Instruction set online circulation method

Abstract

The invention provides an instruction set online circulation method. By acquiring the real-time disaster data and generating the standardized instruction set through the preset instruction process, the core defect that a traditional system is poor in adaptability is overcome. Furthermore, based on a hierarchical communication protocol and a dynamic priority scheduling algorithm, accurate transmission of the instruction in a multi-level node architecture is realized, and particularly, continuity is maintained through a node ad hoc network when communication is interrupted. In the execution link, double verification of identity authentication and role matching is carried out through an authority white list mechanism, and running state data is collected in real time in combination with a structured identifier to form an execution effect evaluation closed loop. According to the method, the whole-process optimization of the emergency instruction from intelligent generation and robust transmission to safe execution and dynamic tracking is realized, information islands and response delay are eliminated from the source, and the flexibility and reliability of disaster response are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of power grid disaster prevention, and in particular to an instruction set online circulation method. Background Art

[0002] Currently, existing mechanisms for generating, issuing, and executing power grid emergency commands are no longer sufficient to meet the needs of modern power grid disaster prevention and mitigation. Emergency management systems currently used by the power industry suffer from aging architectures and insufficient intelligence, particularly when faced with complex disasters. These systems are mostly pre-designed for single-hazard scenarios and lack the flexibility to cope with complex and changing disaster environments.

[0003] For example, in some feasible implementations, a rule-engine-based power emergency command system is used to facilitate the flow of emergency management instructions. This system utilizes a centralized architecture, integrating multiple data sources to monitor disasters and generating response plans based on rules corresponding to pre-set disaster scenarios. When monitoring data triggers specific conditions, the system automatically matches pre-stored response templates. While this type of solution improves the automation and standardization of emergency response, it lacks flexibility and effectiveness. Furthermore, current power emergency command systems often focus on a single dimension in data collection and lack the ability to deeply integrate heterogeneous data from multiple sources, limiting the accuracy of disaster assessments. Furthermore, data exchange often relies on a one-way transmission model, requiring multiple manual relay nodes from command generation to execution. This is not only inefficient but also prone to human error. In cross-regional joint response scenarios, semantic deviations in command transmission are even more pronounced. Furthermore, there is a lack of real-time resource scheduling and command feedback mechanisms. In summary, the real-time, efficiency, and effectiveness of online command flow in power system emergency management urgently need to be improved.

[0004] Therefore, an online instruction set circulation method is needed to realize the full life cycle circulation of emergency instructions in complex disaster scenarios, including generation, issuance, execution and feedback, so as to solve the current problems of low instruction circulation efficiency, flexibility and effectiveness caused by information fragmentation and response delays. Summary of the Invention

[0005] The purpose of this application is to solve at least one of the above-mentioned technical deficiencies, especially the technical deficiencies of low efficiency and effectiveness of existing instruction flow.

[0006] In a first aspect, the present application provides an instruction set online transfer method, the method comprising:

[0007] Acquire real-time disaster data, process the real-time disaster data through a preset instruction process, and generate a standardized instruction set;

[0008] The instruction process includes semantic parsing, knowledge enhancement and instruction generation performed in sequence;

[0009] Determining a target instruction in the standardized instruction set, and sequentially delivering the target instruction to a target node according to a preset node architecture based on a hierarchical communication protocol and a dynamic priority scheduling algorithm;

[0010] Execute the target instruction under the preset permission constraints;

[0011] According to the structured identifier corresponding to the target instruction, the running status data corresponding to the target instruction is collected in real time, and the execution effect of the target instruction is determined according to the running status data.

[0012] As an optional implementation, the real-time disaster data is processed through a preset instruction process to generate a standardized instruction set, including:

[0013] Performing input cleaning and normalization processing on the real-time disaster data to obtain pre-processed first target data;

[0014] Performing semantic parsing on the first target data to parse the operation subject, action predicate, and modification conditions, constructing a structured intermediate representation, and performing knowledge enhancement and compliance verification processes on the structured intermediate representation to obtain second target data;

[0015] generating a standardized instruction set for the second target data according to a dual-channel hybrid generation method;

[0016] Among them, the dual-channel hybrid generation method includes: the template-driven channel fills the pre-compiled instruction template physical slot, the neural network channel generates a sequence based on conditional sampling, and the two outputs are fused through confidence to generate the standardized instruction set.

[0017] As an optional implementation, the method further includes:

[0018] Detect whether the structured intermediate representation has missing parameters. When it is detected that the structured intermediate representation has missing parameters, trigger an interactive clarification process, dynamically inject environment variables through the context management service to supplement the missing parameters, and complete the information completion through an asynchronous callback mechanism.

[0019] As an optional embodiment, the node architecture includes a multi-level node communication channel, and the target instruction is sequentially transmitted to the target node according to the preset node architecture based on the hierarchical communication protocol and dynamic priority scheduling algorithm, including:

[0020] A high-bandwidth protocol is used for real-time synchronization between a first node group of the multi-level node communication channel, and a lightweight protocol is used for adapting to high packet loss scenarios between a second node group of the multi-level node communication channel;

[0021] Through the dynamic priority scheduling algorithm, command priorities are divided and bandwidth resources are allocated according to the degree of disaster impact;

[0022] The dynamic priority scheduling algorithm specifically includes: optimizing the instruction transmission path based on real-time network topology data according to the degree of disaster impact, and interrupting the transmission of low-priority instructions in the scenario of bandwidth resource competition;

[0023] Furthermore, when communication is interrupted, each node in the node architecture starts a self-organizing network to build a local area network to execute the transmission of the target instruction, and incrementally synchronizes the undelivered instructions and the execution log after communication is restored.

[0024] As an optional implementation manner, executing the target instruction under a preset permission constraint condition includes:

[0025] According to the permission whitelist mechanism, the recipient of the target instruction performs identity authentication and role matching double verification after receiving the target instruction;

[0026] When executing the target instruction, real-time feedback confirmation information is given to the preset management unit, wherein the confirmation information includes the identity code of the execution subject and the receiving timestamp;

[0027] After the target instruction is executed, a structured feedback report is submitted to a preset management unit, where the structured feedback report includes an execution timeline and an abnormal event code.

[0028] As an optional implementation manner, determining the execution effect of the target instruction according to the running status data includes:

[0029] Processing the operating status data to obtain a comprehensive score using a multidimensional scoring model;

[0030] The multidimensional scoring model is used to indicate one or more parameters of the target instruction's response time, operation accuracy, and feedback quality;

[0031] Based on the comprehensive score, determine high-frequency abnormal scenarios and high-frequency abnormal nodes, and optimize instruction transmission paths, associated model parameters, and instruction allocation;

[0032] Among them, the associated model parameters are used to indicate the model parameters corresponding to the models related to the instruction generation, allocation, verification and execution process, including at least one parameter corresponding to one or more models in the instruction process, the hierarchical communication protocol, the dynamic priority scheduling algorithm and the permission constraint model corresponding to the permission constraint condition.

