Power supply first-aid repair area scheduling system based on big data coupling

Abstract

The invention relates to the technical field of power maintenance, and discloses a big data coupling-based power supply first-aid repair area scheduling system, which encodes a physical rule into an immutable memory unit, so that model correction always follows a power grid operation common sense; the implicit deviation is controlled at the source through a dominant monitoring and blocking mechanism of an error propagation path; man-machine bidirectional verification and efficiency autonomous evolution are introduced, so that the system has a self-perfection capability. According to the overall improvement, normal form transformation from passive response to active optimization is achieved, the real-time performance and accuracy of scheduling decisions are synchronously improved, and the resource utilization rate and the execution efficiency are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of power maintenance technology, specifically to a power supply emergency repair regional dispatch system based on big data coupling. Background Technology

[0002] Against the backdrop of intelligent upgrading in the energy and power industry, the distribution network emergency repair and dispatch system relies on digital twin technology to construct a virtual mirror of the power grid. This system transforms on-site operational behaviors into standardized event sequences and uses graph computation to perform rapid local corrections to the topology surrounding the fault, attempting to establish a dynamic balance between response speed and computational accuracy. This aims to achieve a minute-level closed-loop dispatch decision-making process and improve fault handling efficiency.

[0003] However, this incremental correction mechanism has structural flaws. In pursuit of real-time performance, the system weakens global consistency verification, causing uncorrected simulation data errors to subtly propagate through grid connection relationships. Simultaneously, frequent local updates make the model overly reliant on recent event characteristics, gradually losing its ability to remember historical operating patterns. This results in the continuous accumulation of implicit deviations between the virtual and actual power grids, affecting the accuracy of the analysis.

[0004] Therefore, we propose a power supply emergency repair area dispatch system based on big data coupling to solve the problems mentioned above. Summary of the Invention

[0005] The purpose of this invention is to provide a power supply emergency repair regional dispatch system based on big data coupling, in order to solve the problem mentioned in the background art where the pursuit of real-time performance weakens global consistency verification, leading to the uncorrected spread of simulation data errors through power grid connection relationships. Simultaneously, frequent local updates cause the model to over-rely on recent event characteristics, gradually losing its ability to remember historical operating patterns, resulting in the continuous accumulation of implicit deviations between the virtual power grid and the actual power grid, affecting the accuracy of judgments.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a power supply emergency repair area dispatching system based on big data coupling, comprising a multi-source data fusion module, a global constraint memory module, an error source tracing and suppression module, a human-machine collaborative verification module, and a performance evolution analysis module. The multi-source data fusion module is used to collect real-time data on power grid operation measurements, meteorological environment, user repair reports and work trajectory. It performs spatiotemporal alignment processing and quality classification labeling on the acquired heterogeneous information to generate raw data streams with confidence labels, providing data support for subsequent modules. The global constraint memory module is used to encode the physical operation rules of the power grid into memory units of a graph neural network. The memory units are stored separately from the dynamic weights. During the local correction process, the update range of the constraint nodes is forced to prevent the model from forgetting the historical operation rules due to frequent iterations, and to ensure that the correction results conform to physical common sense. The error source tracing and suppression module is used to construct an error propagation path diagram and mark the error source and propagation trajectory for each pseudo-measurement node. When a local correction is triggered, the error redistribution magnitude is calculated. When the cumulative error of a node exceeds a preset threshold, the node is frozen and a directional recalculation is started to block the spread of error to the entire network. The human-machine collaborative verification module is used to embed intelligent prompting function into the emergency repair terminal. When there is a conflict between the topology status and the operation semantics, it pushes a list of nodes to be confirmed, receives and processes the results of manual review, and injects the confirmation results as high-weight events into the system to achieve complementary advantages of human and machine decision-making. The performance evolution analysis module is used to monitor the operating performance of each module and statistically analyze the accuracy of error tracing. By analyzing the high-frequency error propagation path, it automatically optimizes the pseudo-measurement weights and memory unit sensitivity parameters, driving the system to continuously improve itself.

[0007] Preferably, the global constraint memory module includes a rule solidification unit and a constraint verification unit; The rule solidification unit is used to store the power grid topology connection relationship, equipment current tolerance threshold and power flow balance law in a non-trainable form, and physically isolates it from the model dynamic weight parameters to form an independent rule memory library. The constraint verification unit is used to call the corresponding constraint conditions from the rule memory when receiving the local correction instruction triggered by the error source suppression module, to implement boundary restrictions on the node state update magnitude, and to force rollback and trigger an alarm when the correction result violates the physical rules.

[0008] Preferably, the rule solidification unit includes a rule compilation subunit and an isolation encapsulation subunit; The rule compilation subunit compiles the power grid topology connection relationship, equipment current tolerance threshold and power flow balance law into non-trainable memory parameters, generates a standard format rule description file, and realizes the creative transformation of physical rules into machine execution code; The isolation and encapsulation subunit separates and deploys the rule description file and the model dynamic weight parameters in the physical storage layer, encapsulates them into an independent rule memory, and establishes a standard interface for the constraint verification unit to call, thus forming a creative isolation mechanism between rule solidification and dynamic learning.

[0009] Preferably, the constraint verification unit includes an intelligent calling subunit and a forced constraint subunit; The intelligent calling subunit is used to intelligently match the constraints related to the current correction node from the rule memory and dynamically generate boundary limit parameters when receiving the local correction instruction triggered by the error source tracing and suppression module; The mandatory constraint subunit is used to compare the actual updated value of the node state with the boundary constraint parameters. When it is determined that the update result violates the physical rules, a mandatory rollback operation is performed, and an alarm signal is sent to the error source tracing and suppression module and the scheduling decision output module.

[0010] Preferably, the error source tracing and suppression module includes a path labeling unit and a suppression control unit; The path labeling unit constructs an error propagation path graph, labels the error sources and propagation trajectories of pseudo-measurement nodes, generates a weighted error association network, and records the propagation path and intensity distribution of errors in the topology. The suppression control unit calculates the error redistribution magnitude when local correction is triggered, monitors the cumulative error of nodes, and performs node freezing and directional recalculation when the error exceeds a preset threshold. It sends correction instructions to the global constraint memory module and sends blocking alarm signals to the scheduling decision output module, interrupts the current scheduling process, and triggers a manual review mechanism.

[0011] Preferably, the path labeling unit includes a path construction subunit and a weight labeling subunit; The path construction subunit constructs an error propagation path diagram based on the power grid topology, marks the error sources and propagation trajectories of pseudo-measurement nodes, and generates a basic error association network. The weighted labeling sub-unit introduces measurement confidence and correction frequency factor into the basic error correlation network to generate a weighted error correlation network, recording the propagation path and intensity distribution of error in the topology.

