A method and system for checking security measures of a smart substation

Abstract

A kind of intelligent substation safety measure checking method and system, comprising: obtaining substation secondary system topology, equipment information and historical safety measure ticket data, construct four-dimensional knowledge graph;According to four-dimensional knowledge graph, combined with memory-guided reflex decision-making reasoning mechanism, the multi-modal features of maintenance task are extracted and the maintenance scene type is identified, the rule reasoning process is optimized by distributed guided local search algorithm, and the optimal safety measure operation set for specific maintenance scene is generated;Through memory-guided decision-making reasoning mechanism, the dependent loop is automatically identified and the minimum safety isolation range is determined, the minimum safety operation set is dynamically generated according to the real-time state of equipment and the operation steps are adaptively adjusted;Combined with the unentangled variational multiple graph representation learning and the distributed guided local search algorithm, an operation dependency relationship model is constructed to realize operation sequence compliance verification, risk level assessment and dynamic visual early warning.The invention realizes the accurate formulation, dynamic adjustment and risk warning of intelligent substation safety measures.

Description

Technical Field

[0001] This invention relates to the field of power system safety operation and maintenance, and in particular to a method and system for verifying safety measures in smart substations, used for the formulation, verification, and risk warning of safety measures for the maintenance of secondary systems in smart substations. Background Technology

[0002] The safe operation and maintenance of power system substations is a crucial link in ensuring the safe and stable operation of the power grid. This is especially true in smart substations, where the formulation of safety measures for the maintenance of secondary equipment directly impacts the safety of equipment and personnel. With the widespread application of smart substations, their complex secondary system structure presents significant challenges to traditional safety management practices.

[0003] Currently, common substation safety management relies primarily on manual comparison with drawings and experience-based judgment. Typical practices include maintenance personnel manually identifying related circuits based on paper drawings and developing operating procedures based on experience; or using simple electronic tools to assist in recording and managing safety tickets, but the core logic still depends on manual judgment. These methods provide some support for basic operating procedures but lack intelligent analysis capabilities.

[0004] Existing, relatively advanced intelligent substation safety management technologies employ basic database management and simple rule matching mechanisms, using preset templates and static rule bases to achieve basic safety ticket generation functions. These systems typically store substation equipment and circuit information in a relational database, generate safety tickets according to predefined operating procedures, and provide simple query and management functions.

[0005] However, this technology has significant shortcomings: First, it struggles to fully capture the complex and ever-changing relationships between secondary equipment, especially the implicit dependencies in networked devices such as GOOSE communication and fiber optic links; second, it lacks adaptability to different maintenance scenarios and cannot dynamically adjust safety measures according to specific maintenance tasks; and third, it lacks real-time verification and risk warning mechanisms during operation, making it difficult to prevent potential risks caused by incorrect operation sequences or omissions of key steps. These shortcomings are particularly prominent in the complex environment of smart substations, necessitating a more intelligent safety management solution. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for verifying safety measures in intelligent substations, aiming to solve the technical problems in the prior art, such as the difficulty in fully capturing the complex relationships between secondary equipment, the lack of scene adaptation capabilities, and the lack of real-time verification and risk warning mechanisms, so as to achieve accurate formulation, dynamic adjustment and risk warning of safety measures in intelligent substations.

[0007] To achieve the above objectives, the present invention provides a method for verifying safety measures in intelligent substations, comprising the following steps:

[0008] The substation secondary system topology, equipment information, and historical safety measure ticket data are acquired. A four-element knowledge graph containing equipment-link-pressure plate-operation is constructed through the untangled multi-spatiotemporal transition graph representation learning method to realize the modeling and representation of complex relationships between equipment.

[0009] Based on the aforementioned quaternary knowledge graph, the multimodal features of the maintenance task are extracted and the maintenance scenario type is identified by combining the memory-guided reflex decision reasoning mechanism. The maintenance scenario identification result is obtained. The rule reasoning process is optimized under complex constraints by the distributed guided local search algorithm to generate the optimal set of safety measures for a specific maintenance scenario.

[0010] Based on the optimal set of safety measures and real-time equipment status information, the associated loops are automatically identified and the minimum safety isolation range is determined through a memory-guided decision-making reasoning mechanism. The minimum safety operation set is dynamically generated according to the real-time equipment status and the operation steps are adaptively adjusted.

[0011] For the aforementioned adaptive adjustment operation steps, an operation dependency model is constructed by combining untangled variational multigraph representation learning and distributed guided local search algorithm to realize operation sequence compliance verification, multi-dimensional risk level assessment, and dynamic visualization early warning.

[0012] Preferably, the construction of a four-element knowledge graph containing device-link-pressure plate-operation using the unwrapped multi-spatiotemporal transition graph representation learning method includes: for the substation secondary system topology, equipment information, and historical safety measure ticket data, extracting entity and relationship information from substation secondary system design documents, wiring diagrams, and equipment manuals to construct an initial knowledge graph containing nodes including IED devices, fiber optic links, soft and hard pressure plates, and operation steps, forming a basic graph structure containing fiber optic connection relationships, pressure plate control relationships, operation dependencies, and functional implementation relationships; and applying an unwrapped variational multigraph autoencoder to decompose the initial knowledge graph into expressions of common relationships. A shared feature subgraph and a private feature subgraph expressing specific relationships are used to generate a multi-spatiotemporal graph representation. Based on the multi-spatiotemporal graph representation, the physical connection relationship and logical dependency relationship between devices are captured simultaneously through a spatiotemporal coding layer. A contrastive learning constraint method is used to optimize the private feature extraction and reduce the impact of noise, resulting in an optimized private feature extraction result. For the optimized private feature extraction result, an entity linking algorithm is used to identify the correspondence between devices, operations and nodes in the initial knowledge graph. An association rule mining algorithm is applied to discover implicit operation dependency patterns, and missing relationships in the initial knowledge graph are dynamically completed to obtain the quadratic knowledge graph.

[0013] Preferably, the step of extracting multimodal features of maintenance tasks and identifying maintenance scenario types by combining the memory-guided reflexive decision-making reasoning mechanism includes: designing a multimodal feature extraction framework for the four-element knowledge graph, extracting semantic features from the maintenance task description text, extracting state features from equipment operating parameters, and extracting experiential features from similar historical cases to form a multidimensional feature representation of the maintenance scenario; based on the multidimensional feature representation of the maintenance scenario, constructing a memory-guided reflexive decision-making model that includes an external memory module storing historical scenario features and category mappings, a reflexive module for fast scenario matching, and a reasoning module for handling complex scenarios, to realize a gradient decision-making process from rapid response to deep reasoning, and obtaining the output results of the gradient decision-making process; and automatically identifying the maintenance scenario type and determining scenario attributes based on the output results of the gradient decision-making process, generating the optimal safety operation set for a specific maintenance scenario.

[0014] Preferably, the optimization of rule reasoning under complex constraints using a distributed guided local search algorithm includes: constructing a hierarchical rule base based on the maintenance scenario identification results, comprising a basic rule layer applicable to all scenarios, a scenario rule layer for different scenario types, and a special case rule layer for handling special situations, forming a three-layered scenario-based rule base; applying a distributed guided local search algorithm based on the three-layered scenario-based rule base, selectively penalizing high-cost constraints through an adaptive violation condition identification mechanism, and controlling the penalty magnitude to prevent over-penalization through a penalty evaporation mechanism, thereby achieving optimal rule reasoning under complex conditions including safety constraints, timing constraints, and resource constraints, and obtaining the optimal rule reasoning result; and using a distributed coordinated synchronization scheme to handle rule conflicts under multiple constraints for the optimal rule reasoning result, generating the optimal safety measure operation set for a specific maintenance scenario that satisfies all constraints.

[0015] Preferably, the automatic identification of associated loops and determination of the minimum safety isolation range through the memory-guided decision-making reasoning mechanism includes: for the optimal set of safety measures, taking the target maintenance equipment as the center, applying a multi-level graph traversal algorithm to automatically identify all loops that have four types of association relationships with the target equipment, including direct connection, functional association, indirect influence, and backup redundancy; determining the minimum safety isolation range according to the maintenance domain isolation principle; based on the minimum safety isolation range, collecting real-time equipment status information of relevant equipment in real time through multi-protocol access methods; applying a data fusion algorithm based on the memory-guided reflection mechanism to process the multi-source heterogeneous data of the real-time equipment status information to form a unified equipment status representation; based on the unified equipment status representation, applying the memory module in the memory-guided reflection decision-making mechanism to generate regular safety measures operation steps; handling special state situations through the reasoning module; and dynamically adjusting the operation content and execution order in combination with the real-time equipment status information to obtain a complete operation step sequence that is adaptively adjusted according to the real-time equipment status.

[0016] Preferably, the step of constructing an operation dependency model by combining untangled variational multigraph representation learning and distributed guided local search algorithm includes: based on the adaptively adjusted operation steps and the quaternary knowledge graph, extracting multidimensional dependencies between operations, including temporal, functional, spatial, resource, and conditional dependencies, using the untangled variational multigraph representation learning method; explicitly defining the preconditions, mutual exclusion relationships, and temporal constraints between operations to form a multidimensional logical dependency network; according to the multidimensional logical dependency network, parsing the input operation record text, using a deep learning sequence labeling model to identify entities and relationships in the operation record text, extracting key information including operation type, operation object, and operation parameters, obtaining operation parsing results, and determining the compliance assessment and system state impact analysis of the current operation; based on the compliance assessment and system state impact analysis of the current operation, matching and verifying the operation parsing results with the multidimensional logical dependency network and the real-time device state, checking whether the operation meets all preconditions, and realizing real-time compliance verification of the operation sequence.

[0017] Preferably, the multi-dimensional risk level assessment includes: applying a distributed guided local search algorithm to transform various risk factors, including operational sequence errors, abnormal equipment status, operational environment risks, personnel factor risks, and system vulnerability risks, into a distributed constrained optimization problem based on the operational analysis results; calculating various operational risk values ​​through an adaptive violation condition identification and penalty evaporation mechanism; handling the complex interactions between risk factors through a distributed coordination mechanism based on the various operational risk values; performing risk pattern matching and predictive analysis by combining similar cases from historical accident data; generating a comprehensive risk assessment result considering risk probability and multi-dimensional risk; and based on the multi-dimensional risk assessment result, applying a risk matrix to map the comprehensive risk value to different risk levels (low, medium, high, and extremely high), identifying major risk contributing factors and key risk points, thereby achieving a multi-dimensional risk level assessment and obtaining the multi-dimensional risk level assessment result.

[0018] Preferably, the dynamic visualization early warning system includes: designing a multi-layered risk visualization interface based on the multi-dimensional risk level assessment results, comprising a global risk situation map, an equipment network topology map, and an operation sequence risk map; using a dynamic topology map based on the substation layout to display the operation impact range and risk distribution in real time, generating intuitive risk status indicators; based on the intuitive risk status indicators, applying a status coding visualization method, marking disconnected links in red, normally operating links in green, links with potential risks in yellow, and key nodes requiring special attention in orange, forming a multi-color coded risk visualization display; based on the multi-color coded risk visualization display, combined with the risk level, issuing graded early warnings through multi-modal methods including visual warnings, sound warnings, and tactile feedback; applying a memory-guided reflection mechanism to match risk mitigation strategies and provide targeted risk mitigation suggestions, thereby achieving intelligent dynamic visualization early warning.

[0019] Preferably, the system also includes system integration and interactive interface implementation: Based on the compliance verification of the operation sequence, multi-dimensional risk level assessment, and dynamic visualization early warning results, a microservice architecture is adopted to design the overall system framework. Functional modules including knowledge graph construction, scene recognition, safety measure generation, and risk verification are encapsulated as independent services. Communication between the functional modules is achieved through RESTful APIs and service registration and discovery mechanisms, thus building a highly available and scalable integrated system platform. For the integrated system platform, a memory-guided reflection decision-making mechanism is applied to design an intelligent human-computer interaction interface. An external memory module stores users' historical operation habits, a reflection module responds to frequently used function accesses, and a reasoning module understands complex user intentions. The layout of the interactive interface and function recommendations are dynamically adjusted according to user behavior patterns, providing personalized intelligent recommendations and auxiliary decision-making functions. Based on the personalized intelligent recommendations and auxiliary decision-making functions, a full-process visualized management interface covering safety measure formulation, approval, execution, and supervision is designed. An interactive device association exploration component based on a knowledge graph is integrated, and the execution progress and system status changes of safety measures are displayed in real time through visual elements, realizing intelligent visualization of the entire safety measure management process and overall integration of system functions.

[0020] Furthermore, the present invention also provides a smart substation safety measure verification system, comprising:

[0021] The knowledge graph construction module is used to acquire the substation secondary system topology, equipment information and historical safety ticket data. It constructs a four-element knowledge graph containing equipment-link-pressure plate-operation through the untangled multi-spatiotemporal transition graph representation learning method to realize the modeling and representation of complex relationships between equipment.

[0022] The scene recognition and rule reasoning module is used to extract multimodal features of maintenance tasks and identify maintenance scene types based on the four-element knowledge graph and memory-guided reflexive decision reasoning mechanism, so as to obtain maintenance scene recognition results. The rule reasoning process is optimized under complex constraints through a distributed guided local search algorithm to generate the optimal set of safety measures for specific maintenance scenes.

[0023] The safety measure adaptive generation module is used to automatically identify related loops and determine the minimum safety isolation range based on the optimal safety measure operation set and real-time equipment status information, through a memory-guided decision reasoning mechanism, and dynamically generate the minimum safety operation set according to the real-time equipment status and adaptively adjust the operation steps.

[0024] The compliance verification and risk warning module is used to construct an operation dependency model based on the adaptively adjusted operation steps, combining untangled variational multigraph representation learning and distributed guided local search algorithm, to realize operation sequence compliance verification, multi-dimensional risk level assessment and dynamic visualization warning.

[0025] By adopting the above technical solution, the present invention has at least the following beneficial effects:

[0026] 1. By constructing a quaternary knowledge graph through the untangling multi-spatiotemporal transition graph representation learning method, the problem of traditional knowledge graphs being unable to accurately express complex spatiotemporal relationships is effectively solved, and the recognition accuracy of implicit dependencies between devices is improved.

[0027] 2. A memory-guided reflex decision-making mechanism is adopted to realize maintenance scenario recognition and safety measure generation. Through the collaboration of external memory module, reflex module and reasoning module, a gradient decision-making process from rapid response to deep thinking is realized, effectively balancing decision-making efficiency and accuracy.

[0028] 3. A distributed guided local search algorithm is applied to optimize rule reasoning and risk assessment. Through adaptive violation condition identification, penalty evaporation mechanism and synchronization coordination scheme, the problems of operational conflicts and risk assessment under multiple constraints are solved.

[0029] 4. It realizes the adaptive generation and dynamic adjustment of safety measures in intelligent substations. By combining real-time equipment status information, it accurately identifies related circuits, determines the minimum safety isolation range, and avoids system function loss caused by excessive isolation.

[0030] 5. It provides multi-dimensional risk assessment and dynamic visualization early warning functions, transforming complex risk assessment results into intuitive visual expressions, and promptly reminding operators through multi-modal early warning methods to improve the safety of safety measures implementation. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart of the intelligent substation safety measure verification method provided in the embodiments of the present invention;

[0033] Figure 2 This is a flowchart illustrating the memory-guided reflection decision-making scenario recognition in an embodiment of the present invention;

[0034] Figure 3 This is a structural diagram of the intelligent substation safety measure verification system provided in an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

[0036] like Figure 1 As shown, the intelligent substation safety measure verification method provided in this embodiment of the invention includes the following steps:

[0037] Step S1: Obtain the substation secondary system topology, equipment information, and historical safety ticket data. Construct a four-element knowledge graph containing equipment-link-pressure plate-operation through the untangled multi-spatiotemporal transition graph representation learning method to realize the modeling and representation of complex relationships between equipment.

[0038] In this step, all information about the substation secondary system is first acquired, including system topology, parameters of various equipment and their connections, and historical safety measure records. This data comes from a wide range of sources, including substation design documents, wiring diagrams, protection setting sheets, equipment manuals, communication configuration files, and historical safety measure records. For this heterogeneous data, specialized data preprocessing techniques are used for format conversion, noise removal, and consistency checks to ensure data quality for subsequent modeling. The untangled multi-spatiotemporal transition graph representation learning method is an innovative graph representation learning technique that can effectively handle various relationship types and spatiotemporal variation characteristics in complex networks. Based on the principle of variational graph autoencoders, this method introduces an untangling mechanism to decompose complex relationship networks into shared and private features, thereby more accurately expressing multiple relationships such as physical connections, logical dependencies, and temporal constraints between devices. A quaternary knowledge graph refers to a knowledge representation structure containing four core entities—equipment, links, pressure plates, and operations—and their interrelationships. It intuitively describes the complex relationships between components in the substation secondary system in a graph form, providing fundamental data support for subsequent safety measure generation and risk assessment.

[0039] Step S2: Based on the quaternary knowledge graph, the multimodal features of the maintenance task are extracted and the maintenance scenario type is identified by combining the memory-guided reflexive decision reasoning mechanism to obtain the maintenance scenario identification result. The rule reasoning process is optimized under complex constraints by the distributed guided local search algorithm to generate the optimal safety operation set for the specific maintenance scenario.

[0040] The core of this step is to achieve accurate identification of maintenance scenarios and generation of optimal safety measures. The memory-guided reflexive decision-making reasoning mechanism is a gradient decision-making mechanism combining rapid response and deep thinking. It comprises three core components: an external memory module, a reflexive module, and a reasoning module. The external memory module stores the mapping relationship between historical scenario features and categories, serving as the knowledge base for decision-making; the reflexive module is responsible for quickly processing common and clear scenarios, rapidly providing decision results through simple pattern matching; the reasoning module handles complex or novel scenarios, providing more accurate decisions through deep reasoning analysis. Multimodal features refer to the feature set describing maintenance tasks from different dimensions, including semantic features extracted from text descriptions, state features extracted from equipment operating parameters, and experiential features extracted from historical cases. The distributed guided local search algorithm is an efficient constraint optimization method, particularly suitable for handling complex decision problems with multiple constraints. This algorithm finds the optimal or near-optimal solution under multiple constraints such as safety constraints, temporal constraints, and resource constraints through adaptive violation condition identification, penalty evaporation mechanism, and distributed coordination strategy. The optimal safety operation set refers to the set of operation steps generated based on the identified maintenance scenario and the optimized rule reasoning results, which both meet safety requirements and minimize the isolation scope.

[0041] Step S3: Based on the optimal safety operation set and real-time equipment status information, the associated loops are automatically identified and the minimum safety isolation range is determined through a memory-guided decision reasoning mechanism. The minimum safety operation set is dynamically generated according to the real-time equipment status and the operation steps are adaptively adjusted.

[0042] In this step, the optimal safety measure operation set generated in step S2 is used as a foundation, combined with real-time acquired equipment status information, to further optimize and adjust the safety measure scheme. Associated loops refer to all circuit connections that are directly or indirectly related to the target maintenance equipment, including physical connections, logical associations, functional dependencies, and other relationships. The system automatically identifies these associated loops from the knowledge graph using a multi-level graph traversal algorithm. The minimum safety isolation range refers to the isolation area that minimizes the impact on the normal function of the system while ensuring safety. Based on the "maintenance domain isolation" principle and combined with the characteristics of the maintenance scenario, the system determines the optimal isolation range. Real-time equipment status information is obtained from the substation automation system through multi-protocol access methods (such as IEC 61850, SNMP, Modbus, etc.) of equipment operating parameters and status data. Differential acquisition, priority sampling, and adaptive sampling frequency technologies are used to ensure the efficiency and real-time nature of data acquisition. The application of a memory-guided decision-making reasoning mechanism here enables the rapid generation of routine safety measure operation steps based on historical experience, while handling special cases through deep reasoning, achieving intelligent generation and dynamic adjustment of safety measure steps. Adaptive adjustment refers to the system's ability to dynamically adjust the operation content and execution order based on changes in the real-time status of the device. For example, "if the switch port is offline, skip the disconnection operation," ensuring the flexibility and adaptability of security measures execution.

[0043] Step S4: For the adaptively adjusted operation steps, an operation dependency model is constructed by combining untangling variational multigraph representation learning and distributed guided local search algorithm to realize operation sequence compliance verification, multi-dimensional risk level assessment and dynamic visualization early warning.

[0044] This step primarily addresses operational safety verification and risk warning. The operational dependency model is a formalized model describing various dependency constraints between operational steps, including temporal dependencies, functional dependencies, spatial dependencies, resource dependencies, and conditional dependencies. Through untangled variational multigraph representation learning, the system can effectively extract and represent these complex dependencies. Operational sequence compliance verification verifies whether the actually executed operations meet predefined dependency constraints, such as checking whether an operation is executed only after all its preceding operations are completed. Natural language understanding technology is used to parse operation records and match them with the dependency model for verification, detecting operational sequence errors in real time. Multi-dimensional risk level assessment evaluates the potential risks of operations from multiple perspectives, including operational sequence risks, equipment status risks, operational environment risks, human factor risks, and system vulnerability risks. The system applies a distributed guided local search algorithm to transform the risk assessment problem into a distributed constraint optimization problem, combining historical accident data and expert experience to generate comprehensive risk assessment results. Dynamic visualization warning transforms the risk assessment results into intuitive visual expressions and timely warning information. A multi-layered risk visualization interface was designed, including a global risk situation map, a device network topology map, and an operation sequence risk map. Through multi-color coding and multi-modal early warning methods, potential risks are alerted in real time and mitigation suggestions are provided.

[0045] The above steps will be explained in detail below with reference to specific embodiments.

[0046] In step S1, a four-element knowledge graph containing device-link-platen-operation is constructed using the untangled multi-spatiotemporal transition graph representation learning method, including:

[0047] Step S1.1: Based on the substation secondary system topology, equipment information, and historical safety measure ticket data, extract entity and relationship information from the substation secondary system design documents, wiring diagrams, and equipment manuals to construct an initial knowledge graph of nodes including IED devices, fiber optic links, soft and hard pressure plates, and operation steps, forming a basic graph structure including fiber optic connection relationships, pressure plate control relationships, operation dependency relationships, and functional implementation relationships.

[0048] This step first requires collecting and organizing all relevant documents for the substation secondary system. These documents form the foundational data source for building the knowledge graph. The substation secondary system refers to the collective control, measurement, protection, and communication equipment excluding main equipment (such as transformers and circuit breakers), and is a critical system for ensuring the safe and stable operation of the substation. Relevant documents for the substation secondary system include design drawings, protection setting sheets, wiring diagrams, equipment manuals, and communication configuration files. Design drawings show the equipment layout and connection methods; protection setting sheets record the parameter settings of protection devices; wiring diagrams describe the connection relationships between devices in detail; equipment manuals provide the functions and operating information of the equipment; and communication configuration files record the communication methods and protocols between devices.

[0049] Next, ontology modeling is used to define the conceptual hierarchy and relation types of the graph. Ontology modeling is a formal method for describing domain concepts and their relationships, enabling computer systems to understand and process domain knowledge. In this invention, the ontology model design follows the IEC 61850 standard, an international standard for intelligent substation automation systems, which defines equipment models, communication protocols, and engineering processes. The system defines four core concepts: equipment, links, terminal blocks, and operations. Equipment includes IED devices (such as line protection devices and transformer protection devices), network devices (such as switches and routers), and secondary circuit devices (such as terminal blocks and terminal blocks). Each equipment entity has unique identifiers, models, and location attributes. Links include fiber optic links, copper cable links, GOOSE links, and MMS links. GOOSE (Generic Object Oriented Substation Event) is a high-speed point-to-point communication mechanism defined in the IEC 61850 standard, used for transmitting state changes and event information; MMS (Manufacturing Message Specification) is a communication protocol for industrial automation systems, used for reading and writing equipment parameters and transmitting control commands. The pressure plate category includes soft pressure plates and hard pressure plates. Pressure plates are control devices used in power systems to switch functional modes. Soft pressure plates are implemented through software, while hard pressure plates are implemented through physical switches. The operation category includes disconnection operations, closing operations, exit operations, and engagement operations, recording attributes such as the type of operation, the object, and preconditions.

[0050] In terms of relational modeling, various relation types are defined, mainly including "fiber optic connection," "plate control," "operational dependency," "functional implementation," and "information exchange." These relations describe various associations between devices, such as physical connections, control relationships, dependencies, functional implementation, and information exchange. Through these relations, the complex interactions of various components in the substation secondary system can be comprehensively described.

[0051] Entity and relation information is extracted from documents using text mining and rule extraction techniques. For highly structured documents (such as protection setting sheets in Excel format), template matching is used for direct extraction; for semi-structured documents (such as design specifications in PDF format), regular expressions and rule extraction methods are applied; for unstructured documents (such as text descriptions), named entity recognition and relation extraction algorithms are combined. Named entity recognition is a task in natural language processing that aims to identify and classify entities (such as device names, operation actions, etc.) in text; relation extraction identifies semantic relationships between entities, such as the relationship "pressure plate A controls function B".

[0052] Finally, the extracted entity and relationship information is integrated to construct an initial knowledge graph. A knowledge graph is a structured knowledge base that stores entities and their relationships in the form of a graph, facilitating knowledge representation, storage, and reasoning. A graph database (such as Neo4j) is used to store the graph data to ensure efficient graph query performance. Neo4j is a database system specifically designed for storing and querying graph data, using graph data structures (nodes, relationships, and attributes) to represent and store data, making it particularly suitable for handling highly interconnected data. Simultaneously, a graph quality verification mechanism is designed, employing methods such as consistency checks, integrity verification, and redundancy elimination to ensure the quality and accuracy of the initial knowledge graph.

[0053] This includes collecting and organizing all relevant documents for the substation secondary system, including but not limited to: design drawings, protection setting sheets, wiring diagrams, equipment manuals, and communication configuration files. An ontology modeling approach is used to define the conceptual hierarchy and relationship types of the graph, primarily including the following core concept categories:

[0054] Equipment category: This includes IED devices (such as line protection devices and transformer protection devices), network equipment (such as switches and routers), and secondary circuit equipment (such as terminal blocks and pressure plates). Each equipment entity has a unique identifier, model number, location, and other attributes.