[0033] As an optional implementation manner, determining the execution effect of the target instruction according to the running status data further includes:

[0034] Determining the timeliness of the execution status of the target instruction based on the running status data, and triggering a hierarchical progressive warning;

[0035] The hierarchical progressive warning includes:

[0036] During the execution of the target instruction, if the first timeout occurs, the corresponding target node will be automatically reminded at the node level; if the second timeout occurs, it will be upgraded to a regional intervention instruction; if the third timeout occurs, the node instruction receiving permission of the target node will be frozen and a negative performance parameter will be generated;

[0037] The negative performance parameter is used to reduce the comprehensive score of the target node in the multidimensional scoring model.

[0038] In a second aspect, the present application provides an instruction set online circulation device, the device comprising:

[0039] An acquisition module is used to acquire real-time disaster data, process the real-time disaster data through a preset instruction process, and generate a standardized instruction set;

[0040] The instruction process includes semantic parsing, knowledge enhancement and instruction generation performed in sequence;

[0041] a processing module, configured to determine a target instruction in the standardized instruction set, and sequentially transmit the target instruction to a target node according to a preset node architecture based on a hierarchical communication protocol and a dynamic priority scheduling algorithm;

[0042] The processing module is further configured to execute the target instruction under preset permission constraints;

[0043] The processing module is further configured to collect, in real time, the running status data corresponding to the target instruction according to the structured identifier corresponding to the target instruction, and determine the execution effect of the target instruction according to the running status data.

[0044] In a third aspect, the present application provides a computer device comprising one or more processors and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method described in the first aspect are performed.

[0045] In a fourth aspect, the present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method described in the first aspect.

[0046] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0047] Based on any of the above embodiments, the online instruction set circulation method proposed in this application, in the context of traditional power emergency systems relying on manual labor for instruction generation, unstable cross-regional transmission, loose execution authority and lack of feedback mechanism due to centralized architecture, rigid rules and data fragmentation, realizes intelligent understanding of disaster data and standardized instruction generation through an instruction process, including semantic analysis, knowledge enhancement and instruction generation steps, breaking through the bottleneck of insufficient adaptability of traditional systems. The hierarchical communication protocol is combined with a dynamic priority scheduling algorithm to ensure the accurate transmission of instructions between multi-level nodes, especially maintaining the responsiveness in network disconnection scenarios through self-organizing networks and incremental synchronization mechanisms. The permission whitelist and double verification mechanism ensure the safe closed loop of instruction execution, and the structured feedback report provides a quantifiable basis for effect evaluation. The multi-dimensional scoring model generates an execution score based on parameters such as timeliness and accuracy, driving the dynamic optimization of parameters such as transmission paths and scheduling algorithms; the hierarchical progressive warning mechanism automates timeliness control to avoid cascading failures. This method deeply integrates semantic understanding, dynamic scheduling, authority constraints and closed-loop optimization to form a full-process closed loop of intelligent generation, robust transmission, safe execution, quantitative feedback and dynamic tuning. It fundamentally solves the problems of information fragmentation, response delay and resource mismatch, significantly improves the intelligence, standardization and disaster resilience of power emergency response, and provides efficient technical support for complex disaster prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0049] Figure 1 A flowchart of an instruction set online transfer method provided in one embodiment of the present application;

[0050] Figure 2 A flowchart of an instruction set online transfer method provided in one embodiment of the present application;

[0051] Figure 3 A flowchart of an instruction set online transfer method provided in one embodiment of the present application;

[0052] Figure 4 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] As global climate change intensifies, extreme natural disasters facing power grids are increasing in frequency and intensity. In recent years, natural disasters such as typhoons, ice storms, and wildfires have inflicted increasing damage to power infrastructure. Traditional emergency command models are no longer adequate for disaster prevention and mitigation in modern power grids. The emergency management systems currently in use in the power industry suffer from aging architectures and insufficient intelligence, particularly in the face of complex disasters. These systems are mostly designed for single-hazard scenarios and lack the flexibility to cope with complex and changing disaster environments.

[0055] Among existing technologies, the most representative is the power emergency command system based on a rules engine. This system utilizes a centralized architecture, integrating multiple data sources to monitor disasters and generating response plans based on pre-set rules. Its core working mechanism is to consolidate historical experience into decision-making rules. When monitoring data triggers specific conditions, the system automatically matches pre-existing response templates. This approach has improved the standardization of emergency response to a certain extent, but its reliance on manually preset rules makes it less adaptable to new or complex disasters. In particular, as a disaster evolves, the system struggles to adjust its decision-making strategies in a timely manner, leading to inappropriate resource allocation.

[0056] The power emergency command system based on the rule engine can include the following steps:

[0057] Rule engine initialization: introduce engine dependencies and initialize the rule base;

[0058] Data model construction: define entity classes to map attributes such as power grid equipment and fault events;

[0059] Rule script development: write rule files and divide them into warning, diagnosis, and disposal modules;

[0060] Real-time data access: connect to various detection systems and convert telemetry data into engine fact objects;

[0061] Rule triggering mechanism: monitors abnormal telemetry data and triggers rule chain matching and execution;

[0062] Asynchronous processing optimization: integrated message middleware buffering of high-concurrency events and asynchronous rule matching;

[0063] Result output integration: call the interface to push instructions and persist logs.

[0064] A rule engine is a software system used to automatically execute and manage business rules. Its core function is to evaluate input data based on a predefined set of rules and trigger corresponding actions or decisions. A data model is a framework used to abstractly describe data characteristics, structure, operations, and constraints. Its core function is to provide a logical organization for data in database systems or software applications, and to ensure data consistency, validity, and operability.

[0065] Clearly, existing power emergency command systems are often limited to a single dimension in their data collection modules, lacking the ability to deeply integrate heterogeneous data from multiple sources, limiting the accuracy of disaster assessments. Decision support modules generally rely on rule engines and static knowledge bases, failing to adapt to real-time trends. This significantly increases the rate of decision-making errors when faced with new, complex disasters.

[0066] At the same time, existing systems mostly use a one-way transmission mode, requiring instructions to pass through multiple manual transfer nodes from generation to execution. This is not only inefficient but also prone to human error. In cross-regional joint response scenarios, the semantic deviation of instruction transmission is large. Resource scheduling algorithms also have obvious shortcomings. Most systems use static allocation strategies based on historical experience and are unable to dynamically respond to the evolution of disasters, resulting in an imbalance in emergency resource allocation. Problems with status tracking are equally serious. Due to the lack of a standardized feedback mechanism, on-site execution status is difficult to transmit back to the command center in a timely and accurate manner, creating a decision-making blind spot.

[0067] In summary, traditional systems rely on manual analysis of disaster data and manual formulation of instructions, which has problems of low efficiency and strong subjectivity; this application realizes the automatic generation of instructions through the linkage of rule engines and real-time multi-source data, significantly shortens decision-making time, eliminates the risk of human misjudgment, and ensures the scientific nature and timeliness of instructions. The existing technology uses a single communication link and a manual translation mechanism, which is prone to information loss and delay; this application optimizes the instruction transmission path and resource allocation sequence through multi-level communication protocols and dynamic priority scheduling algorithms, and integrates redundant transmission link design to ensure the continuity and integrity of instruction transmission in extreme environments. The traditional model relies on manual operation for instruction parsing due to the heterogeneity of on-site equipment interfaces, and the execution consistency is poor; this application uses atomic operation encapsulation and unified adaptation interface technology to break down complex tasks into standardized steps, achieve seamless docking and precise operation of heterogeneous equipment, and greatly improve execution accuracy and efficiency. The existing system lacks real-time feedback and dynamic adjustment mechanisms, and the execution progress relies on manual reporting. This application builds a closed-loop system of "generation-execution-feedback-optimization" through real-time status tracking and abnormal automatic re-dispatching strategies to ensure that tasks are completed compulsorily and avoid response stagnation caused by information gaps in the traditional model.