[0012] Preferably, the suppression control unit includes an error monitoring subunit and a suppression execution subunit; The error monitoring subunit is connected to the path labeling unit, receives the error association network, calculates the error redistribution magnitude when local correction is triggered, identifies nodes whose accumulated error exceeds a preset threshold, generates and outputs a trigger signal containing node identifier and suppression priority. The suppression execution subunit connects with the error monitoring subunit, receives the suppression trigger signal, performs freeze and directional recalculation operations on the identified node, sends a correction instruction to the global constraint memory module and sends a blocking alarm signal to the scheduling decision output module, interrupts the current scheduling process and triggers the manual review mechanism.

[0013] Preferably, the human-machine collaborative verification module includes a conflict alert unit and a feedback injection unit; The conflict alert unit is connected to the global constraint memory module and the scheduling decision output module. It receives topology status data and operation instruction data. When it detects that the node status and operation semantics are inconsistent, it generates a list of nodes to be confirmed and pushes it to the emergency repair terminal. The feedback injection unit is connected to the conflict prompting unit and the error source tracing and suppression module. It receives the manual review results returned by the emergency repair terminal and converts the reviewed and confirmed events into high-weight correction parameters, which are then injected into the error source tracing and suppression module.

[0014] Preferably, the conflict alerting unit includes a scene perception subunit and an intelligent hierarchical subunit; The scene perception subunit is configured to obtain power grid constraint rules from the global constraint memory module, obtain current operation instructions from the scheduling decision output module, integrate multi-dimensional information such as meteorological environment, equipment type and personnel location to generate a set of operation scene feature parameters, and identify high-risk nodes and generate a set of nodes to be confirmed when a conflict between topology state and operation semantics is detected. The intelligent hierarchical subunit is configured to receive the set of nodes to be confirmed from the scene perception subunit, call the constraint rules to classify the risk level of conflicting nodes, optimize the push content and frequency strategy, generate a hierarchical confirmation list and send it to the emergency repair terminal, and link the feedback injection unit to realize the human-machine verification closed loop.

[0015] Preferably, the feedback injection unit includes an intelligent evaluation subunit and a knowledge fusion subunit; The intelligent evaluation subunit receives the manual review results transmitted from the conflict alert unit and the emergency repair terminal, evaluates the reliability level and calculates the confidence weight, and outputs the review items with quality labels. The recognition fusion subunit receives review events with quality labels, weights and fuses them with existing system parameters to generate correction parameters, injects them into the error source tracing and suppression module, and links with the global constraint memory module.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This system encodes physical rules into immutable memory units, ensuring that model corrections always adhere to common-sense power grid operation principles. Through explicit monitoring and blocking mechanisms of error propagation paths, implicit deviations are controlled at their source. The introduction of human-machine bidirectional verification and performance self-evolution enables the system to self-improve. Overall improvements achieve a paradigm shift from passive response to proactive optimization, simultaneously enhancing the real-time performance and accuracy of scheduling decisions, and significantly improving resource utilization and execution efficiency.

[0017] 2. Through the synergistic effect of the rule solidification unit and the constraint verification unit, the global constraint memory module achieves complete closed-loop management from rule storage to rule execution. Compared with the common shortcomings of current technologies, such as the mixing of physical rules and model parameters and the lack of dynamic verification mechanisms, this module brings three substantial improvements. First, the physical isolation storage mechanism of rules and weights ensures the permanence and immutability of physical rules, solving the problem of rules being easily overwritten in traditional systems. Second, the real-time boundary limit and forced rollback mechanism establish a security barrier, making up for the lack of effective interception of violation corrections in existing technologies. Finally, the linkage of the two units forms an organic whole of rule solidification storage and dynamic verification execution, enabling the system to have both flexible learning capabilities and maintain the rigidity of physical constraints, achieving a dual improvement in model adaptation and operational security, and significantly enhancing the decision-making credibility and long-term operational stability of the distribution network emergency repair and dispatch system under complex operating conditions. Attached Figure Description

[0018] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figure 1 A power supply emergency repair area dispatching system based on big data coupling includes a multi-source data fusion module, a global constraint memory module, an error source tracing and suppression module, a human-machine collaborative verification module, and a performance evolution analysis module. The multi-source data fusion module is used to collect real-time data on power grid operation measurements, meteorological environment, user repair reports and work trajectory. It performs spatiotemporal alignment processing and quality classification labeling on the acquired heterogeneous information to generate raw data streams with confidence labels, providing data support for subsequent modules. The global constraint memory module is used to encode the physical operation rules of the power grid into memory units of the graph neural network. The memory units are stored separately from the dynamic weights. During the local correction process, the update range of the constraint nodes is forced to prevent the model from forgetting the historical operation rules due to frequent iterations, and to ensure that the correction results conform to physical common sense. The error source tracing and suppression module is used to construct an error propagation path diagram and mark the error source and propagation trajectory for each pseudo-measurement node. When a local correction is triggered, the error redistribution magnitude is calculated. When the cumulative error of a node exceeds a preset threshold, the node is frozen and a directional recalculation is started to block the spread of error to the entire network. The human-machine collaborative verification module is used to embed intelligent prompting function into the emergency repair terminal. When there is a conflict between the topology status and the operation semantics, it pushes a list of nodes to be confirmed, receives and processes the results of manual review, and injects the confirmation results as high-weight events into the system to achieve complementary advantages of human and machine decision-making. The performance evolution analysis module is used to monitor the operating performance of each module and statistically analyze the accuracy of error tracing. By analyzing the high-frequency error propagation path, it automatically optimizes the pseudo-measurement weights and memory unit sensitivity parameters, driving the system to continuously improve itself.

[0021] In this embodiment, the multi-source data fusion module completes the real-time aggregation and standardization processing of heterogeneous data from multiple sources, including power grid measurements, meteorology, repair reports, and work trajectories. Through spatiotemporal alignment and quality grading labeling mechanisms, it effectively solves the problems of data silos and inconsistent quality in traditional systems, providing a reliable data foundation for subsequent analysis. Its purpose is to build a unified data foundation, enabling all modules to work collaboratively within the same data context and avoiding misjudgments caused by data inconsistencies. The final effect is a significant improvement in data availability and reliability, reducing the sources of error caused by data quality issues, and laying a solid foundation for the accurate operation of the entire system.

[0022] The global constraint memory module encodes physical rules such as Kirchhoff's laws and equipment current-capacity thresholds into non-trainable memory units, storing them separately from the model's dynamic weights. This design creatively combines physical common sense with machine learning models, solving the technical challenge of traditional models gradually forgetting historical patterns during frequent local corrections. Its purpose is to ensure that every topology correction strictly adheres to the fundamental principles of power grid operation, preventing the model from deviating from physical reality due to overfitting recent events. The final result is a significantly enhanced long-term system stability, a substantial increase in the global consistency verification pass rate, and effective prevention of the accumulation of implicit deviations between the virtual and actual power grids.

