A substation centralized control misoperation checking method and system based on device association

Abstract

The application discloses a substation centralized control mistake-proof checking method and system based on equipment association, and relates to the technical field of substation checking. The substation centralized control mistake-proof checking system based on equipment association comprises a substation information construction module and a substation operation checking module. The application constructs a substation collaborative knowledge graph comprising primary equipment electrical association, secondary equipment control association and equipment operation constraint relationship, so that the mistake-proof checking can comprehensively consider the multi-dimensional association relationship between equipment. The application generates an operation state evolution path to analyze the state change process of target equipment and affected associated equipment in the operation execution process to be checked, so that the system can obtain the equipment state change and constraint change caused by the operation in advance, and the discovery ability of potential risks in the continuous operation process is improved.

Description

Technical Field

[0001] This invention relates to the field of substation verification technology, and in particular to a substation centralized control system error prevention verification method and system based on equipment association. Background Technology

[0002] With the development of intelligent substations and centralized control systems, substation operation is gradually shifting from on-site manual operation to remote centralized control. Existing centralized control error prevention and verification methods are mainly based on the topology of primary equipment and preset five-prevention rules. By judging whether the actual operating status meets the operating conditions, the legality of operation tickets and control commands is verified.

[0003] However, existing methods mainly focus on static judgment of a single operation or the current state, making it difficult to analyze the continuous impact of changes in the state of primary equipment on associated equipment and secondary protection and control logic during operation execution. When there are implicit state relationships between multiple operation instructions, traditional methods cannot identify potential mutual exclusion risks caused by state propagation and changes in control constraints in a timely manner. At the same time, after discovering operation conflicts, existing technologies usually adopt direct locking or alarm methods, which cannot optimize and adjust the operation path according to the operating status.

[0004] Therefore, a centralized control and error prevention verification method is needed that can integrate the relationships between primary equipment, secondary equipment, and error prevention constraints. Summary of the Invention

[0005] This invention aims to provide a substation centralized control error prevention verification method and system based on equipment association. By constructing a collaborative knowledge graph of equipment, generating an operation state evolution path, identifying mutually exclusive relationships in the operation process, and generating an operation execution plan that meets safety constraints based on virtual state evolution, the error prevention verification capability under complex operation scenarios is improved.

[0006] A method for verifying the prevention of errors in substation centralized control based on equipment association includes the following steps: The target substation contains several primary and secondary devices; acquire primary device rule data, secondary device rule data, and station-end anti-misoperation rule data; construct a substation collaboration knowledge graph between primary and secondary devices based on the primary and secondary device rule data; Obtain the sequence of operation instructions to be verified; determine the target equipment and affected related equipment corresponding to the sequence of operation instructions to be verified based on the substation collaborative knowledge graph; generate the corresponding operation state evolution path based on the target equipment and affected related equipment; The judgment is made based on the operation state evolution path: when there are mutually exclusive operation instructions to be checked, virtual evolution is performed based on the substation collaborative knowledge graph and the operation state evolution path to obtain the error prevention risk state; the corresponding target operation execution plan is generated according to the error prevention risk state; otherwise, the target operation execution plan is directly output according to the sequence of operation instructions to be checked. Acquire real-time substation operation data; perform anti-misoperation verification based on the target operation execution plan and the real-time substation operation data to obtain the anti-misoperation verification result; when the anti-misoperation verification result is passed, output the sequence of operation instructions to be executed for equipment control; when the anti-misoperation verification result is failed, output anti-misoperation alarm information to correct the instructions.

[0007] As a preferred embodiment of the present invention, the specific steps for constructing a substation collaborative knowledge graph between primary and secondary equipment based on primary equipment rule data and secondary equipment rule data include: Based on the primary equipment rule data, extract the physical entity nodes and primary equipment connection attributes corresponding to the primary equipment; based on the secondary equipment rule data, extract the logical entity nodes and secondary signal protection attributes corresponding to the secondary equipment. Electrical relationships are established between physical entity nodes based on primary equipment connection attributes; control relationships are established between logical entity nodes based on secondary signal protection attributes; state constraints corresponding to primary and secondary equipment are extracted based on station-end anti-misoperation rule data; and equipment operation constraints are obtained by associating the state constraints with the corresponding physical and logical entity nodes. A substation collaborative knowledge graph is constructed based on electrical relationships, control relationships, and equipment operation constraints, with the electrical relationships, control relationships, and equipment operation constraints serving as relation edges in the substation collaborative knowledge graph.

[0008] As a preferred embodiment of the present invention, the specific steps for generating a corresponding operational state evolution path based on the target device and the affected associated devices include: Based on the sequence of operation instructions to be verified, extract the operation object, operation type and target status change information corresponding to each operation instruction to be verified, match the operation object with the physical entity nodes and logical entity nodes in the substation collaborative knowledge graph to obtain the corresponding target device; Based on the relationship edges of the target device in the substation collaborative knowledge graph, the devices that have a state influence relationship with the target device are identified as affected associated devices. Based on the target state change information of the target device and the relationship edges corresponding to all affected associated devices, the propagation direction and influence range of the state change of the operation instruction sequence to be verified are determined. Based on the direction of state change propagation, the state change relationship between the target equipment and the affected related equipment is progressively deduced in the substation collaborative knowledge graph to obtain the predicted equipment states of primary and secondary equipment at different operation stages; based on the predicted equipment states, an operation state evolution path is constructed, which includes operation trigger nodes, state change nodes, and constraint influence nodes.

[0009] As a preferred embodiment of the present invention, the specific steps for determining the operational state evolution path include: Correlation analysis was performed on the operation state evolution paths corresponding to different operation instructions to be verified to determine the set of associated nodes that contain the same state change nodes or constraint influence nodes between different operation state evolution paths. Identifying mutual exclusion relationships based on a set of associated nodes specifically includes: Determine whether the state change nodes corresponding to different operation instructions to be verified act on the same device, and whether there are opposite state directions or conflicting device state conditions in the target state change information; if so, determine that there is a mutual exclusion relationship between the device states of the corresponding operation instructions to be verified; at the same time, determine whether the state change nodes corresponding to different operation instructions to be verified are associated with the same affected associated device, and whether there is a conflict in the predicted state of the corresponding device; if so, determine that there is a mutual exclusion relationship between the state propagation of the corresponding operation instructions to be verified. Determine whether the constraint-affected nodes corresponding to different operation instructions to be verified trigger the device operation constraint relationship of the same device, and whether there is inconsistency in the predicted state of the device within the scope of the corresponding state change influence; if so, determine that there is a mutual exclusion relationship between the corresponding operation instructions to be verified. Based on the mutual exclusion relationships of equipment states, constraints, and state propagation, determine the set of operation instructions to be checked that have mutual exclusion relationships.

[0010] As a preferred embodiment of the present invention, the specific steps for virtual evolution based on the substation collaborative knowledge graph and operation state evolution path include: A virtual operation state space is constructed based on the substation collaborative knowledge graph; the predicted state of the target equipment and the affected related equipment is progressively updated according to the execution order of different operation instructions to be verified, resulting in several virtual operation state sequences; For any virtual operation state sequence, based on the state change nodes and constraint influence nodes in the operation state evolution path, the consistency between the predicted state of each device and the operation constraint relationship of the device is detected, and the corresponding error prevention risk state is determined. Based on the error prevention risk status, the paths of mutually exclusive operation instructions to be verified are adjusted to obtain an adjusted virtual operation state sequence; wherein, the path adjustment includes at least one of adjusting the operation execution order, adding intermediate operations to resolve the mutual exclusion state, and deleting operation instructions that cause state conflicts. Based on the error prevention risk status corresponding to the adjusted virtual operation state sequence, a matching analysis is performed with the virtual error prevention constraint conditions to screen out the virtual operation state sequences that meet the virtual error prevention constraint conditions, and the screened virtual operation state sequences are determined as the target operation execution plan.

[0011] As a preferred embodiment of the present invention, the specific steps for error prevention verification based on the target operation execution plan and real-time substation operation data include: Obtain the sequence of target operation instructions corresponding to the target operation execution plan, and determine the target equipment to be executed, the affected related equipment to be executed, and the operation constraint relationship of the equipment to be executed for each target operation instruction based on the substation collaborative knowledge graph; Based on real-time substation operation data, the actual operating status of the target equipment and the affected related equipment to be executed is verified to determine whether the actual operating status meets the operating conditions corresponding to the target operation execution plan, and the equipment status verification result is obtained; according to the operation constraint relationship of the equipment to be executed, the constraint consistency verification of the target operation instruction sequence in the target operation execution plan is performed to determine whether the primary equipment and secondary equipment meet the coordination requirements, and the constraint consistency verification result is obtained. Based on the equipment status verification results and constraint consistency verification results, generate error prevention verification results.