[0068] The present application constructs an automated and standardized online instruction set circulation system to achieve intelligent generation, precise transmission, efficient execution, and closed-loop tracking of emergency instructions in disaster scenarios, and solve the information fragmentation and response delay problems of the traditional model. Based on the specific implementation method, the technical concept of the present application is that the instruction set online circulation method proposed in the present application, in the context of the traditional power emergency system, which relies on manual instruction generation, unstable cross-regional transmission, loose execution authority, and lack of feedback mechanism due to the centralized architecture, rigid rules, and data fragmentation, realizes intelligent understanding of disaster data and standardized instruction generation through an instruction process, including semantic parsing, knowledge enhancement, and instruction generation steps, breaking through the bottleneck of insufficient adaptability of traditional systems. The hierarchical communication protocol is combined with a dynamic priority scheduling algorithm to ensure the precise transmission of instructions between multi-level nodes, especially through the self-organizing network and incremental synchronization mechanism to maintain the responsiveness in the network disconnection scenario. The permission whitelist and double verification mechanism ensure the safe closed loop of instruction execution, and the structured feedback report provides a quantifiable basis for effect evaluation. The multidimensional scoring model generates execution scores based on parameters such as timeliness and accuracy, driving the dynamic optimization of parameters such as transmission paths and scheduling algorithms. A hierarchical progressive warning mechanism automates timeliness control to avoid cascading failures. This method deeply integrates semantic understanding, dynamic scheduling, authority constraints, and closed-loop optimization to form a full-process closed loop of intelligent generation, robust transmission, secure execution, quantitative feedback, and dynamic tuning. This fundamentally solves the problems of information fragmentation, delayed response, and resource mismatch, significantly improving the intelligence, standardization, and disaster resilience of power emergency response, and providing efficient technical support for complex disaster prevention and control.

[0069] The method provided in this application is described in detail below based on corresponding implementation methods in some actual application scenarios.

[0070] See also Figure 1 , Figure 1 A flow chart of an instruction set online transfer method provided in one embodiment of the present application is shown as follows: Figure 1 As shown, the method includes:

[0071] S101, acquiring real-time disaster data, processing the real-time disaster data through a preset instruction process, and generating a standardized instruction set;

[0072] The instruction process includes semantic parsing, knowledge enhancement and instruction generation performed in sequence;

[0073] S102, determining a target instruction in the standardized instruction set, and sequentially delivering the target instruction to a target node according to a preset node architecture based on a hierarchical communication protocol and a dynamic priority scheduling algorithm;

[0074] S103, executing the target instruction under preset permission constraints;

[0075] S104 . According to the structured identifier corresponding to the target instruction, collect the running status data corresponding to the target instruction in real time, and determine the execution effect of the target instruction according to the running status data.

[0076] The online instruction set transfer method provided by this embodiment addresses the core defect of poor adaptability of traditional power emergency systems, which rely on manually preset rules and have difficulty dynamically adapting to the evolution of complex disasters. By acquiring real-time disaster data and generating a standardized instruction set through a preset instruction process, including semantic parsing, knowledge enhancement, and instruction generation, this method addresses the core defect of poor adaptability of traditional systems. Among them, semantic parsing deeply extracts the operating subjects, action predicates, and modifying conditions in the disaster data, knowledge enhancement supplements domain rule constraints, and the instruction generation process ensures the standardization and executable nature of the instructions. Furthermore, based on hierarchical communication protocols, such as high-bandwidth protocols to ensure real-time synchronization between node groups, lightweight protocols to adapt to high packet loss scenarios, and dynamic priority scheduling algorithms, such as optimizing transmission paths according to the degree of disaster impact and interrupting low-priority instructions to ensure the real-time performance of key instructions, accurate instruction transmission is achieved in a multi-level node architecture, especially maintaining continuity through node self-organizing networks when communication is interrupted. The execution stage uses a permission whitelist mechanism to perform dual verification of identity authentication and role matching, and combines structured identification to collect operating status data in real time, forming a closed loop for execution effect evaluation. This method achieves full-process optimization of emergency instructions from intelligent generation, robust transmission to safe execution and dynamic tracking, eliminating information silos and response delays from the root, and significantly improving the flexibility and reliability of disaster response.

[0077] As an optional implementation, the real-time disaster data is processed through a preset instruction process to generate a standardized instruction set, including:

[0078] Performing input cleaning and normalization processing on the real-time disaster data to obtain pre-processed first target data;

[0079] Performing semantic parsing on the first target data to parse the operation subject, action predicate, and modification conditions, constructing a structured intermediate representation, and performing knowledge enhancement and compliance verification processes on the structured intermediate representation to obtain second target data;

[0080] generating a standardized instruction set for the second target data according to a dual-channel hybrid generation method;

[0081] Among them, the dual-channel hybrid generation method includes: the template-driven channel fills the pre-compiled instruction template physical slot, the neural network channel generates a sequence based on conditional sampling, and the two outputs are fused through confidence to generate the standardized instruction set.

[0082] As an optional implementation, the method further includes:

[0083] Detect whether the structured intermediate representation has missing parameters. When it is detected that the structured intermediate representation has missing parameters, trigger an interactive clarification process, dynamically inject environment variables through the context management service to supplement the missing parameters, and complete the information completion through an asynchronous callback mechanism.

[0084] To address the problem of missing parameters in disaster data due to volatile environments, which traditional systems require manual intervention, this design detects missing parameters in the structured intermediate representation, triggers an interactive clarification process, dynamically injects environment variables through the context management service to supplement the missing parameters, and ensures the consistency of the completion through an asynchronous callback mechanism. This design enables adaptive processing in the case of missing parameters, avoids interruptions in the command generation process, and enhances the system's fault tolerance and the integrity of command generation.

[0085] For details, please refer to Figure 2 , Figure 2 A flowchart of an instruction set online transfer method provided for an embodiment of the present application is used to illustrate the specific process of instruction set generation.

[0086] The back-end technology for intelligent instruction set generation follows a hierarchical and progressive pipelined processing flow. Each link is connected through standardized interfaces and asynchronous communication mechanisms, forming a complete transformation chain from raw input to executable instructions. In the initial stage of the system, user requests are received through a high-throughput message queue. The input real-time disaster data is pre-processed and cleaned and standardized, including encoding format conversion, sensitive word filtering, and basic grammar correction to ensure that the text subsequently processed meets the model input specifications. The pre-processed text enters the semantic parsing engine, which uses a pre-trained language model for context-aware encoding. It utilizes a multi-head attention mechanism to capture the semantic associations between the operation subject, action predicate, and modifying conditions, and combines dependency syntactic analysis to construct a structured intermediate representation. The parsing process synchronously calls the entity recognition service, accurately extracting domain-specific terms through a hybrid strategy of conditional random fields and dictionary matching. The recognition results are then mapped to the standardized entity library of the knowledge graph to eliminate ambiguity caused by synonyms and abbreviations.