[0023] The error source tracing and suppression module has completed the explicit construction and dynamic blocking of error propagation paths. By labeling the error sources and propagation trajectories of pseudo-measurement nodes and initiating node freezing and directional recalculation mechanisms when errors exceed limits, it effectively solves the global distortion problem caused by the implicit propagation of errors. Its purpose is to transform the subtle error flows that are difficult to detect in traditional systems into a monitorable and interventionable explicit process, achieving precise control. The final effect is the successful blocking of error propagation throughout the network, keeping the deviations of key indicators such as line loss calculation within a reasonable range, substantially enhancing the reliability of scheduling path planning, and avoiding resource waste caused by topology errors.

[0024] The human-machine collaborative verification module embeds intelligent prompting functionality into the emergency repair terminal, achieving a complementary integration of human and machine decision-making advantages. When the system detects a conflict between the topology status and operational semantics, it proactively pushes a list of nodes to be confirmed to on-site personnel and injects the results of manual verification as high-weight events into the system. Its purpose is to break through the limitations of traditional one-way command issuance and fully utilize the on-site experience of frontline personnel to compensate for algorithmic blind spots. The final results show a significant reduction in the misjudgment rate of critical nodes, a marked improvement in the executability of scheduling commands, a reduction in personnel cognitive load, and an increase in the overall reliability and efficiency of system decision-making.

[0025] The performance evolution analysis module undertakes the task of continuous monitoring and autonomous optimization of system operating performance. By statistically analyzing key indicators such as error source tracing accuracy and manual review effectiveness, and analyzing high-frequency error propagation paths, it achieves automatic tuning of pseudo-measurement weights and memory unit sensitivity. Its purpose is to build the system's self-evolution capability, changing the passive situation of traditional systems with fixed parameters that cannot adapt to changes in the operating environment. The ultimate effect drives the entire system to form a positive cycle, enabling the scheduling strategy to continuously optimize with data accumulation, achieving a leap from static configuration to dynamic evolution, and continuously improving long-term operational accuracy.

[0026] The scheduling decision output module completes the final task of transforming the optimized topology state into executable scheduling instructions. By generating structured work orders containing optimal routes, material configurations, and safety verification information, and distributing them to the emergency repair execution unit in real time, it achieves seamless integration of decision-making and action. Its purpose is to solve the problem of traditional system scheduling instructions being coarse and lacking fine-grained guidance, ensuring that optimization results are accurately implemented. The final effect is a significant reduction in the idle rate of emergency repair vehicles, a substantial shortening of the average fault location and arrival time, and a comprehensive improvement in the accuracy and execution efficiency of scheduling instructions, forming a complete closed loop from analysis to execution.

[0027] This system encodes physical rules into immutable memory units, ensuring that model corrections always adhere to common-sense power grid operation principles. Through explicit monitoring and blocking mechanisms of error propagation paths, implicit deviations are controlled at their source. The introduction of human-machine bidirectional verification and performance self-evolution enables the system to self-improve. This overall improvement represents a paradigm shift from passive response to proactive optimization, simultaneously enhancing the real-time performance and accuracy of scheduling decisions, significantly improving resource utilization and execution efficiency, and providing a sustainable technological path for intelligent emergency repair of distribution networks.

[0028] Example 2: Please refer to Figure 1 The global constraint memory module includes a rule solidification unit and a constraint verification unit; The rule solidification unit is used to store the power grid topology connection relationship, equipment current tolerance threshold and power flow balance law in a non-trainable form, and physically isolates it from the model dynamic weight parameters to form an independent rule memory library. The constraint verification unit is used to call the corresponding constraint conditions from the rule memory when receiving the local correction instruction triggered by the error source suppression module, to implement boundary restrictions on the node state update magnitude, and to force rollback and trigger an alarm when the correction result violates the physical rules.

[0029] In this embodiment, the rule solidification unit transforms grid topology connections, equipment current tolerance thresholds, and power flow balance rules into non-trainable memory parameters, which are then stored in physical isolation from the model's dynamic weights. This achieves a creative decoupling of physical rules from the machine learning model. This unit addresses the technical shortcomings of traditional systems where physical rules are easily overwritten by model iterations and historical constraints gradually weaken. Its purpose is to construct an immutable rule memory library, ensuring that grid operation common sense remains constant during continuous learning. The final result is a significantly enhanced long-term system stability, a substantial increase in the global consistency verification pass rate, and effective prevention of the accumulation of implicit deviations between the virtual and actual grids due to rule forgetting, providing a reliable physical benchmark for precise scheduling.

[0030] The constraint verification unit, upon receiving a local correction command triggered by the error tracing and suppression module, invokes corresponding constraints from the rule memory to impose boundary limits on the node state update magnitude, thus completing the real-time control of model corrections by physical rules. This unit addresses the safety risks of local corrections potentially exceeding equipment operating limits and violating power flow balance principles. Its purpose is to establish a dynamic verification mechanism to ensure that every topology correction strictly adheres to the basic principles of power grid operation. The final effect is that when the correction result violates physical rules, it can force a rollback and trigger an alarm, preventing unauthorized operations from entering the execution phase. This significantly improves the safety and reliability of system decision-making and reduces the risk of decision errors caused by model overfitting.

[0031] Through the synergistic effect of the rule solidification unit and the constraint verification unit, the global constraint memory module achieves complete closed-loop management from rule storage to rule execution. Compared with the common shortcomings of current technologies, such as the mixing of physical rules and model parameters and the lack of dynamic verification mechanisms, this module brings three substantial improvements. First, the physical isolation storage mechanism of rules and weights ensures the permanence and immutability of physical rules, solving the problem of rules being easily overwritten in traditional systems. Second, the real-time boundary limit and forced rollback mechanism establish a security barrier, making up for the lack of effective interception of violation corrections in existing technologies. Finally, the linkage of the two units forms an organic whole of rule solidification storage and dynamic verification execution, enabling the system to have both flexible learning capabilities and maintain the rigidity of physical constraints, achieving a dual improvement in model adaptation and operational security, and significantly enhancing the decision-making credibility and long-term operational stability of the distribution network emergency repair and dispatch system under complex operating conditions.

[0032] Example 3: Please refer to Figure 1 The rule solidification unit includes a rule compilation subunit and an isolation encapsulation subunit; The rule compilation subunit compiles the power grid topology connection relationship, equipment current tolerance threshold and power flow balance law into non-trainable memory parameters, generates a standard format rule description file, and realizes the creative transformation of physical rules into machine execution code; The isolation and encapsulation subunit separates and deploys the rule description file and the model's dynamic weight parameters in the physical storage layer, encapsulates them into an independent rule memory, and establishes a standard interface for the constraint verification unit to call, thus forming a creative isolation mechanism between rule solidification and dynamic learning.