[0012] A substation centralized control system for error prevention and verification based on device association includes: The substation information construction module includes an information acquisition unit and an operation prediction unit. The target substation contains several primary and secondary devices. The information acquisition unit is used to acquire primary device rule data, secondary device rule data, and substation-side error prevention rule data. Based on the primary and secondary device rule data, it constructs a substation collaborative knowledge graph between primary and secondary devices. The operation prediction unit is used to acquire the sequence of operation instructions to be verified. Based on the substation collaborative knowledge graph, it determines the target device and affected associated devices corresponding to the sequence of operation instructions to be verified. Based on the target device and affected associated devices, it generates the corresponding operation state evolution path. The substation operation verification module includes a risk assessment unit and a verification confirmation unit. The risk assessment unit is used to make judgments based on the operation state evolution path: when there are mutually exclusive operation instructions to be verified, virtual evolution is performed based on the substation collaborative knowledge graph and the operation state evolution path to obtain the error prevention risk state; the corresponding target operation execution plan is generated according to the error prevention risk state; otherwise, the target operation execution plan is directly output according to the operation instruction sequence to be verified. The verification confirmation unit is used to acquire real-time substation operation data; error prevention verification is performed based on the target operation execution plan and the real-time substation operation data to obtain the error prevention verification result; when the error prevention verification result is passed, the operation instruction sequence to be executed is output for equipment control; when the error prevention verification result is failed, error prevention alarm information is output for instruction correction.

[0013] The present invention has the following advantages: 1. This invention constructs a substation collaborative knowledge graph that includes primary equipment electrical associations, secondary equipment control associations, and equipment operation constraints, enabling the anti-misoperation verification to comprehensively consider the multi-dimensional relationships between equipment. By generating operation state evolution paths, the system analyzes the state change process of the target equipment and affected associated equipment during the execution of the verification operation, allowing the system to obtain in advance the possible changes in equipment state and constraints caused by the operation, thereby improving the ability to detect potential risks during continuous operation.

[0014] 2. This invention analyzes the mutual exclusion relationship based on state change nodes, constraint influence nodes, and predicted equipment states. This enables the identification of associated conflicts between different operation commands caused by the propagation of equipment state changes or the triggering of control constraints, reducing the missed detections caused by relying solely on a single rule for matching. By considering both primary equipment states and secondary equipment control logic states during the error prevention verification process, the consistency of equipment states and constraints in the operation scheme is verified, thereby improving the operational reliability of the centralized control system. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of a substation centralized control anti-misoperation verification system based on device association, used in an embodiment of the present invention. Detailed Implementation

[0016] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this invention.

[0017] Example 1: A method for verifying the prevention of errors in substation centralized control based on equipment association, comprising the following steps: The target substation contains several primary and secondary devices; acquire primary device rule data, secondary device rule data, and station-end anti-misoperation rule data; construct a substation collaboration knowledge graph between primary and secondary devices based on the primary and secondary device rule data; The target substation contains several primary and secondary equipment. Primary equipment refers to electrical equipment that directly participates in the process of power generation, transmission and distribution, including circuit breakers, disconnect switches, grounding switches, busbars, transformers and transmission lines. Secondary equipment refers to equipment used to measure, protect, control and monitor the primary equipment, including protection devices, measurement and control devices, intelligent terminals and communication control equipment.

[0018] Primary equipment rule data describes the attributes of primary equipment itself and the electrical connection relationships between equipment, including equipment number, equipment type, bay to which it belongs, connection location, opening and closing state logic, and equipment operation restrictions; secondary equipment rule data describes the control and protection relationships between secondary equipment and between secondary equipment and primary equipment, including protection signal type, control object, protection action conditions, interlocking logic, and signal transmission relationships. Substation-side error prevention rule data describes the safety constraints set for equipment operation within the substation, including prohibited operation conditions, permitted operation conditions, equipment state transition rules, and operation sequence restriction rules. By collecting the above data, data from different sources are associated and matched according to a unified equipment identifier to form a basic data set containing equipment attribute information, equipment association information, and operation constraint information, providing a data foundation for the subsequent construction of a substation collaborative knowledge graph.

[0019] The specific steps for constructing a substation collaborative knowledge graph between primary and secondary equipment based on primary equipment rule data and secondary equipment rule data include: Based on the primary equipment rule data, extract the physical entity nodes and primary equipment connection attributes corresponding to the primary equipment; based on the secondary equipment rule data, extract the logical entity nodes and secondary signal protection attributes corresponding to the secondary equipment. Based on the primary equipment rule data, the information contained therein, such as equipment name, equipment number, equipment type, and equipment installation location, is parsed to convert each actual primary equipment into a physical entity node in the substation collaborative knowledge graph. The physical entity node is used to represent equipment objects in the actual power grid, such as circuit breaker nodes, disconnector nodes, and busbar nodes.

[0020] Further, primary equipment connection attributes are extracted from the primary equipment rule data. These attributes describe the electrical connection relationships between primary equipment, including equipment connection endpoints, voltage levels, line connection directions, and upstream / downstream power supply relationships. For secondary equipment rule data, parsing is performed according to equipment number, protection function type, control object, and signal interaction relationship. Protection devices, measurement and control devices, etc., are converted into logical entity nodes, which represent secondary logical objects that implement protection and control functions. Simultaneously, secondary signal protection attributes are extracted. These attributes describe the signal transmission and protection control relationships between secondary equipment, including protection start conditions, action output signals, interlocking signals, and control feedback signals. Through the above processing, actual equipment objects are converted into data nodes that can be used for correlation analysis, while retaining the connection and control characteristics between equipment.

[0021] Electrical relationships are established between physical entity nodes based on primary equipment connection attributes; control relationships are established between logical entity nodes based on secondary signal protection attributes; state constraints corresponding to primary and secondary equipment are extracted based on station-end anti-misoperation rule data; and equipment operation constraints are obtained by associating the state constraints with the corresponding physical and logical entity nodes. Based on the primary device connection attributes, an electrical association is established between the corresponding physical entity nodes. Specifically, the direct electrical connection between two primary devices is determined based on the device connection endpoint information. If a current transmission path exists between the two physical entity nodes, an electrical association is established between the two nodes, and the connection direction, voltage level, and device connection type are stored as association attributes.

[0022] For example, if there is an actual electrical connection between the circuit breaker node and the bus node, then a corresponding electrical association is established; for secondary equipment, the signal interaction relationship between logical entity nodes is analyzed based on the secondary signal protection attributes, and control association relationships are established between the corresponding logical entity nodes by identifying the sending and receiving ends of protection action signals, control command signals, and interlocking signals.

[0023] For example, when a protection device outputs a trip signal to the circuit breaker control module, a control association is established between the protection logic node and the control logic node. In this way, the knowledge graph can express not only the physical connections between primary devices but also the control logic relationships between secondary devices, providing a foundation for subsequent analysis of the scope of operational impact.

[0024] The station-side anti-misoperation rule data is parsed, and the rules describing equipment operation restrictions are converted into state constraints. State constraints represent the state requirements that equipment must meet to perform a certain operation, such as requiring the circuit breaker to be in the open state before operating a disconnecting switch, or requiring the corresponding line to be de-energized before operating a grounding switch. In the specific processing, the operation object, operation action, prerequisite state, and prohibition conditions are first extracted from the anti-misoperation rules. Then, the prerequisite state and prohibition conditions are converted into structured constraint information, such as "Equipment A is in the open state" or "Equipment B is not in the energized state." Finally, based on the equipment number and functional relationship, the state constraints are associated with the corresponding physical entity node or logical entity node. When a state constraint involves both primary equipment status and secondary protection logic, it is associated with the corresponding primary equipment node and secondary equipment node respectively, forming an equipment operation constraint relationship. The equipment operation constraint relationship describes the safety conditions that must be met during equipment operation, enabling subsequent anti-misoperation verification to determine the legality of the operation based on the current state of the equipment.

