Intelligent measurement and control method and system for frequent power outage of low-voltage distribution network user side

Abstract

The application provides a kind of frequently power failure intelligent measurement and control method and system applied to low-voltage distribution network user side, it is related to low-voltage distribution network technical field, first, the operation correlation signal of user side distribution node is collected, the abnormal conduction path and state change are analyzed, the power failure abnormal conduction trajectory is generated, based on the power failure abnormal conduction trajectory, the power failure risk correlation model is constructed in combination with user side load operation characteristics and distribution line parameters;Then, according to the risk factors determined by the power failure risk correlation model, the power failure cooperative intervention parameters of the linkage user side measurement and control unit and line side regulation and control equipment are generated;Then, the frequently power failure measurement and control instruction is generated in combination with user side distribution scene data;Finally, the frequently power failure measurement and control instruction is issued to the corresponding execution unit, and the distribution operation state data after instruction execution is collected.The application can comprehensively and accurately analyze power failure abnormalities, realize user side and line side cooperative intervention, effectively reduce the frequency of frequent power failure, and improve user power reliability and power system stability.

Description

Technical Field

[0001] This invention relates to the field of low-voltage distribution network technology, and more specifically, to a method and system for intelligent monitoring and control of frequent power outages applied to the user side of a low-voltage distribution network. Background Technology

[0002] In low-voltage distribution networks, frequent power outages are a critical factor affecting user experience and the stable operation of the power system. Traditional power outage detection and control methods have many limitations. On the one hand, existing technologies often focus only on localized power outages at the user end, such as relying solely on simple monitoring equipment installed at the user end to detect outages, lacking in-depth analysis of the causes, especially failing to accurately identify the transmission process of power outage anomalies from the line side to the user side. This makes it difficult to determine the root cause of power outages in the face of complex and ever-changing outage situations, and thus cannot fundamentally solve the problem of frequent power outages.

[0003] On the other hand, existing power outage control strategies are relatively isolated, lacking an effective coordination mechanism between user-side monitoring and control units and line-side control equipment. When dealing with power outages, they typically operate independently, unable to coordinate and control based on the overall power outage risk situation. This results in low efficiency in power outage handling, failure to restore power supply in a timely and effective manner, and consequently, an inability to effectively reduce the frequency of power outages, causing significant losses to both users and the power system. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for intelligent monitoring and control of frequent power outages applied to the user side of a low-voltage distribution network, the method comprising:

[0005] Collect operation-related signals of user-side power distribution nodes, and based on the operation-related signals, analyze the transmission path and state changes of anomalies from the line side to the user side, and generate the power outage anomaly transmission trajectory of the user side of the low-voltage power distribution network.

[0006] Based on the aforementioned power outage anomaly propagation trajectory, and combined with the user-side load operation characteristics and distribution line parameters, a power outage risk correlation model for the user side of the low-voltage distribution network is constructed.

[0007] Based on the power outage risk correlation model, and according to the risk factors determined by the power outage risk correlation model, power outage coordinated intervention parameters are generated to link the user-side monitoring and control unit and the line-side control equipment.

[0008] Based on outage collaborative intervention parameters and combined with user-side power distribution scenario data, frequent outage monitoring and control commands are generated for use on the user side of low-voltage power distribution networks.

[0009] Frequent power outage monitoring and control commands are sent to the corresponding execution units, and power distribution operation status data are collected after the commands are executed.

[0010] Furthermore, embodiments of the present invention also provide an intelligent monitoring and control system for frequent power outages applied to the user side of a low-voltage distribution network, characterized in that it includes:

[0011] A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the above-described intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network by executing the machine-executable instructions.

[0012] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions stored in a computer-readable storage medium, a processor of a frequent power outage intelligent monitoring and control system applied to the user side of a low-voltage distribution network reads the machine-executable instructions from the computer-readable storage medium, the processor executes the machine-executable instructions, causing the frequent power outage intelligent monitoring and control system applied to the user side of a low-voltage distribution network to execute the above-described frequent power outage intelligent monitoring and control method applied to the user side of a low-voltage distribution network.

[0013] Based on the above, by collecting operational correlation signals from user-side distribution nodes, the transmission path and state changes of anomalies from the line side to the user side are analyzed in depth, generating a power outage anomaly transmission trajectory. Based on the power outage anomaly transmission trajectory, a power outage risk correlation model is constructed by combining user-side load operation characteristics and distribution line parameters. This model can comprehensively consider the impact of multiple factors on power outage risk, enabling early identification of potential power outage risks in the face of complex and ever-changing low-voltage distribution network environments. Combined with the power outage risk correlation model, power outage collaborative intervention parameters are generated for the linkage between user-side monitoring and control units and line-side control equipment. Furthermore, frequent power outage monitoring and control commands are generated, realizing coordinated control between the user side and the line side. This allows for precise and timely control based on the overall power outage risk situation, effectively improving power outage handling efficiency, quickly restoring power supply, significantly reducing the frequency of frequent power outages on the user side of the low-voltage distribution network, and improving the reliability of user power supply and the stability of the power system. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the execution flow of the intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network, provided by an embodiment of the present invention.

[0015] Figure 2 This is a schematic diagram of the main control interface of the intelligent monitoring and control system for frequent power outages applied to the user side of a low-voltage distribution network, provided in an embodiment of the present invention. Detailed Implementation

[0016] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a method for intelligent monitoring and control of frequent power outages applied to the user side of a low-voltage distribution network, provided by an embodiment of the present invention. The following is a detailed description of this method for intelligent monitoring and control of frequent power outages applied to the user side of a low-voltage distribution network.