[0033] In this embodiment, the rule compilation subunit compiles the power grid topology connections, equipment current tolerance thresholds, and power flow balance laws into non-trainable memory parameters, thus creatively transforming physical rules from natural language descriptions to machine-executable code. This subunit addresses the technical shortcomings of traditional systems where physical rules and model parameters are stored in a mixed manner and are easily overwritten by iterative updates. Its purpose is to construct standardized and permanent rule description files, giving physical knowledge a structured form that can be directly invoked by computers. The final effect is that the rule transformation process is reproducible and verifiable, significantly improving the standardization and stability of rule storage, providing accurate and unambiguous execution basis for subsequent constraint verification, and fundamentally avoiding the risk of misjudgment caused by ambiguous rule descriptions.

[0034] The isolated encapsulation subunit achieves a creative isolation between the rule memory and the model learning space by separating the rule description file and the model's dynamic weight parameters in the physical storage layer. This subunit solves the structural problem of coupled storage and mutual interference between rules and weights in traditional architectures. Its purpose is to establish an independent rule memory with a standard calling interface, ensuring that physical rules remain constant during model training and correction. The final result is a rule memory with high security and maintainability. The constraint verification unit can efficiently call rules through the standard interface without touching the model parameters, achieving decoupling between rule solidification and dynamic learning, and significantly reducing system maintenance complexity and the risk of accidental rule tampering.

[0035] Through the synergy of the rule compilation subunit and the isolation encapsulation subunit, the rule solidification unit achieves complete closed-loop management of physical rules from compilation to isolated storage. Compared to the common shortcomings of current technologies where physical rules often exist in configuration files or hard-coded form, are mixed with model parameters, and lack standardized interfaces, this unit brings three substantial improvements. First, the compilation and conversion mechanism transforms abstract physical laws into standardized parameters that can be executed by machines, solving the problems of ambiguous rule descriptions and difficulty in automatic parsing, and improving the accuracy and efficiency of rule application. Second, the physical isolation storage mechanism ensures the independence and security of the rule memory, avoiding accidental overwriting of rules by model iteration and enhancing the long-term stability of the system. Finally, the establishment of standard interfaces enables seamless integration between rule invocation and model training, allowing the constraint verification unit to efficiently obtain rules without worrying about the underlying storage details. This achieves a dual improvement in rule management standardization and system architecture clarity, significantly enhancing the reliability and technical maintainability of rule execution in the power distribution network emergency repair and dispatch system under complex operating conditions.

[0036] Example 4: Please refer to Figure 1 The constraint verification unit includes an intelligent calling subunit and a mandatory constraint subunit; The intelligent calling subunit is used to intelligently match the constraints related to the current correction node from the rule memory and dynamically generate boundary limit parameters when receiving the local correction instruction triggered by the error source tracing and suppression module; The mandatory constraint subunit is used to compare the actual updated value of the node state with the boundary constraint parameters. When it is determined that the update result violates the physical rules, a mandatory rollback operation is performed, and an alarm signal is sent to the error source tracing and suppression module and the scheduling decision output module.

[0037] In this embodiment, the intelligent invocation subunit establishes an intelligent matching mechanism between constraints and correction nodes, thereby dynamically extracting relevant constraints from the rule memory and generating boundary limit parameters. This subunit addresses the technical shortcomings of rigid constraint invocation and fixed boundary parameters in traditional methods. Its purpose is to achieve scenario-based adaptive configuration of constraints, avoiding over- or under-constraint caused by a "one-size-fits-all" approach. The final result is a significant improvement in the flexibility and accuracy of constraint invocation. The system can fully leverage data value while ensuring the safety boundaries of physical rules, significantly reducing the probability of misjudgments caused by improper constraints, and providing a scientific and reasonable basis for subsequent mandatory constraint subunits.

[0038] The mandatory constraint subunit, through rigid comparison and immediate interception mechanisms, performs real-time verification of the actual updated values ​​of node states against boundary limit parameters and forces a rollback in case of violations. This subunit addresses the security risks of weak constraints and uninterrupted violation interception in traditional systems. Its purpose is to establish an insurmountable security barrier, ensuring that any topology modification must strictly adhere to common sense regarding power grid operation. The ultimate effect is a substantial guarantee of the security of system decisions, effectively preventing illegal scheduling caused by model bias or data anomalies, significantly improving the credibility and reliability of scheduling commands, and providing a timely and accurate entry point for manual intervention through the alarm mechanism.

[0039] Through the collaborative action of the intelligent invocation subunit and the mandatory constraint subunit, the constraint verification unit achieves a creative combination of flexible constraint recognition and rigid execution. Compared to the common shortcomings of current technologies, such as rigid constraint invocation, fixed boundary parameters, and lack of effective interception of violation corrections, this unit brings three substantial improvements. First, the intelligent invocation mechanism enables dynamic matching of constraints and adaptive generation of boundary parameters, resolving the contradiction between constraints and scenarios in traditional methods, making the verification process both accurate and flexible. Second, the mandatory constraint mechanism establishes a rigid interception barrier against violations, compensating for the lack of effective control over corrections that violate physical rules in existing technologies, fundamentally eliminating safety hazards. Finally, the linkage between the two subunits forms a complete closed loop from constraint extraction, boundary generation, real-time comparison to violation handling, realizing the automation and intelligence of constraint verification, significantly reducing the frequency of manual intervention and system maintenance costs. Overall, this unit enables the distribution network emergency repair and dispatch system to maintain learning flexibility while possessing a strict safety baseline, achieving a dual improvement in model adaptation and operational safety, and significantly enhancing the system's decision-making credibility and long-term operational stability under complex operating conditions.

[0040] Example 5: Please refer to Figure 1 The error source tracing and suppression module includes a path labeling unit and a suppression control unit; The path labeling unit constructs an error propagation path graph, labels the error sources and propagation trajectories of pseudo-measurement nodes, generates a weighted error association network, and records the propagation path and intensity distribution of errors in the topology. The suppression control unit calculates the error redistribution magnitude when local correction is triggered, monitors the cumulative error of nodes, and performs node freezing and directional recalculation when the error exceeds a preset threshold. It sends correction instructions to the global constraint memory module and sends blocking alarm signals to the scheduling decision output module, interrupts the current scheduling process, and triggers a manual review mechanism.

[0041] In this embodiment, the path labeling unit constructs an error propagation path diagram, labeling the error sources and propagation trajectories of pseudo-measurement nodes, thus explicitly representing the error propagation process. This unit addresses the technical shortcomings of traditional systems where error propagation paths are invisible and untraceable. Its purpose is to transform implicit error propagation into a quantifiable and traceable correlation network, making the intensity and scope of error propagation readily apparent. The final result is that the system can accurately identify error sources and propagation paths, providing clear targets for subsequent suppression and control, significantly improving the accuracy and timeliness of error localization, and avoiding blind corrections caused by unclear error sources.