[0025] A substation collaborative knowledge graph is constructed based on electrical relationships, control relationships, and equipment operation constraints, with the electrical relationships, control relationships, and equipment operation constraints serving as relation edges in the substation collaborative knowledge graph.

[0026] Based on the physical entity nodes, logical entity nodes, and various relationships constructed above, a substation collaborative knowledge graph is constructed using a graph structure approach. The substation collaborative knowledge graph consists of nodes and relational edges. Nodes include physical entity nodes representing primary equipment, logical entity nodes representing secondary equipment, and state constraint nodes representing operational constraints. Relationship edges include electrical relationships, control relationships, and equipment operation constraint relationships.

[0027] In the specific construction process, physical entity nodes are connected according to electrical relationships to form a physical relationship network reflecting the actual operating structure of primary equipment; logical entity nodes are connected according to control relationships to form a control relationship network reflecting secondary protection and control logic; then, state constraint condition nodes are connected to corresponding equipment nodes through equipment operation constraint relationships to realize the association and integration between primary equipment status, secondary equipment control logic, and anti-misoperation rules; finally, a substation collaborative knowledge graph that can simultaneously describe equipment physical connection relationships, control logic relationships, and operational safety constraint relationships is formed, providing a unified data model foundation for subsequent operational state evolution analysis, mutual exclusion operation identification, and anti-misoperation verification.

[0028] Obtain the sequence of operation instructions to be verified; determine the target equipment and affected related equipment corresponding to the sequence of operation instructions to be verified based on the substation collaborative knowledge graph; generate the corresponding operation state evolution path based on the target equipment and affected related equipment; The specific steps for generating the corresponding operational state evolution path based on the target device and affected associated devices include: Based on the sequence of operation instructions to be verified, extract the operation object, operation type and target status change information corresponding to each operation instruction to be verified, match the operation object with the physical entity nodes and logical entity nodes in the substation collaborative knowledge graph to obtain the corresponding target device; The system acquires the sequence of operation instructions to be verified in the centralized control system. The sequence of operation instructions to be verified refers to one or more equipment control operation instructions arranged in a predetermined execution order, such as circuit breaker opening and closing instructions, disconnector opening and closing instructions, and protection device activation and deactivation instructions. Each operation instruction to be verified is parsed to extract the operation object, operation type, and target state change information contained therein.

[0029] Among them, the operation object is used to represent the name or number of the controlled equipment; the operation type is used to represent the control action performed on the equipment, such as closing, opening, engaging, and disengaging; the target state change information is used to represent the expected state change of the equipment after the operation is performed, such as the circuit breaker changing from the open state to the closed state, or the protection device changing from the disengaged state to the engaged state.

[0030] The extracted operation objects are matched with physical and logical entity nodes in the substation collaborative knowledge graph. Specifically, similarity matching is performed based on equipment attributes such as equipment number, equipment name, equipment type, and the bay to which it belongs. When the operation object corresponds to primary equipment, it is matched to the corresponding physical entity node; when the operation object corresponds to secondary protection or control equipment, it is matched to the corresponding logical entity node. Finally, the target equipment corresponding to the operation instruction sequence to be verified is determined, providing a basis for subsequent analysis of the operation's impact range.

[0031] Based on the relationship edges of the target device in the substation collaborative knowledge graph, the devices that have a state influence relationship with the target device are identified as affected associated devices. Based on the target state change information of the target device and the relationship edges corresponding to all affected associated devices, the propagation direction and influence range of the state change of the operation instruction sequence to be verified are determined. After identifying the target equipment, an extended analysis is performed based on the association relationship of the target equipment in the substation collaborative knowledge graph to obtain the equipment nodes that have a state influence relationship with the target equipment, and these equipment are identified as affected associated equipment; among them, affected associated equipment refers to associated equipment whose operating status, control logic or anti-misoperation constraints may change after the target equipment performs an operation.

[0032] In the specific expansion process, the target device is used as the starting node, and the relationships are traversed along the electrical relationships, control relationships, and equipment operation constraints in the substation collaborative knowledge graph. When a device node has a direct or indirect relationship with the target device, and the relationship can reflect the transmission of equipment status changes, control signal transmission, or operational constraint effects, the device node is added to the set of affected associated devices. For example, circuit breaker operation may affect the line status through electrical relationships, the protection device status through control relationships, and the grounding switch operation conditions through equipment operation constraints.

[0033] After identifying the affected associated devices, the propagation direction and scope of the state change are analyzed based on the target state change information corresponding to the target device and the relationship edge attributes between each affected associated device and the target device. The propagation direction of the state change is used to indicate the order in which the device state change is transmitted from the target device to the associated devices, and the scope of the state change is used to indicate the set of devices affected by the operation.

[0034] In the specific analysis process, the priority of state propagation is determined according to the type of relation edge. For example, electrical relations are used to analyze the state transmission path of primary equipment, control relations are used to analyze the logic change path of secondary equipment, and equipment operation constraint relations are used to analyze the error prevention condition change path. Starting from the target equipment, the associated equipment nodes are obtained in sequence to form a state change propagation path extending from the target equipment to the affected associated equipment, and the scope of influence of the state change corresponding to the operation instruction sequence to be verified is determined.

[0035] Based on the direction of state change propagation, the state change relationship between the target equipment and the affected related equipment is progressively deduced in the substation collaborative knowledge graph to obtain the predicted equipment states of primary and secondary equipment at different operation stages; based on the predicted equipment states, an operation state evolution path is constructed, which includes operation trigger nodes, state change nodes, and constraint influence nodes.

[0036] Based on the determined direction of state change propagation, the state change relationship between the target equipment and the affected associated equipment is progressively deduced in the substation collaborative knowledge graph to predict the state change process of each equipment during the execution of the operation command to be verified. The progressive deduction refers to the analysis process of updating the equipment state change in stages according to the execution order of the operation command and the equipment association relationship.

[0037] In the specific simulation process, the real-time operation data of the substation is first used as the initial state input to obtain the current operating status of the target equipment and the affected related equipment, including the equipment opening and closing status, energized status, protection activation and deactivation status, and control status. Then, the target equipment is updated according to the target state change information corresponding to the operation command to be verified. Furthermore, the target equipment state change is transmitted to the affected related equipment level by level according to the relationship edges in the substation collaborative knowledge graph.

[0038] During the state transfer process, state calculations are performed based on the influence rules corresponding to different relational edges. For example, when a state change in the target device is transmitted to other primary devices through electrical relationships, the operating state of the associated devices is determined based on the device connection relationships. When a state change is transmitted to secondary devices through control relationships, the corresponding logic changes in the secondary devices are determined based on the relationship between protection logic and control signals. When a state change triggers device operation constraints, the relevant devices are determined based on the error prevention rules to determine whether they meet the new operating conditions. Through the above step-by-step update process, the predicted state of the primary and secondary devices at each operation stage is obtained, forming a dynamic change process from the initial state to the target state.

[0039] Based on the predicted equipment states corresponding to different operation stages, a structured description is provided for the target equipment, affected related equipment, and the corresponding state change process, and an operation state evolution path is constructed. The operation state evolution path is used to represent the complete process of equipment state changes, related influence propagation, and operation constraint changes during the execution of the operation command to be verified, including operation trigger nodes, state change nodes, and constraint influence nodes.

[0040] In the specific construction process, the target device executing the operation command to be verified is used as the operation trigger node to indicate the starting position of the state change; the device predicted state transition process generated by the target device and the affected associated devices at different operation stages is used as the state change node to indicate the state propagation process; and the change of device operation constraints caused by the change of device state is used as the constraint influence node to indicate the change process of the anti-misoperation rules.

[0041] Furthermore, the nodes are connected according to the direction of state change propagation, and the corresponding electrical, control, and equipment operation constraints between the nodes are used as the basis for path connection to form an operation state evolution path that includes "operation triggering - state change - constraint influence". This path can describe how a primary equipment state change affects associated equipment and secondary control logic changes during the execution of a single operation command or a sequence of operation commands, providing an analytical basis for subsequent identification of state conflicts, constraint conflicts, and mutually exclusive state propagation relationships between different operation commands.