[0055] Link types: including fiber optic links, copper links, GOOSE links, MMS links, etc. Each link entity contains attributes such as source port, destination port, bandwidth, and protocol type.

[0056] Pressure plate type: including soft pressure plates and hard pressure plates, recording the pressure plate's identification, name, current status, control function and other attributes.

[0057] Operations include disconnect, close, exit, and engage operations, recording the operation type, object, and preconditions.

[0058] In relational modeling, several relation types are defined, mainly including:

[0059] "Fiber Optic Connection" (connectTo): Describes the physical connection between two device ports;

[0060] "Pressure plate control" (controlBy): describes the control relationship of the pressure plate on a certain function or circuit;

[0061] "Operation dependency" (dependOn): Describes the prerequisite dependencies between operations;

[0062] "Function Implementation" (implementBy): Describes the implementation relationship between functions and devices;

[0063] "CommunicateWith": Describes the information exchange relationship between devices.

[0064] Step S1.2: Apply the unwrapped variational multigraph autoencoder to decompose the initial knowledge graph into a shared feature subgraph expressing common relationships and a private feature subgraph expressing specific relationships, thereby generating a multi-spatiotemporal graph representation.

[0065] The Disentangled Variational Multi-graph Autoencoder (DAE) is an innovative graph representation learning method that effectively handles various spatiotemporal relationships present in substation secondary systems. A Variational Autoencoder (VAE) is a generative model consisting of an encoder and a decoder. The encoder maps input data to a probability distribution in the latent space, while the decoder samples and reconstructs the input data from the latent space. Unlike traditional VAEs, DAE introduces an untangling mechanism, enabling the decomposition of complex features into meaningful independent components.

[0066] The architecture of DAE mainly comprises several key components. First is the Graph Encoder, which transforms the input knowledge graph G into a low-dimensional latent space representation. The encoder employs a Graph Convolutional Network (GCN) structure, designing specific convolutional kernels for different types of nodes and edges to capture the rich semantics in heterogeneous graphs. A Graph Convolutional Network is a neural network used to process graph-structured data; it aggregates neighborhood information of nodes and generates node representations through convolutional operations defined on the graph. When processing heterogeneous graphs (graphs containing different types of nodes and edges), GCNs need to design specific convolutional operations for different types of elements.

[0067] The Temporal-Spatial Encoding Layer is the second important component of DAE, introducing spatiotemporal information encoding during the encoding process. For spatial relationships (such as the physical connection of equipment), location encoding techniques are used; for temporal relationships (such as the sequence of operation steps), sequence encoding techniques are used. Location encoding typically uses sine and cosine functions to embed location information into a continuous vector space; sequence encoding captures the sequential information and long-range dependencies in the sequence through recurrent neural networks or self-attention mechanisms. The introduction of spatiotemporal encoding enables the model to simultaneously capture static topology and dynamic temporal dependencies, which is crucial for understanding the complex relationships in substation systems.

[0068] The Disentanglement Module is the core component of the DAE (Deep Image Engine). It decomposes the latent representation Z output by the encoder into a shared representation Zs and multiple private representations Zp = {Zp1, Zp2, ..., Zpk}, where k represents the number of classes of the private features. The disentanglement process is achieved through variational inference, an approximate Bayesian inference method used to estimate complex posterior distributions. The objective function of the DAE consists of three parts: reconstruction loss Lrecon, KL divergence loss Lkl, and contrastive loss Lcontrast. The reconstruction loss measures the difference between the decoded graph and the original graph; the KL divergence loss ensures a reasonable distribution in the latent space; and the contrastive loss enhances the discriminative power of the private features.

[0069] The Product of Experts (PoE) mechanism is another important component of DAE, which integrates shared features. PoE is a method for combining multiple probabilistic models. Its basic idea is to multiply and normalize the probability distributions of multiple "experts" (probabilistic models) to form a new probability distribution. In DAE, PoE is used to integrate multiple information sources, expressed as p(z|x1,...,xn) ∝ ∏ip(z|xi), where p(z|xi) represents the conditional probability of the latent variable z given the input xi. This mechanism ensures that shared features can fully integrate various types of information, improving the model's expressive power.

[0070] In practical applications, the training process of DAE includes multiple steps. First, the initial knowledge graph constructed in step S1.1 is converted into a format suitable for model input, including a node feature matrix X and an adjacency matrix A. The node feature matrix represents the attributes of each node in the graph, and the adjacency matrix describes the connection relationships between nodes. Then, the model parameters are initialized through a pre-training phase, and a self-supervised learning method (such as random edge prediction) is used to allow the model to learn the basic structure of the graph. Self-supervised learning is a learning method that does not require manual annotation; it trains the model by generating supervision signals from the data itself. Next, end-to-end training is performed using a complete objective function to optimize the untangled representation learning process. Finally, the trained model is applied to the knowledge graph to obtain an enhanced graph representation.

[0071] The multi-spatiotemporal graph representation generated by DAE can better capture the spatiotemporal relationships between devices, especially implicit dependencies that are not obvious in the original graph. This representation method significantly improves the expressive power of knowledge graphs, providing a richer and more accurate knowledge foundation for subsequent scene recognition and rule reasoning.

[0072] Step S1.3: Based on the multiple spatiotemporal graph representation, the physical connection relationship and logical dependency relationship between devices are captured simultaneously through the spatiotemporal coding layer. The contrastive learning constraint method is used to optimize the private feature extraction and reduce the impact of noise, thereby obtaining the optimized private feature extraction result.

[0073] In this step, the feature extraction process is further optimized based on the multiple spatiotemporal graph representation generated in step S1.2. The spatiotemporal coding layer is a neural network layer specifically designed to process spatiotemporal data, capable of simultaneously capturing both physical connections and logical dependencies between devices. Physical connections refer to the actual physical connections between devices, such as fiber optic links and hardware interfaces; logical dependencies refer to the functional and operational dependencies of devices, such as the function of one device depending on the state of another. Both of these relationships are crucial in smart substations, together constituting the complete operational logic of the system.

[0074] The implementation of the spatiotemporal coding layer consists of two parts: spatial coding and temporal coding. Spatial coding uses positional coding techniques to encode the physical location and topological structure information of the devices into feature representations. Common methods for positional coding include absolute positional coding and relative positional coding. Absolute positional coding directly uses the device's coordinates or location ID; relative positional coding uses the distance or topological distance between devices. Temporal coding uses sequence coding techniques to capture the order of operation steps and temporal dependencies. Sequence coding can be implemented using recurrent neural networks (RNNs), long short-term memory networks (LSTMs), or self-attention mechanisms. Recurrent neural networks are a type of neural network capable of processing sequential data, remembering previous information by hiding states; long short-term memory networks are a variant of RNNs, specifically designed to solve long-term dependencies in long sequences; self-attention mechanisms capture the dependencies between elements by calculating the correlation strength between elements in the sequence and are a core component of models such as Transformer.

[0075] Contrastive learning (CLM) is a self-supervised learning technique used to optimize private feature extraction and reduce the impact of noise. The core idea of ​​CLM is to bring the representations of similar samples close together in the feature space, while keeping the representations of dissimilar samples far apart. In this invention, CLM is used to enhance the discriminative power of private features, ensuring that different types of private features can be effectively separated. Specifically, it involves constructing positive and negative sample pairs, where positive pairs belong to the same category and negative pairs belong to different categories. Then, by minimizing the distance between positive sample pairs and maximizing the distance between negative sample pairs, the model is trained to learn discriminative feature representations.

[0076] Contrastive learning constraints are implemented using the following loss function:

[0077] L contrast = -log[exp(sim(z i , z j ) / τ) / Σ k≠i exp(sim(z i , z k ) / τ)]

[0078] Where, sim(z) i , z j ) represents the eigenvector z i and z j The similarity between them is usually expressed using cosine similarity; τ is a temperature parameter that controls the smoothness of the distribution; z i and z j It is the feature representation of positive sample pairs, z k It is a feature representation of negative samples.

[0079] By applying contrastive learning constraints, the impact of noise on feature extraction can be effectively reduced. Noise refers to irrelevant or erroneous information in the data, which may originate from errors in the data acquisition process, interference during transmission, or corruption during storage. In knowledge graphs, noise may manifest as incorrect entity relationships, inaccurate attribute values, or redundant connections. Contrastive learning, by emphasizing the discriminative features between samples, naturally suppresses common noise components, thereby improving the robustness of feature extraction.

[0080] After processing with a spatiotemporal coding layer and contrastive learning constraints, optimized private feature extraction results were obtained. These features more accurately reflect the physical connections and logical dependencies between devices, providing a reliable foundation for subsequent knowledge graph completion and inference. The optimized private features have higher discriminative power and lower noise levels, enabling more effective support for the accurate formulation of substation safety measures and risk assessment.

[0081] Step S1.4: Based on the optimized private feature extraction results, the entity linking algorithm is used to identify the correspondence between the devices and operations in the historical security tickets and the nodes in the initial knowledge graph. The association rule mining algorithm is applied to discover the implicit operation dependency patterns, and the missing relationships in the initial knowledge graph are dynamically completed to obtain the quadratic knowledge graph.

[0082] This step aims to further enrich and improve the knowledge graph using historical safety measure tickets. Historical safety measure tickets are documents recording safety measures taken during substation maintenance, containing rich operational experience and implicit rules. This data typically exists in structured or semi-structured document format and is a valuable source of knowledge. The system first preprocesses this data, including document format standardization, key field extraction, error record cleaning, and text standardization. Natural language processing technology is then used to convert unstructured text into structured data, facilitating subsequent analysis.

[0083] Entity linking is a technique for mapping entities mentioned in text to existing entities in a knowledge base. In this invention, entity linking is used to identify the correspondence between devices and operations mentioned in historical security tickets and nodes in the initial knowledge graph. The algorithm comprises three key steps: candidate entity generation, candidate entity sorting, and entity mapping confirmation.

[0084] Candidate entity generation is the first step in entity linking. It uses methods such as character matching and lexical similarity calculation to generate a candidate set of knowledge graph nodes that may correspond to the terms "equipment," "operation," etc., mentioned in the security ticket. Character matching includes various methods such as exact matching, prefix matching, suffix matching, and fuzzy matching; lexical similarity calculation uses metrics such as edit distance, Jaccard similarity, or cosine similarity to evaluate the similarity between the text mentions and the candidate entities.

[0085] Candidate entity ranking is the second step in entity linking. It calculates a matching score for each candidate entity by combining contextual information and device attributes. The ranking model employs a BiLSTM network enhanced with an attention mechanism, which effectively captures contextual semantic information. BiLSTM (Bidirectional Long Short-Term Memory) is a special type of recurrent neural network that processes sequential data through two LSTM layers (forward and backward) to capture dependencies between contextual information. The attention mechanism is a neural network component that mimics human selective attention, allowing the model to focus on the most relevant parts when processing sequential data, thus improving model performance.

[0086] Entity mapping confirmation is the final step in entity linking. Based on scores and thresholds, the best-matching entity is selected, and a mapping relationship is established between text mentions and knowledge graph nodes. For mappings with low confidence, the system retains multiple candidates and further verifies them in subsequent processes. This approach reduces the impact of erroneous mappings on subsequent analysis and improves the overall accuracy of the system.

[0087] Association rule mining algorithms are techniques for discovering frequent patterns and relationships in a dataset. In this invention, the algorithm is used to discover implicit operational dependencies and scene association patterns in historical security tickets. Association rule mining mainly includes three parts: frequent pattern mining, association rule generation, and scene association analysis.

[0088] Frequent pattern mining is the foundation of association rule mining, identifying frequently occurring itemsets in a dataset. This system applies an improved Apriori algorithm to mine frequently occurring operation combinations in safety tickets, such as {exiting the line protection soft pressure plate, disconnecting the line CT circuit}. The Apriori algorithm is a classic association rule mining algorithm that identifies frequent itemsets based on the property that "all subsets of a frequent itemset are also frequent" using a layer-by-layer search strategy. The improved Apriori algorithm optimizes performance through the FP-Growth tree structure. FP-Growth (Frequent Pattern Growth) is a tree-based frequent itemset mining algorithm that significantly improves mining efficiency by requiring only two data scans and using a compact data structure.

[0089] Association rule generation is the second step in association rule mining. It calculates support and confidence based on frequent patterns, generating association rules in the form of "If line protection verification is performed, the fault recording circuit must be disconnected." Support represents the frequency of a rule's occurrence across all transactions, reflecting its generality; confidence represents the probability that the conclusion is true when the condition is true, reflecting the rule's reliability. The system uses lift values ​​and chi-square tests to filter rules with high significance. The lift value is the ratio of support to the expected support when the condition and conclusion are independent, used to measure the rule's relevance; the chi-square test is a statistical method used to test whether the association between two categorical variables is significant.

[0090] Scene association analysis is an extension of association rule mining. It uses clustering algorithms to identify typical operating patterns in specific maintenance scenarios and establishes a mapping relationship between scenes and operation sets. The clustering algorithm used is Density Peak Clustering (DPC), which effectively handles non-convex data distributions. DPC is a density-based clustering method that identifies density peaks (points with high local density and far distances from other high-density points) as cluster centers, then assigns other points to the cluster containing the nearest center point. It is particularly suitable for discovering irregularly shaped clusters.

[0091] Finally, the mined association rules and scenario patterns are transformed into new relationships in the knowledge graph, dynamically completing the missing relationships in the graph. This process includes four steps: rule transformation, scenario association, confidence labeling, and conflict detection and resolution.

[0092] Rule transformation converts association rules into "operational dependencies" in a knowledge graph, such as adding dependOn edges from "disconnecting the fault recording circuit" to "line protection verification" in the graph. These newly added dependencies reflect the implicit operation sequence and dependency constraints in historical experience, and are an important basis for safety measure generation and risk assessment.

[0093] The scenario association adds a "Scenario" class node and establishes the association between scenarios and operation steps. For example, a "Line Protection Verification" scenario node is created, and a hasOperation edge is added between it and related operation steps. This scenario-operation mapping enables the system to quickly identify relevant operation steps for specific maintenance scenarios, improving the relevance and efficiency of safety measure generation.

[0094] Confidence labeling adds a confidence attribute to newly added relation edges, reflecting the reliability of the relation and facilitating trade-offs during subsequent inference. Confidence values ​​are typically calculated based on the support and confidence of association rules, or determined according to expert evaluation and historical validation results. Relationships with high confidence have higher priority in the inference process, while relationships with low confidence require more validation or supplementary conditions.

[0095] Conflict detection and resolution detects and resolves potential knowledge conflicts arising from newly added relationships, such as circular dependencies or logical contradictions, ensuring the consistency of the knowledge graph. Conflict detection methods include cycle detection, contradictory rule identification, and constraint verification; conflict resolution strategies include confidence-based prioritization, expert rule coverage, and condition refinement.

[0096] Through the above steps, a complete quaternary knowledge graph was obtained, containing four types of entities: equipment, links, pressure plates, and operations, along with their complex relationships. This comprehensively expresses the structure and operational dependencies of the substation's secondary system. This knowledge graph serves as the foundation for subsequent safety measure generation and risk assessment, providing comprehensive and accurate knowledge support for the management of safety measures in smart substations.

[0097] like Figure 2 As shown, in step S2, the multimodal features of the maintenance task are extracted and the maintenance scenario type is identified by combining the memory-guided reflexive decision-making reasoning mechanism, including:

[0098] Step S2.1: For the four-element knowledge graph, design a multimodal feature extraction framework to extract semantic features from the maintenance task description text, state features from the equipment operating parameters, and experience features from similar historical cases to form a multidimensional feature representation of the maintenance scenario.

[0099] The multimodal feature extraction framework is a system capable of extracting and integrating features from different data sources, aiming to comprehensively characterize maintenance scenarios. The core idea of ​​this framework is to construct a comprehensive feature representation of the maintenance scenario by integrating multiple information sources such as text descriptions, equipment status, and historical experience, providing rich input information for subsequent scene recognition. The framework includes three parallel feature extraction channels: a text semantic feature extraction channel, an equipment status feature extraction channel, and a historical experience feature extraction channel.

[0100] The text semantic feature extraction channel processes textual descriptions of maintenance tasks, such as "calibration of cabinet A for 220kV line protection." These descriptions typically include key information such as the maintenance object, voltage level, equipment type, and maintenance type. This channel employs a BERT-based bidirectional encoder architecture, fine-tuned for domain adaptation, enabling it to accurately understand power system terminology and expressions. BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that captures deep semantic information from text through bidirectional contextual understanding. Domain-adaptive fine-tuning refers to further training the pre-trained model using power system documentation to better adapt it to the specialized language of the power sector. The specific implementation of this channel includes four steps: text preprocessing, hierarchical encoding, attention enhancement, and semantic vector generation. Text preprocessing includes word segmentation, terminology recognition, and standardization, converting the original text into a standardized input format; hierarchical encoding sequentially extracts word-level features, phrase-level features, and sentence-level features to form a multi-level semantic representation; attention enhancement introduces an attention mechanism based on knowledge in the power field to highlight keywords and relationships, improving the targeting of feature extraction; semantic vector generation finally generates a fixed-dimensional semantic feature vector, which serves as the input for scene recognition.

[0101] The equipment status feature extraction channel processes equipment operating parameters and status information related to maintenance tasks. Equipment status refers to the current operating condition of the equipment, including online / offline status, operating parameter values, alarm information, etc. This information is crucial for determining the type of maintenance scenario and the scope of safety measures. This channel combines time-series feature extraction and status coding techniques to capture the dynamic characteristics of the equipment. Time-series feature extraction refers to extracting features from the time series of equipment operating parameters to reflect the changing trends and patterns of the parameters; status coding converts the discrete states of the equipment (such as running / stopping, normal / alarm, etc.) into numerical vector representations. The specific implementation of this channel includes four steps: status data collection, time-series pattern extraction, abnormal status detection, and status feature fusion. Status data collection obtains the real-time status of relevant equipment through SCADA systems, relay protection information systems, etc.; time-series pattern extraction uses Long Short-Term Memory (LSTM) networks to extract time-series pattern features of equipment status; abnormal status detection identifies potential abnormal status patterns through autoencoders. Autoencoders are unsupervised learning models that can effectively detect abnormal data by learning compressed representations and reconstructions of data; status feature fusion integrates the status features of different equipment into a unified representation, forming a comprehensive status feature vector.

[0102] The historical experience feature extraction channel analyzes records of similar historical maintenance tasks to extract experiential features. Historical experience refers to information such as operation records, result evaluations, and problem summaries of similar past maintenance tasks, containing rich experiential knowledge and best practices. This channel is based on the principle of case-based reasoning, using similarity metrics to find similar historical cases and extract valuable experiential information from them. Case-based reasoning is an artificial intelligence method that finds historical cases similar to the current problem and uses their solutions to solve new problems. The specific implementation of this channel includes four steps: historical case indexing, similarity calculation, experiential feature extraction, and experiential knowledge representation. Historical case indexing establishes an efficient historical maintenance case index, supporting fast retrieval; similarity calculation uses a hybrid similarity metric method, comprehensively considering multi-dimensional similarity such as equipment type, operation type, and fault type; experiential feature extraction extracts key success factors and common problems from similar historical cases; and experiential knowledge representation transforms the extracted experiential knowledge into structured feature representations for easy subsequent processing and application.

[0103] The feature fusion layer integrates the features extracted from the three channels to form a multi-dimensional feature representation of the maintenance scenario. Feature fusion employs an adaptive weighting mechanism, which dynamically adjusts the fusion weights based on the importance of various features in different scenarios. The adaptive weighting mechanism is a method that automatically adjusts the importance of each feature channel based on the characteristics of the input data. It achieves the optimal feature combination by learning the contribution of various features in different scenarios. The fusion process includes four steps: feature normalization, adaptive weight calculation, weighted fusion, and dimensionality adjustment. Feature normalization normalizes features from different sources to ensure consistent numerical ranges; adaptive weight calculation calculates the weight coefficients of each feature channel based on an attention mechanism; weighted fusion applies the calculated weight coefficients to perform weighted feature fusion; and dimensionality adjustment optimizes the dimensionality of the fused features through dimensionality reduction or feature selection techniques, reducing computational complexity.

[0104] Finally, feature enhancement and optimization are performed to improve the expressive power and discriminative power of the features. Feature enhancement refers to improving the quality and expressive power of features through various technical means; optimization involves adjusting features for specific tasks to make them more suitable for subsequent processing. Specifically, this includes four steps: feature complementarity enhancement, noise suppression, feature importance assessment, and feature representation optimization. Feature complementarity enhancement analyzes the complementary relationships between different feature channels to enhance the expression of complementary information; noise suppression applies regularization methods to suppress noise components in the features; feature importance assessment uses interpretable AI technology to evaluate the importance of each feature dimension, providing an interpretive basis for subsequent scene recognition; and feature representation optimization optimizes the feature representation based on the assessment results, improving its ability to distinguish different maintenance scenarios.

[0105] Multimodal feature extraction comprehensively captures various features of the maintenance scene, providing rich and accurate input information for subsequent scene recognition. This multi-dimensional feature representation significantly improves the accuracy and robustness of scene recognition, especially when facing complex or novel scenes.

[0106] Step S2.2: Based on the multidimensional feature representation of the maintenance scenario, a memory-guided reflection decision model is constructed, which includes an external memory module to store historical scene features and category mappings, a reflection module to achieve fast scene matching, and an inference module to handle complex scenarios. This realizes a gradient decision-making process from fast response to deep reasoning, and obtains the output results of the gradient decision-making process.

[0107] The memory-guided reflexive decision-making model is an innovative decision-making architecture that integrates the advantages of rapid reflexive decision-making and deep reasoning decision-making, achieving efficient and accurate scene recognition. The core idea of ​​this model is to simulate two decision-making modes in the human cognitive process: rapid reaction and deep thinking, and adaptively select the appropriate decision path based on the complexity of the scene. The model comprises three key modules: an external memory module, a reflexive decision-making module, and a reasoning decision-making module.

[0108] The External Memory Module is the knowledge base of the memory-guided reflexive decision-making model, storing the mapping relationship between historical scene features and categories. External memory refers to explicit knowledge stored and accessed by the system, unlike the implicitly encoded knowledge within the neural network; it can be directly queried and updated. This module adopts a key-value storage structure, where the key is the scene feature vector and the value is the scene category and related attributes. Key-value storage is an efficient data organization method, allowing for quick lookup of the corresponding value using the key, suitable for achieving fast memory retrieval. The specific implementation of this module includes four parts: memory structure design, hierarchical memory organization, memory update mechanism, and memory compression and optimization. The memory structure design determines the basic organizational form and storage format of the memory; the hierarchical memory organization organizes the memory according to scene category and complexity, forming a multi-level index structure for efficient retrieval; the memory update mechanism uses an incremental learning algorithm to support dynamic updates of memory content, maintaining the timeliness of knowledge; and memory compression and optimization apply knowledge distillation technology to compress redundant memory, improving retrieval efficiency. Knowledge distillation is a technique that transfers knowledge from a complex model (teacher model) to a simple model (student model). By having the student model mimic the output of the teacher model, it achieves effective compression and transfer of knowledge.

[0109] The Reflexive Decision Module is responsible for quickly processing common and well-defined scenarios, similar to human conditioned reflexes. Conditioned reflexes refer to automated responses formed through repeated training, allowing for rapid decision-making without deep thought. This module is based on the nearest neighbor retrieval principle, quickly matching input features with known patterns in memory. Nearest neighbor retrieval is a distance-based retrieval method that finds the data point in the feature space closest to the query point. The specific implementation of this module includes four parts: approximate nearest neighbor retrieval, similarity threshold control, fast scene matching, and confidence assessment. Approximate nearest neighbor retrieval uses the Locality Sensitive Hash (LSH) algorithm to achieve efficient feature vector retrieval. LSH is a hashing technique that maps high-dimensional data to a low-dimensional space, so that similar data points have a high probability of being mapped to the same bucket after hashing, greatly improving retrieval efficiency. Similarity threshold control sets a dynamic similarity threshold to control the triggering conditions of reflexive decision-making. For scenarios with similarity exceeding the threshold, fast scene matching directly outputs the matching result without further analysis. Confidence assessment calculates the confidence of the matching result; when the confidence is low, the decision-making task is transferred to the inference module to ensure the reliability of the decision.

[0110] The Reasoning Decision Module handles complex or novel scenarios, making decisions through deep reasoning. Deep reasoning refers to the process of deeply understanding a problem and making a decision through logical analysis, causal inference, and other methods, suitable for handling complex or unprecedented situations. This module adopts a deep neural network structure, capable of handling ambiguous or unseen scenario patterns. The specific implementation of this module includes four parts: deep feature extraction, context-aware reasoning, rule-integrated reasoning, and uncertainty modeling. Deep feature extraction uses a multilayer perceptron (MLP) to further extract deep representations of features. An MLP is a feedforward neural network composed of multiple fully connected layers, capable of learning complex nonlinear mappings of data. Context-aware reasoning introduces contextual information to enhance reasoning capabilities, such as considering external factors like the current power grid operating status and weather conditions. Rule-integrated reasoning combines predefined power system expert rules for reasoning, enhancing interpretability. Uncertainty modeling estimates the uncertainty of the decision through a Bayesian neural network, providing a credibility assessment of the decision. A Bayesian neural network is a model that integrates Bayesian statistics and neural networks, not only outputting prediction results but also providing the probability distribution of the prediction and uncertainty estimates.