[0087] The intermediate representation generated by semantic parsing then enters the knowledge enhancement phase, where the domain knowledge graph built on a graph database plays a central role. The system uses graph traversal algorithms to analyze dependencies and execution order constraints between operation objects. For example, this can detect whether permission levels need to be verified before file operations or identify the logic for service startup and shutdown. The rules engine simultaneously loads the business policy library to perform validity checks on the intermediate representation, including permission compliance review, resource quota verification, and operational risk prediction. When missing parameters or logical conflicts are detected, the context management service dynamically injects environment variables (such as the current user role and system configuration parameters) or triggers an interactive clarification process, collaborating with the front-end to complete information completion through an asynchronous callback mechanism.

[0088] When it comes to instruction generation, the system adopts a dual-channel hybrid generation strategy. The template-driven channel screens candidate templates from a pre-compiled instruction pattern library based on intent classification labels, fills entity slots with placeholders through the parameter binding engine, and performs type matching and range checking. The neural network channel generates sequences based on the Transformer decoder architecture, using the intermediate representation as input conditions. It uses a self-attention mechanism to perform probabilistic sampling in the instruction space and combines it with a beam search algorithm to balance generation quality and diversity. The two outputs are confidence-weighted by the fusion controller, and a reinforcement learning reward mechanism is introduced to evaluate syntactic correctness, functional completeness, and execution feasibility, ultimately generating an optimized candidate instruction set. During this process, the dependency analyzer detects resource contention and sequence constraints between instructions in real time, inserting synchronization lock mechanisms or adjusting execution timing when necessary.

[0089] The generated instruction sequence enters a multi-level verification system. The static verification layer simulates the execution instruction stream in a sandbox environment through formal methods. Code instrumentation is used to capture runtime exceptions (such as race conditions and resource leaks). Abstract interpretation techniques are used to verify the legality of memory access boundaries and state transitions. Performance metrics are recorded and used to generate policy feedback optimization. Verification results are pushed to the knowledge base update service via an event bus, driving the incremental expansion of the template library and the dynamic reinforcement of constraint rules, forming a closed-loop mechanism for continuous learning.

[0090] During the instruction set generation process, performance optimization measures are implemented throughout. During the semantic parsing phase, a quantized compression model and cache warming mechanism are deployed; knowledge graph queries use a hybrid indexing strategy to accelerate graph traversal; and the instruction generation module enables GPU-accelerated inference and instruction fragment cache reuse.

[0091] At the same time, the security system builds multi-layer protection. The entry gateway implements syntax analysis and operation whitelist verification, the isolated test environment configures resource isolation and operation auditing, and two-way transport layer security protocol encryption is enabled for communication between key services. The monitoring system collects the number of queries per second, latency, and error rate indicators of each node in real time, and combines it with an adaptive current limiting algorithm to achieve dynamic load balancing. Through the application programming interface gateway, RESTful and WebSocket dual-protocol access can be provided to the outside world, supporting synchronous instant response and asynchronous task queue modes, and built-in retry mechanisms and transaction compensation logic to ensure system fault tolerance. This technical architecture that integrates data-driven and rule-constrained technology ensures efficient instruction conversion while ensuring generation accuracy, providing reliable back-end support for intelligent system automation.

[0092] In addition, the system architecture adopts a microservice design, with key modules encapsulated as independent containers and communicating through a service grid.

[0093] This implementation addresses the problem of semantic ambiguity caused by the lack of unified standards in traditional disaster data processing processes. It generates first target data through input cleaning and normalization, thereby improving the quality of original data. Furthermore, semantic parsing constructs a structured intermediate representation containing operation subjects, action predicates, and modifying conditions. Knowledge enhancement injects domain rules and verifies compliance to produce high-quality second target data. The dual-channel hybrid generation method combines the advantages of template-driven and neural network sequence generation, taking into account both standardization and scenario adaptability. The confidence fusion mechanism optimizes output quality. This solution significantly improves the accuracy and robustness of instruction generation, providing an effective semantic data foundation for subsequent transmission and execution.

[0094] As an optional embodiment, the node architecture includes a multi-level node communication channel, and the target instruction is sequentially transmitted to the target node according to the preset node architecture based on the hierarchical communication protocol and dynamic priority scheduling algorithm, including:

[0095] A high-bandwidth protocol is used for real-time synchronization between a first node group of the multi-level node communication channel, and a lightweight protocol is used for adapting to high packet loss scenarios between a second node group of the multi-level node communication channel;

[0096] Through the dynamic priority scheduling algorithm, command priorities are divided and bandwidth resources are allocated according to the degree of disaster impact;

[0097] The dynamic priority scheduling algorithm specifically includes: optimizing the instruction transmission path based on real-time network topology data according to the degree of disaster impact, and interrupting the transmission of low-priority instructions in the scenario of bandwidth resource competition;

[0098] Furthermore, when communication is interrupted, each node in the node architecture starts a self-organizing network to build a local area network to execute the transmission of the target instruction, and incrementally synchronizes the undelivered instructions and the execution log after communication is restored.

[0099] For details, please refer to Figure 3 , Figure 3 A flowchart of an instruction set online transfer method provided for an embodiment of the present application is used to illustrate the specific process of instruction transmission based on an example of an actual application scenario.

[0100] The precise delivery of emergency command sets for power grid disaster prevention and mitigation relies on a multi-layered communication architecture and intelligent transmission strategies, ensuring that commands reach target nodes efficiently and reliably even in complex disaster environments. The system employs a hybrid communication protocol to adapt to the transmission requirements of different tiers, establishing a five-tiered channel: province, prefecture, county, station, and team. The high-bandwidth, secure HTTPS protocol is deployed between the provincial, prefecture, and county nodes, leveraging its multiplexing and header compression features to support the real-time synchronization of massive commands. Full-duplex communication ensures the stability of bidirectional data flow. To address the mobile terminals and harsh network environments at the county, station, and team levels, the system incorporates the lightweight CoAP protocol. This connectionless transport layer communication protocol, with transmission and retransmission mechanisms adapted to high packet loss scenarios, is particularly well-suited for the intermittent connectivity requirements of mobile devices such as repair vehicles and drones. Data chunking technology is used to break large command sets into 512-byte blocks, prioritizing the transmission of critical operation fields (such as equipment shutdown commands) when bandwidth is limited. The remaining data is transmitted incrementally upon network recovery.