[0042] The judgment is made based on the operation state evolution path: when there are mutually exclusive operation instructions to be checked, virtual evolution is performed based on the substation collaborative knowledge graph and the operation state evolution path to obtain the error prevention risk state; the corresponding target operation execution plan is generated according to the error prevention risk state; otherwise, the target operation execution plan is directly output according to the sequence of operation instructions to be checked. The specific steps for determining the operational state evolution path include: Correlation analysis was performed on the operation state evolution paths corresponding to different operation instructions to be verified to determine the set of associated nodes that contain the same state change nodes or constraint influence nodes between different operation state evolution paths. Obtain the operation state evolution path corresponding to each operation instruction to be verified. The operation state evolution path is used to describe the process in which the device state changes due to the operation trigger node during the execution of a single operation instruction, and further affects the operation state of associated devices and the changes in device operation constraints. Since different operation instructions to be verified may act on the same device or affect the same device through association, it is necessary to perform association analysis on the node relationships between different operation state evolution paths.

[0043] Specifically, state change nodes and constraint impact nodes are extracted from each operational state evolution path. State change nodes represent predicted state changes of the target device or affected associated devices during operation, while constraint impact nodes represent changes in device operation constraints triggered by the device state change. Node attribute matching is performed on the state change nodes and constraint impact nodes in different operational state evolution paths. These node attributes include device identifier, device type, state change type, constraint type, and association type. When nodes in different paths have the same device identifier or the same associated device object, and the corresponding state change process or constraint condition is associated, the corresponding node is identified as an associated node. Through this method, a set of associated nodes containing the same state change nodes or constraint impact nodes is obtained between different operational state evolution paths, providing an analytical basis for subsequently identifying the mutual exclusion relationships between operational instructions.

[0044] Identifying mutual exclusion relationships based on a set of associated nodes specifically includes: Determine whether the state change nodes corresponding to different operation instructions to be verified act on the same device, and whether there are opposite state directions or conflicting device state conditions in the target state change information; if so, determine that there is a mutual exclusion relationship between the device states of the corresponding operation instructions to be verified; at the same time, determine whether the state change nodes corresponding to different operation instructions to be verified are associated with the same affected associated device, and whether there is a conflict in the predicted state of the corresponding device; if so, determine that there is a mutual exclusion relationship between the state propagation of the corresponding operation instructions to be verified. Based on the set of associated nodes, device state conflict analysis is performed on the state change nodes corresponding to different operation commands to be verified. Among them, device state mutual exclusion relationship refers to the operation conflict relationship that arises when multiple operation commands to be verified act on the same device at the same time, due to inconsistent target state change directions or the inability to simultaneously meet device state conditions.

[0045] In the specific analysis process, firstly, the state change nodes corresponding to different operation commands to be verified are obtained, and the equipment identifier and target state change information corresponding to the state change nodes are extracted; it is determined whether different state change nodes correspond to the same equipment object, such as the same circuit breaker, disconnector, or protection device; when it is determined that multiple operation commands act on the same equipment, the target state change information corresponding to each operation command is further compared, including the equipment target state type, state transition direction, and state constraint conditions.

[0046] For example, if one operation command requires the circuit breaker to change from the open state to the closed state, while another operation command requires the same circuit breaker to remain in the open state, then the state changes corresponding to the two operation commands are in opposite directions; or if two operation commands require the equipment to simultaneously meet mutually exclusive state conditions, then it is considered that there is a conflict in the equipment state. When any of the above situations are detected, it is determined that there is a mutually exclusive relationship between the corresponding operation commands to be checked.

[0047] Based on the set of associated nodes, the potential conflicts of different operation commands in the state propagation process are further analyzed. Among them, the state propagation mutual exclusion relationship refers to the predicted state of the same affected associated equipment that is mutually conflicting after different operation commands act on different objects, due to the propagation of equipment state changes along the electrical or control associations in the substation collaborative knowledge graph.

[0048] In the specific analysis process, the set of affected associated devices corresponding to each operation state evolution path is first obtained, and it is determined whether the state change nodes corresponding to different operation instructions to be verified are associated with the same affected associated device after state propagation. If the same affected associated device exists, the predicted device state corresponding to the device under different operation state evolution paths is further obtained, and the consistency between different predicted states is compared.

[0049] The predicted equipment states include the equipment's open / closed state, energized state, protection logic state, and control state. When the same affected related equipment produces different state results under different operating commands, or when the predicted state produced by one operating command prevents another operating command from achieving its target state, a state propagation conflict is considered to exist between the two operating commands, and a mutual exclusion relationship in state propagation is determined between the corresponding operating commands to be verified. This method can identify implicit conflicts caused by the transmission of state changes, avoiding omissions caused by judging solely based on the direct operating object.

[0050] Determine whether the constraint-affected nodes corresponding to different operation instructions to be verified trigger the device operation constraint relationship of the same device, and whether there is inconsistency in the predicted state of the device within the scope of the corresponding state change influence; if so, determine that there is a mutual exclusion relationship between the corresponding operation instructions to be verified. Based on the constraint-affected nodes, the changes in anti-misoperation rules generated after the execution of different operation commands are analyzed. Among them, the mutual exclusion relationship of constraint conditions refers to the conflict relationship that occurs when different operation commands can change the state of different devices, but because they trigger the same device operation constraint relationship, the related devices cannot meet the operation requirements at the same time.

[0051] In the specific analysis process, firstly, the constraint-affected nodes in the evolution path of different operation states are extracted, and the equipment operation constraint relationship corresponding to each constraint-affected node is obtained. The equipment operation constraint relationship is used to describe the state conditions that the equipment needs to meet when performing operations. For example, before operating a certain disconnecting switch, the corresponding circuit breaker is required to be in the open state, or before operating a certain grounding switch, the line is required to be in the de-energized state.

[0052] Determine whether the constraint-affected nodes corresponding to different operation instructions are associated with the same device operation constraint relationship; if an association exists, further obtain the predicted state of the device within the influence range corresponding to the constraint relationship, and determine whether the predicted state of the device is consistent after different operation instructions are executed; when the predicted state of one device satisfies the constraint condition, while the predicted state of another device causes the same constraint condition to be unsatisfied, then it is determined that there is a mutual exclusion relationship between the corresponding operation instructions.

[0053] Based on the mutual exclusion relationships of equipment states, constraints, and state propagation, determine the set of operation instructions to be checked that have mutual exclusion relationships.

[0054] Based on the mutual exclusion relationship identification results, the conflict relationships between each operation instruction to be checked are summarized to form a set of operation instructions to be checked that have mutual exclusion relationships. Specifically, operation instructions identified as mutually exclusive relationships of equipment status, state propagation, or constraint conditions are associated and recorded, and stored according to operation instruction number, conflict type, associated equipment, and corresponding operation state evolution path to obtain a set of mutually exclusive operation instructions. The set of mutually exclusive operation instructions is used to represent the combination of operation instructions that cannot be executed simultaneously in the original execution order under the current substation operating state and the conditions of the operation to be executed. Subsequently, based on the mutually exclusive operation instructions in this set, different operation sequences can be simulated and risk analysis can be performed through a virtual operating state space to generate a target operation execution plan that meets the anti-misoperation constraint conditions.

[0055] The specific steps for virtual evolution based on substation collaborative knowledge graph and operation state evolution path include: A virtual operation state space is constructed based on the substation collaborative knowledge graph; the predicted state of the target equipment and the affected related equipment is progressively updated according to the execution order of different operation instructions to be verified, resulting in several virtual operation state sequences; Based on the established substation collaborative knowledge graph, the current operating status of the substation and the equipment relationships are mapped to the virtual operating state space. The virtual operating state space is a state model used to simulate the execution process of the operation instructions to be verified. It uses physical entity nodes, logical entity nodes and corresponding relationship edges in the substation collaborative knowledge graph as its basic structure, and loads the real-time operating status of the equipment, the predicted status of the equipment, and the operating constraints of the equipment as state attributes to describe the equipment state change process after the execution of different operation schemes.

[0056] In the specific construction process, the real-time operation data of the substation is first acquired, and the opening and closing status, energized status and operation status information of the primary equipment are mapped to the corresponding physical entity nodes. The protection status, control status and signal status information of the secondary equipment are mapped to the corresponding logical entity nodes. Then, the status change nodes and constraint influence nodes in the operation status evolution path are used as the basis for state transition, and equipment status change rules are established so that the virtual operation status space can dynamically update the equipment status according to the operation instructions.