[0111] The reflex-reasoning collaborative decision-making process is the core mechanism of the memory-guided reflexive decision-making model. It achieves seamless cooperation between the two decision-making modes, forming a gradient decision-making process from rapid reaction to deep thinking. The specific implementation of this process includes four parts: a decision flow mechanism, an outcome fusion strategy, a decision feedback loop, and a conflict resolution mechanism. The decision flow mechanism establishes the decision flow standards from the reflex module to the reasoning module, including triggering conditions such as insufficient similarity and low confidence. The outcome fusion strategy performs weighted fusion based on confidence and contextual adaptability when both modules provide results simultaneously. The decision feedback loop feeds the decision results back to the memory module, dynamically updating the memory content for continuous learning. The conflict resolution mechanism uses voting or hierarchical judgment mechanisms to resolve conflicts when they occur, ensuring the consistency and accuracy of the final decision.

[0112] The gradient decision-making process refers to a system adaptively selecting decision paths of varying depths based on the complexity of the scenario, progressing from simple reflective decision-making to complex reasoning-based decision-making, gradually deepening the analysis of the problem. This gradient decision-making mechanism ensures both efficiency in handling simple scenarios and accuracy in handling complex scenarios, which is the core advantage of the memory-guided reflective decision-making model. This mechanism achieves a good balance between resource consumption and decision quality, making it particularly suitable for applications such as substation safety management that require both high efficiency and accuracy.

[0113] Step S2.3: Based on the output of the gradient decision process, automatically identify the maintenance scenario type and determine the scenario attributes, and generate the optimal safety operation set for the specific maintenance scenario.

[0114] In this step, based on the output of the gradient decision process, the maintenance scenario is identified and safety measures are generated. Scenario identification refers to determining the type of scenario the current maintenance task belongs to, such as "protection verification," "modification and shutdown," or "periodic testing." Scenario attributes refer to the specific characteristics of the scenario, such as the range of equipment involved, operational complexity, and risk level. The optimal set of safety measures refers to the set of operations that minimize the isolation scope and operational steps for a specific maintenance scenario while meeting safety requirements.

[0115] Scene type identification is the first step in scene recognition, outputting the identified scene category, such as "protection and verification" or "modification and accompaniment." Different scene types correspond to different safety measures and operating procedures; accurate scene type identification is a prerequisite for generating appropriate safety measures. Through a memory-guided reflexive decision model, the system matches and infers from the input multimodal features with known scene patterns to determine the scene type. For common standard scenes, the system quickly identifies them using the reflexive module; for complex or novel scenes, the inference module performs in-depth analysis to ensure the accuracy of the identification results.

[0116] Scene attribute determination is the second step in scene identification. Building upon the identified scene type, it further determines the scene's specific attributes, such as the range of equipment involved and operational complexity. This attribute information is crucial for developing accurate safety measures. The system extracts various scene attributes by analyzing the input multimodal features and relevant information in the knowledge graph. For example, for the "220kV line protection cabinet A verification" scenario, the system will determine that the involved equipment is the line protection device cabinet A and its related circuits, the operational complexity is medium, and the risk level is medium, etc. This attribute information provides important reference for subsequent safety measure generation.

[0117] Uncertainty handling is a crucial step in scene recognition. When the recognition result is uncertain, multiple possible scenarios and their probability distributions are provided. Uncertainty may stem from ambiguity in the input information, incomplete data, or limitations of the model. By using Bayesian neural networks or ensemble learning methods, the probability distribution of the recognition result is estimated. For cases with high uncertainty, manual intervention or gathering more information for reassessment may be necessary. This uncertainty handling mechanism enhances the robustness and reliability of the system, avoiding security risks caused by misidentification.

[0118] The ultimate goal of this step is to generate a set of safety measures operations. Based on the identified scenario type and attributes, an optimal set of safety measures operations is generated for a specific maintenance scenario. First, a set of rules applicable to the current scenario is extracted from the rule base. These rules come from expert experience, industry standards, and historical case analysis. Then, combined with equipment association information and real-time equipment status from the knowledge graph, the range of equipment and loops requiring operation is determined. Next, the system applies a distributed guided local search algorithm to optimize the operation sequence under complex constraints, obtaining the optimal safety measure solution that satisfies safety requirements while minimizing operational complexity. Finally, the generated safety measure solution is formatted and standardized to form a structured set of safety measures operations, facilitating subsequent execution and monitoring.

[0119] Through scene recognition and safety measure generation, customized safety measures can be provided based on the specific circumstances of the maintenance task, significantly improving the accuracy and efficiency of safety measure formulation. The application of a memory-guided reflex decision-making mechanism enables the system to quickly handle common scenarios as well as accurately respond to complex or novel scenarios, providing intelligent support for the safe operation and maintenance of substations.

[0120] In step S2, the rule reasoning process is optimized under complex constraints using a distributed guided local search algorithm, including:

[0121] Step S2.4: Based on the inspection scene identification results, construct a hierarchical rule base, which includes a basic rule layer applicable to all scenarios, a scenario rule layer for different scenario types, and a special case rule layer for handling special situations, forming a three-layer scenario-based rule base.

[0122] A hierarchical rule base is a knowledge base that organizes rules hierarchically, improving the flexibility and scalability of rule management. A rule base is a system for storing and managing rules, which are knowledge expressed in the form of "if...then...". In this invention, rules are used to guide the generation and optimization of security measures. The system adopts a three-layer architecture for the rule base, including a basic rule layer, a scenario rule layer, and a special case rule layer.

[0123] The basic rules layer contains general rules applicable to all scenarios, such as "live equipment must be isolated" and "operations must follow the principle of outside before inside, and high before low." These rules originate from the basic safety guidelines and operating procedures of the power industry, have the highest priority, and ensure basic safety requirements. Basic rules are usually more abstract and general, with a wide range of applications, and form the foundation of the rule base. For example, the rule "any maintenance of live equipment must first ensure power isolation" applies to all maintenance scenarios and is a fundamental principle of safe power operation.

[0124] The scenario rule layer contains specialized rules defined for different scenario types (such as protection verification, modification shutdown, periodic testing, etc.). Each scenario type corresponds to a set of rules, which reflect the operational requirements and best practices under specific scenarios. Scenario rules are more targeted than basic rules and can provide more precise guidance based on scenario characteristics. For example, the rule "In the protection verification scenario, if the circuit has a soft pressure plate, the operation steps are 'remove the pressure plate → disconnect the terminal'; if there is no pressure plate, directly 'disconnect the terminal'" is specifically applicable to the protection verification scenario and provides specific operational sequence guidance.

[0125] The special case rule layer contains rules for handling specific equipment models, special operating conditions, or abnormal situations. These rules have the highest specificity and are used to cover special cases that cannot be handled by the basic rules and scenario rules. Special case rules are usually targeted at specific equipment models or non-standard situations, and there are many of them, but their scope of application is relatively narrow. For example, the rule "For a specific model of protection device from a certain manufacturer, a self-test program must be performed before verification, and then the relevant circuit must be disconnected" is a special case rule for a specific equipment model.

[0126] Extensible Markup Language (XML) is used as the rule definition language to achieve structured expression and flexible configuration of rules. XML is a markup language used to mark up electronic documents to give them structure; it provides a structured, self-describing way to represent data. Using XML to define rules has advantages such as unified format, clear structure, ease of parsing, and extensibility. XML rule definitions include the following core elements:

[0127] Rule ID: Uniquely identifies each rule for easy reference and management. Rule IDs are usually named hierarchically, such as "Base.001" representing the first rule in the basic rule layer, and "Scene.Protect.001" representing the first rule in the protection verification scenario.

[0128] ApplicableScenario: Specifies the type and conditions under which the rule applies. The applicable scenario can be a specific type of maintenance task, such as protection verification, modification and shutdown, or it can be a situation that meets specific conditions, such as "the line protection device has a soft pressure plate".

[0129] Trigger Condition: Defines the trigger condition for a rule using XPath expressions. XPath is a language for selecting nodes in an XML document that allows precise specification of the conditions under which a rule is triggered. Trigger conditions can be combinations of various factors such as device status, operation type, and environmental factors.

[0130] Action: The operation or step to be performed after the rule is triggered. The action can be a specific operation command, such as "exit the soft clamp", or it can be a call to other rules or functions, such as "apply the minimum isolation range algorithm".

[0131] Priority: The execution priority of a rule, used to resolve rule conflicts. When multiple rules simultaneously meet their triggering conditions, the system determines the execution order or overriding relationship based on priority. Priority is usually represented by a number, with larger numbers indicating higher priority.

[0132] Dependent rules: These specify other rules that the current rule depends on. The dependencies between rules form a logical chain for rule execution, ensuring that rules are applied in the correct order. Dependent rules can be preconditions or components of combined rules.

[0133] Version information: The rule's version number and update time. Version information is used for rule version control and update management, ensuring that the system uses the latest and most valid rules.

[0134] By constructing a hierarchical rule base, precise rule guidance can be provided for different maintenance scenarios, ensuring the accuracy and compliance of safety measures generation. The three-tier architecture design makes rule management both holistic and targeted, comprehensively covering various scenarios and situations, and providing a solid foundation for rule reasoning under complex constraints.

[0135] Step S2.5: Based on the scenario-based rule base of the three-layer architecture, a distributed guided local search algorithm is applied. Through an adaptive violation condition identification mechanism, high-cost constraints are selectively punished. Combined with a penalty evaporation mechanism, the penalty magnitude is controlled to prevent over-punishment. This achieves optimal rule reasoning under complex conditions including security constraints, temporal constraints, and resource constraints, and obtains the optimal rule reasoning result.

[0136] Distributed Guided Local Search (DGLS) is a heuristic optimization algorithm particularly suitable for solving complex decision problems with multiple constraints. Heuristic optimization refers to using empirical rules or heuristic principles to guide the search process and find a near-optimal solution within a reasonable computational time. In substation safety management, the rule-based reasoning process involves various constraints, such as safety constraints, timing constraints, and resource constraints. Traditional rule-based reasoning methods struggle to find optimal solutions under these complex constraints. The DGLS algorithm effectively overcomes this challenge by introducing a penalty mechanism and a distributed coordination strategy.

[0137] First, the rule-based reasoning problem is modeled as a distributed constraint optimization problem (DCOP). A DCOP is a constraint optimization problem solved collaboratively by multiple agents. Each agent controls a subset of variables and coordinates with other agents to find a solution that satisfies the constraints and optimizes the objective function. In this model, variables represent the operational steps or parameters to be decided, such as whether to remove a pressure plate or disconnect a circuit; constraints represent rule conditions and requirements, including safety constraints, sequence constraints, and resource constraints; the objective function comprehensively considers factors such as safety, operational efficiency, and resource utilization to evaluate the merits of the decision scheme. Safety constraints are limitations ensuring the safety of personnel and equipment, such as "live equipment must not be operated directly"; sequence constraints are limitations on the order of operations, such as "the soft pressure plate must be removed before disconnecting the circuit"; resource constraints are limitations on the availability of resources such as personnel and tools, such as "specific operations require personnel with the appropriate qualifications to perform."

[0138] One of the core components of the DGLS algorithm is the adaptive violation identification mechanism. This mechanism intelligently identifies and assesses the severity of constraint violations, providing a basis for subsequent penalty adjustments. The system designs a heuristic evaluation function f(s,c) to calculate the degree to which solution s violates constraint c. The evaluation function considers factors such as the severity, scope of impact, and consequences of the violation, providing a comprehensive score. A dynamic threshold mechanism is also introduced to automatically adjust the violation judgment criteria based on problem complexity and solution progress. In the early stages of the solution process, the threshold may be relatively lenient, allowing a certain degree of constraint violation to explore a wider solution space; as the solution progresses, the threshold gradually becomes stricter, guiding the search towards solutions without violations. A conflict tracking algorithm is implemented to record historical patterns of constraint conflicts and predict potential conflicts. Conflict tracking identifies frequently violated constraint combinations by analyzing conflict patterns that appear during the historical search process, providing valuable guidance for the search.

[0139] The penalty evaporation mechanism is another important component of the DGLS algorithm. It prevents the search process from getting trapped in local optima by dynamically adjusting the penalty value. The system maintains a penalty value p(c) for each constraint c, initially set to 0. When the local search gets trapped in a local optimum, the system increases the penalty value of the violated constraint: p(c) = p(c) + 1, guiding the search away from the current local optimum. The system introduces a penalty evaporation factor λ (0 < λ < 1), periodically reducing the penalty value: p(c) = λ·p(c), to prevent some constraints from being over-penalized. Over-penalization can lead to an overly restricted search space, missing potential high-quality solutions. An adaptive evaporation strategy is designed to dynamically adjust the λ value based on the search state. For example, when the search is progressing well, a larger λ value can be used to maintain the search direction; when the search is stuck, a smaller λ value is used to accelerate penalty evaporation and increase exploration.

[0140] The guided local search strategy is the core search mechanism of the DGLS algorithm. It guides the search direction by modifying the objective function, avoiding getting trapped in local optima. The modified objective function is: h(s) = g(s) + λ·∑c∈C p(c)·f(s,c), where g(s) is the original objective function, λ is the penalty coefficient, p(c) is the penalty value of constraint c, and f(s,c) is the degree to which solution s violates constraint c. This modification ensures that the search process considers not only the original quality of the solution but also the penalty value of the penalized feature, guiding the search away from explored local optima. A variable neighborhood search strategy is designed to dynamically adjust the search direction and step size according to the problem characteristics. Variable neighborhood search is a search strategy that systematically changes the neighborhood structure, enabling a more comprehensive exploration of the solution space. The system implements a simulated annealing mechanism, accepting non-improved solutions with a certain probability to avoid getting trapped in local optima. Simulated annealing is an optimization algorithm inspired by the metallurgical annealing process, which probabilistically accepts inferior solutions, allowing for a broader exploration of the search space. The system also incorporates an early stopping criterion, which terminates the search promptly when a satisfactory solution is reached or computational resources are limited, thereby improving algorithm efficiency.

[0141] The DGLS algorithm, through the components and mechanisms described above, can effectively perform rule reasoning and optimization under complex constraints. First, it initializes the search, constructing an initial solution (an operational set) based on scene recognition results and a rule base. Then, the system enters an iterative optimization process: evaluating the objective function value and constraint violations of the current solution; generating neighborhood solutions based on penalty values ​​and heuristic information; evaluating neighborhood solutions and selecting the optimal neighborhood solution to update the current solution; updating the penalty value and coordination information; and checking the termination condition, ending the iteration if satisfied. Finally, it outputs the optimal solution as the rule reasoning result, including the set of operational steps and their execution order, and generates a reasoning explanation, illustrating the considerations and trade-offs at each decision point.

[0142] By using distributed guided local search to optimize rule reasoning, the optimal or near-optimal security measures can be found under complex constraints. This effectively balances multiple objectives such as security, operational efficiency, and resource utilization, significantly improving the accuracy and adaptability of rule reasoning, especially in highly complex scenarios or those with conflicting constraints.

[0143] Step S2.6: Based on the optimal rule reasoning result, a distributed coordination synchronization scheme is used to handle rule conflicts under multiple constraints, and generate the optimal safety operation set for a specific maintenance scenario that satisfies all constraints.

[0144] In complex rule-based reasoning processes, conflicts or contradictions may arise between different rules, requiring resolution through distributed coordination mechanisms. Rule conflict refers to two or more rules offering contradictory decision recommendations, such as one rule requiring the disconnection of a loop while another requires maintaining its connection. Distributed coordination refers to the process by which multiple independent decision-making units reach a consensus through information exchange and negotiation. Synchronization schemes refer to schemes where decision-making units coordinate according to a synchronized clock or unified signal, ensuring the consistency and reliability of the decision-making process.

[0145] The distributed coordination synchronization scheme first decomposes the problem into multiple sub-problems, which are then solved in parallel by different agents. Problem decomposition is a fundamental strategy in distributed computing; by breaking down complex problems into relatively independent sub-problems, parallel computing resources can be fully utilized, improving solution efficiency. In this invention, problem decomposition may be performed according to dimensions such as device type, functional area, or operation type. For example, the operation of protection devices, network devices, and power circuits may be assigned to different agents for processing.

[0146] Design a message-passing-based coordination protocol to enable agents to share penalty information and solutions. Message passing is the fundamental mechanism for inter-agent communication in distributed systems, achieving efficient information sharing and exchange by defining standard message formats and delivery rules. The coordination protocol defines the rules and processes for inter-agent communication, including message types, sending timing, and processing methods. Typical message types include status information (current solutions and constraint violations), decision information (planned actions), and coordination information (conflict detection and resolution suggestions). The coordination protocol typically uses a round-robin approach, where each agent sends messages, receives and processes messages from other agents, and updates its own decisions until consensus is reached or termination conditions are met.

[0147] Implement a conflict detection and resolution mechanism to handle decision-making conflicts among agents. Conflict detection is the process of identifying inconsistencies or contradictions in decisions among agents, typically achieved by comparing the decision outcomes of each agent or checking whether global constraints are met. Conflict resolution is the process of resolving decision-making conflicts according to predetermined strategies or through dynamic negotiation. Common conflict resolution strategies include priority arbitration (deciding according to rule priority), majority voting (adopting the decision of the majority of agents), compromise (finding a middle ground acceptable to all parties), and hierarchical decision-making (resolving the conflict by a higher-level decision-maker). Based on the nature and severity of the conflict, select an appropriate resolution strategy to ensure the consistency and rationality of the final decision.

[0148] An asynchronous update strategy is adopted to reduce communication overhead and improve system response speed. Asynchronous updates mean that agents do not need to wait for all other agents to complete their current round of processing; they can update their decisions based on the information they have received. This approach reduces waiting time between agents, improves the overall efficiency of the system, and is particularly suitable for handling large-scale distributed problems with high communication costs. An information diffusion mechanism is designed to ensure that key decision information can be quickly disseminated to relevant agents, guaranteeing the timeliness and effectiveness of coordination.

[0149] By handling rule conflicts through distributed coordination, the system can comprehensively consider multiple constraints and requirements to find a globally optimal or near-optimal solution. The final set of optimal safety measures includes the following:

[0150] Operation Steps List: This section details all the operations that need to be performed, including the operation type, the object being operated on, and the operation parameters. Examples include "Exit Line 1 Protection Device Soft Pressure Plate X" and "Disconnect Line 1 CT Circuit Terminals."

[0151] Execution order: Explicitly define the execution order of operation steps to ensure that operations conform to timing constraints and dependencies. Execution order can be represented as a complete order (the determined order of all steps) or a partial order (defining only the necessary sequence, allowing some steps to be executed in parallel).

[0152] Execution conditions: Specify the execution conditions for each operation step, including preconditions (which must be met for execution) and trigger conditions (which require execution when met). Execution conditions enhance the adaptability of the safety measures solution, enabling dynamic adjustments to the execution process based on actual circumstances.

[0153] Verification criteria: Provide verification methods and checkpoints for each key step to ensure correct operation and prevent omissions or errors. Verification criteria may include observing device status indicators, measuring specific parameters, and checking communication status.

[0154] Resource Requirements: This section outlines the personnel, tools, time, and other resources required to execute each step, facilitating resource allocation and preparation. Resource requirements information aids in the feasibility assessment and implementation management of the safety measures plan.

[0155] The generated optimal set of safety measures not only satisfies various safety constraints, ensuring safe and reliable operation, but also optimizes the operation steps and sequence, reducing unnecessary operations and waiting time. Furthermore, it considers resource constraints to ensure the feasibility of the solution. This comprehensively optimized safety measure scheme significantly improves the safety and efficiency of maintenance work, providing strong support for the safe operation and maintenance of smart substations.

[0156] In step S3, the associated loops are automatically identified and the minimum safe isolation range is determined through a memory-guided decision-making reasoning mechanism, including:

[0157] Step S3.1: For the optimal set of safety measures, with the target maintenance equipment as the center, apply a multi-level graph traversal algorithm to automatically identify all loops that have four types of relationships with the target equipment, including direct connection, functional association, indirect influence and backup redundancy, and determine the minimum safety isolation range according to the maintenance domain isolation principle.

[0158] In this step, a multi-level graph traversal is first performed on the knowledge graph, centered on the target maintenance equipment, to identify all associated loops and devices. The target maintenance equipment refers to the equipment planned for maintenance, such as specific protection devices, communication equipment, or measuring devices. Multi-level graph traversal is an algorithm that explores graph structure data hierarchically, discovering all nodes and edges related to the starting node by systematically visiting nodes in the graph. In this invention, an improved breadth-first search (BFS) algorithm is used to implement multi-level graph traversal. BFS is a graph search algorithm that starts from the starting node, visits all neighboring nodes first, and then visits nodes at the next level, making it suitable for discovering shortest paths and comprehensively exploring graph structures.

[0159] The multi-level graph traversal strategy includes four layers of exploration, covering different types of relationships: The first layer explores directly connected physical loops, such as the connection between a protection device and a terminal block, or fiber optic links. Direct connections refer to relationships where devices are directly connected through physical media (such as cables or optical fibers); these relationships are the most obvious and easy to identify. The second layer explores functionally related loops, such as logical relationships between devices communicating via GOOSE messages. Functional relationships refer to relationships where devices, although not directly physically connected, are mutually dependent through functional logic, such as one device's function depending on information or services provided by another device. GOOSE (Generic Object Oriented Substation Event) is a high-speed point-to-point communication mechanism defined in the IEC 61850 standard for event transmission between intelligent electronic devices (IEDs), supporting time-critical applications. The third layer explores indirectly affected loops, such as devices related through a common bus or power supply. Indirect effects refer to relationships between devices generated through shared resources or intermediate nodes; these relationships are more subtle and require in-depth analysis to discover. The fourth layer explores backup or redundant loops, such as hot standby protection devices and redundant communication paths. Redundancy refers to redundant devices or paths set up to improve system reliability, providing backup functionality when the primary device or path fails.

[0160] A correlation strength scoring function S(e1,e2) is defined to quantify the degree of correlation between two devices. Correlation strength refers to the closeness or importance of the relationship between two devices; high correlation strength means that the state or operation of one device has a significant impact on the other. The correlation strength calculation considers multiple factors: physical distance (actual or topological distance between devices), logical dependency (degree of functional dependence), functional influence range (the range of impact of a change in the function of one device on the other), and historical operation relevance (the frequency of simultaneous operation of the two devices in historical security measures). An attenuation factor α (0 < α < 1) is introduced to cause the correlation strength to decrease as the correlation path increases: S(path) = Πi S(ei,ei+1)·αi, where path represents the correlation path between devices, S(ei,ei+1) represents the correlation strength of adjacent devices on the path, and αi represents the attenuation factor at step i. This attenuation mechanism reflects the reality that indirect correlations are usually weaker than direct correlations. A correlation strength threshold θ is set; traversal stops when S(path) < θ, controlling the boundary of the correlation range. The threshold θ needs to be set in a balance between comprehensiveness and accuracy. An excessively high threshold may miss important correlations, while an excessively low threshold may introduce irrelevant devices.

[0161] Importance analysis and priority ranking are performed on the identified associated devices and loops. Importance refers to the criticality of a device or loop within the system; high importance means that its state or operation has a significant impact on system safety and functionality. An importance index I(e) is calculated for the identified associated devices and loops, considering factors such as the criticality of device functions and the scope of impact of a fault. Functional criticality reflects the importance of a device's function; for example, main protection devices are usually more critical than auxiliary devices. The scope of impact of a fault reflects the range of systems that a device fault may affect; the wider the scope, the higher the importance. Associated loops are prioritized based on the importance index to ensure that critical loops are treated first. The system also establishes a dependency graph to identify pre-dependencies between loops. A dependency graph is a directed graph that represents the dependencies between entities and is used to analyze system structure and determine the order of operations. A topological sorting algorithm is applied to ensure that the order of operations satisfies dependency constraints. Topological sorting is an algorithm that sorts all vertices of a directed acyclic graph such that all directed edges point from the element at the beginning to the element at the end; it is often used to determine the execution order of tasks with dependencies.

[0162] The minimum safe isolation range is determined based on the "maintenance domain isolation" principle. Maintenance domain isolation is an isolation strategy that minimizes the impact on normal system functions while ensuring safety. Its core idea is to limit the isolation range to the necessary minimum, avoiding system function loss due to excessive isolation. The system calls the corresponding isolation strategy template based on the scenario identification results. The isolation strategy template is a predefined isolation strategy framework for different maintenance scenarios, including scenario characteristics, isolation principles, and operation guidelines. Specific isolation rules are designed for different scenarios, such as retaining backup protection functions in protection verification scenarios and considering temporary solutions for the transition period in modification scenarios. The system implements a scenario-strategy mapping table, supporting quick selection of the appropriate isolation strategy for the current scenario.

[0163] The final isolation scheme is determined through multi-objective optimization decision-making. Multi-objective optimization refers to the problem of simultaneously optimizing multiple potentially conflicting objective functions. In this invention, it is necessary to balance the contradictory requirements of safety and impact scope. A multi-objective evaluation function F(R) is defined to comprehensively evaluate factors such as the safety, impact scope, and operational complexity of the isolation scheme R. The Pareto optimization method is applied to find the balance point that minimizes safety and impact. Pareto optimization is a multi-objective optimization method that seeks a solution that cannot improve any objective without reducing any objective, i.e., the Pareto optimal solution. A risk assessment model is introduced to perform risk quantification analysis on candidate isolation schemes. Risk quantification analysis combines risk probability and consequence severity to provide a risk dimension reference for decision-making. Finally, the isolation scheme with controllable risk and minimal impact is selected as the final decision.

[0164] Furthermore, a dynamic adjustment mechanism for isolation boundaries is designed to improve the adaptability of the isolation scheme. An elastic isolation boundary mechanism is designed to allow adjustment of the isolation range based on real-time conditions. Critical isolation points are identified, as their state directly determines the isolation effectiveness. These critical isolation points are typically nodes controlling important connections or functions, such as main circuit breakers and communication hubs. An isolation redundancy index is established to evaluate the reliability and fault tolerance of the isolation scheme. Isolation redundancy reflects the safety margin of the isolation scheme; high redundancy means that even if some isolation measures fail, basic isolation effectiveness can still be guaranteed. An adaptive boundary adjustment algorithm is implemented to dynamically optimize the isolation range while ensuring safety.