[0042] The suppression and control unit calculates the error redistribution magnitude when local corrections are triggered, monitors the accumulated error at nodes, and performs node freezing and targeted recalculation when the error exceeds a preset threshold, thus dynamically blocking error propagation. This unit overcomes the technical limitations of traditional methods that rely solely on post-event processing to prevent uncontrolled error accumulation. Its purpose is to establish a real-time intervention mechanism to proactively curb errors before they cause global impact. The final effect is that the system can promptly interrupt abnormal correction processes, preventing the spread of errors across the entire network, while simultaneously triggering a manual review mechanism to ensure thorough problem investigation, significantly improving the system's security and reliability.

[0043] Through the synergistic action of the path labeling unit and the suppression control unit, the error source tracing and suppression module achieves complete closed-loop management from error source tracing to suppression execution. Compared to the common shortcomings of current technologies, such as invisible error propagation, delayed suppression measures, and lack of proactive intervention capabilities, this module brings three substantial improvements. First, the path labeling mechanism visualizes the error propagation process, solving the blind spot of traditional systems being completely unaware of error propagation paths, and making suppression measures clearly targeted. Second, the dynamic monitoring and threshold triggering mechanism automates and enables real-time error suppression, compensating for the lag in post-event handling of existing technologies and moving the risk control point forward. Finally, the linkage between the two sub-units forms an organic whole of source tracing analysis and suppression execution, achieving full automation of error detection, assessment, blocking, and alarm, significantly reducing the burden of manual investigation and system operation risks. Overall, this module enables the distribution network emergency repair and dispatch system to have error self-awareness and proactive defense capabilities, achieving a dual improvement in topology correction accuracy and operational safety, and significantly enhancing the system's anti-interference capability and decision-making reliability under complex operating conditions.

[0044] Example 6: Please refer to Figure 1 The path labeling unit includes a path construction subunit and a weight labeling subunit; The path construction subunit constructs an error propagation path diagram based on the power grid topology, marks the error sources and propagation trajectories of pseudo-measurement nodes, and generates a basic error association network. The weighted labeling sub-unit introduces measurement confidence and correction frequency factor into the basic error correlation network to generate a weighted error correlation network, recording the propagation path and intensity distribution of error in the topology.

[0045] In this embodiment, the path construction subunit constructs an error propagation path diagram based on the power grid topology, marking the error source and propagation trajectory for pseudo-measurement nodes, thus completing the explicit representation of the error propagation link. This subunit addresses the technical shortcomings of traditional systems where error propagation paths are invisible and untraceable. Its purpose is to transform the implicit error diffusion process into a structured, traceable network, enabling the system to clearly grasp the complete transmission chain of error from the source node to the target node. The final effect is that the system can accurately identify the error source and diffusion range, providing a clear target for subsequent suppression and control, significantly improving the accuracy and timeliness of error location, and avoiding blind corrections and resource waste caused by unclear error paths.

[0046] The weighted labeling subunit quantifies the assessment of error propagation intensity by introducing measurement confidence and correction frequency factors into the basic error correlation network. This subunit overcomes the limitations of traditional methods, which cannot differentiate the impact of errors along different paths or identify key propagation channels. Its aim is to establish a differentiated assessment mechanism for error propagation, enabling high-risk propagation paths to be prioritized for identification and control. The final result is that the system can accurately identify the main channels and weak points of error spread based on weighted labels, achieving optimized allocation of suppression resources, significantly improving the targeting and efficiency of error control, and avoiding the low control effectiveness caused by applying equal effort to all aspects.

[0047] By leveraging the synergistic effect of the path construction subunit and the weight labeling subunit, the path labeling unit achieves a progressive analysis of error propagation, moving from explicit path identification to quantitative intensity analysis. Compared to the common shortcomings of current technologies, such as invisible error propagation, inability to assess the degree of impact, and lack of targeted suppression measures, this unit brings three substantial improvements. First, the path construction mechanism transforms the abstract error propagation process into a concrete topological network, solving the blind spot of traditional systems' lack of knowledge about error propagation paths and enabling targeted suppression measures. Second, the weight labeling mechanism achieves quantifiable assessment of propagation intensity through confidence level and correction frequency factor, compensating for the inability of existing technologies to identify key propagation channels and making resource allocation more precise and efficient. Finally, the two subunits work together to form a complete analysis chain from path discovery and intensity assessment to priority ranking, achieving full visualization and differentiated control of error propagation, significantly reducing the burden of manual investigation and system operation risks. Overall, this unit enables the distribution network emergency repair and dispatching system to possess error self-awareness and intelligent assessment capabilities, achieving a dual improvement in topology correction accuracy and operational safety, and significantly enhancing the system's anti-interference capability and decision-making credibility under complex operating conditions.

[0048] Example 7: Please refer to Figure 1 The suppression control unit includes an error monitoring subunit and a suppression execution subunit; The error monitoring subunit is connected to the path labeling unit, receives the error association network, calculates the error redistribution magnitude when local correction is triggered, identifies nodes whose accumulated error exceeds a preset threshold, generates and outputs a trigger signal containing node identifier and suppression priority. The suppression execution subunit connects with the error monitoring subunit, receives the suppression trigger signal, performs freeze and directional recalculation operations on the identified node, sends a correction instruction to the global constraint memory module and sends a blocking alarm signal to the scheduling decision output module, interrupts the current scheduling process and triggers the manual review mechanism.

[0049] In this embodiment, the error monitoring subunit establishes a data connection with the path labeling unit, receives error correlation networks in real time, dynamically calculates the error redistribution magnitude when local corrections are triggered, actively identifies nodes where accumulated errors exceed a preset threshold, and generates a trigger signal containing node identifiers and suppression priorities. This subunit completes the online monitoring and risk assessment of the error propagation process, solving the technical shortcomings of traditional systems where error spread cannot be perceived in real time and risk nodes are difficult to locate accurately. Its purpose is to build an error early warning mechanism, transforming post-event handling into pre-event intervention, and providing clear targets for suppression execution. The final effect is that the system can proactively discover risk sources before errors cause global impact, significantly improving the timeliness and accuracy of error identification, avoiding the problem of error runaway due to monitoring lag, and laying a reliable foundation for subsequent suppression operations.