[0057] After constructing the virtual operating state space, the predicted state of the target device and affected associated devices is progressively updated according to the execution order of different operation instructions to be verified. Specifically, the sequence of operation instructions to be verified is arranged in the order of execution time, and the target state change information corresponding to each operation instruction is loaded in turn. When an operation instruction is executed, the predicted state of the device corresponding to the target device node is updated first. Then, based on the electrical association, control association, and device operation constraint relationship in the substation collaborative knowledge graph, the state change is propagated to the corresponding affected associated devices, and the predicted state of the affected associated devices is updated. By changing the execution order of different operation instructions to be verified, the device state change process under different operation paths is simulated, forming multiple virtual operating state sequences that gradually change from the initial operating state to the target state, providing a data foundation for different execution schemes for subsequent error prevention risk analysis.

[0058] For any virtual operation state sequence, based on the state change nodes and constraint influence nodes in the operation state evolution path, the consistency between the predicted state of each device and the operation constraint relationship of the device is detected, and the corresponding error prevention risk state is determined. For each virtual operation state sequence, the state change nodes and constraint influence nodes in the operation state evolution path are used to analyze whether the equipment state changes during the virtual operation meet the error prevention requirements. Among them, the error prevention risk state is used to represent the degree of matching between the equipment state changes and the equipment operation constraint relationship during the execution of a certain virtual operation state sequence, and is used to determine whether there is a potential error risk in the operation sequence.

[0059] In the specific detection process, the predicted equipment states corresponding to each operation stage in the virtual operation state sequence are first extracted, and the equipment object corresponding to each state change node and the target state change process are determined according to the operation state evolution path. Then, the equipment operation constraint relationships in the substation collaborative knowledge graph are invoked to match the predicted equipment states with the corresponding constraint conditions. For example, when the predicted state of a disconnector changes during the virtual operation, it is checked whether the corresponding circuit breaker state meets the disconnector operation requirements; when the secondary protection logic state changes, it is checked whether the corresponding control constraints meet the protection blocking conditions.

[0060] Furthermore, the risk status for error prevention is determined based on the detection results. When the predicted state of the equipment satisfies all equipment operation constraints, the corresponding virtual operation state sequence is determined as a low-risk state. When there is an inconsistency between the predicted state of the equipment and the equipment operation constraints, the corresponding risk status is determined based on the location of the inconsistency, the number of affected equipment, and the constraint triggering situation. For example, when an operation causes the state of a single equipment to fail to meet the constraints, it can be determined as a local risk state. When a state change propagates through correlation relationships and causes the constraints of multiple equipment to fail simultaneously, it can be determined as a correlated risk state. Through the above methods, risk assessment of the execution results of different virtual operation state sequences is achieved.

[0061] Based on the error prevention risk status, the paths of mutually exclusive operation instructions to be verified are adjusted to obtain an adjusted virtual operation state sequence; wherein, the path adjustment includes at least one of adjusting the operation execution order, adding intermediate operations to resolve the mutual exclusion state, and deleting operation instructions that cause state conflicts. Based on the error prevention risk status corresponding to each virtual operation state sequence, the operation paths of the operation instructions to be verified that have mutual exclusion relationships are optimized and adjusted to reduce equipment state conflicts and constraint conflicts generated during the operation. Among them, path adjustment refers to the process of changing the operation execution method for operation instructions with mutual exclusion relationships so that the equipment state change process can meet the equipment operation constraint relationship again.

[0062] In the specific adjustment process, firstly, the location of the risk is determined based on the risk status of error prevention, and the cause of the conflict is analyzed in conjunction with the state change nodes and constraint impact nodes in the operation state evolution path. If the mutual exclusion relationship is caused by the operation execution order, the execution order of the mutually exclusive operation instructions to be checked is adjusted so that the equipment state changes satisfy the sequential constraint relationship. If the mutual exclusion relationship is caused by the current state of the equipment not being able to meet the operation conditions, intermediate operations to change the equipment state are added between the original operation instructions, such as adding disconnect, confirmation, or state transition operations, so that subsequent operations have the execution conditions. If a certain operation instruction continuously causes the predicted state of the equipment to conflict with the equipment operation constraint relationship, the operation instruction is deleted or replaced with other operation methods that meet the constraint conditions.

[0063] After the path adjustment is completed, the state is updated again in the virtual operation state space according to the adjusted operation path to obtain the adjusted virtual operation state sequence, and the corresponding device prediction state and anti-misoperation risk state are recalculated. Through iterative adjustment and verification, the risk level of the virtual operation state sequence is gradually reduced, and the security of the generated operation execution plan is improved.

[0064] Based on the error prevention risk status corresponding to the adjusted virtual operation state sequence, a matching analysis is performed with the virtual error prevention constraint conditions to screen out the virtual operation state sequences that meet the virtual error prevention constraint conditions, and the screened virtual operation state sequences are determined as the target operation execution plan.

[0065] The system acquires the anti-misoperation risk status corresponding to each adjusted virtual operation state sequence and matches it with the virtual anti-misoperation constraints to determine the operation plan that meets the safety execution requirements. The virtual anti-misoperation constraints are safety judgment conditions pre-set according to the substation operation rules, equipment operation requirements, and station-end anti-misoperation rules. They are used to limit the range of equipment state changes and the constraint states that can be triggered during operation. In the specific matching process, the anti-misoperation risk status corresponding to each adjusted virtual operation state sequence is compared with the virtual anti-misoperation constraints item by item to determine whether there are abnormal equipment states, equipment operation constraints failures, or inconsistencies between the states of primary and secondary equipment during the virtual operation.

[0066] When the anti-misoperation risk state corresponding to a certain virtual operation state sequence meets all virtual anti-misoperation constraints, the virtual operation state sequence is determined as a candidate operation scheme. When there are multiple virtual operation state sequences that meet the conditions, further screening can be carried out according to the risk level, the number of operation steps, and the complexity of state changes. The virtual operation state sequence with lower risk and simpler operation process is selected as the target operation execution scheme. Finally, the determined target operation execution scheme is output to the subsequent anti-misoperation verification process for final safety verification before actual equipment control. In this way, the transformation from prohibiting operation after discovering risk to predicting risk and generating executable safe operation schemes is realized.

[0067] Acquire real-time substation operation data; perform anti-misoperation verification based on the target operation execution plan and the real-time substation operation data to obtain the anti-misoperation verification result; when the anti-misoperation verification result is passed, output the sequence of operation instructions to be executed for equipment control; when the anti-misoperation verification result is failed, output anti-misoperation alarm information to correct the instructions; The specific steps for error prevention verification based on the target operation execution plan and real-time substation operation data include: Obtain the sequence of target operation instructions corresponding to the target operation execution plan, and determine the target equipment to be executed, the affected related equipment to be executed, and the operation constraint relationship of the equipment to be executed for each target operation instruction based on the substation collaborative knowledge graph; The process involves obtaining the target operation execution plan, determined after virtual evolution and risk screening. This plan includes several target operation instruction sequences arranged in execution order. These sequences represent the specific operations to be performed during actual equipment control, including the operation object, operation type, and target state change information. The target operation instruction sequences are then parsed to extract the operation object and action corresponding to each instruction. The operation object is then matched with physical and logical entity nodes in the substation collaborative knowledge graph to determine the corresponding target equipment to be executed. The target equipment to be executed refers to primary or secondary equipment that actually performs control actions, such as circuit breakers, disconnectors, and protection devices.

[0068] Furthermore, based on the electrical, control, and equipment operation constraints in the substation collaborative knowledge graph, the associations of each target device to be executed are extended to identify the affected associated devices that may experience state changes after the corresponding operation is executed. Specifically, taking the target device to be executed as the starting node, the knowledge graph is queried along the equipment associations to obtain associated devices that have state propagation relationships, control logic relationships, or anti-misoperation constraint relationships with the target device, and these devices are identified as the affected associated devices to be executed. Based on the equipment operation conditions corresponding to the target operation instruction, the equipment operation constraint relationships associated with the target operation are extracted from the substation collaborative knowledge graph as the basis for constraint judgment in the subsequent anti-misoperation verification process.