[0165] Finally, a structured dataset of associated loops and isolation schemes are generated, serving as the foundation for subsequent security measure generation. The associated loop representation adopts a hierarchical structure, including core loops, auxiliary loops, and indirectly associated loops. Core loops are critical loops that must be isolated; auxiliary loops are support loops that need to be isolated to ensure security; and indirectly associated loops are peripheral loops that may be affected but do not necessarily need to be completely isolated. Key attributes are labeled for each loop: loop type, association strength, importance, etc., and a relationship matrix between loops is established to describe dependencies, mutual exclusions, and other relationships between loops. Isolation scheme standardization converts the isolation scheme into a standard format, including a list of isolated devices, operation sequences, verification methods, etc., adds scheme metadata (such as applicable scenarios, security levels, required resources, etc.), and generates explanations of the scheme's decision-making basis, improving the interpretability and credibility of the scheme.

[0166] By using multi-level graph traversal and maintenance domain isolation principles, all loops related to the maintenance equipment can be identified comprehensively and accurately, and the optimal isolation range can be determined. This provides a reliable foundation for the generation of subsequent safety measures, effectively avoids safety risks caused by missing related loops, and minimizes the impact on the normal function of the system.

[0167] Step S3.2: Based on the minimum security isolation range, real-time device status information of relevant devices is collected in real time through multi-protocol access. The multi-source heterogeneous data of the real-time device status information is processed by a data fusion algorithm based on memory-guided reflection mechanism to form a unified device status representation.

[0168] In this step, based on the minimum safety isolation range determined in step S3.1, the range of devices that need to be monitored is first determined. The minimum safety isolation range includes devices and loops that must be isolated, as well as related devices whose status needs to be monitored to ensure safety. Real-time device status information refers to the current working status, operating parameters, and configuration information of the devices. This information is crucial for developing accurate safety measures and monitoring the implementation process of safety measures.

[0169] A multi-protocol device status acquisition framework is designed to support the acquisition of real-time status data from various smart substation devices. Multi-protocol access refers to the system's ability to exchange data with different types of devices through multiple communication protocols, adapting to the access needs of various devices within the substation. This framework boasts high compatibility and scalability, capable of accommodating devices from different manufacturers and using different protocols. The multi-protocol access layer includes various protocol adapters: IEC 61850 protocol adapter, SNMP protocol adapter, Modbus protocol adapter, OPC UA adapter, and a custom protocol adapter.

[0170] The IEC 61850 protocol adapter supports sub-protocols such as MMS, GOOSE, and SMV for collecting status data from protection devices, smart terminals, and other equipment. IEC 61850 is an international standard for intelligent substation automation systems, defining equipment models, communication protocols, and engineering processes. MMS (Manufacturing Message Specification) is a communication protocol for industrial automation systems, used for reading and writing equipment parameters and transmitting control commands; GOOSE (Generic Object Oriented Substation Event) is a high-speed point-to-point communication mechanism used for transmitting status changes and event information; SMV (Sampled Measured Values) is a protocol for transmitting digital measurement values, primarily used for transmitting digital sampled values ​​of analog quantities such as current and voltage.

[0171] SNMP protocol adapters are primarily used for network device status monitoring, such as switch port status and link bandwidth usage. SNMP (Simple Network Management Protocol) is an internet standard protocol used to manage devices on IP networks and is widely used in network device monitoring and management. Modbus protocol adapters are used to collect status information from traditional RTUs and older devices. Modbus is a serial communication protocol commonly used for connection between industrial electronic devices, particularly suitable for data acquisition from traditional automation equipment. OPC UA adapters provide standardized interfaces to industrial control systems, supporting cross-platform data exchange. OPC UA (OPC Unified Architecture) is an industrial communication standard that provides a platform-independent, secure way to transmit information, suitable for data integration in complex industrial control systems. Custom protocol adapters support manufacturer-specific protocols, achieving flexible adaptation through protocol parsing configuration, ensuring the system can handle the data acquisition needs of non-standard devices.

[0172] Design efficient data acquisition strategies to optimize data acquisition efficiency and reliability. Differential acquisition only transmits changed data, reducing network load. Differential acquisition is a data transmission optimization technique that compares current data with previously transmitted data, sending only the changed portions, significantly reducing the amount of data transmitted. Priority sampling assigns different sampling priorities based on device importance and data timeliness requirements. High-priority data (such as critical protection status) is sampled more frequently to ensure timely acquisition of important information. Adaptive sampling frequency dynamically adjusts the sampling frequency based on system load and data change rate. The sampling frequency is increased when data changes rapidly or the system load is light; the sampling frequency is decreased when data is stable or the system load is heavy, optimizing system resource utilization. Batch data requests optimize the data request strategy, reducing communication frequency and improving acquisition efficiency. Multiple individual data requests are merged into batch requests, reducing the overhead of communication establishment and disconnection, and improving data acquisition efficiency. An exponential backoff algorithm is designed to handle communication anomalies, ensuring data acquisition reliability. The exponential backoff algorithm is a mechanism that gradually increases the retry interval after communication failure, avoiding network congestion caused by frequent retries while ensuring that data is eventually acquired.

[0173] Implement data quality assessments to ensure the accuracy and reliability of collected data. Timestamp verification verifies the reasonableness of data timestamps and identifies outdated data. By comparing data timestamps with system time, it detects abnormally delayed or expired data, avoiding the use of untimely information. Range checks check whether data values ​​are within reasonable ranges according to equipment parameter specifications. Data exceeding the expected range may indicate equipment malfunction or measurement errors, requiring special marking or handling. Rate of change analysis detects whether the rate of change of data conforms to physical characteristics, identifying abnormal fluctuations. Under normal operating conditions, the rate of change of most parameters follows a certain pattern; sudden and drastic changes may indicate equipment failure or data errors. Consistency verification improves data reliability through cross-validation using redundant information or correlated data. For example, multiple measurements of the same physical quantity should be basically consistent, and related parameters should satisfy certain physical relationships. Quality labeling adds a quality label to each data point, indicating the reliability level of the data. Quality labels typically include levels such as "good," "questionable," and "bad," assisting in subsequent processing decisions.

[0174] After acquiring device status data, a multi-source heterogeneous data fusion algorithm is implemented to integrate device status data from different sources and in different formats into a unified device status representation. Multi-source heterogeneous data refers to data from different devices, different protocols, and different formats. This data needs to be processed and fused to form a consistent system view. This algorithm draws on the idea of ​​memory-guided reflexive decision-making, enabling it to quickly handle routine data fusion scenarios while processing complex or conflicting status information through deep reasoning.

[0175] Data preprocessing and standardization are the first steps in data fusion, transforming heterogeneous data into a unified internal representation format. Unit conversion converts measurements from different units to a unified standard unit, ensuring data comparability. For example, current values ​​reported by different devices are uniformly converted to amperes (A), and voltage values ​​are uniformly converted to volts (V). Time alignment aligns data from different timestamps along the timeline, ensuring temporal consistency of states. Time alignment typically employs methods such as interpolation, extrapolation, or nearest-nearest-value imputation to map data from different time points to a unified time point. Data structure transformation converts data in different formats into a unified internal representation structure, facilitating subsequent processing. This typically involves operations such as data model mapping, field renaming, and structural reorganization. Outlier handling identifies and processes outliers using statistical methods, such as outlier smoothing and missing value imputation. Common outlier handling methods include median filtering, moving averages, interpolation, and predictive imputation.

[0176] A memory-based fast state fusion module enables rapid processing of common state combinations, improving system efficiency. A state pattern memory stores common device state combination patterns and their explanations. These patterns originate from historical operational data and expert knowledge, representing common system state combinations and their significance. A pattern matching mechanism matches the currently collected multi-source state data with patterns in the memory, identifying similar state combinations. The matching process typically uses similarity metrics, such as cosine similarity and Euclidean distance, to assess the closeness between the current state and patterns in the memory. For common state combinations with high matching degrees, rapid state determination directly outputs the fusion result without complex calculations. This "fast track" processing mechanism significantly improves the system's efficiency in handling common states. The memory is dynamically updated, continuously enriching and optimizing the state pattern memory based on operational experience, improving the system's adaptability and accuracy. Newly emerging state combinations, after verification, can be added to the memory, continuously expanding the system's knowledge base.

[0177] The deep reasoning state fusion module handles complex or unknown state combinations, ensuring the system can cope with various situations. The device state reasoning model is based on a Bayesian network to construct a causal relationship model between device states. A Bayesian network is a probabilistic graphical model that represents conditional dependencies between variables through a directed acyclic graph, suitable for expressing causal relationships and uncertainties between states. Multi-source data weight calculation dynamically calculates the weight of each data source based on factors such as data quality and source reliability. High-quality, high-reliability data sources receive higher weights and have a greater impact during the fusion process. The conflict resolution strategy is designed based on a conflict data processing method using evidence theory to reasonably handle inconsistent state information. Evidence theory (Dempster-Shafer theory) is an uncertainty reasoning method capable of handling uncertain and conflicting information, suitable for conflict resolution in multi-source data fusion. For complex state combinations that cannot be concluded through rapid matching, the state reasoning engine initiates deep reasoning analysis. The reasoning process considers the logical relationships between devices, physical constraints, and historical experience to arrive at the most probable state interpretation.

[0178] Finally, a unified equipment status representation model is constructed to provide comprehensive and real-time status basis for safety measure generation. The equipment status representation standard defines a unified standard for describing equipment status, including basic status (e.g., online / offline), operational status (e.g., normal / alarm / fault), and functional status (e.g., the enabled / disabled status of each functional module). A multi-dimensional state space is designed to comprehensively describe the various aspects of the equipment's status characteristics. A correlation model between states is established to describe the logical relationships between different state dimensions. State change detection and event generation implement a state change detection algorithm to promptly identify key state changes. An event classification framework is designed to transform state changes into structured events. An event priority evaluation mechanism is established to highlight key state change events. An event notification mechanism is implemented to ensure that important state changes trigger corresponding processing in a timely manner. State history management and trend analysis design an efficient state history storage structure to support long-term storage and rapid retrieval of state data. A state trend analysis algorithm is implemented to predict the evolution trend of equipment status. A state baseline model is established for rapid identification of abnormal states. State backtracking queries are supported for easy post-event analysis and problem localization.

[0179] By utilizing multi-protocol access and data fusion based on memory-guided reflection mechanisms, accurate and comprehensive real-time device status information can be obtained, forming a unified device status representation. This representation provides a reliable status basis for safety measure generation, enabling safety measure operations to be dynamically adjusted according to the actual device conditions, thereby improving the relevance and effectiveness of safety measures.

[0180] Step S3.3: Based on the unified device status representation, the memory module in the memory-guided reflex decision-making mechanism generates routine safety operation steps. The reasoning module handles special state situations, and the operation content and execution order are dynamically adjusted in combination with the real-time device status information to obtain a complete sequence of operation steps that are adaptively adjusted according to the real-time device status.

[0181] In this step, a memory-guided reflex decision-making mechanism is innovatively applied to the safety measure generation process, achieving efficient and intelligent safety measure generation functionality. The memory-guided reflex decision-making mechanism is a decision-making mechanism combining rapid response and deep thinking, composed of a memory module, a reflex module, and a reasoning module, similar to the human decision-making process. This mechanism originates from the field of visual decision-making reasoning, and this invention transfers and optimizes its core ideas for application in the substation safety measure generation scenario. Visual decision-making reasoning refers to the cognitive process of making decisions based on visual information, emphasizing the combination of rapid response and deep analysis from perception to decision. This mechanism forms a progressive decision-making process from rapid response to deep thinking, making it particularly suitable for safety measure generation tasks that require both efficiency and accuracy.

[0182] First, a safety measure memory module is constructed to store historical safety measure experiences and typical operating patterns. This memory module serves as a knowledge base for the memory-guided reflex decision-making mechanism, storing historical experiences and regularities to provide a basis for rapid decision-making. The system adopts a multi-level memory structure design, including three categories: episodic memory, semantic memory, and procedural memory. Episodic memory stores complete historical safety measure cases, including scenario descriptions, equipment status, operating steps, and results. Episodic memory records specific experiences in specific scenarios, similar to human recollections of past experiences. Semantic memory stores abstract operating rules and best practices, such as "circuit breaker maintenance must first disconnect the isolating switches on both sides." Semantic memory is a conceptual understanding of rules and knowledge, independent of specific experiences. Procedural memory stores standard operating procedures and sequence of steps, such as "standard operating procedures for disabling protective devices." Procedural memory is knowledge about how to perform specific tasks, usually represented by procedural steps.

[0183] The design incorporates a memory index and retrieval mechanism to ensure rapid retrieval of historical experiences relevant to the current scenario. A multi-dimensional feature index is constructed based on device type, operation type, and scenario features, supporting multi-condition queries and similarity retrieval. Semantic similarity calculation employs a vector space model to calculate the similarity between the query scenario and remembered cases. The vector space model is an information retrieval method that represents documents and queries as vectors and assesses relevance by calculating the similarity between vectors. Context-sensitive retrieval considers contextual factors such as power grid operating status and seasonal time for memory retrieval, improving the adaptability of retrieval results. A hierarchical caching strategy stores frequently used items in a fast cache, improving retrieval efficiency. Caching is a temporary storage mechanism that reduces data access time by storing frequently used data in faster storage media.

[0184] Implement memory quality management to ensure the accuracy and effectiveness of memory content. Memory reliability scoring assesses the reliability of memory items based on historical application performance, prioritizing high-scoring items. Redundancy elimination identifies and merges semantically similar memory items to reduce redundancy and improve storage and retrieval efficiency. A memory update mechanism dynamically updates memory content based on new operational experience and expert feedback to ensure the timeliness of knowledge. A memory forgetting strategy archives or deletes long-unused and low-reliability memory items, optimizing the size and quality of the memory bank. Memory forgetting mimics the natural decay of human memory, selectively forgetting unimportant or outdated information to maintain the efficiency and relevance of the memory system.

[0185] The reflective decision-making module handles routine and explicit safety measure generation scenarios. This module is the rapid response part of the memory-guided reflective decision-making mechanism, achieving rapid decision-making through simple pattern matching, similar to human conditioned reflexes. Pattern matching and rapid response are the core mechanisms of reflective decision-making. Feature extraction extracts key feature vectors from the current scene and device state, capturing the essential features of the scene. Pattern matching quickly matches the feature vectors with patterns in the memory bank, finding the most similar historical cases. Similarity threshold control sets a dynamic threshold to control the triggering conditions of reflective decision-making, ensuring that reflective decision-making is only triggered in cases of high matching. Step template instantiation, upon successful matching, instantiates an operation step template based on the current specific device and state, forming a preliminary safety measure plan. Template instantiation is the process of applying a general template to a specific situation, generating a specific plan suitable for the current situation by replacing variables or parameters in the template.

[0186] Context adaptation is a crucial function of the reflection decision module, ensuring that the generated security measures are suitable for the current environment. Device status adaptation adjusts operation steps based on real-time device status, such as "skipping the disconnection operation if the switch port is offline." Resource constraint adaptation considers resource constraints such as personnel qualifications and tool availability, adjusting the operation schedule accordingly. Timing constraint handling ensures that the generated step sequence meets necessary timing dependencies, avoiding incorrect operation order. Security verification performs basic security rule checks on the rapidly generated steps to ensure there are no obvious risks and improve the reliability of the solution.

[0187] Feedback loops are the learning mechanism of the reflexive decision-making module, continuously optimizing the decision-making process through practical experience. Execution effect feedback collects feedback on the effectiveness of the safety measures generated by reflexive decision-making, evaluating the quality of the decisions. Memory reinforcement strengthens the memory of effective decisions, increasing their matching priority in similar scenarios. Reinforcement is a learning mechanism that improves the accuracy of decisions in similar future situations by increasing the weight of successful experiences. Anomaly marking records execution anomalies, used to improve decision-making rules and avoid repeating errors.

[0188] The design incorporates a reasoning and decision-making module to handle complex or novel security measure generation scenarios. This module represents the deep thinking component of the memory-guided reflex decision-making mechanism, handling complex situations through logical reasoning and knowledge deduction, similar to human deliberation. Deep reasoning is the core function of the reasoning and decision-making process. Knowledge graph-based reasoning utilizes the knowledge graph constructed in step S1 for path analysis and relationship deduction, discovering implicit relationships between devices. Rule engine reasoning applies the rule base from step S2 for formal reasoning, generating operational plans that conform to rule constraints. The rule engine is a system that processes data using rules, reasoning and transforming input data through a series of "if-then" rules. Causal reasoning analyzes causal relationships between devices, predicts the scope of operational impact, and assesses the chain reaction of operations. Risk assessment performs multi-dimensional risk assessments on the reasoning-generated steps to ensure the security of the plan.

[0189] Decomposition and recombination strategies are crucial methods for reasoning and decision-making modules to handle complex problems. Problem decomposition breaks down complex safety measures scenarios into multiple sub-problems, reducing overall problem complexity. Sub-problem solving addresses each sub-problem separately, generating local solutions. Solution integration integrates the sub-problem solutions into a complete sequence of safety measures steps, ensuring consistency among all parts. Conflict detection and resolution identify and resolve potential conflicts that may arise during the integration process, ensuring the consistency and feasibility of the final solution.

[0190] Innovative solution generation is the reasoning and decision-making module's ability to handle novel scenarios. Analogical reasoning identifies similar but not perfectly matching historical cases and generates solutions through analogy. Analogical reasoning is a similarity-based reasoning method that finds similarities between the current problem and previously solved problems, drawing on existing solutions to address new issues. Combinatorial innovation combines multiple memory fragments to creatively generate new operational sequences to handle unprecedented scenarios. Expert knowledge fusion integrates the experience and rules of power industry experts to guide the generation of innovative solutions, ensuring their professionalism and feasibility. Solution verification uses formal methods to verify the safety and effectiveness of innovative solutions, ensuring they comply with basic safety principles and operational specifications.

[0191] Reflection-inference coordination is a key mechanism for achieving seamless cooperation between the two decision-making modes. Decision flow triggering conditions define the conditions under which the reflection module transfers decision tasks to the inference module, such as insufficient matching or high-risk scenarios. Partial result reuse utilizes some results from the reflection module during inference, improving efficiency and avoiding redundant calculations. Inference result feedback feeds inference results back to the memory bank, enriching experience accumulation and providing reference for similar scenarios in the future. Learning optimization optimizes decision-making strategies and parameters based on the inference process and results, improving the system's adaptability.

[0192] Based on a unified equipment status representation and memory-guided decision-making output, the operation content and execution sequence are dynamically adjusted in conjunction with real-time equipment status information. Dynamic adjustment refers to adaptively modifying the operation plan according to real-time status changes to ensure the adaptability and effectiveness of safety measures. For example, when it is found that a device is already in the desired state (such as a pressure plate being deactivated), the corresponding operation steps are automatically skipped; when an abnormal equipment state is detected, necessary processing steps are added; when resource conditions change, the division of labor and sequence of operations are adjusted. This dynamic adjustment capability greatly improves the flexibility and adaptability of safety measure execution, enabling it to cope with various changes and uncertainties in the complex environment of substations.

[0193] Ultimately, a complete sequence of operating procedures is obtained, adaptively adjusted based on the real-time status of the equipment. This sequence includes detailed operating instructions, execution conditions, sequence constraints, and verification points, guiding maintenance personnel to complete maintenance tasks safely and efficiently. Adaptive adjustment ensures the targeted and effective implementation of safety measures, avoids unnecessary operations and potential risks, and improves the overall quality and efficiency of maintenance work.

[0194] By employing a memory-guided decision-making reasoning mechanism, this approach combines efficiency and flexibility: for routine scenarios, it quickly generates standard operating procedures; for complex or novel scenarios, it generates customized solutions through deep reasoning. This progressive decision-making mechanism, moving from rapid response to in-depth thinking, significantly improves the intelligence and adaptability of safety measure generation, providing strong support for the management of safety measures in smart substations.

[0195] In step S4, an operation dependency model is constructed by combining untangled variational multigraph representation learning and a distributed guided local search algorithm, including:

[0196] Step S4.1: Based on the adaptively adjusted operation steps and the quaternary knowledge graph, the multidimensional dependency relationships between operations, including temporal dependency, functional dependency, spatial dependency, resource dependency and conditional dependency, are extracted by the unentangled variational multigraph representation learning method. The preconditions, mutual exclusion relationships and temporal constraints between operations are clearly defined to form a multidimensional logical dependency network.

[0197] In this step, an operation dependency model is constructed based on the adaptive operation steps generated in step S3 and the four-element knowledge graph built in step S1. The operation dependency model is a formalized model describing various dependency constraints between operations, used to guide operation execution and risk assessment. Through the untangled variational multigraph representation learning method, multidimensional dependencies between operations are extracted to form a complete dependency network.

[0198] First, multidimensional dependencies are defined, including five main types: temporal dependency, functional dependency, spatial dependency, resource dependency, and conditional dependency. Temporal dependency describes the sequential constraints of operation execution, such as "exiting the soft pressure plate must be completed before disconnecting the terminals." Temporal dependency is the most basic type of dependency, directly affecting the execution order of operations; violating temporal dependency may lead to operational failure or safety risks. Functional dependency describes the functional association between operations, such as "fault recording shutdown depends on the communication function of the protection device." Functional dependency reflects the functional correlation between operational objects; the result of one operation may affect the condition or effect of another operation. Spatial dependency describes dependencies based on physical location, such as "disconnecting the A-phase disconnector should precede disconnecting the B and C-phase disconnectors." Spatial dependency considers the physical layout and spatial relationships of equipment, which is important for optimizing operation paths and improving operational efficiency. Resource dependency describes the competitive relationship between operations for shared resources, such as multiple operations requiring the same operator or tool. Resource dependencies affect the possibility of parallel execution of operations; proper arrangement of resource dependencies can improve execution efficiency. Conditional dependency describes dynamic dependencies based on specific conditions, such as "heat dissipation measures need to be implemented only when the temperature exceeds a threshold." Conditional dependency introduces dynamic judgment, making dependency relationships more flexible and adaptable to changing environmental conditions.

[0199] Initial dependency extraction is performed using multiple methods. Rule-driven extraction uses expert rules from the power system to identify standard dependencies. These rules, derived from industry standards, operating procedures, and expert experience, reflect generally accepted operational dependency knowledge. Knowledge graph derivation derives implicit dependencies based on relationships within the knowledge graph. The system utilizes entity relationships and attribute information from the knowledge graph, through path analysis and relational reasoning, to discover potential dependencies between operational objects. Historical data mining analyzes operational sequence patterns in historical safety tickets to identify empirical dependencies. The system applies sequence pattern mining algorithms to discover frequently occurring operational sequence patterns from a large number of historical operation records, serving as a basis for empirical dependencies. Equipment manual parsing extracts operational constraints and dependencies from equipment operation manuals. Operation manuals typically contain detailed operating procedures and precautions, providing an important source of knowledge about operational dependencies.

[0200] Next, an unentangled variational multigraph representation learning method is applied to construct and optimize the operation dependency model. This method can effectively handle the complex entanglement problem in operation dependencies, improving the model's expressive power and accuracy. The system first initializes the operation dependency graph, representing operations as graph nodes and dependencies as directed edges. Dependency type and strength attributes are assigned to edges to reflect the nature and importance of the dependencies. An initial multigraph structure is constructed, with different types of dependencies forming different sub-layers. Feature representations of graph nodes are designed, including information such as operation type, target device, and execution conditions.

[0201] Then, an unwrapped variational autoencoder (DAE) is applied to process the initial multigraph. Shared and private features are separated to identify commonalities and unique characteristics in different types of dependencies. Spatiotemporal encoding enhancement introduces temporal and spatial encodings to capture the spatiotemporal relationships of operations. Contrastive learning optimization enhances the model's ability to distinguish similar dependency patterns through contrastive learning methods. Contrastive learning is a self-supervised learning method that constructs positive and negative sample pairs to learn to distinguish between similar and dissimilar representations, helping to improve the model's ability to distinguish different dependency types in dependency modeling.

[0202] A Product of Experts (PoE) fusion mechanism is implemented to integrate information from multiple dependencies. For each operation pair (Oi, Oj), the system calculates the joint probability of multiple dependencies. The PoE formula is applied: p(Oi→Oj) ∝ ∏kp(Oi→Oj|Rk), where Rk represents the k-th type of dependency. A Product of Experts is a method of combining multiple probabilistic models by multiplying and normalizing the probability distributions of each model to form a more accurate joint distribution. In this invention, PoE is used to integrate probability estimates of different types of dependencies to obtain a more comprehensive representation of dependencies. Trust weights are introduced to adjust the impact of dependencies in the fusion process based on their reliability. High-reliability dependencies (such as those explicitly specified in the equipment manual) receive higher weights, while low-reliability dependencies (such as those inferred only from limited historical data) receive lower weights. A probability threshold is set to retain dependencies exceeding the threshold in the final model, filtering out low-probability noise dependencies.

[0203] Model optimization and validation are performed to ensure the accuracy and completeness of the dependency model. Conflict detection identifies and resolves circular or contradictory dependencies in the model. Circular dependencies are closed-loop dependencies, such as A depending on B, B depending on C, and C depending on A, which logically cannot be satisfied simultaneously. Conflicting dependencies are mutually conflicting constraints, such as one constraint requiring A to precede B, and another requiring B to precede A. The system uses graph algorithms to detect circular dependencies and applies methods based on priority, reliability, or expert rules to resolve conflicts. Completeness checks verify that critical operations have necessary dependency constraints. All important operations have clearly defined preconditions and subsequent effects to avoid risks caused by missing dependencies. Expert review combines power industry expert knowledge to manually review and correct the model, supplementing any professional knowledge that the algorithm may have missed. Model performance testing uses historical cases to verify the accuracy and completeness of the model, ensuring that the model can correctly predict operational dependencies.

[0204] Finally, a formal representation of operation dependencies is constructed to provide a standardized basis for subsequent compliance verification. The formal description of dependencies defines the Operation Dependency Description Language (ODDL), which uniformly represents various types of dependencies. ODDL is a formal language specifically designed to express operation dependencies, possessing clear syntax and semantics, facilitating computer processing and reasoning. The system converts dependencies into formal constraints, such as...