[0101] In order to cope with sudden network congestion and resource competition during disasters, the system embeds a dynamic priority scheduling algorithm, which divides instructions into special, first, and second levels according to the degree of disaster impact, corresponding to trunk line emergency repairs, secondary trunk operation and maintenance scheduling, and routine monitoring tasks, respectively. For example, bandwidth resources are allocated through a weighted polling mechanism. Special-level instructions monopolize more than 60% of the bandwidth and support preemptive transmission, which can interrupt the transmission channel of low-priority instructions and dynamically optimize the transmission path based on real-time network topology data. In terms of transmission security, the content of the instruction uses the national secret algorithm to encrypt sensitive parameters and is digitally signed using the elliptic curve algorithm. The receiving end can only parse and execute after the signature is verified, effectively resisting man-in-the-middle attacks and data tampering; the Obfs4 obfuscation protocol can be introduced at the traffic level to disguise the transmission characteristics and avoid the identification and blocking of malicious traffic in the public network environment.

[0102] In the extreme case of a complete communication outage, nodes in the node architecture capable of edge computing activate local emergency mode. On-site terminals establish a network through local area communication ad hoc networking. Once communication is restored, edge nodes transmit local execution logs and undelivered commands back to the cloud via an incremental synchronization protocol. Conflict detection algorithms (such as vector clocks) resolve data inconsistencies and ensure global state synchronization. The entire process can be tracked in real time via the command center's visual monitoring platform. A GIS map dynamically renders the command transmission path, link health status (color coding), and node load indicators. Abnormal events (such as command timeouts) trigger audible and visual alarms and automatically activate backup routes. The logging system fully records command lifecycle data (sending time, nodes passed through, arrival delay), supporting post-audit and transmission efficiency analysis.

[0103] This implementation addresses the vulnerability of cross-regional command transmission to network fluctuations. By designing multi-level node communication channels, this approach combines a high-bandwidth protocol to ensure real-time synchronization between node groups with a lightweight protocol that adapts to high packet loss scenarios, dynamically adapting to network environments. A dynamic priority scheduling algorithm allocates bandwidth resources based on the severity of the disaster's impact, interrupting low-priority commands to ensure the real-time transmission of critical commands. When communication is interrupted, nodes self-organize to form a local network to maintain transmission, and upon restoration, incremental synchronization of missed commands. This solution significantly improves the stability and anti-interference capabilities of command transmission, ensuring continuous response in disaster scenarios.

[0104] As an optional implementation manner, executing the target instruction under a preset permission constraint condition includes:

[0105] According to the permission whitelist mechanism, the recipient of the target instruction performs identity authentication and role matching double verification after receiving the target instruction;

[0106] When executing the target instruction, real-time feedback confirmation information is given to the preset management unit, wherein the confirmation information includes the identity code of the execution subject and the receiving timestamp;

[0107] After the target instruction is executed, a structured feedback report is submitted to a preset management unit, where the structured feedback report includes an execution timeline and an abnormal event code.

[0108] For example, based on actual application scenarios, the efficient execution of the power grid's typhoon disaster prevention and mitigation instruction set is centered on organizational structure and process rules. Through the synergy of standardized instruction generation, authority constraint transmission, execution feedback verification, and dispute resolution channels, a vertically integrated and horizontally coordinated operating system is established. During the instruction generation phase, a structured template is used to define operational elements, clearly marking the execution entity's qualification level, task objectives, geographic scope, time window, and acceptance criteria. A permission whitelist mechanism is used to limit the scope of recipients that can be reached by the instruction, ensuring a strict match between the responsible entity and the operational authority.

[0109] The system has a built-in three-level vertical transmission link: the provincial command node pushes global instruction packages to the municipal dispatch unit, the municipal node decomposes it into operational task sets based on regional characteristics, and the county-level execution unit further refines it to the team-level action guidelines, forming a step-by-step deconstruction instruction adaptation path. Each level must confirm the compliance and enforceability of the target instructions through a dual verification mechanism of form and content.

[0110] Dynamic permission management is implemented during the instruction delivery process, requiring recipients to undergo dual verification through identity authentication and role matching before accessing the instruction content. A mechanism for leaving operational traces is established during the execution phase. Upon receiving an instruction, on-site terminals must provide confirmation within a specified timeframe. This confirmation includes the identity code of the executing entity, the timestamp of task receipt, and a statement of the environmental status.

[0111] The execution feedback mechanism incorporates a multi-dimensional verification system. Upon task completion, a structured feedback report must be submitted within a specified time window. This report covers the actual execution timeline, a list of participating identities, a record of abnormal events, and a snapshot of the environmental status. For feedback data, the quality supervisor at the corresponding level conducts a formal review of the data format integrity, focusing on verifying the temporal logic consistency, user qualification compliance, and the completeness of required fields.

[0112] The dispute resolution channel sets up hierarchical response rules. When the execution progress deviation exceeds the preset threshold or encounters force majeure, the execution unit can initiate an emergency upload request. Major dispute operations require a temporary review group to be formed by randomly extracting expert models from the expert database. Through a joint diagnosis mechanism, a multi-dimensional analysis of the operation basis, execution records and environmental parameters is conducted. After the review conclusion is electronically signed by multiple parties, a supplementary instruction is generated and takes effect immediately. The performance evaluation module continuously tracks the data of the entire life cycle of the instruction, constructs a three-dimensional evaluation model of response timeliness, operation accuracy, and feedback quality, and incorporates the comprehensive score of the execution unit into the plan optimization factor library to drive the iterative upgrade of process rules.

[0113] To prevent unauthorized operations caused by lax permission management during command execution, this implementation utilizes a whitelist mechanism to perform dual verification of identity and role matching prior to execution, ensuring the legitimacy of the operator. Real-time confirmation feedback, including identity codes and timestamps, enhances process transparency. Upon command completion, a structured feedback report is submitted, including execution timelines and exception codes, providing complete traceability. This closed-loop permission constraint effectively mitigates human error and improves execution security and compliance.

[0114] As an optional implementation manner, determining the execution effect of the target instruction according to the running status data includes:

[0115] Processing the operating status data to obtain a comprehensive score using a multidimensional scoring model;

[0116] The multidimensional scoring model is used to indicate one or more parameters of the target instruction's response time, operation accuracy, and feedback quality;

[0117] Based on the comprehensive score, determine high-frequency abnormal scenarios and high-frequency abnormal nodes, and optimize instruction transmission paths, associated model parameters, and instruction allocation;

[0118] Among them, the associated model parameters are used to indicate the model parameters corresponding to the models related to the instruction generation, allocation, verification and execution process, including at least one parameter corresponding to one or more models in the instruction process, the hierarchical communication protocol, the dynamic priority scheduling algorithm and the permission constraint model corresponding to the permission constraint condition.

[0119] This implementation addresses the lack of a quantitative evaluation mechanism in traditional systems. Using a multi-dimensional scoring model that integrates response timeliness, operational accuracy, and feedback quality, it generates a comprehensive execution performance score, accurately identifying high-frequency abnormal scenarios and nodes. Based on the scoring results, it optimizes instruction delivery paths, adjusts instruction process parameters, and dynamically prioritizes scheduling strategies, forming a closed-loop feedback mechanism. This design continuously improves the system's adaptability and response accuracy, enabling dynamic resource allocation.