[0069] Based on real-time substation operation data, the actual operating status of the target equipment and the affected related equipment to be executed is verified to determine whether the actual operating status meets the operating conditions corresponding to the target operation execution plan, and the equipment status verification result is obtained; according to the operation constraint relationship of the equipment to be executed, the constraint consistency verification of the target operation instruction sequence in the target operation execution plan is performed to determine whether the primary equipment and secondary equipment meet the coordination requirements, and the constraint consistency verification result is obtained. The system acquires real-time operational data of the target substation during its current operation. This data reflects the actual operating status of primary and secondary equipment, including equipment open / close status, energized status, protection activation / deactivation status, control status, and remote signaling status. Based on the equipment identification information, the real-time operational data is mapped to the corresponding target equipment node and affected associated equipment node in the substation collaborative knowledge graph to obtain the actual operating status of each device.

[0070] Based on the operational conditions corresponding to the target operation execution plan, the current state of the above-mentioned equipment is checked for consistency. In the specific verification process, firstly, the target state that each target device to be executed needs to achieve and the initial state conditions that need to be met before the operation are determined according to the target operation instruction sequence. Then, the current state of the equipment is matched and analyzed with the corresponding operational conditions to determine whether the equipment has the conditions to execute the corresponding operation. For example, for circuit breaker closing operation, it is necessary to determine whether the circuit breaker is currently in a closing state, and at the same time, it is necessary to determine whether the state of the associated isolation equipment meets the closing requirements. For protection device commissioning operation, it is necessary to determine whether the current operating state of the corresponding secondary equipment meets the commissioning conditions.

[0071] Furthermore, for the affected associated devices to be executed, based on the state change propagation relationship in the target operation execution plan, it is determined whether the current state of the associated devices may cause anomalies in the subsequent operation process; when the current states of both the target device to be executed and the affected associated devices to be executed meet the operation conditions corresponding to the target operation execution plan, a device state verification pass result is generated; otherwise, a device state verification fail result is generated, and the device object that caused the state anomaly and the corresponding state difference information are recorded as the basis for subsequent error prevention judgment.

[0072] Based on the operational constraints of the equipment to be executed extracted from the substation collaborative knowledge graph, a constraint consistency check is performed on the sequence of target operation instructions in the target operation execution plan. This constraint consistency check determines whether the changes in primary equipment status, secondary equipment control logic, and anti-misoperation rules remain consistent during the execution of the target operation instructions. Specifically, according to the operation stage corresponding to the target operation instruction sequence, the equipment operation constraints involved in each stage are obtained, and the constraint conditions corresponding to each equipment operation constraint are extracted, including equipment status requirements, operation sequence relationships, and inter-equipment interlocking conditions.

[0073] The predicted state change process of the equipment corresponding to the target operation execution plan is matched and analyzed with the above constraints to determine whether there are any constraints that fail during the operation. For example, when the target operation instruction involves a change in the state of primary equipment, it is determined whether the corresponding secondary protection logic synchronously meets the control requirements; when the secondary equipment generates a change in control state, it is determined whether the corresponding primary equipment has the execution conditions; when multiple devices have interlocking relationships, it is determined whether the execution order of the target operation instruction satisfies the equipment operation constraints.

[0074] If all device operation constraints are met during the execution of the target operation instruction sequence, it is determined that the primary and secondary devices meet the coordination requirements, and a constraint consistency check result is generated. If any device operation constraint cannot be met, it is determined that there is a coordination anomaly between the primary and secondary devices, and a constraint consistency check result is generated.

[0075] Based on the equipment status verification results and constraint consistency verification results, a prevent-misoperation verification result is generated. Based on the equipment status verification results and constraint consistency verification results, a comprehensive judgment is made on the target operation execution plan, and the final prevent-misoperation verification result is generated.

[0076] Specifically, the device status verification results and constraint consistency verification results are correlated and analyzed. When the device status verification results show that the current status of the target device to be executed and the affected associated devices to be executed meet the operating conditions, and the constraint consistency verification results show that the primary device and the secondary device meet the coordination requirements, it is determined that the target operation execution plan meets the error prevention requirements, and an error prevention verification pass result is generated.

[0077] If either the equipment status verification result or the constraint consistency verification result fails to meet the requirements, it is determined that the target operation execution plan has a risk of error prevention, generating an error prevention verification failure result, and outputting error prevention alarm information based on the corresponding abnormal information. The error prevention alarm information includes the abnormal equipment object, abnormal status information, the triggered equipment operation constraint relationship, and the corresponding conflicting operation instructions, used to guide operators to adjust the target operation execution plan. Through the above verification method, dual verification of the equipment status before operation and the constraints during operation is achieved, improving the safety and reliability of remote operation processes in the centralized control system.

[0078] The virtual error prevention constraints differ from those in the judgment objects and application stages of subsequent error prevention verification steps. Specifically, the virtual error prevention constraints are used for scheme selection during the virtual evolution stage. Their function is to constrain and judge the state change process of different combinations of operating instructions in the virtual operating state space without changing the actual substation operating state. They are primarily used to evaluate the rationality of the operating instruction sequence during theoretical execution.

[0079] Specifically, the virtual anti-misoperation constraints are pre-constructed based on the substation operation rules, equipment operation logic, and historical operation constraint relationships. They are used to limit the range of changes in the predicted state of the equipment during the virtual operation process and to determine whether different operation sequences, operation combinations, and intermediate adjustment measures may lead to equipment state conflicts, constraint failures, or inconsistencies between the logic of primary and secondary equipment. Since this process is based on the analysis of the predicted state of the equipment and the simulated operating environment, its purpose is to select candidate operation execution schemes with lower risks and that meet the theoretical operating requirements from multiple possible operation paths, rather than directly determining whether the actual equipment has the conditions for execution.

[0080] The subsequent error prevention verification steps are used to verify the actual execution of the target operation plan after it has been determined. The judgment is based on the current real-time operating status of the substation and the actual error prevention rules. It is mainly used to confirm whether the operation plan after virtual evolution screening is suitable for the current substation operating environment.

[0081] Specifically, before actual execution, it is necessary to reacquire the real-time operating data of the target equipment and the affected related equipment. Since the operating status of the equipment may be affected by load changes, equipment maintenance, protection status adjustments and other real-time operations, the actual equipment status may differ from the initial status used during virtual evolution. Therefore, the error prevention verification step determines whether the current equipment status meets the execution requirements of the target operation execution plan through real-time equipment status verification and actual equipment operation constraint consistency verification, and further confirms whether the primary equipment and secondary equipment meet the actual collaborative control conditions.

[0082] Virtual error prevention constraints and error prevention verification steps correspond to the scheme generation stage and the execution licensing stage, respectively. The former focuses on optimizing the operation path based on historical rules and predicted states to avoid generating operation schemes that are theoretically risky. The latter focuses on finalizing the generated scheme in conjunction with the current actual operating state to avoid the risk of misoperation during the actual execution of the virtual security scheme due to changes in the real-time operating environment. Although both involve error prevention rule judgment, the judgment objects, data sources, and technical purposes are different. Virtual error prevention constraints cannot replace the error prevention verification steps before actual execution.

[0083] The preset parameters involved in this embodiment mainly include equipment status matching threshold, equipment association extension range threshold, anti-misoperation risk judgment threshold, and virtual anti-misoperation constraint conditions. The above parameters are not set directly using fixed empirical values, but are determined in combination with the substation's historical operating data, equipment operating specifications, and on-site commissioning results.

[0084] Specifically, the following steps are taken: First, historical operation ticket data, equipment remote signaling change data, anti-misoperation interlocking records, and manual review results are collected from the target substation during its normal operation cycle. The data are then categorized according to operation type, equipment type, and operating condition. Next, the patterns of equipment status changes and the occurrence of anti-misoperation events under different operating conditions are statistically analyzed to obtain the initial value range of each parameter. Finally, the parameters are corrected during the on-site trial operation phase to ensure that they can adapt to different substation equipment structures and operating environments.

[0085] The device status matching threshold is used to determine whether the operation object in the operation instruction to be verified belongs to the same device object as the knowledge graph node. This threshold can be determined by the matching results between historical device names, device numbers, and device attributes. The specific method is as follows: select historically confirmed device data as samples, normalize the similarity of device names, consistency of device numbers, consistency of device types, and consistency of installation locations, and calculate the comprehensive matching score between different devices; statistically analyze the score distribution corresponding to the correctly matched samples, and take the lowest score when the correct matching rate reaches the preset requirement as the device status matching threshold.