[0205] This indicates that if operation Oi depends on operation Oj, then Oj must be executed before Oi. A dependency priority system is established to handle the priority relationships between multiple dependency constraints. High-priority dependencies (such as safety-critical dependencies) are given priority in conflict situations to ensure that critical constraints are not violated. Conditional dependency expressions, such as "if condition then Oi→Oj else Oj→Oi", are supported to enhance the flexibility and adaptability of the dependency model.

[0206] Design a dependency query interface to support efficient queries for operation compliance verification and risk assessment. Design an efficient dependency query API to support complex condition queries, such as "find all operations that have a temporal dependency on operation X". Implement dependency path analysis functionality to identify indirect dependencies, such as "operation A depends on B, B depends on C, then A indirectly depends on C". Provide dependency strength assessment to quantify the importance of dependencies, aiding in risk assessment and prioritization. Support dependency visualization, intuitively displaying the dependency network between operations, facilitating the understanding and analysis of complex dependency structures.

[0207] Implement a real-time update mechanism to ensure the dependency model can adapt to changing environments and requirements. Design an incremental update mechanism for the dependency model, supporting the dynamic addition or modification of dependencies without rebuilding the entire model. Implement a dependency propagation algorithm to automatically update affected indirect dependencies, ensuring model consistency. Establish dependency version control to support historical tracing of dependencies, facilitating auditing and backtracking analysis. Design a change impact analysis function to assess the scope of impact of dependency changes and avoid unexpected consequences caused by changes.

[0208] The operation dependency model constructed using the unentangled variational multigraph representation learning method can comprehensively and accurately express various dependency constraints between operations, forming a multidimensional logical dependency network. This network explicitly defines the preconditions, mutual exclusion relationships, and temporal constraints between operations, providing a reliable theoretical foundation for subsequent compliance verification and risk assessment. The application of the unentangled variational multigraph representation learning method significantly improves the model's ability to handle complex dependencies, especially showing a clear advantage in scenarios where multiple dependency types are intertwined.

[0209] Step S4.2: Based on the multidimensional logical dependency network, parse the input operation record text, use a deep learning sequence labeling model to identify entities and relationships in the operation record text, extract key information including operation type, operation object, and operation parameters, obtain operation parsing results, and determine the compliance assessment and system state impact analysis of the current operation.

[0210] In this step, the input operation log text is parsed and analyzed based on the constructed multidimensional logical dependency network. The operation log text refers to the operation description entered by maintenance personnel or the operation log recorded by the system, such as "Distance protection soft pressure plate of Line 1 protection device has been deactivated." Natural language understanding technology is used to parse this text, extract structured information, and conduct compliance assessments and impact analyses.

[0211] First, a Natural Language Understanding (NLU) system is designed to parse the operation records of maintenance personnel. NLU is a branch of artificial intelligence that focuses on enabling computers to understand and process human language, including tasks such as semantic analysis and intent recognition. This system can understand technical terms and industry expressions, accurately extracting operational information. The system constructs a professional domain lexicon, including power system technical terms, a thesaurus, a terminology hierarchy, and terminology attribute information. Technical terms refer to specialized vocabulary in specific fields, such as "soft pressure plate" and "GOOSE communication," which may not be correctly understood by ordinary language processing systems. The thesaurus collects different words expressing the same concept, such as "exit / release / disable pressure plate," ensuring the system can understand different expressions. The terminology hierarchy reflects the inclusion relationship between concepts; for example, "line protection" includes "distance protection" and "zero-sequence protection," helping the system understand the hierarchical relationship between terms. Terminology attribute information records the relevant attributes of terms, such as equipment type, voltage level, manufacturer, and model, providing richer semantic information.

[0212] Implement a semantic parsing model to identify entities and relationships in the text. A deep learning sequence labeling model (BiLSTM-CRF) is used to identify entities and relationships in the text. Sequence labeling is a task that assigns each element in a sequence to a predefined category, suitable for named entity recognition, part-of-speech tagging, and other problems. BiLSTM-CRF is a model that combines a bidirectional long short-term memory network (BiLSTM) and a conditional random field (CRF), making it particularly suitable for sequence labeling tasks. BiLSTM processes the input sequence through two LSTM layers (forward and backward) to capture contextual information; the CRF layer considers the transition probabilities between labels to ensure the rationality of the output label sequence. The system implements an intent recognition module to understand the basic purpose of an operation, such as "isolate," "verify," and "restore." Intent recognition is one of the core tasks of natural language understanding, aiming to determine the user's basic intent or purpose. The system designs a contextual understanding mechanism to handle omissions and references, such as the specific pressing object in "the pressing plate has been removed." Contextual understanding refers to the ability to understand the current content considering the surrounding context, which is crucial for handling linguistic phenomena such as omissions and references. The system incorporates an attention mechanism to enhance its ability to extract key information and improve the accuracy of understanding complex expressions. Attention mechanisms, a technique in deep learning, allow models to focus on the most relevant parts of input data and have achieved significant success in the field of natural language processing.

[0213] The system designs a standardized operation representation, converting natural language into a structured format. The standard operation representation format is: <Operation Type, Operation Object, Operation Parameters, Operation Time, Operator>, converting text information into structured data easily processed by computers. The operation type specifies the basic behavior of the operation, such as "exit," "disconnect," and "close"; the operation object is the target device or component, such as "Line 1 protection device" or "CT circuit terminal"; the operation parameters provide specific settings or conditions for the operation, such as "30-second delay" or "forced mode"; the operation time records the time point of operation execution; and the operator records the information of the personnel performing the operation. The system also implements expression standardization, converting natural language into a structured operation representation. This process involves technologies such as entity extraction, relation recognition, and attribute mapping, converting unstructured text into a standardized operation representation. Finally, the system designs a fuzzy expression processing mechanism, using contextual inference to complete incomplete information. Fuzzy expressions refer to expressions with unclear meanings or incomplete information, such as omissions, pronouns, and vague qualifiers. This information is completed through contextual analysis and knowledge reasoning. The system establishes operation verification rules to ensure the logical rationality of the parsed results. The verification rules check the internal consistency and logical rationality of the operation representation, such as the matching of the operation type with the object, and the reasonable range of parameter values, filtering out obviously erroneous parsed results.

[0214] Next, based on a memory-guided reflective decision-making mechanism, real-time matching of operations and system states is achieved. This mechanism can quickly handle routine state matching while processing complex state changes through deep reasoning. The system is designed with a fast state matching reflective module to handle common state matching scenarios. An operation-state change mapping library is established to store state change patterns caused by common operations, such as "exiting the protection device soft pressure plate X causes function Y to be disabled." A pattern matching algorithm is designed to quickly identify the matching degree between the current operation and known patterns. Reflective state updates are implemented; for routine operations with high matching degrees, predefined state changes are directly applied without complex calculations. A confidence evaluation mechanism is established to determine whether further reasoning verification is needed; low-confidence matching results trigger deep reasoning.

[0215] Implement a deep state reasoning module to handle complex or unknown state changes. Construct a device state transition model to describe the impact of each operation on the device state. A state transition model is a model that describes how a system changes from one state to another, typically represented using a finite state machine (FSM) or a Markov model. Design a knowledge graph-based reasoning engine to analyze the impact of operations on related devices and loops. The knowledge graph reasoning engine uses entity relationships in a graph structure for reasoning, discovering implicit knowledge through path analysis, relationship derivation, and other methods. Implement state consistency checks to verify whether the reasoning results conform to physical and logical constraints. State consistency checks ensure that the reasoning results do not violate known physical laws or logical relationships, such as certain state combinations that are physically impossible to coexist. Establish a state conflict resolution strategy to handle situations where multiple sources of information are inconsistent. State conflicts refer to contradictory state information from different information sources; the system resolves these conflicts through methods such as reliability weights, majority voting, or expert rules.

[0216] Design a reflection-inference collaborative mechanism to achieve seamless cooperation between the two decision-making modes. Design decision flow criteria to define when to switch from the reflection module to the inference module, such as when the matching degree is below a threshold or when state changes are complex. Establish a partial result sharing mechanism, allowing the inference module to utilize the preliminary judgment of the reflection module to avoid redundant calculations. Implement a result fusion strategy to combine the outputs of both modules to obtain the final state judgment, especially when the two modules give different results. Design a learning feedback loop to continuously optimize the reflection and inference strategies based on actual results, improving the system's adaptability.

[0217] Finally, the operation parsing results are matched with the multidimensional logical dependency network and the real-time status of the devices to determine the compliance and impact of the current operation. The dependency compliance check queries the dependency model constructed in step S4.1 to check whether the current operation satisfies all prerequisite dependencies. For example, if the current operation is "disconnecting the CT circuit terminal," it checks whether necessary prerequisite operations (such as "exiting the relevant soft pressure plate") have been executed. A method for assessing the severity of dependency violations is designed to distinguish between critical and minor violations. Critical violations may lead to security risks or operational failures and require immediate correction; minor violations have less impact and may only require warnings or logging. A dependency tracking function is implemented to determine the specific reasons for dependency violations and related operations, helping users understand and correct problems. A dependency exemption mechanism is established to handle reasonable exceptions in special cases, increasing the system's flexibility. In some special cases, regular dependencies may not be applicable; through predefined exemption conditions or expert authorization, certain dependency constraints can be bypassed when specific conditions are met.

[0218] Equipment status consistency verification compares the operation parsing results with the real-time equipment status collected in step S3.2. A status prediction model is designed to predict equipment status changes after the operation. Based on the operation type and object, the prediction model forecasts the expected equipment status after the operation, serving as a verification reference standard. Status difference detection is implemented to identify inconsistencies between the actual and expected states. Status differences may indicate incomplete operation execution, execution errors, or abnormal equipment response. An anomaly explanation mechanism is established to analyze possible causes of status inconsistencies and provide diagnostic information and solution suggestions.

[0219] Operational impact analysis assesses the impact of the current operation on various parts of the system. An operational impact propagation model is constructed to analyze the impact of the current operation on each part of the system. The propagation model describes how the operation's effects diffuse to related devices and functions through inter-device relationships, assessing the scope and depth of the operation's impact. Key parameter monitoring points are designed to focus on tracking significantly affected system parameters and promptly detect abnormal changes. Cascade effect prediction is implemented to assess the chain reactions that the operation may trigger, especially situations that could lead to system instability or functional degradation. Safety boundary monitoring is established to ensure that the operation does not cause the system to cross safety boundaries, such as complete failure of critical protection functions or entry into an unsafe state.

[0220] Real-time feedback generation provides users with timely feedback on operation analysis and evaluation. A multi-level feedback mechanism is designed, including confirmation, warning, and error messages, selecting the appropriate feedback level based on operational compliance and risk level. Context-sensitive feedback content generation is implemented, providing targeted guidance and suggestions, such as suggesting the correct operation sequence when errors occur. Multimodal feedback presentation is designed, including text, audio, and visual cues, ensuring key information is effectively conveyed. Feedback prioritization is established to ensure critical information receives timely attention and prevents important warnings from being overwhelmed by a large amount of secondary information.

[0221] By parsing operation logs and matching states, the operational intentions of maintenance personnel can be accurately understood, the compliance of operations can be verified, and the impact of operations on system state can be monitored in real time, providing reliable input for subsequent risk assessment and early warning. The application of the memory-guided reflex decision-making mechanism significantly improves the efficiency and accuracy of state matching, especially in handling a large number of routine operations without losing the ability to deeply analyze complex situations.

[0222] Step S4.3: Based on the compliance assessment and system state impact analysis of the current operation, the operation parsing result is matched and verified with the multidimensional logical dependency network and the real-time state of the device to check whether the operation meets all the prerequisite dependency conditions, thereby realizing the real-time compliance verification of the operation sequence.

[0223] In this step, based on the compliance assessment and impact analysis results from step S4.2, the system further matches and verifies the real-time status of the multi-dimensional logically dependent network and devices to achieve real-time compliance verification of the operation sequence. Compliance verification refers to verifying whether the operation conforms to predefined rules, standards, and constraints to ensure the safety and effectiveness of the operation.

[0224] First, perform pre-operation dependency verification to ensure that the current operation meets all necessary preconditions. Query the multi-dimensional logical dependency network to obtain all pre-operations of the current operation. Pre-dependencies are other operations or conditions that must be completed before the current operation and are fundamental to ensuring the operation's safety and effectiveness. Check the satisfaction status of each pre-dependency, including whether the dependent operation has been executed, whether the execution result meets expectations, and whether the dependency condition is met. For time-series dependencies, verify whether the dependent operation has been completed and whether its completion time is earlier than the current operation; for functional dependencies, check whether the dependent function is in a correct state; for conditional dependencies, the system evaluates whether the condition is met. Classify the verification results into three states: "satisfied," "partially satisfied," and "not satisfied," corresponding to fully compliant, slightly deviated, and seriously violated requirements, respectively.

[0225] This process involves assessing the risks of dependency violations and quantifying the potential risks of not meeting dependency criteria. A risk assessment model is defined, considering the type, severity, and possible consequences of dependency violations. Different types of dependency violations have different risk characteristics; for example, violations of safety-critical dependencies typically carry higher risks. A comprehensive risk score is calculated, combining the severity of the violation with the importance of the dependency to quantify the overall risk level. Risk scores are typically calculated using a weighted approach, with violations of highly important dependencies receiving higher weights. Risk scores are mapped to risk levels, such as "low risk," "medium risk," "high risk," and "extremely high risk," for easier understanding and decision-making. A risk interpretation report is generated, detailing the sources of risk, possible consequences, and recommended measures to improve the interpretability of the risk assessment.

[0226] Design real-time state consistency verification to ensure that operations are appropriate for the current system state. Check the consistency between the current operation and the real-time state of the device to verify whether the operation matches the current state of the device. For example, if the operation is "restore a disconnected loop," but the device status shows that the loop is not currently disconnected, there is a state inconsistency. The system identifies redundant and invalid operations, such as making the same state change to a device already in the target state. While such operations are not necessarily harmful, they may indicate that the operation plan does not match the actual situation and require attention. Assess the reasonableness of the impact of the operation on the current state and determine whether the operation will cause the device to enter an unreasonable or dangerous state. For example, performing certain operations in certain operating modes may cause protection functions to fail; the system needs to identify and warn of such situations. Implement state prediction verification, simulate the system state after the operation is executed, and verify whether it meets safety and functional requirements. State prediction uses a device state transition model to predict the new state after the operation based on the current state and operation type, and evaluates its reasonableness.

[0227] Perform compliance analysis on the operation sequence to assess its rationality and completeness. Construct an operation sequence dependency graph to represent all dependencies and execution order between operations. The dependency graph is a directed graph where nodes represent operations and edges represent dependencies, used to analyze the structural characteristics of the operation sequence. Apply a topological sorting algorithm to check for circular dependencies in the operation sequence. Circular dependencies represent logically unsatisfactory dependencies, such as operation A depending on B, B depending on C, and C depending on A, forming an unsolvable cycle. Verify the completeness of the operation sequence by checking for missing necessary operation steps. Completeness verification ensures that all necessary operations are included in the sequence, avoiding security risks caused by missing critical steps. System evaluates the efficiency of the operation sequence, identifying redundant steps and optimization opportunities. Efficiency evaluation aims to discover unnecessary operations or optimizeable execution orders to improve operational efficiency.

[0228] The system designs exception handling and corrective suggestions to help users resolve compliance issues. Compliance issues are categorized by severity, such as "Fatal Error" (must be resolved before continuing), "Critical Warning" (strongly recommended for resolution), and "Notes" (can continue but require caution). Targeted corrective suggestions are generated, such as adding missing steps, adjusting the order of operations, and modifying operation parameters. Suggestions are generated based on the nature of the problem and the system's knowledge base, providing practical solutions. Interactive problem-solving is supported, allowing users to select different corrective strategies and preview the effects. Interactive problem-solving enhances the system's flexibility, enabling users to choose the most suitable solution based on the actual situation. An automatic correction function is implemented; for simple problems, corrective solutions can be automatically generated and executed, reducing user intervention. Automatic correction is typically suitable for clearly defined problems, such as adding explicitly missing steps or adjusting the order of obviously erroneous steps.

[0229] Establish a compliance verification record and audit mechanism to ensure the traceability of the operational process. The system records all compliance verification activities, including verification time, content, results, and handling measures. Detailed verification records provide necessary data for subsequent audits and analysis. Generate compliance verification reports to summarize verification results and handling status, facilitating management and review. Verification reports typically include a verification overview, a list of issues, handling measures, and recommendations. The system implements a verification history query function, supporting the retrieval of historical verification records by various criteria (such as time, device, issue type, etc.). The historical query function facilitates issue tracking and experience accumulation, contributing to continuous improvement of compliance management. Establish a compliance statistical analysis function to identify common problem patterns and trends, providing a basis for rule optimization and training. Statistical analysis can identify systemic problems and improvement opportunities, improving the overall compliance level.

[0230] Real-time compliance verification can promptly identify issues such as incorrect operation sequences, missing necessary steps, or violations of security constraints, preventing potential security risks. The combination of a multi-dimensional logical dependency network and real-time state matching verification ensures the comprehensiveness and accuracy of compliance verification, providing strong support for secure operations. It not only identifies problems but also provides specific corrective suggestions and automatic correction functions, significantly improving operational safety and efficiency.

[0231] In step S4, a multi-dimensional risk level assessment is implemented, including:

[0232] Step S4.4: Based on the operation analysis results, a distributed guided local search algorithm is applied to transform various risk factors, including operation sequence errors, abnormal equipment status, operational environment risks, human factor risks, and system vulnerability risks, into a distributed constraint optimization problem. Various operation risk values ​​are calculated through adaptive violation condition identification and penalty evaporation mechanisms.

[0233] In this step, the operational risk assessment problem is modeled as a distributed constrained optimization problem (DCOP) and solved using a distributed guided local search algorithm. Risk assessment is the process of systematically analyzing potential hazards, their likelihood of occurrence, and the severity of their consequences to identify and manage risks. Distributed constrained optimization problems are a class of optimization problems solved collaboratively by multiple agents, each controlling some variables and coordinating to find a solution that satisfies the constraints and optimizes the objective function.

[0234] First, formal modeling of the risk assessment problem is performed. A risk variable set V = {v1, v2, ..., vn} is defined, where each variable vi represents a risk factor, such as incorrect operation sequence or abnormal equipment status. Risk factors are conditions or behaviors that may lead to adverse consequences and are the basic unit of risk assessment. A variable domain Di = {d1, d2, ..., dm} is defined, representing the possible value range of each risk factor, usually normalized to the interval [0,1], where 0 represents no risk and 1 represents the highest risk. A constraint set C = {c1, c2, ..., ck} is constructed, where each constraint ci defines a risk assessment rule under specific conditions. Constraints can represent the mutual influence between risk factors, conditional triggering relationships, or risk thresholds. An objective function F(V) is designed to calculate the overall risk value by integrating all risk factors, typically a weighted sum or a weighted maximum value. The objective function reflects the overall risk level of the system and is the final output of the risk assessment.

[0235] Risk factors are identified and quantified, comprehensively considering all potential risks. Operation sequence risk, based on the dependency model in step S4.1, assesses the severity of violations of the operation sequence. Operation sequence risk stems from incorrect execution of operations, such as operating equipment without first isolating the power supply, which may lead to safety accidents. Equipment status risk, based on the status matching results in step S4.2, assesses the danger of abnormal equipment status. Equipment status risk stems from equipment being in an abnormal or unsafe state, such as protection devices not being activated or communication links being interrupted. Operating environment risk considers the impact of environmental factors such as weather conditions and power grid load on operational risk. Environmental factors can significantly affect operational safety; for example, the risk of outdoor operation in severe weather significantly increases. Human factor risk assesses the risks caused by factors such as operator qualifications, experience, and fatigue. Human factors are a significant source of operational risk, including insufficient skills, lack of experience, and fatigued operation. System vulnerability risk considers the fault tolerance and safety margin of the current system configuration. System vulnerability reflects the system's ability to resist and recover from failures; highly vulnerable systems are more prone to serious consequences when faced with problems.

[0236] This design uses risk constraint modeling to express the complex interactions between risk factors. Single-factor constraints define the threshold and calculation rules for a single risk factor, such as "if the operation sequence error exceeds 2 steps, the risk value increases by 0.3." Single-factor constraints are the most basic type of constraint, directly limiting and evaluating a single risk factor. Inter-factor constraints define the interaction relationships between multiple risk factors, such as "if an operation sequence error and equipment malfunction coexist, the risk value increases by an additional 0.2." Inter-factor constraints capture the synergistic effect of risk factors, reflecting the phenomenon that the combined effect of multiple factors may amplify risk. Time-series constraints define the pattern of risk factor changes over time, such as "continuous operation errors lead to cumulative risk growth." Time-series constraints consider the time dimension of risk; some risks may accumulate or decay over time, rather than remaining static. Environmental condition constraints define the moderating effect of environmental factors on risk, such as "all operational risks increase by 50% under thunderstorm weather." Environmental condition constraints reflect the adjustment rules for risk assessment under specific environmental conditions, making the assessment results more consistent with reality.

[0237] Next, the Distributed Guided Local Search (DGLS) algorithm is applied for risk assessment optimization. DGLS is a heuristic algorithm suitable for solving complex constraint optimization problems, capable of effectively finding high-quality solutions in large-scale search spaces. An adaptive violation condition identification mechanism is implemented to intelligently identify constraint violations. A risk assessment function f(s,c) is designed to calculate the degree of constraint c violation under the current state s. The risk assessment function transforms constraint violations into numerical form, facilitating subsequent calculation and comparison. A dynamic threshold mechanism is implemented to automatically adjust the risk judgment criteria based on system state and historical data. The dynamic threshold adapts to the risk assessment needs of different scenarios, avoiding oversensitivity or insensitivity issues that may arise from fixed thresholds. A violation pattern library is established to record typical risk combination patterns, supporting rapid identification. Based on historical experience and expert knowledge, the violation pattern library stores known high-risk combination patterns, facilitating rapid identification of similar risk situations. An adaptive sampling strategy is designed to focus on exploring high-risk areas, improving algorithm efficiency. Adaptive sampling concentrates computational resources on potentially high-risk areas, improving the efficiency and accuracy of risk assessment.

[0238] To prevent the search process from getting stuck in local optima, a penalty evaporation mechanism is implemented. A penalty value p(c) is maintained for each constraint c, initially set to 0. The penalty value reflects the algorithm's focus on a specific constraint; a higher penalty value guides the algorithm to prioritize solving that constraint. A penalty increase strategy is designed: when the search gets stuck in a local optimum, the penalty value of the violated constraint is increased: p(c) = p(c) + 1. Increasing the penalty encourages the algorithm to leave the current local optimum and explore new solution spaces. A penalty evaporation function is implemented: p(c) = λ·p(c), where λ is the evaporation factor (0 < λ < 1). Penalty evaporation prevents certain constraints from being over-penalized, maintaining the algorithm's exploratory ability. An adaptive evaporation control is designed to dynamically adjust the value of λ based on the search progress. The adaptive evaporation control adjusts the evaporation rate based on the algorithm's convergence and violation trend, balancing exploration and utilization.

[0239] Design a distributed risk assessment coordination mechanism to improve computational efficiency and result accuracy. Decompose the risk assessment task into multiple sub-tasks and assign them to different computing nodes. Task decomposition is typically based on risk type or system region, allowing each node to focus on specific aspects of risk assessment. Design a message passing protocol to enable nodes to share assessment results and penalty information. Message passing is a fundamental mechanism for communication between nodes in a distributed system; by defining standard message formats and passing rules, effective information sharing is achieved. Implement a conflict detection and coordination mechanism to resolve conflicts in assessment results from different nodes. Conflict detection identifies contradictory assessment results from different nodes, while the coordination mechanism resolves these conflicts through predefined rules or dynamic negotiation. Establish a global consistency maintenance strategy to ensure the overall consistency of the final assessment results. Global consistency ensures that the assessment results from all nodes can be integrated into a logically consistent whole, avoiding biases caused by local optima or local perspectives.

[0240] Optimize the local search strategy to improve the efficiency and accuracy of risk assessment. Modify the risk assessment objective function: h(s) = g(s) + λ·∑c∈C p(c)·f(s,c). The modified objective function considers the combined effect of the original risk assessment (g(s)) and the penalty term, guiding the search direction. Design a neighborhood generation function to effectively explore the risk state space. Neighborhood generation defines how to generate candidate next states from the current state; a good neighborhood generation function can efficiently explore the solution space. Implement a hill-climbing search strategy to gradually optimize the risk assessment results. Hill-climbing search is a basic local search method that gradually approaches the optimal solution by continuously selecting neighborhood states that can improve the objective function. Introduce a random restart mechanism to avoid getting trapped in local optima. When the search gets trapped in a local optimum, random restart randomly selects a new starting point to restart the search, increasing the chance of finding the global optimum.

[0241] By employing a distributed guided local search algorithm, various operational risks can be efficiently assessed, and the risk values ​​of each risk factor can be calculated. The application of adaptive violation condition identification and penalty evaporation mechanisms enables the algorithm to effectively handle complex risk constraints and find more accurate risk assessment results. This method is particularly suitable for handling multi-dimensional and highly complex risk assessment problems, providing a reliable foundation for subsequent risk level assessment and early warning.

[0242] Step S4.5: Based on the various operational risk values, the complex interaction between risk factors is processed through a distributed coordination mechanism. Risk pattern matching and predictive analysis are performed by combining similar cases in historical accident data to generate a comprehensive risk assessment result that considers risk probability and multi-dimensional risk.

[0243] In this step, based on the various operational risk values ​​calculated in step S4.4, the complex interactions between risk factors are further processed, and in-depth analysis is conducted in conjunction with historical experience to generate a comprehensive risk assessment result. The interaction between risk factors refers to the nonlinear effect produced when multiple risk factors act together, which may amplify or suppress risk.