[0050] The suppression execution subunit establishes an instruction connection with the error monitoring subunit. Upon receiving the suppression trigger signal, it immediately performs freeze and directed recalculation operations on the identified node. Simultaneously, it sends a correction instruction to the global constraint memory module and a blocking alarm signal to the scheduling decision output module, interrupting the current scheduling process and triggering a manual review mechanism. This subunit completes the error blocking and process control tasks, solving the security risks of traditional systems lacking effective interception methods and allowing abnormal corrections to still enter the execution stage. Its purpose is to establish a rigid constraint mechanism to ensure that violations are stopped in time and subject to manual confirmation. The final effect is that the system can quickly cut off the error propagation path, preventing the risk from spreading to the entire network. At the same time, it links multiple modules to form a closed-loop control, significantly improving the security and reliability of system operation and reducing the probability of scheduling errors caused by algorithm deviations.

[0051] Through the synergistic action of the error monitoring subunit and the suppression execution subunit, the suppression control unit achieves a complete closed loop of error perception and blocking execution. Compared with the common shortcomings of current technologies, such as invisible error propagation, delayed suppression measures, and lack of linkage mechanisms, this unit brings three substantial improvements. First, the separation design of data connection and instruction connection decouples monitoring and execution, enabling the two subunits to both collaborate efficiently and optimize independently, solving the structural problems of traditional systems with overly tight functional coupling and difficulty in flexible adjustment. Second, the dynamic monitoring and priority marking mechanism based on the error correlation network upgrades error control from passive response to proactive early warning, making up for the shortcomings of existing technologies in terms of delayed risk identification and insufficient targeting, enabling suppression resources to be accurately deployed to high-risk nodes. Finally, the multi-module linkage blocking alarm and manual review triggering mechanism constructs a complete security defense line from anomaly detection and process interruption to manual intervention, changing the limitations of traditional systems with single-module handling and lack of coordinated response, and achieving system-level security protection. Overall, this unit enables the distribution network emergency repair and dispatch system to have the capabilities of error self-sensing, autonomous blocking, and coordinated control, achieving a dual improvement in topology correction accuracy and operational safety. It significantly enhances the system's anti-interference capability and decision-making credibility under complex operating conditions, providing a solid safety guarantee for intelligent emergency repair of the distribution network.

[0052] Example 8: Please refer to Figure 1 The human-machine collaborative verification module includes a conflict alert unit and a feedback injection unit; The conflict alert unit is connected to the global constraint memory module and the scheduling decision output module. It receives topology status data and operation instruction data. When it detects that the node status and operation semantics are inconsistent, it generates a list of nodes to be confirmed and pushes it to the emergency repair terminal. The feedback injection unit is connected to the conflict prompting unit and the error source tracing and suppression module. It receives the manual review results returned by the emergency repair terminal and converts the reviewed and confirmed events into high-weight correction parameters, which are then injected into the error source tracing and suppression module.

[0053] In this embodiment, the conflict alerting unit establishes a data connection with the global constraint memory module and the scheduling decision output module, receives topology status data and operation command data in real time, and uses a consistency comparison mechanism to detect conflicts between node status and operation semantics. It automatically generates a list of nodes to be confirmed and pushes it to the emergency repair terminal. This unit completes the real-time verification and risk alerting tasks of topology status and operation commands, solving the technical defects of traditional systems where algorithm decisions are disconnected from actual on-site conditions and potential conflicts cannot be detected in a timely manner. Its purpose is to build an information bridge between algorithms and human intervention, enabling on-site personnel to intervene in suspicious decisions promptly. The final effect is a significant improvement in system decision transparency, a substantial decrease in the misjudgment rate of key nodes, and a substantial enhancement in the executability of scheduling commands. It also reduces the risk of decision-making errors caused by algorithm blind spots and provides high-quality human verification samples for subsequent feedback.

[0054] The feedback injection unit establishes a data connection with the conflict alert unit and the error tracing and suppression module, receives the manual review results returned by the emergency repair terminal, converts the reviewed and confirmed events into high-weight correction parameters, and injects them into the error tracing and suppression module. This unit completes the creative transformation of human experience into system knowledge, solving the technical problem of the ineffective accumulation of human experience and the disconnect between human and machine decision-making in traditional systems. Its purpose is to build a two-way cognitive enhancement mechanism between humans and machines, enabling the system to continuously learn and optimize from human intervention. The final effect is that human experience is structured into correction parameters that the system can recognize, the judgment accuracy of the error tracing and suppression module is continuously improved, and the system as a whole forms a positive cycle of becoming more accurate with use, significantly enhancing the environmental adaptability and long-term operational accuracy of the scheduling strategy.

[0055] Through the synergistic effect of the conflict alerting unit and the feedback injection unit, the human-machine collaborative verification module achieves a complete closed loop from risk discovery and human verification to experience feedback. Compared with the common shortcomings of current technologies, such as one-way instruction issuance, ineffective utilization of human experience, and lack of self-improvement capabilities, this module brings three substantial improvements. First, the conflict alerting mechanism makes the algorithm decision-making process transparent, resolving the contradiction of traditional systems operating in a black box and on-site personnel only being able to passively execute instructions. This allows for precise human intervention in high-risk areas, improving the credibility of decisions. Second, the feedback injection mechanism transforms discrete human experience into structured system knowledge, compensating for the shortcomings of experience loss and inability to accumulate in existing technologies, and achieving a creative integration of human and machine advantages. Finally, the linkage between the two sub-units forms a continuously optimized evolutionary closed loop, changing the limitations of traditional static and rigid systems that are difficult to adapt to environmental changes, and enabling the system to have self-learning and improvement capabilities. Overall, this module enables the distribution network emergency repair dispatch system to leap from one-way command to two-way cognition, and from static configuration to dynamic evolution, achieving a dual improvement in decision-making accuracy and execution efficiency. It significantly enhances the system's environmental adaptability and long-term operational stability under complex operating conditions, providing a sustainable development path for intelligent distribution network emergency repair through human-machine collaboration.

[0056] Example 9: Please refer to Figure 1 The conflict alert unit includes a scene perception subunit and an intelligent hierarchical subunit; The scene perception subunit is configured to obtain power grid constraint rules from the global constraint memory module, obtain current operation instructions from the scheduling decision output module, integrate multi-dimensional information such as meteorological environment, equipment type and personnel location to generate a set of operation scene feature parameters, and identify high-risk nodes and generate a set of nodes to be confirmed when a conflict between topology state and operation semantics is detected. The intelligent hierarchical subunit is configured to receive the set of nodes to be confirmed from the scene perception subunit, call the constraint rules to classify the risk level of conflicting nodes, optimize the push content and frequency strategy, generate a hierarchical confirmation list and send it to the emergency repair terminal, and link the feedback injection unit to realize the human-machine verification closed loop.