[0120] As an optional implementation manner, determining the execution effect of the target instruction according to the running status data further includes:

[0121] Determining the timeliness of the execution status of the target instruction based on the running status data, and triggering a hierarchical progressive warning;

[0122] The hierarchical progressive warning includes:

[0123] During the execution of the target instruction, if the first timeout occurs, the corresponding target node will be automatically reminded at the node level; if the second timeout occurs, it will be upgraded to a regional intervention instruction; if the third timeout occurs, the node instruction receiving permission of the target node will be frozen and a negative performance parameter will be generated;

[0124] The negative performance parameter is used to reduce the comprehensive score of the target node in the multidimensional scoring model.

[0125] This implementation addresses the cascading failures caused by command execution timeouts through a hierarchical, progressive warning mechanism: the first timeout triggers an automatic node-level alert, a second timeout escalates to a regional intervention order, and a third timeout freezes node permissions and generates a negative performance parameter. This gradient response mechanism embeds timeliness control throughout the execution process, preventing interruptions to disaster response. The negative performance parameter, coupled with a multi-dimensional scoring model, drives node performance optimization, significantly improving the automation of timeliness management.

[0126] The implementation logic of the closed-loop tracking system for power grid typhoon disaster prevention and mitigation instructions focuses on status tracking and quality control throughout the instruction lifecycle, using digital means to establish a self-verifying and self-optimizing management chain. The system generates standard operating blueprints based on structured instruction templates. Each instruction is embedded with a unique hash identifier, an execution entity authority matrix, and a time window threshold. Asymmetric encryption algorithms are used to ensure the authenticity of the instruction source and the tamper-proof nature of the content.

[0127] The status tracking process establishes a multi-dimensional monitoring matrix. The system collects command flow timestamps, node response codes, and operation status tags in real time, and detects abnormal patterns through a time series analysis engine. If a command fails to complete the state transition within the preset time window, a hierarchical progressive warning system is automatically triggered: the first timeout triggers an automatic node-level reminder, the second timeout escalates to regional-level node intervention, and the third timeout freezes the node's permission to receive subsequent commands and generates a negative performance evaluation indicator. Tracking data is mapped to the command center's digital dashboard in real time, graphically displaying the distribution of command processing time in each region. Areas with concentrated abnormalities automatically increase the frequency of supervisory command issuance.

[0128] The feedback verification system employs a dual-verification mechanism. Feedback data packets submitted by execution units must include the operation timeline, personnel qualifications, and exception event codes. The system implements automated verification through an intelligent verification engine. The timing analysis module verifies the conformance of operation steps with the time window, calculating the deviation between actual execution time and the preset standard. The authority matching engine examines the consistency between the operator's qualifications and the instruction authority matrix, identifying risks of unauthorized operations. The exception code parser maps event types to a pre-set knowledge base to assess the rationality of event handling solutions.

[0129] The closed-loop optimization phase establishes a data-driven self-improvement mechanism. The system uses machine learning models to analyze historical instruction flow data and identify high-frequency anomalies and performance bottlenecks. The response time model continuously optimizes the instruction delivery path selection strategy. The precision control algorithm dynamically adjusts the sensitivity threshold of the verification rules. The resource scheduling engine automatically balances instruction distribution density based on regional execution performance. The performance evaluation module generates quantitative scores based on timeliness, accuracy, and completeness. The overall performance index of the execution unit is directly linked to the priority of subsequent instruction reception, forming an incentive mechanism that promotes the survival of the fittest.

[0130] In summary, based on the description of actual application scenarios, this application provides a dynamic priority scheduling algorithm for multi-level instruction delivery, covering instruction classification, transmission path optimization, and dual-link redundant switching methods, ensuring the efficiency and robustness of instruction delivery, and realizing full-process exception tracking. It should be noted that there are theoretically several alternative solutions for the instruction generation, delivery, execution and closed-loop tracking involved in the technical solution of the present invention, but they all face significant limitations in achieving the purpose of the invention. For example, the instruction generation link can use a manually preset template library to match disaster scenarios, but this method relies on historical experience and cannot dynamically adapt to real-time data changes, resulting in response delays and insufficient adaptability; the instruction delivery link may adopt a centralized broadcast mode, which simplifies authority management but has a single point failure risk. In extreme environments, it is easy to cause system paralysis due to communication interruption. Although completely decentralized user-to-user transmission avoids dependence on central nodes, it is difficult to achieve accurate access and dynamic priority scheduling of cross-level instructions. If the execution link adopts a general middleware adaptation technology, although it can be compatible with heterogeneous device protocols, the delay and maintenance cost introduced by the conversion layer will weaken the real-time performance of the system, and manual analysis and execution will further amplify the risk of misoperation. Although the closed-loop tracking link can partially realize the feedback function by using periodic polling or offline log synchronization, the former increases the network load and the feedback is delayed, while the latter loses the real-time optimization value and has the risk of data tampering. In summary, although the alternative solutions are feasible in local links, they cannot systematically solve the synergistic contradiction between efficiency, reliability and real-time performance. The present invention builds a full-chain adaptive emergency management system through the technical integration of rule engine driven generation, multi-level dynamic transmission, atomic execution encapsulation and real-time closed-loop tracking. Its core innovation lies in breaking through the fragmentation defects of traditional solutions with standardization, automation and redundant design, forming an irreplaceable technical integration advantage.

[0131] The present application also provides an instruction set online circulation device to implement the method described in any embodiment, the device comprising:

[0132] An acquisition module is used to acquire real-time disaster data, process the real-time disaster data through a preset instruction process, and generate a standardized instruction set;

[0133] The instruction process includes semantic parsing, knowledge enhancement and instruction generation performed in sequence;

[0134] a processing module, configured to determine a target instruction in the standardized instruction set, and sequentially transmit the target instruction to a target node according to a preset node architecture based on a hierarchical communication protocol and a dynamic priority scheduling algorithm;

[0135] The processing module is further configured to execute the target instruction under preset permission constraints;

[0136] The processing module is further configured to collect, in real time, the running status data corresponding to the target instruction according to the structured identifier corresponding to the target instruction, and determine the execution effect of the target instruction according to the running status data.

[0137] The online instruction set transfer method provided by this embodiment addresses the core defect of poor adaptability of traditional power emergency systems, which rely on manually preset rules and have difficulty dynamically adapting to the evolution of complex disasters. By acquiring real-time disaster data and generating a standardized instruction set through a preset instruction process, including semantic parsing, knowledge enhancement, and instruction generation, this method addresses the core defect of poor adaptability of traditional systems. Among them, semantic parsing deeply extracts the operating subjects, action predicates, and modifying conditions in the disaster data, knowledge enhancement supplements domain rule constraints, and the instruction generation process ensures the standardization and executable nature of the instructions. Furthermore, based on hierarchical communication protocols, such as high-bandwidth protocols to ensure real-time synchronization between node groups, lightweight protocols to adapt to high packet loss scenarios, and dynamic priority scheduling algorithms, such as optimizing transmission paths according to the degree of disaster impact and interrupting low-priority instructions to ensure the real-time performance of key instructions, accurate instruction transmission is achieved in a multi-level node architecture, especially maintaining continuity through node self-organizing networks when communication is interrupted. The execution stage uses a permission whitelist mechanism to perform dual verification of identity authentication and role matching, and combines structured identification to collect operating status data in real time, forming a closed loop for execution effect evaluation. This method achieves full-process optimization of emergency instructions from intelligent generation, robust transmission to safe execution and dynamic tracking, eliminating information silos and response delays from the root, and significantly improving the flexibility and reliability of disaster response.