[0244] First, a distributed coordination mechanism is used to handle the complex interactions between risk factors. Distributed coordination refers to the process by which multiple independent decision-making units reach a consensus through information exchange and negotiation, and is used in risk assessment to integrate multi-dimensional risk information. A factor interaction model is designed to express the mutual influence relationships between risk factors. Interaction models typically use graph structures or matrix forms to represent the direction, intensity, and conditions of influence between factors. The system implements risk factor clustering analysis to identify groups of risk factors with strong correlations. Clustering analysis groups similar or related risk factors, facilitating more effective analysis and handling of related risks. A risk propagation network is established to simulate how risks spread and evolve within the system. The risk propagation network describes the process by which risk influences other factors from an initial factor through various paths, helping to understand the dynamic development of risk. A collaborative risk assessment algorithm is designed to calculate the overall risk considering the synergistic effect between factors. Synergistic effects refer to the effect produced by the combined action of multiple factors exceeding the sum of the individual effects of each factor, which may lead to non-linear growth of risk.

[0245] Design a distributed coordination strategy to ensure that all evaluation nodes reach a consistent risk assessment result. Implement a consensus-based risk aggregation algorithm to integrate the results from multiple evaluation nodes. Consensus algorithms are the fundamental mechanism for reaching consensus decisions in distributed systems, and in risk assessment, they are used to integrate evaluation results from different perspectives. Design a conflict detection and resolution mechanism to handle evaluation discrepancies between different nodes. Conflict detection identifies significantly different evaluation results given by different nodes, and the resolution mechanism eliminates these discrepancies through predefined rules or dynamic negotiation. Establish a global risk view update protocol to ensure that all nodes obtain consistent risk assessment results. Global view updates are a key mechanism for maintaining consistency in distributed systems, ensuring that all nodes make decisions based on the same information. Implement an incremental update mechanism that only propagates changed risk assessment results, reducing communication overhead. Incremental updates significantly reduce data transmission volume and improve system response speed by transmitting only the changed parts.

[0246] Next, risk pattern matching and predictive analysis are performed using historical accident data. Historical accident data is a valuable resource for risk assessment, containing detailed records of past events and lessons learned. The system analyzes historical data and extracts patterns to learn risk patterns from past cases. Historical accident and risk event data are collected and preprocessed to ensure data quality and consistency. Preprocessing includes data cleaning, standardization, anomaly detection, and handling of missing values. Data mining techniques are applied to identify accident precursor patterns and combinations of risk factors, discovering potential risk indicators. Commonly used data mining techniques include association rule mining, sequence pattern mining, cluster analysis, and anomaly detection. A risk factor-accident type association model is established to predict the accident types that specific risk combinations may lead to. The association model quantifies the relationship between risk factors and accident types, facilitating the prediction of possible accident types based on the current risk situation. Similar case retrieval is implemented to find historical risk events similar to the current situation, drawing on their experiences and lessons. Similar case retrieval is typically based on multi-feature vector similarity calculations to find the historical cases most closely related to the current situation.

[0247] This system enables quantitative risk assessment and classification, providing measurable and comparable risk results. It integrates DGLS algorithm results and historical data analysis to calculate risk probability and severity. Risk probability represents the likelihood of a risk event occurring, typically based on historical statistics and model predictions; severity represents the degree of impact after an event occurs, considering factors such as personnel safety, equipment loss, and systemic impact. A risk matrix is ​​designed to map risks to different levels (e.g., low, medium, high, very high). The risk matrix is ​​a commonly used tool for risk assessment; by combining risk probability and severity, it categorizes risks into different levels, facilitating management and decision-making. The system also implements risk decomposition functionality, analyzing the components and main contributing factors of overall risk. Risk decomposition helps understand the internal structure of risk, identify major risk sources, and provide a basis for targeted risk management. Finally, a risk trend prediction model is established to assess the evolution trend of risk over time. Trend prediction considers the impact of time factors on risk, predicting the direction and speed of risk development, and providing a time window for preventative measures.

[0248] A risk assessment risk evaluation mechanism is designed, combining data-driven approaches with expert experience. An expert knowledge representation format is designed to transform power industry expert experience into computable rules. Knowledge representation utilizes rule bases, case bases, and semantic networks to make tacit knowledge explicit and formalized. A knowledge fusion algorithm is implemented to integrate data-driven results with expert rules. Knowledge fusion employs methods such as weighted combination, Bayesian update, or fuzzy inference to integrate knowledge from different sources. A risk assessment calibration mechanism is established to adjust assessment model parameters based on expert feedback. Calibration ensures that the model output aligns with expert judgments, achieving model optimization through parameter adjustments or structural modifications. An uncertainty representation method is designed to reflect the confidence and reliability of the risk assessment. Uncertainty representation typically uses probability distributions, confidence intervals, or fuzzy sets to express the degree of uncertainty in the assessment results.

[0249] Design a risk mitigation suggestion generation function to provide specific measures to address identified risks. Based on risk assessment results, identify key risk points and major risk sources. Key risk points are high-risk and significantly impactful links or factors, and are the focus of risk management. Design a risk mitigation strategy library to store mitigation measures for different risk types. The library includes preventative, corrective, and contingency measures, covering all stages of risk management. Implement a mitigation strategy matching algorithm to generate targeted suggestions for identified risks. Strategy matching selects the most suitable mitigation measure based on risk type, severity, and system environment. Establish a mitigation effectiveness evaluation model to predict the effects of different mitigation measures. The effectiveness evaluation simulates risk changes after the implementation of mitigation measures to help select the most effective response strategy.

[0250] Through a distributed coordination mechanism and historical accident data analysis, comprehensive risk assessment results are generated, taking into account risk probability and multiple dimensions. These results include not only quantified risk values ​​and levels, but also comprehensive information such as risk composition analysis, development trends, similar case references, and mitigation suggestions, providing a reliable basis for subsequent risk level assessment and decision-making. This comprehensive assessment method, combining data-driven approaches and expert experience, significantly improves the accuracy and practicality of risk assessment.

[0251] Step S4.6: Based on the multi-dimensional risk assessment results, apply the risk matrix to map the comprehensive risk value to different risk levels of low, medium, high, and extremely high, and identify the main risk contributing factors and key risk points to achieve multi-dimensional risk level assessment and obtain multi-dimensional risk level assessment results.

[0252] In this step, based on the multi-dimensional risk assessment results from step S4.5, a risk matrix is ​​further applied to classify risks and identify key risk points, resulting in the final risk level assessment. Risk level assessment transforms quantitative risk values ​​into qualitative risk levels, facilitating management decision-making and risk communication.

[0253] First, a risk matrix is ​​applied to map the overall risk value to different risk levels. A risk matrix is ​​a two-dimensional table where the horizontal axis represents risk probability and the vertical axis represents risk impact or severity. By combining these two dimensions, risks are categorized into different levels. The system designs a standardized risk matrix, defining the criteria for risk level classification. A typical risk matrix classifies risks into four levels: "low," "medium," "high," and "extremely high," each corresponding to different management strategies and response measures. A probability-impact assessment model is implemented to evaluate the probability and impact of the current risk. Probability assessments are typically based on historical statistics, expert judgment, or model predictions, using quantitative or semi-quantitative scales; impact assessments consider the degree of impact on personnel safety, equipment integrity, system functionality, and economic losses. Risk level mapping rules are designed to map the risk assessment results to their positions within the risk matrix. These mapping rules establish a correspondence between risk assessment values ​​and matrix coordinates, ensuring that the assessment results can accurately locate specific units within the matrix. An adaptive risk threshold is implemented to dynamically adjust the risk level classification criteria based on system status and environmental conditions. The adaptive threshold considers differences in risk acceptance across different scenarios, and may employ stricter risk standards in critical scenarios.

[0254] This project implements a multi-dimensional risk level representation to comprehensively showcase all aspects of risk. A multi-dimensional risk representation format is designed to simultaneously display the overall risk level and the risk levels of each dimension. This multi-dimensional representation not only provides the overall risk rating but also displays the various dimensions that constitute the overall risk, such as operational risk, equipment risk, and environmental risk, facilitating a comprehensive understanding of the risk situation. A dimension weight adjustment mechanism is implemented to adjust the weight of each dimension in the overall assessment based on the characteristics of the scenario. Dimension weights reflect the relative importance of each risk dimension under different scenarios; for example, the weight of environmental risk may increase under severe weather conditions. A risk radar chart representation method is designed to visually display the multi-dimensional risk distribution. Radar charts are an effective way to represent multi-dimensional data; each axis represents a risk dimension, and the area and shape of the resulting polygons visually reflect the multi-dimensional characteristics of risk. Inter-dimensional correlation analysis is established to identify the mutual influence and dependencies between different risk dimensions. Correlation analysis reveals the interactions between risk dimensions, helping to understand the systemic characteristics of risk and avoiding the fragmented consideration of each dimension of risk.

[0255] Next, we identify the main risk contributing factors and key risk points to provide a basis for targeted risk management. We will conduct risk factor contribution analysis to quantify the proportion of each factor's contribution to the overall risk. Contribution analysis typically employs methods such as sensitivity analysis or variance decomposition to calculate the degree of influence of each risk factor on changes in overall risk. We will design a key risk point identification algorithm to identify the most critical risk points based on risk level and system impact. Key risk points typically have high risk levels, a wide range of impact, or are located on the system's critical path, making them the focus of risk management. We will establish a risk factor correlation network to analyze the mutual influence and propagation paths among risk factors. The correlation network illustrates the causal relationships and influence chains among risk factors, helping to understand the risk propagation mechanism and cascading effects. We will implement risk ranking and priority allocation to determine the priority order of risk responses. Risk prioritization typically considers factors such as risk level, controllability, and resource constraints to help optimize risk management strategies with limited resources.

[0256] Design a visual representation of risks to enhance the intuitiveness and comprehensibility of risk information. Implement multi-level risk heatmaps to intuitively display the risk distribution across different parts of the system. Risk heatmaps use color coding to represent risk levels, typically green (low risk), yellow (medium risk), orange (high risk), and red (extremely high risk), enabling rapid communication of risk status. Design risk trend charts to show how risks change over time. Trend charts combine historical data and predictive models to demonstrate historical changes and future trends in risks, helping to predict risk development directions. Establish a key indicator dashboard to monitor the status of key risk indicators in real time. The dashboard centrally displays the most important risk indicators, typically using intuitive elements such as dashboards, progress bars, or status lights for quick understanding of the risk status. Implement an interactive risk exploration interface to allow users to delve deeper into risk details as needed. The interactive interface allows users to flexibly explore risk information from overview to detail through clicks, filters, zooming, and other operations.

[0257] Generate comprehensive risk reports, providing complete risk assessment conclusions and recommendations. Design a hierarchical risk report structure to meet the information needs of different users. Hierarchical reports typically include a summary layer (key conclusions and recommendations), a management layer (risk overview and main findings), and a technical layer (detailed analysis and data support), adapting to different user needs. Implement an automated report generation function, automatically generating standardized reports based on assessment results. Automated generation improves report efficiency and consistency, ensuring no key information is omitted. Establish a report content customization mechanism, tailoring report content according to user roles and concerns. Content customization considers the different concerns of different users; for example, managers may focus on the overall risk situation, while technical personnel may need detailed technical risk analysis. Design a report distribution and tracking mechanism to ensure that key risk information is promptly communicated to relevant personnel. Distribution and tracking ensure that risk information reaches those who should know and allows tracking of the status of information delivery and processing.

[0258] By applying risk matrices and conducting multi-dimensional risk analysis, comprehensive risk level assessment results were generated. These results not only provide the overall risk level but also offer rich information such as risk distribution across various dimensions, key contributing factors, critical risk points, and risk development trends. The multi-dimensional risk level assessment results provide decision-makers with a clear overall risk picture, supporting precise risk management decisions. The generated risk reports and visualizations make complex risk information intuitive and easy to understand, promoting risk communication and consensus building, and providing strong support for safety measure management.

[0259] In step S4, dynamic visual early warning is implemented, including:

[0260] Step S4.7: Based on the multi-dimensional risk level assessment results, design a multi-level risk visualization interface that includes a global risk situation map, an equipment network topology map, and an operation sequence risk map. Use a dynamic topology map based on the substation layout to display the scope of operation impact and risk distribution in real time, and generate intuitive risk status indicators.

[0261] This step involves designing a multi-layered risk visualization interface to transform complex risk assessment results into intuitive visual representations, helping operations and maintenance personnel quickly understand the risk situation. Visualization is a technique that converts data into graphical representations, leveraging the advantages of the human visual system to improve the efficiency and comprehensibility of information delivery. Multi-layered visualization refers to providing multiple complementary views according to different levels of abstraction and points of interest to meet diverse information needs.

[0262] First, a global risk situation map is designed to provide an overall view of system risks. A risk heatmap based on the substation layout is designed to visually display risk distribution. A heatmap is a visualization method that uses color gradients to represent numerical intensity, used in risk visualization to show the risk level of different areas. The system uses the physical layout of the substation as a base map and uses color coding to represent the risk level of each area, such as green for low-risk areas, yellow for medium-risk areas, and red for high-risk areas, visually displaying the spatial distribution of risks. Automatic risk area division is implemented, classifying areas within the substation into different risk levels based on risk assessment results. Area division is based on equipment function grouping, physical location, or system topology, dividing the substation into multiple functional areas or equipment groups, and determining the risk level of each area based on the comprehensive risk assessment results. A risk indicator dashboard is designed to display the real-time status of key risk indicators. The dashboard typically includes intuitive elements such as dashboard panels, progress bars, and status lights, centrally displaying the most critical risk indicators, such as the overall risk level, the number of high-risk devices, and the number of operations violating dependencies. Time-based display is supported, allowing users to view risk change trends and historical comparisons. The time dimension display uses a timeline control or trend chart to show how the risk situation changes over time, helping to understand the dynamic characteristics and development trends of risk.

[0263] Design a device network topology visualization to display the connections and statuses between devices. Construct a device relationship topology graph based on a knowledge graph to show the connections and dependencies between devices. The topology graph uses nodes to represent devices and edges to represent connections or dependencies between devices, intuitively displaying the system's structure and composition. Design a multi-level zoom function to support a smooth transition from a global view to local details. Multi-level zoom allows users to seamlessly switch between different levels of abstraction, gaining both a macroscopic understanding of the overall system structure and in-depth viewing of detailed information about specific devices or connections. Implement status coding visualization, using different colors to mark links and devices in different states. Status coding uses visual attributes such as color, shape, or size to represent the status of devices or links; for example, red indicates disconnection or failure, green indicates normal operation, and yellow indicates potential risk. Design interactive node expansion to display detailed device information and relationships as needed. Interactive nodes allow users to obtain additional information about nodes, such as device parameters, historical status, and associated devices, through clicking, hovering, or expanding, providing flexibility in information exploration.

[0264] This system visualizes the risks of operational sequences, showcasing the order and risk status of operational steps. A timeline-based operational sequence display clearly presents the order and status of operational steps. The timeline arranges operations chronologically, forming a linear sequence that intuitively displays the execution order and progress. Operational dependencies are visualized using connecting lines to represent relationships between operations. Directed edges represent the sequential relationships or dependency constraints between operations, aiding in understanding the logical structure and execution constraints of the operational sequence. A risk marking system is designed to add prominent markers to risky operational steps. Risk markers use visual elements such as color, icons, or labels to highlight high-risk operational steps, attracting user attention. An operational comparison function is supported, allowing the side-by-side display of differences between planned and actual operations. Operational comparisons, through side-by-side display or highlighting differences, show the discrepancies between planned and actual operations, helping to identify execution deviations and potential problems.

[0265] Design personalized risk views to meet the specific needs of different users. Design user-configurable risk monitoring views, allowing users to select the risk indicators they want to focus on and how they are displayed. Configurable views allow users to customize display content, layout, and update frequency, personalizing the risk monitoring interface according to individual preferences and job requirements. Implement role-adaptive display, providing customized views for different roles. Role-adaptive display automatically adjusts the displayed content and level of detail based on the user's responsibilities and permissions; for example, managers may need a high-level overview, while technical personnel may need detailed technical data. Support multi-device adaptation, providing optimal display effects on devices of different sizes and resolutions. Multi-device adaptation, through responsive design, ensures that the visualization interface adapts to various display environments from large screens to mobile devices, maintaining good usability. Design view saving and sharing mechanisms to facilitate team collaboration and experience transfer. View saving allows users to save valuable view configurations, while the sharing mechanism supports sharing views with team members, promoting collaboration and knowledge transfer.

[0266] Implement dynamic interaction and real-time updates to ensure the timeliness and interactivity of risk visualization. Design a real-time data update mechanism to ensure the visualization interface reflects the latest system status and risk assessment results. Real-time updates, through data push or periodic polling, keep the visualization interface synchronized with the underlying data, promptly reflecting status changes. Implement interactive risk exploration functionality, allowing users to explore risk information through clicks, hovering, and dragging. Interactive exploration empowers users to actively explore data, obtaining the information they need through direct operation, improving user engagement and depth of understanding. Design animated transition effects to smoothly display status changes and risk evolution processes. Animated transitions, through effects such as gradation, movement, or deformation, intuitively demonstrate the process of status changes, helping users understand the dynamic behavior of the system. Implement context-sensitive interactive prompts, providing relevant information and suggestions based on user actions and system status. Contextual prompts provide appropriate information support when users need it, such as explaining terminology, providing background information, or suggesting the next step, enhancing the user experience.

[0267] Through a multi-layered risk visualization interface, complex risk assessment results are transformed into intuitive and easy-to-understand visual representations, enabling maintenance personnel to quickly grasp the risk situation, identify key issues, and make correct decisions. Based on a dynamic topology diagram of the substation layout and a combination of multiple complementary views, comprehensive and in-depth risk information is provided, from the overall situation to specific operational procedures, meeting information needs at different levels. Personalized configuration and interactive exploration functions further enhance the flexibility and applicability of the visualization interface, adapting to the needs and preferences of different users.

[0268] Step S4.8: Based on the intuitive risk status identification, apply the status coding visualization method to mark disconnected links in red, normally operating links in green, links with potential risks in yellow, and key nodes that require special attention in orange, forming a multi-color coded risk visualization display.

[0269] In this step, a status coding visualization method is applied, using color coding to represent different equipment statuses and risk levels, forming an intuitive multi-color risk visualization display. Status coding is a visualization technique that uses visual attributes (such as color, shape, and size) to represent data status or characteristics, with color coding being one of the most commonly used and intuitive coding methods.

[0270] First, design a color coding scheme to assign appropriate colors to different states and risk levels. Red is used to mark disconnected links, indicating that the link is currently unavailable. Red is a warning color, typically used to indicate stop, prohibition, or danger; in visualization, it represents a disconnected or failed state. Green is used to mark normally operating links, indicating that the link is working correctly. Green represents safety and permissibility, used to indicate a normal or healthy state. Yellow is used to mark links with potential risks, indicating that although the link is currently normal, there are safety hazards. Yellow is a warning color, indicating a state that requires attention or may have problems. Orange is used to mark critical nodes requiring special attention, indicating that the node has a significant impact on system security. Orange is between yellow and red, indicating a state that is more serious than a warning but does not reach a dangerous level.

[0271] The system implements multi-attribute status coding, combining various visual attributes to express rich status information. In addition to color, the system uses shape, size, and transparency to enhance status representation. Shape distinguishes equipment type (e.g., square represents protection devices, circle represents communication equipment); size indicates equipment importance or the scope of risk impact; transparency indicates data certainty, with low transparency indicating uncertain or missing data. Line style coding is designed, using different line types to represent link type and status. Solid lines represent physical connections, dashed lines represent logical connections; line thickness indicates bandwidth or importance; line animation (e.g., flowing effects) indicates data transmission status. An icon labeling system is implemented, using professional icons to represent equipment type and function. Standard power industry icons are used to represent substation equipment, such as circuit breakers, disconnect switches, and transformers, ensuring rapid identification by professional users. A composite status representation is designed to simultaneously display multi-dimensional status information. For example, the shape of the equipment icon represents the type, color represents the status, and the border represents the risk level, achieving integrated expression of multi-dimensional information.

[0272] Design dynamic state encoding to express state changes and the time dimension. Implement state change animation to intuitively demonstrate the state transition process of a device or link. State change animation uses smooth transition effects to show the process of a device changing from one state to another, enhancing the perception of change. Design flashing or pulsating effects to draw attention to specific states. Flashing is often used to indicate warnings or situations requiring immediate attention, such as newly emerging high risks or state abrupt changes. Implement gradient color representation to express continuously changing states or risk levels. Gradient colors are suitable for representing continuously changing values, such as a gradient from low to high risk, providing more granular state differentiation. Design a timeline integration to display the history of state changes over time. The timeline allows users to view historical state changes, understand the state evolution process, and discover temporal patterns.

[0273] Implement context-aware state coding, adjusting the coding scheme according to the environment and user needs. Design environment-adaptive display, adjusting color and contrast based on the display environment. Automatically adjust the color scheme under different lighting conditions (such as bright or dark environments) or on different display devices to ensure good visibility and differentiation. Implement user preference settings, allowing users to adjust the color scheme and coding rules. User preferences consider individual visual habits and special needs, such as colorblind-friendly modes or high-contrast modes. Design role-adaptive coding, adjusting information density and professionalism according to the user's role. Different user roles may require different levels of detail and professionalism; the system automatically adjusts the displayed content and expression based on the user's role. Implement task context awareness, highlighting relevant state information based on the current task. Task context awareness can identify the user's current task, highlighting task-related state information and reducing cognitive load.

[0274] Design an information hierarchy to organize and display status information of varying importance. Implement a visual hierarchy design, making important information stand out and relegating secondary information to the background. Visual hierarchy, through the design of factors such as contrast, size, and position, creates a hierarchy of information importance, guiding users to notice the most crucial information first. Design interactive detail unfolding, allowing users to access more detailed status information as needed. Interactive details adopt a progressive disclosure principle, initially displaying only advanced information, with users able to access more detailed content through interactive operations. Implement focus highlighting, automatically or manually highlighting the currently focused device or area. Focus highlighting uses effects such as highlighting, magnification, or sharpening to distinguish the user's current focus from other content. Design a context-preserving mechanism to maintain awareness of the overall situation while delving into details. Context preservation uses techniques such as thumbnails, navigation bars, or perspective transformations to ensure users do not lose sight of the overall picture when viewing detailed information.

[0275] Implement accessibility design to ensure users with varying abilities can effectively understand state codes. Design a colorblind-friendly color scheme to ensure colorblind users can distinguish different states. Colorblind-friendly design selects color combinations that are sufficiently distinguishable for colorblind users, or adds auxiliary codes such as shapes and textures. Implement a high-contrast mode to accommodate the needs of visually impaired users. High-contrast mode enhances the contrast between foreground and background, making content easier to perceive and read. Design multimodal feedback that combines visual, auditory, and tactile feedback to convey state information. Multimodal feedback relies not only on the visual channel but also utilizes auditory cues and tactile feedback to provide suitable information delivery methods for users with different sensory abilities. Implement configurable text labels to provide textual descriptions of states. Text labels provide clear state descriptions for users who may not be able to accurately perceive color or shape differences, ensuring barrier-free information delivery.

[0276] A state-coded visualization method was used to create a multi-color-coded risk visualization display, making complex equipment status and risk information intuitive and easy to understand. Red, green, yellow, and orange color codes directly correspond to different states such as disconnected, normal, potential risk, and critical nodes, facilitating rapid identification. Combining state codes with various visual attributes such as shape, size, and line type further enriches the information expression, enabling the delivery of more dimensions of information within a limited visual space. Dynamic state coding and context-aware coding adjustments improve the timeliness and adaptability of the visualization, enabling it to respond to changing environments and needs. Multi-layered information organization and accessibility design ensure the effectiveness and inclusiveness of the visualization for different users.

[0277] Step S4.9: Based on the multi-color encoded risk visualization display, and combined with the risk level, issue graded warnings through multimodal methods including visual warnings, sound warnings, and tactile feedback. Apply a memory-guided reflex mechanism to match risk mitigation strategies and provide targeted risk mitigation suggestions to achieve intelligent dynamic visualization warnings.

[0278] In this step, based on the results of risk visualization and assessment, multimodal early warning and intelligent risk mitigation suggestions are implemented, forming a complete dynamic visualization early warning system. An early warning refers to a warning message issued before a risk event occurs, aiming to alert relevant personnel to potential risks and take preventative measures. Multimodal early warning refers to simultaneously delivering warning information through multiple sensory channels (such as vision, hearing, and touch), improving the effectiveness and reliability of information transmission.

[0279] First, an intelligent early warning system based on a memory-guided reflex mechanism is implemented. This system can quickly respond to common risk patterns and handle complex risk situations through in-depth analysis. The system defines an early warning level system, classifying risks into different levels. Typical early warning levels include: Info (indicating a low-risk situation requiring attention); Attention (indicating some risk requiring attention); Warning (indicating significant risk requiring preventative measures); Danger (indicating high risk requiring immediate action); and Emergency (indicating extremely high risk requiring urgent handling). The system designs early warning trigger rules, mapping risk assessment results to corresponding early warning levels. Trigger rules determine appropriate early warning levels based on factors such as risk assessment value, risk type, scope of impact, and system importance. The system implements an early warning escalation mechanism, automatically raising the early warning level when the risk persists or intensifies. The escalation mechanism monitors the development trend of the risk, automatically raising the early warning level to a higher level when the risk situation worsens or persists for a set time, ensuring timely response. The system establishes early warning priority management to ensure that critical early warnings are handled first. Priority management assigns processing priorities to early warnings based on the severity, scope of impact, and urgency of the risks, ensuring that the most important early warnings receive priority attention and processing.