[0057] In this embodiment, the scene perception subunit obtains power grid constraint rules from the global constraint memory module, current operation instructions from the scheduling decision output module, and integrates multi-dimensional information such as meteorological environment, equipment type, and personnel location to generate a set of operation scene feature parameters. This completes the task of comprehensive perception of the emergency repair operation scene and accurate identification of conflict nodes. This subunit solves the technical defects of traditional systems where prompts are detached from the actual scene and risk identification is based on a single dimension. Its purpose is to construct a scene profile with multi-factor linkage, making conflict detection more in line with the actual situation on site. The final effect is that the system can accurately identify high-risk nodes and generate a set of nodes to be confirmed under complex working conditions, significantly improving the pertinence and accuracy of conflict prompts, avoiding misjudgments and omissions caused by missing scene information, and providing high-quality input data for subsequent intelligent classification.

[0058] The intelligent risk classification subunit receives the set of nodes to be confirmed from the scene perception subunit, applies constraint rules to classify conflict nodes into risk levels, optimizes the push content and frequency strategy, generates a graded confirmation list, and sends it to the emergency repair terminal. This completes the differentiated management of conflict nodes and the dynamic optimization of push strategies. This subunit solves the technical problems of indiscriminate push of prompts and excessive cognitive load on personnel in traditional systems. Its purpose is to achieve risk classification management, prioritizing critical nodes. The final result is that the system can intelligently adjust the intensity and frequency of prompts according to the risk level, significantly reducing the decision-making pressure on on-site personnel, improving human-machine verification efficiency and the ability to identify misjudgments of critical nodes, forming a closed-loop optimization from risk perception to accurate push notifications.

[0059] Through the synergy of the scene-aware subunit and the intelligent grading subunit, the conflict alert unit achieves progressive optimization from scene construction and risk identification to tiered push notifications. Compared to the common shortcomings of current technologies, such as alerts being detached from the scene, indiscriminate push notifications, and heavy manual screening burdens, this unit brings three substantial improvements. First, the multi-dimensional scene fusion mechanism incorporates on-site elements such as weather, equipment, and personnel into conflict detection, resolving the contradiction between traditional system alerts and actual working conditions, making risk alerts more accurate and relevant. Second, the intelligent grading mechanism achieves differentiated risk assessment through constraint rule invocation, compensating for the shortcomings of existing technologies that apply equal effort to all situations without highlighting key points, ensuring that critical nodes are prioritized. Finally, the push strategy optimization mechanism dynamically adjusts the content and frequency of alerts, changing the limitations of the fixed pattern of traditional systems and increasing the burden on personnel, achieving intelligent and efficient human-computer interaction. Overall, this unit enables the distribution network emergency repair dispatch system to have scenario adaptability and risk classification management capabilities, achieving a dual improvement in decision accuracy and execution efficiency. It significantly enhances the system's environmental adaptability and long-term operational stability under complex working conditions, and provides an optimized path for human-machine collaboration for intelligent distribution network emergency repair.

[0060] Example 10: Please refer to Figure 1 The feedback injection unit includes an intelligent evaluation subunit and a knowledge fusion subunit; The intelligent evaluation subunit receives the manual review results transmitted from the conflict alert unit and the emergency repair terminal, evaluates the reliability level and calculates the confidence weight, and outputs the review items with quality labels. The recognition fusion subunit receives review events with quality labels, weights and fuses them with existing system parameters to generate correction parameters, injects them into the error source tracing and suppression module, and links with the global constraint memory module.

[0061] In this embodiment, the intelligent evaluation subunit receives the manual review results transmitted from the conflict alert unit and the emergency repair terminal, and uses a multi-dimensional evaluation mechanism to calculate the reliability level and confidence weight, thus completing the creative transformation task of vectorizing human experience. This subunit solves the technical defects of inconsistent quality of manual review results and the inability to distinguish between high and low credibility in traditional systems. Its purpose is to establish a scientific quality evaluation system, enabling the system to accurately identify and quantify the value of human intervention. The final effect is that the system can dynamically adjust the weight of review events based on factors such as the operating environment, personnel qualifications, and historical accuracy, significantly improving the accuracy and effectiveness of human experience injection, avoiding negative interference from low-quality human intervention, and providing a reliable quality foundation for subsequent parameter fusion.

[0062] The knowledge fusion subunit receives review events with quality labels and weights them with existing system parameters to generate corrective parameters, thus completing the creative integration of human and machine knowledge. This subunit solves the technical problem of the disconnect between human experience and machine models in traditional systems, preventing them from forming a synergy. Its purpose is to build a human-machine collaborative knowledge evolution mechanism, enabling the system to continuously learn and optimize from high-quality human intervention. The final result is that human experience is seamlessly integrated into the system's decision-making framework, the accuracy of the error source tracing and suppression module is continuously improved, and the system as a whole forms a positive cycle of becoming more accurate with use, significantly enhancing the environmental adaptability and long-term operational accuracy of the scheduling strategy.

[0063] Through the synergistic effect of the intelligent evaluation subunit and the knowledge fusion subunit, the feedback injection unit achieves a complete closed loop from human experience quality assessment to human-machine knowledge fusion injection. Compared with the common shortcomings of current technologies, such as the inability to effectively quantify human experience, the disconnect between human and machine decision-making, and the lack of self-improvement capabilities, this unit brings three substantial improvements. First, the intelligent evaluation mechanism scientifically quantifies the credibility of human review results, resolving the contradiction that traditional systems cannot distinguish between high and low quality human intervention, making experience access more precise and controllable. Second, the weighted fusion mechanism transforms high-quality human experience into system-recognizable correction parameters, compensating for the shortcoming of the inability to share human and machine knowledge in existing technologies, and achieving a creative combination of human and machine advantages. Finally, the linkage between the two subunits forms a continuously optimized evolutionary closed loop, changing the limitations of traditional systems that are static, rigid, and unable to adapt to environmental changes, enabling the system to have self-learning and improvement capabilities. Overall, this unit enables the distribution network emergency repair and dispatch system to leap from one-way command to two-way cognition, and from experience loss to knowledge accumulation, achieving a dual improvement in decision-making accuracy and execution efficiency. It significantly enhances the system's environmental adaptability and long-term operational stability under complex operating conditions, providing a sustainable development path of human-machine collaboration for intelligent distribution network emergency repair.