[0086] The equipment association extension range threshold is used to determine the range of associated equipment affected by the target equipment. This parameter is determined through the impact range of historical operations. Specifically, the changes in remote signaling data before and after the target equipment changes during historical operations are collected, and the equipment that actually undergoes state changes is taken as the set of affected equipment. Based on the association distance, relationship type, and state change probability between the target equipment and other equipment in the substation collaborative knowledge graph, statistics are performed to determine the proportion of equipment state changes at different association levels. When the probability of equipment state changes falls below a preset proportion after the association level continues to expand, the corresponding level is taken as the termination condition for association extension.

[0087] The error prevention risk assessment threshold is used to determine whether there is an error prevention risk in the virtual operation state sequence. This threshold is determined through training with historical error prevention event data and normal operation data. Specifically, the changes in equipment state, the satisfaction of constraints, and the final manual confirmation results during historical operations are used as sample inputs to construct the correspondence between equipment state changes and risk results. The deviation range between the predicted state of the equipment and the equipment operation constraint relationship under normal operation is statistically analyzed, and the maximum deviation value under normal operation is used as the risk assessment reference value. When the state deviation during the virtual operation exceeds this reference value, it is determined that there is an error prevention risk.

[0088] The virtual anti-misoperation constraints are determined based on the existing anti-misoperation rules and operating procedures of the substation, including constraints on equipment operation sequence, equipment status, and coordination constraints between primary and secondary equipment. In the specific determination process, the interlocking logic rules and operation ticket review rules in the substation anti-misoperation system are first obtained. Then, the rules are classified according to equipment type and operation scenario and converted into structured constraints, such as "the circuit breaker must be in the open state before the disconnecting switch is operated" and "the control circuit must be normal before the protection device is put into operation". Finally, through historical operation verification and on-site operation testing, constraints that cannot be applied in practice or are repeated are deleted to form a set of virtual anti-misoperation constraints applicable to the target substation.

[0089] The substation collaborative knowledge graph construction process in this embodiment is based on a graph data model. Specifically, primary equipment, secondary equipment, and anti-misoperation rules are converted into nodes in the graph model, and the electrical connection relationships, control logic relationships, and equipment operation constraint relationships between equipment are converted into relationship edges between nodes.

[0090] For the device entity recognition process, a method based on rule matching and attribute similarity calculation is adopted. First, feature information such as device name, number, type, voltage level and installation location are extracted. Then, the features between different device objects are standardized. Finally, the degree of association between device objects is obtained by feature weighting calculation, and the corresponding node relationship is determined according to the device status matching threshold.

[0091] The operation state evolution path generation process is implemented using a graph traversal-based state propagation algorithm. The specific steps are as follows: First, the target device corresponding to the operation instruction to be verified is taken as the starting node, and the current real-time operating status of the device is loaded. Then, path searching is performed according to the relationship edge types in the substation collaborative knowledge graph, where electrical associations are used to propagate primary device state changes, control associations are used to propagate secondary device logic changes, and device operation constraint relationships are used to propagate changes in anti-misoperation conditions. During each node expansion process, the predicted device state is updated based on the target state change information corresponding to the operation instruction, and state change nodes and constraint-affected nodes are recorded. Finally, an operation state evolution path is formed, consisting of operation trigger nodes, state change nodes, and constraint-affected nodes.

[0092] The mutual exclusion relationship identification process employs a path node association analysis method. Specifically, the operation state evolution paths corresponding to different operation instructions to be verified are compared node by node. First, it is determined whether different paths have state change nodes corresponding to the same device. If so, the direction of the target state change is further compared to see if they are consistent. For example, the device state change is represented as a "current state - target state" transition relationship, and the conflict is determined by comparing the state transition directions. Second, it is analyzed whether different paths propagate to the same affected associated device through relational edges, and the predicted states of the corresponding devices are compared to see if they are consistent. Finally, based on the device operation constraint relationships corresponding to the constraint-affected nodes, it is determined whether different operation paths cause the same constraint condition to be unable to be satisfied simultaneously.

[0093] The virtual operation state space is constructed using a state space simulation method. Specifically, the equipment nodes in the substation collaborative knowledge graph are used as state variable carriers, the real-time state of the equipment is used as the initial state, and the operation instructions are used as state transition conditions. For each sequence of operation instructions to be verified, the states of the target equipment and affected related equipment are updated step by step according to the execution order, generating virtual operation state sequences corresponding to different execution orders. By comparing the consistency between the predicted state of the equipment and the equipment operation constraint relationship in different virtual state sequences, the corresponding error prevention risk state is determined.

[0094] The operation path adjustment process employs a constraint satisfaction optimization method. Specifically, mutually exclusive operation instructions are taken as the objects to be adjusted, and equipment operation constraints are taken as limiting conditions. By changing the operation execution order, inserting intermediate operations to resolve conflicts, or deleting operation instructions that cause conflicts, a virtual operation state sequence is regenerated. Then, the predicted equipment state and the error prevention risk state are recalculated, and the adjustment and verification process is repeated until the target operation execution scheme that satisfies the virtual error prevention constraints is obtained.

[0095] The data involved in this embodiment includes equipment status data, operation instruction data, equipment attribute data, and anti-misoperation rule data. Since the data formats and units differ from different data sources, they need to be standardized before calculation. Specifically, the collected data is first cleaned to remove duplicate, abnormal, and missing key fields. Then, a unified data description format is established based on the data type, converting non-numerical data such as equipment numbers and names into standard encoding forms, and converting status description information into unified status identifiers. For example, different expressions such as "closed," "closed," and "engaged" are uniformly mapped to corresponding status codes.

[0096] For numerical data, such as equipment operating parameters, time intervals, and the number of status changes, normalization is required before performing correlation calculations or risk analysis due to the differences in the units of measurement for different parameters. Specifically, a maximum-minimum normalization method is used to convert data from different ranges to a unified numerical range. During the processing, the maximum and minimum values ​​in historical data are first calculated; then, the proportion of the current data within the historical range is calculated; finally, the transformed data is used for equipment correlation calculations, risk scoring calculations, and status consistency assessments, avoiding deviations in calculation results due to different data units.

[0097] For time series data, since different devices may have different data acquisition periods, time alignment processing is required. Specifically, the unified timestamp of the centralized control system is used as a benchmark to map the data collected by different devices to a unified time window; for time nodes with missing data, adjacent time states are maintained or supplemented based on historical change trends; for data with a high sampling period, state aggregation is performed according to a preset time window to obtain a sequence of device operating states at a unified time scale.

[0098] For equipment relationship data, since primary and secondary equipment are described differently, unified entity mapping processing is required. Specifically, an equipment mapping table is established using equipment number, equipment name, electrical bay, and control object relationship to associate primary and secondary equipment nodes into the same equipment object system. This allows electrical relationships, control relationships, and anti-misoperation constraints to be analyzed uniformly within the same knowledge graph. Through the above data standardization processing, it is ensured that data from different sources, formats, and units can be integrated and analyzed within the same computational model.

[0099] Example 2: A substation centralized control system for error prevention and verification based on device association (see [link]). Figure 1 As shown, it includes: The substation information construction module includes an information acquisition unit and an operation prediction unit. The target substation contains several primary and secondary devices. The information acquisition unit is used to acquire primary device rule data, secondary device rule data, and substation-side error prevention rule data. Based on the primary and secondary device rule data, it constructs a substation collaborative knowledge graph between primary and secondary devices. The operation prediction unit is used to acquire the sequence of operation instructions to be verified. Based on the substation collaborative knowledge graph, it determines the target device and affected associated devices corresponding to the sequence of operation instructions to be verified. Based on the target device and affected associated devices, it generates the corresponding operation state evolution path. The substation operation verification module includes a risk assessment unit and a verification confirmation unit. The risk assessment unit is used to make judgments based on the operation state evolution path: when there are mutually exclusive operation instructions to be verified, virtual evolution is performed based on the substation collaborative knowledge graph and the operation state evolution path to obtain the error prevention risk state; the corresponding target operation execution plan is generated according to the error prevention risk state; otherwise, the target operation execution plan is directly output according to the operation instruction sequence to be verified. The verification confirmation unit is used to acquire real-time substation operation data; error prevention verification is performed based on the target operation execution plan and the real-time substation operation data to obtain the error prevention verification result; when the error prevention verification result is passed, the operation instruction sequence to be executed is output for equipment control; when the error prevention verification result is failed, error prevention alarm information is output for instruction correction.