[0280] Design a multimodal warning output to convey warning information through multiple sensory channels. Design visual warning elements, including color coding, flashing effects, and highlighting. Visual warnings are the most basic form of warning, attracting user attention through eye-catching visual effects. Common visual warning elements include: color changes (such as a gradient from green to red), flashing or pulsating effects, size or shape changes, pop-ups or banners, etc. Implement an audio warning system that emits sound cues of different intensities and frequencies based on the warning level. Audio warnings convey information through the auditory channel and are particularly suitable for providing alerts when users are not looking directly at the screen or are under high attention load. Audio warnings typically include different tones, rhythms, or volumes of audible alerts corresponding to different warning levels and types. Support haptic feedback, providing haptic warnings such as vibration on mobile devices. Haptic warnings convey information through the tactile channel and are suitable for use in noisy environments or situations requiring quiet. Common haptic warnings include vibration patterns (such as short vibrations, long vibrations, or vibration sequences), force feedback, or pressure changes, etc. Design warning message push notifications to send warning information through channels such as SMS and application notifications. Push notifications ensure that alerts are delivered to relevant personnel in a timely manner, even if they are not physically present in front of the system. Push messages typically include key information such as the alert level, a summary, and recommended actions.

[0281] A warning pattern memory is implemented to store common risk scenarios and corresponding warning templates. Based on historical experience and expert knowledge, the memory records information such as characteristics, warning levels, and handling suggestions for typical risk scenarios. The warning pattern memory serves as the knowledge foundation for a memory-guided reflex mechanism, supporting rapid identification and response to common risk patterns. The system is designed for rapid pattern matching, triggering immediate warnings for known risk patterns. Pattern matching compares the current risk situation with patterns in the memory; for high-match cases, the system can immediately generate the corresponding warning without complex analysis. This "reflexive" response significantly improves the system's response speed to common risks. The system sets a warning reflex threshold to control the sensitivity of automatic warnings. The reflex threshold determines when an automatic warning is triggered; threshold settings need to balance timeliness and accuracy to avoid excessive false alarms or missed alarms. The system establishes a warning effect feedback mechanism to optimize warning strategies based on user responses. Effect feedback evaluates the effectiveness of warnings by recording user responses to warnings (e.g., ignoring, confirming, handling) and the occurrence of actual risk events, and adjusts warning strategies accordingly, achieving system self-optimization.

[0282] Design a deep analysis and early warning module to handle complex or novel risk situations. Implement risk trend analysis to predict the direction and speed of risk development. Trend analysis, based on historical data and current status, uses time series analysis or predictive models to estimate the future development path of risks and provide forward-looking early warnings. Design a correlation risk identification algorithm to discover potential risk correlations and cascading effects. Correlation risk identification analyzes the mutual influence and propagation paths between risk factors, identifies possible chain reactions or cascading failures, and provides early warnings of potential systemic risks. Establish an anomaly pattern detection system to identify atypical but potentially dangerous situations. Anomaly detection uses machine learning algorithms to identify states or behaviors that deviate from normal patterns, discovering novel risk patterns that may be ignored by rule-based reflection mechanisms. Implement context-aware analysis, comprehensively assessing environmental conditions, system status, and other contextual factors. Context awareness combines risk assessment with the current system operating environment and status, generating early warnings that are more consistent with reality and avoiding mechanical early warnings divorced from context.

[0283] Provides intelligent risk mitigation suggestions to help users effectively address identified risks. A risk-countermeasure knowledge base is built to store effective mitigation measures for various risks. The knowledge base includes various risk scenarios and their corresponding mitigation strategies, derived from industry best practices, expert experience, and historical case analysis. A context-aware suggestion generation algorithm is designed to provide the most suitable mitigation suggestions based on the current context. Context awareness considers factors such as system state, available resources, and operating environment to ensure that the generated suggestions are appropriate for the current situation and have practical feasibility. Suggestion prioritization is implemented, displaying the most critical and effective measures first. Prioritization considers factors such as the effectiveness of the measures, implementation difficulty, resource requirements, and time urgency, helping users focus on the most important actions within a limited time. Suggestion execution simulation is supported, allowing users to preview changes in system state after implementing the suggestions. Execution simulation uses a system model to predict the possible results after suggestion implementation, helping users evaluate the effects and potential impacts of different suggestions and make more informed decisions.

[0284] Design interactive decision support features to enhance users' decision-making capabilities. Design a decision tree visualization to show different decision paths and their possible consequences. A decision tree is a tool that intuitively displays decision options and results. It represents the decision-making process in a tree structure, with each branch representing a possible choice and leaf nodes representing the final outcome. Through decision trees, users can clearly see the possible consequences of different choices and make more informed decisions. Implement risk trade-off analysis to compare the costs and benefits of different mitigation strategies. Risk trade-off analysis assesses the implementation costs (such as resource consumption and time investment) and expected benefits (such as the degree of risk reduction) of each mitigation strategy, helping users choose the most cost-effective solution. Support hypothetical scenario simulation to evaluate the decision-making effect of "what if X were executed?" Hypothetical scenario simulation allows users to explore the possible consequences of different actions without actually executing them, providing a low-risk decision exploration method. Provide a collaborative decision-making platform to support multiple participants in key decisions. The collaborative decision-making platform provides features such as shared views, real-time communication, and collaborative editing, enabling multiple experts to jointly analyze risk situations, brainstorm, and reach better decisions.

[0285] Achieve integrated emergency response processes to ensure early warnings effectively trigger emergency actions. Seamlessly integrate the early warning system with the emergency response process to form a complete risk management chain. Integration ensures that early warning information directly triggers corresponding emergency response procedures, shortening response time and improving processing efficiency. Design an automated emergency plan activation mechanism triggered by early warnings, activating the corresponding plan based on the warning level and type. The automated activation mechanism transforms early warning information into specific emergency action instructions, automatically activating the appropriate level of emergency plan according to predefined rules. Implement automatic notification functionality for key personnel to ensure relevant responsible persons are promptly informed of risk situations. Automatic notification identifies key personnel requiring notification based on the warning content and level, sending notifications through multiple channels (such as telephone, SMS, and app push notifications) to ensure timely information delivery. Support emergency response recording and retrospection for easy post-event analysis and improvement. Response records detail the entire process of early warning triggering and emergency handling, including timestamps, executed operations, and personnel participation, providing a basis for subsequent analysis and improvement.

[0286] Design an early warning effectiveness evaluation and optimization mechanism to continuously improve the performance of the early warning system. Design an early warning effectiveness tracking mechanism to record the accuracy and timeliness of early warnings. Effectiveness tracking compares early warning information with the actual occurrence of events to evaluate the accuracy (whether the risk event was correctly predicted) and timeliness (whether sufficient warning time was provided). Implement a self-learning function for the early warning system, automatically adjusting early warning parameters based on historical effects. The self-learning function automatically optimizes early warning rules, thresholds, and parameters based on effectiveness evaluation results, enabling the system to continuously improve performance with use. Establish an early warning false alarm and missed alarm analysis model to continuously optimize early warning accuracy. False alarm and missed alarm analysis identifies systemic problems in the early warning system, such as high false alarm rates or low detection rates for specific types of risks, providing direction for targeted improvements. Support user feedback collection, incorporating expert experience into the early warning system optimization process. User feedback collection includes user evaluations and suggestions on early warnings; this input from frontline professionals is a valuable resource for improving the early warning system.

[0287] Through multimodal early warning and intelligent risk mitigation suggestions, intelligent dynamic visual early warning is achieved. Multi-color encoded risk visualization provides an intuitive visual foundation for early warning, while multimodal early warning involving sight, sound, and touch ensures effective communication of warning information. The application of a memory-guided reflex mechanism enables the early warning system to respond quickly to common risks while also deeply analyzing complex situations, balancing response speed and analytical depth. Intelligent risk mitigation suggestions and interactive decision support functions not only inform users of the existence of risks but also provide specific solutions for addressing them, significantly enhancing the practical value of the early warning system. The integration of the early warning system with emergency response processes and continuous effectiveness evaluation and optimization ensure the closed-loop operation and continuous improvement of the entire risk management process.

[0288] In a preferred embodiment, system integration and user interface implementation are also included:

[0289] Step S5: Based on the compliance verification of the operation sequence, the multi-dimensional risk level assessment, and the dynamic visualization early warning results, a microservice architecture is adopted to design the overall system framework. Functional modules including knowledge graph construction, scene recognition, security measure generation, and risk verification are encapsulated as independent services. Communication between the functional modules is realized through RESTful APIs and service registration and discovery mechanisms, thereby building a highly available and scalable integrated system platform.

[0290] In this step, based on the implementation results of the aforementioned functional modules, the overall system architecture is designed and constructed, integrating the various functional modules into a unified platform. System integration refers to the process of integrating multiple independent system components or subsystems into a coordinated overall system, with the aim of achieving seamless collaboration and information sharing among the components.

[0291] First, a microservices architecture is adopted for the overall framework design. Microservices architecture is a software development methodology that designs applications as a series of loosely coupled, independently deployable small services. Each service runs in its own process and communicates through lightweight mechanisms (usually HTTP APIs). This architectural pattern offers better flexibility, scalability, and maintainability compared to traditional monolithic architectures. The system encapsulates its main functional modules into independent microservices, including a knowledge graph service, a scene recognition service, a safety measure generation service, and a risk verification service. The knowledge graph service is responsible for maintaining and querying the four-element knowledge graph constructed in step S1, providing functions such as entity relationship querying and path analysis. The scene recognition service implements the maintenance scene recognition and rule reasoning functions in step S2, analyzing and matching the input maintenance tasks to the scene. The safety measure generation service is responsible for the associated loop identification and safety measure operation generation in step S3, dynamically generating safety measure solutions based on the scene and equipment status. The risk verification service implements the compliance verification and risk warning functions in step S4, verifying operations and providing risk assessments.

[0292] Design an inter-service communication mechanism to ensure efficient collaboration among services. RESTful APIs are adopted as the primary method for inter-service communication. RESTful APIs (Representational State Transfer APIs) are lightweight communication methods based on the HTTP protocol. They use standard HTTP methods (such as GET, POST, PUT, DELETE) to operate on resources and are characterized by simplicity, scalability, and statelessness. Define standardized API interfaces for each microservice, including request formats, response formats, error handling specifications, etc., to ensure interface consistency and understandability. Implement request verification and authentication mechanisms to ensure API security and reliability. Verification checks the legality of request formats and parameters, while authentication verifies the identity and permissions of the requester. Design an asynchronous communication pattern to handle long-running tasks or business processes that need decoupling. Asynchronous communication allows non-blocking interaction between services; the sender does not need to wait for the receiver to complete processing before continuing execution, improving system responsiveness and throughput. Establish an event-driven model to achieve loosely coupled communication between services. The event-driven model is based on a publish / subscribe mechanism, allowing services to publish events or subscribe to events of interest, reducing direct dependencies between services.

[0293] Implement a service registration and discovery mechanism to achieve dynamic service management. Service registration refers to a service instance registering its information (such as hostname, port, health status, etc.) with the registry center upon startup; service discovery refers to a client finding available instances of the service it needs through the registry center. This mechanism enables the system to dynamically add or remove service instances, achieving flexible expansion and fault recovery. Design a registry center service to maintain the instance information and status of all microservices. The registry center is the core component of service registration and discovery; it stores information about all service instances and provides query and notification functions. Implement an automatic service registration mechanism, where service instances automatically register their information with the registry center upon startup. Automatic registration reduces the need for manual configuration, making service management more automated and reliable. Design a health check mechanism to monitor the health status of service instances and promptly detect faulty instances. Health checks periodically probe the response of service instances to determine whether they are operating normally, and abnormal instances can be automatically removed from the available list. Establish a service discovery client to enable service consumers to dynamically find and connect to service providers. The service discovery client is responsible for obtaining service information from the registry center and selecting appropriate service instances for connection based on load balancing strategies.

[0294] Design a high-availability architecture to ensure system stability and continuity. High availability refers to the system's ability to continuously run and provide services, maintaining service availability even in the face of failures or maintenance. Implement redundant service deployment to ensure that multiple instances of each critical service run simultaneously. Redundant deployment maintains service availability even when some instances fail by deploying multiple service instances with the same functionality. Design a load balancing mechanism to rationally distribute requests across multiple service instances. Load balancing uses different strategies (such as round-robin, least connections, response time, etc.) to distribute client requests to different service instances, improving system throughput and preventing overload of a single instance. Establish a fault detection and recovery mechanism to quickly respond to service failures and automatically recover. Fault detection identifies abnormal services through monitoring and health checks, while the recovery mechanism maintains system functionality through automatic restart, failover, or service degradation. Implement a circuit breaker pattern to prevent cascading failures. The circuit breaker pattern monitors the failure rate of service calls; when the failure rate exceeds a threshold, it temporarily cuts off calls to failed services to prevent the spread of failures and automatically resumes service calls when conditions permit.

[0295] Implement a scalable design to support system expansion as business needs grow. Scalability refers to the system's ability to handle increased load or functional expansion, categorized into horizontal scaling (adding more instances of the same type) and vertical scaling (enhancing the capabilities of a single instance). Implement an elastic scaling mechanism to automatically adjust the number of service instances based on load. Elastic scaling monitors system resource utilization and request volume, adding instances during high loads and reducing instances during low loads to achieve efficient resource utilization. Design a stateless service model, allowing service instances to be replaced and expanded without distinction. Stateless services do not store session state or business data locally; all necessary state is passed through requests or stored in external systems, allowing service instances to be freely added or removed without affecting functionality. Establish a data partitioning strategy to support horizontal scaling of the data layer. Data partitioning distributes data across multiple nodes, with each node responsible for processing a portion of the data; data processing capacity can be linearly scaled by adding nodes. Implement an asynchronous processing model to improve system throughput and responsiveness. Asynchronous processing separates request reception from processing, enabling the system to handle a large number of concurrent requests more efficiently through mechanisms such as message queues or event streams.

[0296] Design a security architecture to ensure the security of the system and data. System security refers to the ability to protect system resources and data from unauthorized access, use, disclosure, interruption, modification, or destruction. Implement multi-layered authentication to ensure that only authorized users can access system functions. Authentication verifies user identity and may combine various factors such as passwords, certificates, and biometrics to improve the reliability of authentication. Design fine-grained access control to restrict the scope of operations based on user roles and permissions. Access control defines which users can access which resources and perform which operations, ensuring that users can only act within their authorized scope. Establish a data encryption mechanism to protect the security of sensitive data. Data encryption uses algorithms to convert plaintext into ciphertext, protecting the security of data during storage and transmission and preventing unauthorized access or eavesdropping. Implement security auditing functions to record and monitor all critical operations. Security auditing tracks who performed what operations at what time, providing a basis for security incident analysis and compliance verification.

[0297] Design monitoring and operations support to ensure system observability and maintainability. Observability refers to the ability to understand the internal state of the system from the outside, achieved through data such as logs, metrics, and traces; maintainability refers to the system's ease of modification, expansion, and repair. Implement unified log management to centrally collect and analyze log information from various services. Unified log management aggregates scattered service logs into a central system, facilitating querying, analysis, and correlation, accelerating problem localization and resolution. Design a real-time monitoring system to monitor service health status and performance metrics. Real-time monitoring collects various system operating metrics (such as CPU utilization, memory usage, request response time, etc.), providing intuitive dashboards and alarm mechanisms to help operations personnel promptly identify and respond to problems. Establish a distributed tracing mechanism to track the propagation path of requests between microservices. Distributed tracing records the complete path and processing time of requests between different services, helping to understand system behavior and performance bottlenecks. Implement automated operations tools to simplify deployment, upgrades, and maintenance. Automated operations and maintenance automate routine tasks through scripts and tools, reducing manual intervention and improving efficiency and reliability.

[0298] Through microservice architecture and advanced system design, various functional modules are integrated into a unified platform, achieving high availability, scalability, and security. Standardized communication interfaces and registration / discovery mechanisms between services ensure flexible combination and dynamic management of system components. Unified monitoring and operation and maintenance support makes the system easier to maintain and manage. This integrated system platform provides a solid technical foundation for substation safety management, supporting the continuous evolution and expansion of business needs.

[0299] Step S6: For the integrated system platform, an intelligent human-computer interaction interface is designed using a memory-guided reflex decision-making mechanism. The external memory module stores the user's historical operation habits, the reflex module responds to frequently used function accesses, the reasoning module understands complex user intentions, and the layout of the interaction interface and function recommendations are dynamically adjusted according to the user's behavior patterns to provide personalized intelligent recommendations and auxiliary decision-making functions.

[0300] In this step, based on the integrated platform built in step S5, an intelligent human-computer interaction interface is designed and implemented to improve user experience and system usability. Human-Computer Interaction (HCI) is a discipline that studies the ways in which humans interact with computer systems, with the goal of designing convenient, efficient, and satisfying interactive experiences. Intelligent HCI further incorporates artificial intelligence technology to make the interaction process more intelligent, personalized, and natural.

[0301] First, the memory-guided reflex decision-making mechanism is applied to human-computer interaction design. This mechanism integrates three core components—memory, reflex, and reasoning—to achieve gradient intelligent interaction, from rapid response to deep thinking. The system implements an external memory module to store and manage user interaction history and preferences. This external memory module forms the knowledge foundation for intelligent interaction, recording user operating habits, interface preferences, and frequently used functions, providing a basis for subsequent intelligent responses. This module includes user behavior records, tracking and recording user operation sequences, click paths, and dwell times. This behavioral data reflects user usage patterns and preferences, forming a crucial foundation for personalized interaction. The module also includes interface customization records, saving user-defined settings and adjustments to the interface. Interface customization includes layout adjustments, component display / hiding, and color theme selection, reflecting user visual preferences and work habits. The module also saves task context records, recording user operating behaviors and decision-making patterns in different work scenarios. Context records are linked to specific work scenarios and user behaviors, facilitating the system's understanding of contextualized user needs. The module also maintains user feedback records, storing user feedback on system recommendations and auxiliary functions. Feedback information, including acceptance / rejection of recommendations, ratings, or comments, is an important basis for the system to optimize its intelligent functions.

[0302] Design a memory management mechanism to ensure the relevance and timeliness of memory content. Implement a memory weighting mechanism, adjusting the weight of memory items based on time decay and usage frequency. Recent memories and frequently used memories receive higher weights and have a greater influence on intelligent responses. Design a hierarchical memory storage system to manage memories of different types and importance in layers. Hierarchical storage allows the system to employ different storage strategies and access mechanisms for different types of memories, optimizing storage efficiency and access performance. Establish a memory association network to build semantic associations and dependencies between memory items. The association network connects related memory items to form a knowledge network, supporting more complex memory retrieval and reasoning. Implement a memory forgetting mechanism to periodically clean up low-value or outdated memory content. The forgetting mechanism is a crucial part of optimizing memory management, preventing performance degradation and decision bias caused by the accumulation of useless information.

[0303] Implement a reflection module to quickly respond to users' routine operational needs. The reflection module is a rapid response layer for intelligent interaction, achieving instantaneous reactions through simple pattern matching, similar to human conditioned reflexes. This module includes rapid pattern recognition, identifying users' regular operation patterns and usage habits. Pattern recognition identifies recurring behavioral patterns by analyzing operation sequences, time patterns, and triggering conditions. The module designs an interface adaptation strategy, dynamically adjusting interface elements based on identified user patterns. Adaptation strategies include highlighting frequently used functions, simplifying operation processes, and creating shortcuts, making the interface more aligned with user habits. Implement quick operation prediction, anticipating the user's possible next action and providing shortcuts. Operation prediction is based on the user's current context and historical behavior patterns, preparing potentially needed functions in advance and reducing user operation steps. Design reflection trigger conditions, defining when to initiate automatic reflection response. Trigger conditions consider the determinism, frequency, and importance of user behavior to ensure the accuracy and appropriateness of the reflection response.

[0304] Implement a reasoning module to understand and respond to complex user intentions and needs. This module acts as a deep thinking layer for intelligent interaction, handling complex interaction scenarios through logical analysis and reasoning, much like human deliberation. It includes an intent understanding engine that analyzes the goals and intentions behind user behavior. Intent understanding combines user action sequences, contextual information, and historical patterns to infer the user's current work goals and needs. Design context-aware decision-making to formulate response strategies based on the current work scenario and system state. Context awareness considers factors such as task type, system state, and environmental conditions, making system responses more consistent with the actual situation. Implement a knowledge reasoning mechanism that performs logical reasoning based on domain knowledge and user models. Knowledge reasoning utilizes power domain knowledge and user preference models to derive solutions that meet user needs, particularly suitable for complex or novel interaction scenarios. Establish a learning adaptation mechanism to learn from and optimize reasoning strategies from user feedback. Learning adaptation adjusts and improves reasoning rules and strategies by analyzing user feedback on system responses, enhancing the system's adaptability and accuracy.

[0305] Design dynamic interface adjustment functionality to optimize the interactive experience based on user behavior patterns. Implement a layout adaptive mechanism to adjust the interface layout according to user habits. Layout adaptation includes expanding frequently used function areas, collapsing infrequently used functions, and displaying related functions in combination, optimizing screen space utilization and operational efficiency. Design personalized visual elements to adjust the interface's visual style according to user preferences. Visual personalization includes customizing visual elements such as color themes, icon styles, and font sizes, improving user visual comfort and recognition efficiency. Establish interaction mode adaptation to optimize interaction methods based on user operating habits. Interaction mode adaptation identifies user-preferred operation methods (such as keyboard shortcuts, mouse operations, gesture control, etc.) and prioritizes providing matching interaction modes. Implement information density adjustment to adjust the information display density based on user information processing capabilities and preferences. Information density adapts to the user's cognitive load capacity; advanced users may prefer high-density information display, while novice users may need a clearer and simpler interface.

[0306] Design a personalized intelligent recommendation function to proactively provide potentially useful information and features. Implement a feature recommendation engine that recommends relevant features based on user history and current context. Feature recommendations analyze the user's current tasks and historical usage patterns to predict potentially needed features and proactively recommend them, reducing user search and navigation time. Design intelligent content filtering to filter and sort information content based on user interests. Content filtering analyzes user reading and interaction patterns to identify the types and priorities of information that users are interested in, placing the most relevant content in prominent positions. Establish a collaborative filtering mechanism to recommend based on the behavioral patterns of similar users. Collaborative filtering utilizes the principle that "similar users like similar things," enriching the recommendation basis with the behavioral data of other users, especially suitable for new users or new features. Implement a recommendation explanation function to explain the reasons and basis for the recommendations. Recommendation explanations improve the transparency and credibility of recommendations, allowing users to understand why the system makes specific recommendations and facilitating more accurate user feedback.

[0307] The system is designed to assist users in making better decisions in complex situations. A solution comparison tool helps users evaluate the advantages and disadvantages of different security measures. The solution comparison visually presents the differences between solutions from multiple dimensions, including security, operational complexity, and resource requirements, assisting users in making more informed choices. Decision risk warnings are designed to identify and alert users to potential risks in their decisions. These warnings are based on historical cases and expert rules to identify potential risks in the user's current decision and provide timely alerts. Best practice recommendations are established, providing reference solutions based on historical success stories. Best practices extract high-quality solutions from historical records and recommend them to users as references in similar situations, passing on successful experiences. Intelligent diagnostic suggestions are implemented to analyze the causes of problems and provide solutions. Intelligent diagnostics combine system status, operation records, and domain knowledge to analyze possible causes of abnormal situations and provide targeted solutions.

[0308] Implement a natural user interface to make interaction more intuitive and user-friendly. Design natural language interaction to support user operation and query using natural language. Natural language interaction allows users to interact with the system in a manner close to everyday conversation, such as "display the protection status of line 1," reducing the learning cost of interaction. Implement voice control functionality to support voice commands and queries. Voice control is particularly suitable for scenarios where both hands are busy or where long-distance operation is required, improving the flexibility and accessibility of interaction. Establish a gesture recognition interface to support intuitive gesture operation control. Gesture recognition makes interaction more intuitive and natural, especially suitable for touchscreen and large-screen display environments. Design augmented reality (AR) interaction to overlay virtual information onto the real environment. AR interaction is particularly valuable in field operations, as it can intuitively associate system information with actual equipment, supporting on-site decision-making and operation.

[0309] Design learning and adaptation mechanisms to continuously optimize and evolve the user interface. Collect user feedback through multiple channels, including explicit feedback (such as ratings and reviews) and implicit feedback (such as usage frequency and dwell time), to comprehensively understand the user experience. Design an A / B testing mechanism to compare the effectiveness of different interaction solutions. A / B testing scientifically evaluates the actual impact of design changes by showing different versions of the interface or functionality to different users and comparing user reactions and performance metrics. Establish a continuous optimization cycle to continuously improve the interaction design based on user data and feedback. Continuous optimization forms a closed loop between user feedback, usage data, and design improvements, ensuring the continuous evolution and refinement of the interface design. Implement dynamic updates to the user model, adjusting personalized strategies according to changes in user behavior. Dynamic updates to the user model ensure the system can adapt to changes in user preferences and needs, maintaining the timeliness and accuracy of personalized strategies.

[0310] By applying a memory-guided reflex decision-making mechanism, an intelligent human-computer interaction interface is achieved, providing users with a personalized, efficient, and easy-to-use operating experience. An external memory module stores users' historical operating habits, providing a knowledge base for intelligent interaction; a reflex module quickly responds to frequently used function accesses, improving the efficiency of routine operations; and a reasoning module understands complex user intentions and handles unconventional interaction needs. Dynamic interface adjustments, personalized recommendations, and decision support functions further enhance the system's intelligence level and user experience. A natural interactive interface and continuous learning mechanism make the system more user-friendly and capable of continuous evolution. This intelligent human-computer interaction design significantly improves the usability and user satisfaction of the substation safety measures management system, making complex technical systems more user-friendly and efficient.

[0311] Step S7: Based on the personalized intelligent recommendation and decision-making assistance functions, design a full-process visual management interface covering the formulation, approval, execution and supervision of safety measures. Integrate an interactive device relationship exploration component based on knowledge graphs, and display the execution progress and system status changes of safety measures in real time through visual elements to realize intelligent visualization of the entire safety measure management process and overall integration of system functions.