[0064] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0065] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A power supply repair area scheduling system based on big data coupling, characterized in that: It includes a multi-source data fusion module, a global constraint memory module, an error source tracing and suppression module, a human-machine collaborative verification module, and a performance evolution analysis module: The multi-source data fusion module is used to collect real-time data on power grid operation measurements, meteorological environment, user repair reports and work trajectory. It performs spatiotemporal alignment processing and quality classification labeling on the acquired heterogeneous information to generate raw data streams with confidence labels, providing data support for subsequent modules. The global constraint memory module is used to encode the physical operation rules of the power grid into memory units of a graph neural network. The memory units are stored separately from the dynamic weights. During the local correction process, the update range of the constraint nodes is forced to prevent the model from forgetting the historical operation rules due to frequent iterations, and to ensure that the correction results conform to physical common sense. The error source tracing and suppression module is used to construct an error propagation path diagram and mark the error source and propagation trajectory for each pseudo-measurement node. When a local correction is triggered, the error redistribution magnitude is calculated. When the cumulative error of a node exceeds a preset threshold, the node is frozen and a directional recalculation is started to block the spread of error to the entire network. The human-machine collaborative verification module is used to embed intelligent prompting function into the emergency repair terminal. When there is a conflict between the topology status and the operation semantics, it pushes a list of nodes to be confirmed, receives and processes the results of manual review, and injects the confirmation results as high-weight events into the system to achieve complementary advantages of human and machine decision-making. The performance evolution analysis module is used to monitor the operating performance of each module and statistically analyze the accuracy of error tracing. By analyzing the high-frequency error propagation path, it automatically optimizes the pseudo-measurement weights and memory unit sensitivity parameters, driving the system to continuously improve itself. 2.The power supply repair area dispatching system based on big data coupling according to claim 1, characterized in that: The global constraint memory module includes a rule solidification unit and a constraint verification unit; The rule solidification unit is used to store the power grid topology connection relationship, equipment current tolerance threshold and power flow balance law in a non-trainable form, and physically isolates it from the model dynamic weight parameters to form an independent rule memory library. The constraint verification unit is used to call the corresponding constraint conditions from the rule memory when receiving the local correction instruction triggered by the error source suppression module, to implement boundary restrictions on the node state update magnitude, and to force rollback and trigger an alarm when the correction result violates the physical rules.

3. The power supply emergency repair area dispatching system based on big data coupling according to claim 2, characterized in that: The rule solidification unit includes a rule compilation subunit and an isolation encapsulation subunit; The rule compilation subunit compiles the power grid topology connection relationship, equipment current tolerance threshold and power flow balance law into non-trainable memory parameters, generates a standard format rule description file, and realizes the creative transformation of physical rules into machine execution code; The isolation and encapsulation subunit separates and deploys the rule description file and the model dynamic weight parameters in the physical storage layer, encapsulates them into an independent rule memory, and establishes a standard interface for the constraint verification unit to call, thus forming a creative isolation mechanism between rule solidification and dynamic learning.

4. A power supply emergency repair area dispatching system based on big data coupling according to claim 3, characterized in that: The constraint verification unit includes an intelligent invocation subunit and a mandatory constraint subunit; The intelligent calling subunit is used to intelligently match the constraints related to the current correction node from the rule memory and dynamically generate boundary limit parameters when receiving the local correction instruction triggered by the error source tracing and suppression module. The mandatory constraint subunit is used to compare the actual updated value of the node state with the boundary constraint parameters. When it is determined that the update result violates the physical rules, a mandatory rollback operation is performed, and an alarm signal is sent to the error source tracing and suppression module and the scheduling decision output module.

5. A power supply emergency repair area dispatching system based on big data coupling according to claim 4, characterized in that: The error source tracing and suppression module includes a path labeling unit and a suppression control unit; The path labeling unit constructs an error propagation path graph, labels the error sources and propagation trajectories of pseudo-measurement nodes, generates a weighted error association network, and records the propagation path and intensity distribution of errors in the topology. The suppression control unit calculates the error redistribution magnitude when local correction is triggered, monitors the cumulative error of nodes, and performs node freezing and directional recalculation when the error exceeds a preset threshold. It sends correction instructions to the global constraint memory module and sends blocking alarm signals to the scheduling decision output module, interrupts the current scheduling process, and triggers a manual review mechanism.

6. A power supply emergency repair area dispatching system based on big data coupling according to claim 5, characterized in that: The path labeling unit includes a path construction subunit and a weight labeling subunit; The path construction subunit constructs an error propagation path diagram based on the power grid topology, marks the error sources and propagation trajectories of pseudo-measurement nodes, and generates a basic error association network. The weighted labeling sub-unit introduces measurement confidence and correction frequency factor into the basic error correlation network to generate a weighted error correlation network, recording the propagation path and intensity distribution of error in the topology.

7. A power supply emergency repair area dispatching system based on big data coupling according to claim 6, characterized in that: The suppression control unit includes an error monitoring subunit and a suppression execution subunit; The error monitoring subunit is connected to the path labeling unit, receives the error association network, calculates the error redistribution magnitude when local correction is triggered, identifies nodes whose accumulated error exceeds a preset threshold, generates and outputs a trigger signal containing node identifier and suppression priority. The suppression execution subunit connects with the error monitoring subunit, receives the suppression trigger signal, performs freeze and directional recalculation operations on the identified node, sends a correction instruction to the global constraint memory module and sends a blocking alarm signal to the scheduling decision output module, interrupts the current scheduling process and triggers the manual review mechanism.

8. A power supply emergency repair area dispatching system based on big data coupling according to claim 7, characterized in that: The human-machine collaborative verification module includes a conflict alert unit and a feedback injection unit; The conflict alert unit is connected to the global constraint memory module and the scheduling decision output module. It receives topology status data and operation instruction data. When it detects that the node status and operation semantics are inconsistent, it generates a list of nodes to be confirmed and pushes it to the emergency repair terminal. The feedback injection unit is connected to the conflict prompting unit and the error source tracing and suppression module. It receives the manual review results returned by the emergency repair terminal and converts the reviewed and confirmed events into high-weight correction parameters, which are then injected into the error source tracing and suppression module.

9. A power supply emergency repair area dispatching system based on big data coupling according to claim 8, characterized in that: The conflict alert unit includes a scene perception subunit and an intelligent hierarchical subunit; The scene perception subunit is configured to obtain power grid constraint rules from the global constraint memory module, obtain current operation instructions from the scheduling decision output module, integrate multi-dimensional information such as meteorological environment, equipment type and personnel location to generate a set of operation scene feature parameters, and identify high-risk nodes and generate a set of nodes to be confirmed when a conflict between topology state and operation semantics is detected. The intelligent hierarchical subunit is configured to receive the set of nodes to be confirmed from the scene perception subunit, call the constraint rules to classify the risk level of conflicting nodes, optimize the push content and frequency strategy, generate a hierarchical confirmation list and send it to the emergency repair terminal, and link the feedback injection unit to realize the human-machine verification closed loop.

10. A power supply emergency repair area dispatching system based on big data coupling according to claim 9, characterized in that: The feedback injection unit includes an intelligent evaluation subunit and a knowledge fusion subunit; The intelligent evaluation subunit receives the manual review results transmitted from the conflict alert unit and the emergency repair terminal, evaluates the reliability level and calculates the confidence weight, and outputs the review items with quality labels. The recognition fusion subunit receives review events with quality labels, weights and fuses them with existing system parameters to generate correction parameters, injects them into the error source tracing and suppression module, and links with the global constraint memory module.

Images