[0138] As an optional implementation, the acquisition module processes the real-time disaster data through a preset instruction process to generate a standardized instruction set, including:

[0139] Performing input cleaning and normalization processing on the real-time disaster data to obtain pre-processed first target data;

[0140] Performing semantic parsing on the first target data to parse the operation subject, action predicate, and modification conditions, constructing a structured intermediate representation, and performing knowledge enhancement and compliance verification processes on the structured intermediate representation to obtain second target data;

[0141] generating a standardized instruction set for the second target data according to a dual-channel hybrid generation method;

[0142] Among them, the dual-channel hybrid generation method includes: the template-driven channel fills the pre-compiled instruction template physical slot, the neural network channel generates a sequence based on conditional sampling, and the two outputs are fused through confidence to generate the standardized instruction set.

[0143] This implementation addresses the problem of semantic ambiguity caused by the lack of unified standards in traditional disaster data processing processes. It generates first target data through input cleaning and normalization, thereby improving the quality of original data. Furthermore, semantic parsing constructs a structured intermediate representation containing operation subjects, action predicates, and modifying conditions. Knowledge enhancement injects domain rules and verifies compliance to produce high-quality second target data. The dual-channel hybrid generation method combines the advantages of template-driven and neural network sequence generation, taking into account both standardization and scenario adaptability. The confidence fusion mechanism optimizes output quality. This solution significantly improves the accuracy and robustness of instruction generation, providing an effective semantic data foundation for subsequent transmission and execution.

[0144] As an optional implementation manner, the processing module is further configured to:

[0145] Detect whether the structured intermediate representation has missing parameters. When it is detected that the structured intermediate representation has missing parameters, trigger an interactive clarification process, dynamically inject environment variables through the context management service to supplement the missing parameters, and complete the information completion through an asynchronous callback mechanism.

[0146] This implementation addresses the problem of missing parameters in disaster data due to volatile environments, which traditional systems require manual intervention. By detecting missing parameters in the structured intermediate representation, this approach triggers an interactive clarification process. The context management service dynamically injects environment variables to supplement the missing parameters, and an asynchronous callback mechanism ensures consistent completion. This design enables adaptive processing in the case of missing parameters, avoids interruptions in the command generation process, and enhances the system's fault tolerance and the integrity of command generation.

[0147] As an optional embodiment, the node architecture includes a multi-level node communication channel, and the processing module transmits the target instruction to the target node in sequence according to the preset node architecture based on the hierarchical communication protocol and the dynamic priority scheduling algorithm, including:

[0148] A high-bandwidth protocol is used for real-time synchronization between a first node group of the multi-level node communication channel, and a lightweight protocol is used for adapting to high packet loss scenarios between a second node group of the multi-level node communication channel;

[0149] Through the dynamic priority scheduling algorithm, command priorities are divided and bandwidth resources are allocated according to the degree of disaster impact;

[0150] The dynamic priority scheduling algorithm specifically includes: optimizing the instruction transmission path based on real-time network topology data according to the degree of disaster impact, and interrupting the transmission of low-priority instructions in the scenario of bandwidth resource competition;

[0151] Furthermore, when communication is interrupted, each node in the node architecture starts a self-organizing network to build a local area network to execute the transmission of the target instruction, and incrementally synchronizes the undelivered instructions and the execution log after communication is restored.

[0152] This implementation addresses the vulnerability of cross-regional command transmission to network fluctuations. By designing multi-level node communication channels, this approach combines a high-bandwidth protocol to ensure real-time synchronization between node groups with a lightweight protocol that adapts to high packet loss scenarios, dynamically adapting to network environments. A dynamic priority scheduling algorithm allocates bandwidth resources based on the severity of the disaster's impact, interrupting low-priority commands to ensure the real-time transmission of critical commands. When communication is interrupted, nodes self-organize to form a local network to maintain transmission, and upon restoration, incremental synchronization of missed commands. This solution significantly improves the stability and anti-interference capabilities of command transmission, ensuring continuous response in disaster scenarios.

[0153] As an optional implementation manner, the specific manner in which the processing module executes the target instruction under preset permission constraints includes:

[0154] According to the permission whitelist mechanism, the recipient of the target instruction performs identity authentication and role matching double verification after receiving the target instruction;

[0155] When executing the target instruction, real-time feedback confirmation information is given to the preset management unit, wherein the confirmation information includes the identity code of the execution subject and the receiving timestamp;

[0156] After the target instruction is executed, a structured feedback report is submitted to a preset management unit, where the structured feedback report includes an execution timeline and an abnormal event code.

[0157] To prevent unauthorized operations caused by lax permission management during command execution, this implementation utilizes a whitelist mechanism to perform dual verification of identity and role matching prior to execution, ensuring the legitimacy of the operator. Real-time confirmation feedback, including identity codes and timestamps, enhances process transparency. Upon command completion, a structured feedback report is submitted, including execution timelines and exception codes, providing complete traceability. This closed-loop permission constraint effectively mitigates human error and improves execution security and compliance.

[0158] As an optional implementation manner, the processing module determines a specific manner of executing the target instruction according to the running status data, including:

[0159] Processing the operating status data to obtain a comprehensive score using a multidimensional scoring model;

[0160] The multidimensional scoring model is used to indicate one or more parameters of the target instruction's response time, operation accuracy, and feedback quality;

[0161] Based on the comprehensive score, determine high-frequency abnormal scenarios and high-frequency abnormal nodes, and optimize instruction transmission paths, associated model parameters, and instruction allocation;

[0162] Among them, the associated model parameters are used to indicate the model parameters corresponding to the models related to the instruction generation, allocation, verification and execution process, including at least one parameter corresponding to one or more models in the instruction process, the hierarchical communication protocol, the dynamic priority scheduling algorithm and the permission constraint model corresponding to the permission constraint condition.

[0163] This implementation addresses the lack of a quantitative evaluation mechanism in traditional systems. Using a multi-dimensional scoring model that integrates response timeliness, operational accuracy, and feedback quality, it generates a comprehensive execution performance score, accurately identifying high-frequency abnormal scenarios and nodes. Based on the scoring results, it optimizes instruction delivery paths, adjusts instruction process parameters, and dynamically prioritizes scheduling strategies, forming a closed-loop feedback mechanism. This design continuously improves the system's adaptability and response accuracy, enabling dynamic resource allocation.

[0164] As an optional implementation manner, the specific manner in which the processing module determines the execution effect of the target instruction according to the running status data further includes:

[0165] Determining the timeliness of the execution status of the target instruction based on the running status data, and triggering a hierarchical progressive warning;

[0166] The hierarchical progressive warning includes:

[0167] During the execution of the target instruction, if the first timeout occurs, the corresponding target node will be automatically reminded at the node level; if the second timeout occurs, it will be upgraded to a regional intervention instruction; if the third timeout occurs, the node instruction receiving permission of the target node will be frozen and a negative performance parameter will be generated;

[0168] The negative performance parameter is used to reduce the comprehensive score of the target node in the multidimensional scoring model.