[0312] In this step, based on the intelligent human-computer interaction framework designed in step S6, a full-process safety measure management visualization interface is further developed to achieve intelligent and visualized management of the entire safety measure process. Full-process management refers to a management method that covers the entire lifecycle of a business, including the entire process from safety measure formulation, approval, execution to supervision in safety measure management. Visualized management refers to presenting management information and processes through intuitive visual expression, improving information transmission efficiency and decision-making quality.

[0313] First, a safety measure creation interface was designed to support efficient and intelligent safety measure solution creation. A safety measure template library was implemented, providing standard safety measure templates for various maintenance scenarios. Based on historical successful cases and expert experience, the template library provides predefined safety measure solution templates for common maintenance scenarios, reducing repetitive work and ensuring solution quality. An interactive safety measure editor was designed, supporting flexible creation and modification of safety measure solutions. The safety measure editor provides an intuitive interface, supporting drag-and-drop operations, step sorting, parameter settings, and other functions, making the safety measure solution creation process more efficient and flexible. An intelligent recommendation function was established, recommending safety measure steps based on the maintenance scenario and equipment status. Intelligent recommendation, based on scenario recognition and safety measure generation technology in steps S2 and S3, provides users with intelligent safety measure suggestions, combined with the user's professional judgment to form the final solution. Real-time compliance checks were implemented to verify the compliance of the safety measure solution during the editing process. Real-time checks applied the compliance verification technology of step S4, dynamically verifying the compliance of the safety measure solution while the user edits it, promptly identifying and correcting potential problems.

[0314] Design a safety measure approval interface to support an efficient and transparent approval process. Visualize the approval process, intuitively displaying the approval path and current status. Process visualization transforms complex approval processes into intuitive graphical representations, clearly showing approval nodes, sequences, and statuses, enabling all participants to understand the current progress. Design multi-role collaborative approval, supporting participation from different roles and with varying permissions. Multi-role approval considers the division of responsibilities among different departments and positions within the power system, providing corresponding interfaces and functions for various approval personnel to ensure the comprehensiveness and authority of the approval process. Establish intelligent approval assistance, providing approval suggestions based on historical data and rules. Intelligent assistance analyzes the differences between current safety measure plans and similar historical plans, identifying potential risk points and providing decision-making references for approval personnel. Implement approval opinion tracking, recording and managing approval opinions at all levels and modification history. Opinion tracking records detailed approval opinions, modification requirements, and actual changes for each approval stage, forming a complete approval process record for easy traceability and learning.

[0315] Design an execution interface for safety measures to support the orderly and efficient execution of safety measures. Visualize execution steps, clearly displaying the current progress and the next step. Step visualization uses timelines, flowcharts, or checklists to intuitively show the entire execution process and current location, guiding operators to execute the measures systematically. Design an operation guidance function, providing detailed operation guidelines for each step. Operation guidance includes text descriptions, illustrations, and precautions to ensure operators understand the specific requirements and execution methods for each step. Establish real-time status feedback, displaying operation results and equipment status changes. Status feedback uses a graphical interface to display real-time changes in equipment status after the operation, allowing operators to intuitively verify the operation effect and promptly identify anomalies. Implement an anomaly handling wizard to guide users in handling anomalies during execution. The anomaly handling wizard provides preset handling procedures and suggestions for possible anomalies, helping operators quickly respond to unexpected problems.

[0316] Design a safety measure supervision interface to support comprehensive real-time supervision and evaluation. Implement a real-time monitoring panel to monitor the implementation status of safety measures and changes in system status. The monitoring panel centrally displays information such as the progress of safety measure implementation, the status of key equipment, and changes in system parameters, providing a comprehensive real-time monitoring view. Design a risk warning function to promptly identify and alert to potential risks. The risk warning applies the risk assessment and early warning technology from step S4 to monitor the risk status during the implementation of safety measures in real time and issue timely warning information. Establish a deviation analysis tool to compare the differences between the plan and the actual implementation. Deviation analysis compares the safety measure plan with the actual implementation to identify implementation deviations and potential problems, supporting timely intervention and adjustments. Implement a safety measure evaluation report, automatically generating an evaluation report on the effectiveness of safety measure implementation. The evaluation report comprehensively analyzes the completeness, compliance, safety, and efficiency of safety measure implementation, summarizes lessons learned, and provides a reference for future safety measure management.

[0317] Design an interactive device relationship exploration component based on a knowledge graph to enhance users' understanding of the system structure. This component, based on the quaternary knowledge graph constructed in step S1, provides an intuitive interface for exploring complex relationships between devices. Implement a graphical knowledge graph browser to visualize the connections and dependencies between devices. The graphical browser transforms entities and relationships in the knowledge graph into intuitive graphical representations; nodes represent devices or components, and edges represent the relationships between them. Users can intuitively understand the system structure through this interface. Design an interactive query tool to support users in flexibly querying device relationship information. The query tool allows users to find specific devices and their related devices by clicking, searching, or filtering, understanding their position and role in the system. Establish a path analysis function to discover indirect relationships and influence paths between devices. Path analysis allows users to select two devices and automatically find the connection path between them, understanding their indirect relationship methods, which is particularly valuable for understanding the chain effects in complex systems. Implement relationship visualization to intuitively display the strength and importance of relationships between devices. Relationship visualization uses visual attributes such as edge thickness, color, or labels to represent the strength or importance of relationships between devices, helping users identify key connections and dependencies.

[0318] Design real-time progress and status visualization components to intuitively display the implementation status of safety measures and system changes. Implement a safety measure implementation progress dashboard to display the completion status of each stage and step in real time. The progress dashboard uses progress bars, Gantt charts, or milestone charts to intuitively display the overall progress of safety measure implementation and the completion status of each sub-task, facilitating overall control. Design an equipment status change timeline to record and display the status change history of key equipment. The status timeline records the change process of equipment status in chronological order, forming a visualized status change history, which facilitates understanding the evolution process and causal relationships of equipment status. Establish dynamic system topology updates to reflect changes in system structure and connection status in real time. Dynamic topology updates the system topology diagram in real time as operations and status changes occur during safety measure implementation, intuitively displaying changes in system structure, such as link disconnection and equipment isolation. Implement a key parameter trend chart to monitor the changing trends of important system parameters. The trend chart uses time as the horizontal axis and parameter values ​​as the vertical axis to display the changing trends of parameters over time, facilitating the detection of abnormal fluctuations and long-term change patterns.

[0319] Design a collaborative workspace to support collaborative management across multiple roles and teams. Implement role-adaptive interfaces, providing customized work interfaces for different roles. Role adaptation considers the differences in responsibilities and needs of personnel in different positions, providing tailored functions and views for safety management personnel, on-site operators, supervisors, etc. Design a shared workspace to support multiple users simultaneously viewing and collaboratively processing safety information. The shared workspace allows multiple users to view the same information and operation interface in real time, facilitating team collaboration and information sharing, especially suitable for cross-departmental collaboration scenarios. Establish real-time communication tools to support instant communication and coordination among team members. Communication tools include instant messaging, voice calls, video conferencing, etc., facilitating timely communication, coordination of actions, and problem-solving among team members. Implement knowledge sharing functionality to support the recording and sharing of experience and best practices. Knowledge sharing allows users to record and share experiences, skills, and solutions in their work, forming a knowledge base and promoting team learning and capacity building.

[0320] The system is designed for mobile adaptation, supporting safety management anytime, anywhere. It features responsive design to adapt to different devices and screen sizes. Responsive design allows the interface to automatically adapt to the screen size and resolution of various devices, from desktop computers to tablets and smartphones, maintaining good usability. Mobile-first functionality is designed to optimize key operational processes for mobile scenarios. Mobile-first design considers the usage characteristics and limitations of mobile devices, optimizing touch operations, simplifying processes, and reducing data input, making mobile operation more convenient. An offline working mode is established to support work in environments with unstable networks. Offline mode allows users to continue working in environments without or with unstable networks; data is automatically synchronized after the connection is restored, making it particularly suitable for field operations. Location-aware services are implemented to provide relevant information and services based on the user's location. Location awareness uses GPS or indoor positioning technology to identify the user's location and automatically provides device information, operating instructions, or safety tips related to the location, enhancing information support for field work.

[0321] The design ensures overall integration and consistency, guaranteeing harmony and uniformity across the entire interface. A unified design language is implemented to ensure consistent style across all functional modules. This language includes color schemes, icon systems, layout principles, and interaction patterns, ensuring visual and interactive consistency throughout the system and reducing user learning costs. A seamless navigation system is designed to support smooth switching between different functional modules. The navigation system uses clear menu structures, breadcrumb navigation, and contextual links to help users understand their current location and easily navigate to relevant pages, maintaining workflow continuity. A centralized data display is established to ensure consistent data sources and representation methods across modules. Data display uses unified data sources, update mechanisms, and presentation formats to avoid confusion and errors caused by data inconsistencies. System status synchronization is implemented to ensure consistent system status information displayed across all interfaces. Status synchronization is achieved through centralized status management and real-time push mechanisms, ensuring all user interfaces display the same latest system status and preventing decision-making errors due to inconsistent information.

[0322] Implement user experience optimization to ensure interface usability and satisfaction. Implement user behavior analysis to monitor and analyze user interaction patterns and identify improvement opportunities. Behavior analysis collects user interaction data, such as click paths, dwell time, and error rates, to analyze user habits and potential problems, providing data support for interface optimization. Design a contextualized help system to provide context-specific help information when needed. Contextualized help provides targeted help information based on the user's current interface and operation, including text instructions, video tutorials, or interactive guidance, helping users quickly understand and use functions. Establish a user satisfaction assessment system to regularly collect and analyze user feedback on the interface. Satisfaction assessment uses questionnaires, rating systems, or direct feedback to understand user satisfaction with the interface and suggestions for improvement, serving as a basis for continuous optimization. Implement accessibility design to ensure the interface is inclusive of users with different abilities. Accessibility design considers the special needs of users with color blindness, visual impairments, hearing impairments, etc., ensuring system friendliness to all types of users through high-contrast interfaces, screen reader support, keyboard navigation, and other functions.

[0323] The design of a fully visualized management interface enables intelligent and visual management of the entire safety measures process. The entire process, from safety measure formulation, approval, execution to supervision, is systematically supported, with each stage closely linked to form a closed-loop management system. An interactive device relationship exploration component based on a knowledge graph enhances users' understanding of complex system structures, providing intuitive knowledge support for safety measure decision-making. Real-time progress and status visualization makes the safety measure execution process and system changes transparent and visible, facilitating monitoring and adjustments. Collaborative workspaces and mobile device adaptation support flexible collaboration across multiple roles and scenarios, improving team efficiency. Overall integration and consistency assurance ensure system coordination and coherence, providing a smooth user experience. This comprehensive visualized management interface transforms complex technical functions into easy-to-use tools, significantly improving the efficiency and quality of substation safety measure management.

[0324] like Figure 3 As shown, the present invention also provides a smart substation safety measure verification system, comprising:

[0325] The knowledge graph construction module 10 is used to acquire the substation secondary system topology, equipment information and historical safety ticket data. It constructs a four-element knowledge graph containing equipment-link-pressure plate-operation through the untangled multi-spatiotemporal transition graph representation learning method to realize the modeling and representation of complex relationships between equipment.

[0326] The scene recognition and rule reasoning module 20 is used to extract multimodal features of maintenance tasks and identify maintenance scene types based on the four-element knowledge graph and memory-guided reflex decision reasoning mechanism, obtain maintenance scene recognition results, optimize the rule reasoning process under complex constraints through distributed guided local search algorithm, and generate the optimal safety operation set for a specific maintenance scene.

[0327] The safety measure adaptive generation module 30 is used to automatically identify related loops and determine the minimum safety isolation range based on the optimal safety measure operation set and real-time equipment status information through a memory-guided decision reasoning mechanism, dynamically generate the minimum safety operation set according to the real-time equipment status and adaptively adjust the operation steps.

[0328] The compliance verification and risk warning module 40 is used to construct an operation dependency model for the adaptively adjusted operation steps by combining untangled variational multigraph representation learning and distributed guided local search algorithm, so as to realize operation sequence compliance verification, multi-dimensional risk level assessment and dynamic visualization warning.

[0329] In practical applications, taking "220kV line protection cabinet A verification" as an example, the system processing flow is as follows:

[0330] First, the knowledge graph construction module acquires the substation secondary system topology, equipment information, and historical safety measure ticket data. It then constructs a quaternary knowledge graph using a detangling multi-spatiotemporal transition graph representation learning method. This graph accurately expresses the complex relationships between line protection devices and related equipment (such as CTs, PTs, switches, and pressure plates).

[0331] Then, the scene recognition and rule reasoning module, based on the maintenance task description "220kV line protection cabinet A verification", combined with multimodal feature extraction, identifies this as a "protection verification type" scenario. The system optimizes the rule reasoning process through a distributed guided local search algorithm, generating the optimal set of safety measures for the line protection verification scenario, including operation steps such as exiting the relevant soft pressure plate, disconnecting the CT circuit, and isolating the communication link.

[0332] Next, the adaptive generation module automatically identifies all related circuits, including CT circuits, PT circuits, communication links, and power supply circuits, centered on the line protection cabinet A. Based on the "maintenance domain isolation" principle, the system determines the minimum safe isolation range, isolating only necessary circuits and retaining backup protection functions. The system collects the status information of relevant equipment in real time. If it detects that a communication port is offline, it automatically adjusts the operation steps, skipping the corresponding link disconnection operation.

[0333] Finally, the compliance verification and risk warning module performs compliance verification on the adaptively generated operation steps. For example, if the system detects that an operator attempts to disconnect the CT circuit before removing the soft pressure plate, it immediately issues an operation sequence error warning. Simultaneously, a risk assessment is performed on each operation step. If an operation is identified as potentially affecting the protection function of adjacent lines, the risk is marked as "medium risk," and the relevant links are highlighted in yellow on the visualization interface. Visual and audible warnings are provided to alert the operator, along with risk mitigation suggestions.

[0334] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for checking security measures of a smart substation, characterized by, The method comprises the following steps: obtaining substation secondary system topology, device information and historical safety ticket data, constructing a four-dimensional knowledge graph containing devices-links-panels-operations through a multi-time-space transition graph representation learning method to realize modeling and representation of complex association relationships between devices, including: for the substation secondary system topology, device information and historical safety ticket data, through extracting entity information and relationship information in the substation secondary system design document, wiring diagram and device manual, an initial knowledge graph containing IED devices, optical fiber links, soft and hard panels and operation steps is constructed, forming a basic graph structure containing optical fiber connection relationship, panel control relationship, operation dependency relationship and function implementation relationship; applying a multi-time-space graph auto-encoder to decompose the initial knowledge graph into a shared feature subgraph expressing common relationships and a private feature subgraph expressing specific relationships, generating a multi-time-space graph representation; based on the multi-time-space graph representation, the physical connection relationship and logical dependency relationship between devices are captured simultaneously through a space-time coding layer, a contrast learning constraint method is used to optimize private feature extraction and reduce noise influence, and an optimized private feature extraction result is obtained; for the optimized private feature extraction result, the correspondence between devices and operations in the historical safety ticket and the nodes in the initial knowledge graph is identified through an entity linking algorithm, an association rule mining algorithm is applied to discover the implicit operation dependency mode, and the missing relationships in the initial knowledge graph are dynamically completed, and the four-dimensional knowledge graph is obtained; According to the four-dimensional knowledge graph, the multi-modal features of the maintenance task are extracted and the maintenance scene type is identified by combining a memory-guided reflex decision reasoning mechanism, a maintenance scene recognition result is obtained, a distributed guide local search algorithm is used to optimize the rule reasoning process under complex constraint conditions, and an optimal safety operation set for a specific maintenance scene is generated; Based on the optimal safety operation set and real-time device state information, the memory-guided decision reasoning mechanism is used to automatically identify the associated loop and determine the minimum safety isolation range, and the minimum safety operation set is dynamically generated according to the real-time state of the device and the operation steps are adaptively adjusted. For the adaptive adjustment operation steps, an operation dependency relationship model is constructed in combination with the disentangled variational multiple graph representation learning and the distributed guided local search algorithm to realize operation sequence compliance checking, multi-dimensional risk level assessment and dynamic visualized early warning, including: based on the adaptive adjustment operation steps and the four-element knowledge graph, the multi-dimensional dependency relationships between operations including time sequence dependency, functional dependency, spatial dependency, resource dependency and conditional dependency are extracted through the disentangled variational multiple graph representation learning method, the preconditions, mutual exclusion relationships and time sequence constraints between operations are clearly defined, and a multi-dimensional logical dependency network is formed; according to the multi-dimensional logical dependency network, the input operation record text is parsed, a deep learning sequence labeling model is used to identify entities and relationships in the operation record text, key information including operation type, operation object and operation parameter is extracted, an operation parsing result is obtained, and compliance evaluation and system state influence analysis of the current operation are determined; based on the compliance evaluation and system state influence analysis of the current operation, the operation parsing result is matched and verified with the multi-dimensional logical dependency network and the real-time state of the equipment, whether the operation meets all pre-dependent conditions is checked, and real-time compliance checking of the operation sequence is realized.

2. The method of claim 1, wherein, The combination of the memory-guided reflex decision-making mechanism extracts multi-modal features of the maintenance task and identifies the type of the maintenance scene, including: For the four-element knowledge graph, a multi-modal feature extraction framework is designed to extract semantic features from maintenance task description texts, state features from equipment operation parameters, and experience features from historical similar cases to form a multi-dimensional feature representation of the maintenance scene; Based on the multi-dimensional feature representation of the maintenance scene, a memory-guided reflex decision-making model is constructed, including an external memory module to store historical scene features and category mapping, a reflex module to realize fast scene matching, and an inference module to handle complex scenes, realizing a gradient decision-making process from fast reaction to deep reasoning, and obtaining the output result of the gradient decision-making process; According to the output result of the gradient decision-making process, the type of the maintenance scene is automatically identified and the scene attributes are determined, and the optimal set of safety precautions for the specific maintenance scene is generated.

3. The method of claim 1, wherein, The distributed guided local search algorithm is used to optimize the rule inference process under complex constraints, including: According to the maintenance scene recognition result, a hierarchical rule base is constructed, including a basic rule layer applicable to all scenes, a scene rule layer for different scene types, and a special case rule layer for handling special cases, forming a three-layer architecture of the scene-based rule base; Based on the three-layer architecture of the scene-based rule base, the distributed guided local search algorithm is applied, the adaptive violation condition identification mechanism is used to selectively punish high-cost constraints, and the punishment evaporation mechanism is used to control the punishment amplitude to prevent excessive punishment, realizing optimal rule inference under complex conditions including safety constraints, time sequence constraints and resource constraints, and obtaining the optimal rule inference result; For the optimal rule inference result, a distributed coordination synchronization scheme is used to handle rule conflicts under multi-constraint conditions, and the optimal set of safety precautions for the specific maintenance scene that meets all constraint conditions is generated.

4. The method of claim 1, wherein, The automatic identification of the closed loop and the determination of the minimum safe isolation range by the memory-guided decision-making reasoning mechanism comprises: For the optimal set of safety precautions, a multi-level graph traversal algorithm is applied to automatically identify all loops with the target maintenance equipment as the center, including four types of association relationships between the target equipment, such as direct connection, functional association, indirect influence and backup redundancy, and to determine the minimum safe isolation range according to the maintenance domain isolation principle; Based on the minimum safe isolation range, real-time device state information of related devices is collected in real time through multi-protocol access, and a data fusion algorithm based on the memory-guided reflex decision-making mechanism is applied to process the multi-source heterogeneous data of the real-time device state information to form a unified device state representation; According to the unified device state representation, a memory module in the memory-guided reflex decision-making mechanism generates a regular safety precaution operation step, a reasoning module processes special state conditions, and the operation content and execution order are dynamically adjusted in combination with the real-time device state information to obtain a complete operation step sequence that is adaptively adjusted according to the real-time state of the device.

5. The method of claim 1, wherein, Multi-dimensional risk level assessment is achieved, including: For the operation analysis result, a distributed guided local search algorithm is applied to convert various risk factors including operation sequence errors, device state abnormalities, operation environment risks, personnel factor risks and system vulnerability risks into a distributed constraint optimization problem, and to calculate the operation risk values of various types through an adaptive violation condition identification and punishment evaporation mechanism; According to the operation risk values of various types, the complex interaction relationship between risk factors is processed through a distributed coordination mechanism, and risk pattern matching and prediction analysis are performed in combination with similar cases in historical accident data to generate a multi-dimensional risk assessment result considering risk probability and multi-dimensional risk assessment. Based on the multi-dimensional risk assessment result, a risk matrix is applied to map the comprehensive risk value to different risk levels of low, medium, high and extremely high, and to identify the main risk contribution factors and key risk points, to achieve multi-dimensional risk level assessment, and to obtain a multi-dimensional risk level assessment result.

6. The method of claim 5, wherein, Dynamic visual early warning is achieved, including: For the multi-dimensional risk level assessment result, a multi-level risk visualization interface including a global risk situation map, a device network topology map and an operation sequence risk map is designed, a dynamic topology map based on the substation layout is used to display the operation influence range and risk distribution in real time, and an intuitive risk state identifier is generated; Based on the intuitive risk state identifier, a state coding visualization method is applied to mark the disconnected links with red, the normally operating links with green, the links with potential risks with yellow, and the key nodes that need to be focused on with orange, to form a multi-color coded risk visualization display; According to the multi-color coded risk visualization display, a hierarchical early warning is issued through a multi-modal method including visual early warning, sound early warning and tactile feedback, a memory-guided reflex mechanism is applied to match risk mitigation strategies and provide targeted risk mitigation suggestions, and intelligent dynamic visual early warning is achieved.

7. The method of claim 1, wherein, It also includes system integration and interaction interface implementation: Based on the operation sequence compliance verification, multi-dimensional risk level assessment and dynamic visual early warning results, the overall framework of the system is designed using micro-service architecture, the functional modules including knowledge graph construction, scene recognition, safety measure generation and risk verification are encapsulated as independent services, the communication between the functional modules is realized through RESTful API and service registration and discovery mechanism, and an integrated system platform with high availability and scalability is constructed; For the integrated system platform, an intelligent human-computer interaction interface is designed using a memory-guided reflex decision mechanism, the external memory module stores the user's historical operation habits, the reflex module responds to common function access, the reasoning module understands complex user intent, and the interaction interface layout and function recommendation are dynamically adjusted according to the user behavior pattern, providing personalized intelligent recommendation and auxiliary decision functions; According to the personalized intelligent recommendation and auxiliary decision functions, a full-process visual management interface covering safety measure development, approval, execution and supervision is designed, an interactive device association relationship exploration component based on knowledge graph is integrated, the safety measure execution progress and system state changes are displayed in real time through visual elements, and the intelligent visualization of the safety measure management full process and the overall integration of system functions are realized.

8. A smart substation safety measure verification system, characterized in that, It includes: A knowledge graph construction module is used to obtain substation secondary system topology, device information and historical safety measure ticket data, and a four-dimensional knowledge graph containing devices-links-panels-operations is constructed through a multi-time-space transition graph representation learning method, realizing modeling and representation of complex association relationships between devices, including: for the substation secondary system topology, device information and historical safety measure ticket data, entity information and relationship information in the substation secondary system design document, wiring diagram and device manual are extracted to construct an initial knowledge graph containing IED devices, optical fiber links, soft and hard panels and operation steps, forming a basic graph structure containing optical fiber connection relationship, panel control relationship, operation dependency relationship and function implementation relationship; the initial knowledge graph is decomposed into shared feature sub-graphs expressing common relationships and private feature sub-graphs expressing specific relationships by applying the multi-time-space graph auto-encoder, and a multi-time-space graph representation is generated; based on the multi-time-space graph representation, the physical connection relationship and logical dependency relationship between devices are captured simultaneously through a time-space coding layer, the private feature extraction is optimized and the noise influence is reduced by using a contrast learning constraint method, and an optimized private feature extraction result is obtained; for the optimized private feature extraction result, the correspondence between devices and operations in the historical safety measure ticket and the nodes in the initial knowledge graph is identified through an entity linking algorithm, an association rule mining algorithm is applied to discover the implicit operation dependency pattern, the missing relationships in the initial knowledge graph are dynamically completed, and the four-dimensional knowledge graph is obtained; The scene recognition and rule inference module is configured to extract multi-modal features of the maintenance task and recognize a maintenance scene type according to the quadruple knowledge graph and in combination with a memory-guided reflex decision inference mechanism, to obtain a maintenance scene recognition result, and to optimize a rule inference process under complex constraint conditions through a distributed guided local search algorithm, and to generate an optimal set of safety precautions for a specific maintenance scene. The safety precautions adaptive generation module is configured to automatically identify a dependent loop and determine a minimum safety isolation range through a memory-guided decision inference mechanism based on the optimal set of safety precautions and real-time device state information, to dynamically generate a minimum safety operation set and adaptively adjust operation steps according to a real-time device state. The compliance verification and risk early warning module is configured to construct an operation dependency relationship model in combination with an untangling variational multi-graph representation learning and a distributed guided local search algorithm for the adaptively adjusted operation steps, to implement operation sequence compliance verification, multi-dimensional risk level evaluation and dynamic visualized early warning, including: extracting multi-dimensional dependency relationships including time sequence dependency, functional dependency, spatial dependency, resource dependency and conditional dependency between operations through an untangling variational multi-graph representation learning method based on the adaptively adjusted operation steps and the quadruple knowledge graph, to clearly define preconditions, mutual exclusion relationships and time sequence constraints between operations, to form a multi-dimensional logical dependency network; parsing an input operation record text according to the multi-dimensional logical dependency network, to identify entities and relationships in the operation record text using a deep learning sequence labeling model, to extract key information including operation types, operation objects and operation parameters, to obtain an operation parsing result, to determine compliance evaluation of a current operation and system state influence analysis; matching and verifying the operation parsing result with the multi-dimensional logical dependency network and a real-time device state based on the compliance evaluation of the current operation and the system state influence analysis, to check whether the operation meets all pre-dependent conditions, to implement real-time compliance verification of operation sequence.

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