[0100] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Parts not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A method for verifying the prevention of malfunctions in substation centralized control based on equipment association, characterized in that, Includes the following steps: The target substation contains several primary and secondary devices; acquire primary device rule data, secondary device rule data, and station-end anti-misoperation rule data; construct a substation collaboration knowledge graph between primary and secondary devices based on the primary and secondary device rule data; Obtain the sequence of operation instructions to be verified; determine the target equipment and affected related equipment corresponding to the sequence of operation instructions to be verified based on the substation collaborative knowledge graph; Generate the corresponding operational state evolution path based on the target device and the affected associated devices; Judgment is made based on the operation state evolution path: when there are mutually exclusive operation instructions to be verified, virtual evolution is performed based on the substation collaborative knowledge graph and the operation state evolution path to obtain the error prevention risk state; Based on the risk status of error prevention, a corresponding target operation execution plan is produced; Otherwise, the target operation execution plan is directly output based on the sequence of operation instructions to be checked; Obtain real-time operation data of substations; Based on the target operation execution plan and the real-time operation data of the substation, a misoperation prevention verification is performed to obtain the misoperation prevention verification result. When the misoperation prevention verification result is passed, the sequence of operation instructions to be executed is output for equipment control. When the misoperation prevention verification result is failed, a misoperation prevention alarm message is output to correct the instructions.

2. The substation centralized control anti-misoperation verification method based on equipment association according to claim 1, characterized in that, The specific steps for constructing a substation collaborative knowledge graph between primary and secondary equipment based on primary equipment rule data and secondary equipment rule data include: Based on the primary equipment rule data, extract the physical entity nodes and primary equipment connection attributes corresponding to the primary equipment; based on the secondary equipment rule data, extract the logical entity nodes and secondary signal protection attributes corresponding to the secondary equipment. Electrical relationships are established between physical entity nodes based on primary equipment connection attributes; control relationships are established between logical entity nodes based on secondary signal protection attributes; state constraints corresponding to primary and secondary equipment are extracted based on station-end anti-misoperation rule data; and equipment operation constraints are obtained by associating the state constraints with the corresponding physical and logical entity nodes. A substation collaborative knowledge graph is constructed based on electrical relationships, control relationships, and equipment operation constraints, with the electrical relationships, control relationships, and equipment operation constraints serving as relation edges in the substation collaborative knowledge graph.

3. The substation centralized control anti-misoperation verification method based on equipment association according to claim 2, characterized in that, The specific steps for generating the corresponding operational state evolution path based on the target device and affected associated devices include: Based on the sequence of operation instructions to be verified, extract the operation object, operation type and target status change information corresponding to each operation instruction to be verified, match the operation object with the physical entity nodes and logical entity nodes in the substation collaborative knowledge graph to obtain the corresponding target device; Based on the relationship edges of the target device in the substation collaborative knowledge graph, the devices that have a state influence relationship with the target device are identified as affected associated devices. Based on the target state change information of the target device and the relationship edges corresponding to all affected associated devices, the propagation direction and influence range of the state change of the operation instruction sequence to be verified are determined. Based on the direction of state change propagation, the state change relationship between the target equipment and the affected related equipment is progressively deduced in the substation collaborative knowledge graph to obtain the predicted equipment states of primary and secondary equipment at different operation stages; based on the predicted equipment states, an operation state evolution path is constructed, which includes operation trigger nodes, state change nodes, and constraint influence nodes.

4. The substation centralized control anti-misoperation verification method based on equipment association according to claim 3, characterized in that, The specific steps for determining the operational state evolution path include: Correlation analysis was performed on the operation state evolution paths corresponding to different operation instructions to be verified to determine the set of associated nodes that contain the same state change nodes or constraint influence nodes between different operation state evolution paths. Identifying mutual exclusion relationships based on a set of associated nodes specifically includes: Determine whether the state change nodes corresponding to different operation instructions to be verified act on the same device, and whether there are opposite state directions or conflicting device state conditions in the target state change information; if so, determine that there is a mutual exclusion relationship between the device states of the corresponding operation instructions to be verified; at the same time, determine whether the state change nodes corresponding to different operation instructions to be verified are associated with the same affected associated device, and whether there is a conflict in the predicted state of the corresponding device; if so, determine that there is a mutual exclusion relationship between the state propagation of the corresponding operation instructions to be verified. Determine whether the constraint-affected nodes corresponding to different operation instructions to be verified trigger the device operation constraint relationship of the same device, and whether there is inconsistency in the predicted state of the device within the scope of the corresponding state change influence; if so, determine that there is a mutual exclusion relationship between the corresponding operation instructions to be verified. Based on the mutual exclusion relationships of equipment states, constraints, and state propagation, determine the set of operation instructions to be checked that have mutual exclusion relationships.

5. The substation centralized control anti-misoperation verification method based on equipment association according to claim 4, characterized in that, The specific steps for virtual evolution based on substation collaborative knowledge graph and operation state evolution path include: A virtual operation state space is constructed based on the substation collaborative knowledge graph; the predicted state of the target equipment and the affected related equipment is progressively updated according to the execution order of different operation instructions to be verified, resulting in several virtual operation state sequences; For any virtual operation state sequence, based on the state change nodes and constraint influence nodes in the operation state evolution path, the consistency between the predicted state of each device and the operation constraint relationship of the device is detected, and the corresponding error prevention risk state is determined. Based on the error prevention risk status, the paths of mutually exclusive operation instructions to be verified are adjusted to obtain an adjusted virtual operation state sequence; wherein, the path adjustment includes at least one of adjusting the operation execution order, adding intermediate operations to resolve the mutual exclusion state, and deleting operation instructions that cause state conflicts. Based on the error prevention risk status corresponding to the adjusted virtual operation state sequence, a matching analysis is performed with the virtual error prevention constraint conditions to screen out the virtual operation state sequences that meet the virtual error prevention constraint conditions, and the screened virtual operation state sequences are determined as the target operation execution plan.

6. The substation centralized control anti-misoperation verification method based on equipment association according to claim 5, characterized in that, The specific steps for error prevention verification based on the target operation execution plan and real-time substation operation data include: Obtain the sequence of target operation instructions corresponding to the target operation execution plan, and determine the target equipment to be executed, the affected related equipment to be executed, and the operation constraint relationship of the equipment to be executed for each target operation instruction based on the substation collaborative knowledge graph; Based on real-time substation operation data, the actual operating status of the target equipment and the affected related equipment to be executed is verified to determine whether the actual operating status meets the operating conditions corresponding to the target operation execution plan, and the equipment status verification result is obtained; according to the operation constraint relationship of the equipment to be executed, the constraint consistency verification of the target operation instruction sequence in the target operation execution plan is performed to determine whether the primary equipment and secondary equipment meet the coordination requirements, and the constraint consistency verification result is obtained. Based on the equipment status verification results and constraint consistency verification results, generate error prevention verification results.

7. A substation centralized control system for error prevention and verification based on equipment association, characterized in that, The system employs a substation centralized control anti-misoperation verification method based on device association as described in any one of claims 1-6, comprising: The substation information construction module includes an information acquisition unit and an operation prediction unit. The target substation contains several primary and secondary devices. The information acquisition unit is used to acquire primary device rule data, secondary device rule data, and substation-side error prevention rule data. Based on the primary and secondary device rule data, it constructs a substation collaborative knowledge graph between primary and secondary devices. The operation prediction unit is used to acquire the sequence of operation instructions to be verified. Based on the substation collaborative knowledge graph, it determines the target device and affected associated devices corresponding to the sequence of operation instructions to be verified. Based on the target device and affected associated devices, it generates the corresponding operation state evolution path. The substation operation verification module includes a risk assessment unit and a verification confirmation unit. The risk assessment unit is used to make judgments based on the operation state evolution path: when there are mutually exclusive operation instructions to be verified, virtual evolution is performed based on the substation collaborative knowledge graph and the operation state evolution path to obtain the error prevention risk state; the corresponding target operation execution plan is generated according to the error prevention risk state; otherwise, the target operation execution plan is directly output according to the operation instruction sequence to be verified. The verification confirmation unit is used to acquire real-time substation operation data; error prevention verification is performed based on the target operation execution plan and the real-time substation operation data to obtain the error prevention verification result; when the error prevention verification result is passed, the operation instruction sequence to be executed is output for equipment control; when the error prevention verification result is failed, error prevention alarm information is output for instruction correction.

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