[0169] This implementation addresses the cascading failures caused by command execution timeouts through a hierarchical, progressive warning mechanism: the first timeout triggers an automatic node-level alert, a second timeout escalates to a regional intervention order, and a third timeout freezes node permissions and generates a negative performance parameter. This gradient response mechanism embeds timeliness control throughout the execution process, preventing interruptions to disaster response. The negative performance parameter, coupled with a multi-dimensional scoring model, drives node performance optimization, significantly improving the automation of timeliness management.

[0170] It should be noted that it should be understood that the division of the various modules of the above device is merely a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. Moreover, these modules can all be implemented in the form of software called by a processing element; or they can all be implemented in the form of hardware; or some modules can be implemented in the form of software called by a processing element, and some modules can be implemented in the form of hardware. For example, the processing module can be a separately established processing element, or it can be integrated into a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called by a processing element of the above device to perform the functions of the above-mentioned module. The implementation of other modules is similar. In addition, these modules can all or partly be integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed by the hardware integrated logic circuit in the processor element or by instructions in the form of software.

[0171] Schematically, as Figure 4 As shown, Figure 4 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 can be provided as a server. Figure 4 Computer device 300 includes a processing component 302, which further includes one or more processors, and a memory resource represented by memory 301 for storing instructions executable by processing component 302, such as an application. The application stored in memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, processing component 302 is configured to execute the instructions to perform the method of any of the above embodiments.

[0172] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or the like.

[0173] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0174] An embodiment of the present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute a method as provided in any embodiment.

[0175] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0176] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0177] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for online transfer of instruction sets, characterized in that: The method comprises: Acquire real-time disaster data, process the real-time disaster data through a preset instruction process, and generate a standardized instruction set; The instruction process includes semantic parsing, knowledge enhancement and instruction generation performed in sequence; Determining a target instruction in the standardized instruction set, and sequentially delivering the target instruction to a target node according to a preset node architecture based on a hierarchical communication protocol and a dynamic priority scheduling algorithm; Execute the target instruction under the preset permission constraints; According to the structured identifier corresponding to the target instruction, the running status data corresponding to the target instruction is collected in real time, and the execution effect of the target instruction is determined according to the running status data.

2. The method according to claim 1, characterized in that The real-time disaster data is processed through a preset instruction process to generate a standardized instruction set, including: Performing input cleaning and normalization processing on the real-time disaster data to obtain pre-processed first target data; Performing semantic parsing on the first target data to parse the operation subject, action predicate, and modification conditions, constructing a structured intermediate representation, and performing knowledge enhancement and compliance verification processes on the structured intermediate representation to obtain second target data; generating a standardized instruction set for the second target data according to a dual-channel hybrid generation method; Among them, the dual-channel hybrid generation method includes: the template-driven channel fills the pre-compiled instruction template physical slot, the neural network channel generates a sequence based on conditional sampling, and the two outputs are fused through confidence to generate the standardized instruction set.

3. The method according to claim 2, characterized in that The method further comprises: Detect whether the structured intermediate representation has missing parameters. When it is detected that the structured intermediate representation has missing parameters, trigger an interactive clarification process, dynamically inject environment variables through the context management service to supplement the missing parameters, and complete the information completion through an asynchronous callback mechanism.

4. The method according to claim 1, wherein The node architecture includes a multi-level node communication channel, and the target instruction is sequentially transmitted to the target node according to the preset node architecture based on the hierarchical communication protocol and dynamic priority scheduling algorithm, including: A high-bandwidth protocol is used for real-time synchronization between a first node group of the multi-level node communication channel, and a lightweight protocol is used for adapting to high packet loss scenarios between a second node group of the multi-level node communication channel; Through the dynamic priority scheduling algorithm, command priorities are divided and bandwidth resources are allocated according to the degree of disaster impact; The dynamic priority scheduling algorithm specifically includes: optimizing the instruction transmission path based on real-time network topology data according to the degree of disaster impact, and interrupting the transmission of low-priority instructions in the scenario of bandwidth resource competition; Furthermore, when communication is interrupted, each node in the node architecture starts a self-organizing network to build a local area network to execute the transmission of the target instruction, and incrementally synchronizes the undelivered instructions and the execution log after communication is restored.

5. The method according to claim 1, wherein The executing the target instruction under the preset permission constraint condition includes: According to the permission whitelist mechanism, the recipient of the target instruction performs identity authentication and role matching double verification after receiving the target instruction; When executing the target instruction, real-time feedback confirmation information is given to the preset management unit, wherein the confirmation information includes the identity code of the execution subject and the receiving timestamp; After the target instruction is executed, a structured feedback report is submitted to a preset management unit, where the structured feedback report includes an execution timeline and an abnormal event code.

6. The method according to any one of claims 1 to 5, characterized in that Determining the execution effect of the target instruction according to the running status data includes: Processing the operating status data to obtain a comprehensive score using a multidimensional scoring model; The multidimensional scoring model is used to indicate one or more parameters of the target instruction's response time, operation accuracy, and feedback quality; Based on the comprehensive score, determine high-frequency abnormal scenarios and high-frequency abnormal nodes, and optimize instruction transmission paths, associated model parameters, and instruction allocation; Among them, the associated model parameters are used to indicate the model parameters corresponding to the models related to the instruction generation, allocation, verification and execution process, including at least one parameter corresponding to one or more models in the instruction process, the hierarchical communication protocol, the dynamic priority scheduling algorithm and the permission constraint model corresponding to the permission constraint condition.

7. The method according to claim 6, characterized in that The determining the execution effect of the target instruction according to the running status data further includes: Determining the timeliness of the execution status of the target instruction based on the running status data, and triggering a hierarchical progressive warning; The hierarchical progressive warning includes: During the execution of the target instruction, if the first timeout occurs, the corresponding target node will be automatically reminded at the node level; if the second timeout occurs, it will be upgraded to a regional intervention instruction; if the third timeout occurs, the node instruction receiving permission of the target node will be frozen and a negative performance parameter will be generated; The negative performance parameter is used to reduce the comprehensive score of the target node in the multidimensional scoring model.

8. An instruction set online circulation device, characterized in that: The device comprises: An acquisition module is used to acquire real-time disaster data, process the real-time disaster data through a preset instruction process, and generate a standardized instruction set; The instruction process includes semantic parsing, knowledge enhancement and instruction generation performed in sequence; a processing module, configured to determine a target instruction in the standardized instruction set, and sequentially transmit the target instruction to a target node according to a preset node architecture based on a hierarchical communication protocol and a dynamic priority scheduling algorithm; The processing module is further configured to execute the target instruction under preset permission constraints; The processing module is further configured to collect, in real time, the running status data corresponding to the target instruction according to the structured identifier corresponding to the target instruction, and determine the execution effect of the target instruction according to the running status data.

9. A computer device, characterized in that: The method comprises one or more processors and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method according to any one of claims 1 to 7 are performed.

10. A storage medium, characterized in that: The storage medium stores computer-readable instructions, which, when executed by one or more processors, enable the one or more processors to perform the steps of the method according to any one of claims 1 to 7.

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