[0017] Step S110: Collect the operation-related signals of the user-side power distribution nodes. Based on the operation-related signals, analyze the transmission path and state changes of the anomaly from the line side to the user side, and generate the power outage anomaly transmission trajectory of the user side of the low-voltage power distribution network.

[0018] In this embodiment, a user-side power distribution system in a residential community is used as the application scenario. This community includes multiple residential buildings, each equipped with a distribution box, and each user's home is equipped with a smart meter and related power distribution equipment. First, it is necessary to collect the operational correlation signals of the user-side power distribution nodes. These signals include voltage, current, leakage current, temperature, and load operation signals. For example, corresponding sensors are installed in the community's main distribution box, the distribution boxes in each building, and the smart meters in each user's home to collect data in real time, such as three-phase voltage, three-phase current, zero-sequence current (leakage current), circuit breaker contact temperature in the distribution box, ambient temperature, and the load power of each user. Simultaneously, the location of the power distribution node corresponding to each signal is recorded, such as the main distribution box being located in the community's central distribution room, and the distribution box for Unit 1 of Building 1 being located in the first-floor corridor of that unit, as well as the operational scenarios, such as power consumption scenarios during different times of the weekday, weekday night, and weekend, thus forming a set of power outage correlation signals for the user side of the low-voltage power distribution network.

[0019] Step S111: Collect voltage, current, leakage current, temperature and load operation signals of the user-side power distribution nodes, and synchronously record the location and operation scenario of the power distribution nodes corresponding to each signal to form a set of power outage related signals for the user side of the low-voltage power distribution network.

[0020] Specifically, voltage signals are acquired through voltage sensors at a sampling frequency of 10kHz to obtain instantaneous values ​​of the three-phase voltages, such as real-time data of phase A, phase B, and phase C voltages. Current signals are acquired through current sensors, also at a sampling frequency of 10kHz to acquire instantaneous values ​​of the three-phase currents. Leakage current signals, i.e., zero-sequence current, are acquired through zero-sequence current transformers at a sampling frequency of 2kHz. Temperature signals are acquired through temperature sensors, including circuit breaker contact temperature and ambient temperature, at a sampling frequency of 1Hz. Load operation signals are acquired through high-precision metering chips, including active power, reactive power, and power factor, with an update frequency of 1Hz. During the acquisition process, a corresponding distribution node location label is added to each acquired signal data, such as "Main Distribution Box - Phase A Voltage" or "Building 1, Unit 1 Distribution Box - Contact Temperature," and the operating scenario during acquisition is recorded, such as "Weekday - 14:00 - Residential Electricity Peak." The above data are then integrated to form a power outage-related signal set.

[0021] Step S112: Based on the power outage associated signal set, divide multiple power outage anomaly conduction dimensions to form a power outage anomaly conduction dimension division result. The multiple power outage anomaly conduction dimensions include power outage anomaly conduction dimensions of voltage, current, leakage current and temperature.

[0022] After obtaining the set of power outage-related signals, the data is analyzed, and the power outage anomaly propagation dimensions are divided according to the type and characteristics of the signals. The voltage dimension mainly focuses on abnormal conditions such as voltage amplitude changes, sags, swells, and interruptions; the current dimension includes anomalies such as overcurrent, undercurrent, and current imbalance; the leakage current dimension addresses abnormal increases in zero-sequence current; and the temperature dimension involves abnormal increases in contact temperature and ambient temperature. Through this division, the complex set of power outage-related signals is classified according to different physical characteristics and propagation mechanisms, forming a clear division of power outage anomaly propagation dimensions, laying the foundation for subsequent feature extraction and propagation path analysis.

[0023] Step S113: Extract the features reflecting abnormal conduction under each conduction dimension from the various signals in the power outage-related signal set to form a core feature set of abnormal conduction of power outage.

[0024] For each defined dimension of power outage anomaly conduction, features reflecting the anomaly conduction are extracted from the power outage-related signal set. In the voltage dimension, features such as the amplitude, duration, and occurrence time of voltage dips, and the duration of voltage interruptions are extracted. In the current dimension, features such as the amplitude, duration, and rate of change of overcurrent are extracted. In the leakage current dimension, features such as the maximum value and trend of leakage current are extracted. In the temperature dimension, features such as the rate of temperature rise and the highest temperature value are extracted. For example, in the voltage dimension, when a sudden drop in phase A voltage from 220V to 150V is detected, lasting 500ms, the dip amplitude (220V-150V=70V), duration (500ms), and occurrence time (14:30:20) are used as anomaly conduction features in the voltage dimension. These features extracted from each dimension are summarized to form the core feature set of power outage anomaly conduction.

[0025] Step S114: Based on the results of the power outage anomaly transmission dimension classification, establish the correspondence between the anomaly characteristics of different dimensions and the transmission path from the line side to the user side, and form an anomaly characteristic transmission path association table.

[0026] Based on the previously defined dimensions of power outage anomaly propagation, and combined with the line structure of the community's power distribution system, a correspondence between anomaly characteristics and propagation paths for different dimensions is established. For example, voltage anomalies are usually related to the propagation paths of main lines and branch lines, such as the lines from the main distribution box to the distribution boxes of each building; current anomalies may be related to user access lines and near-load lines, such as the lines from the unit distribution box to the user's home. By analyzing historical data and line topology, the specific propagation path segments that may correspond to each anomaly characteristic are determined, and the node information on the path is recorded, such as nodes like the main distribution box, the distribution box of Building 1, and user meters, thus forming an anomaly characteristic propagation path association table.

[0027] Step S1141: Divide the results according to the power outage anomaly conduction dimension, classify the core feature set of power outage anomaly conduction to the corresponding dimension, form voltage, current, leakage current and temperature anomaly feature subsets, and output the dimension-specific anomaly feature set.

[0028] Based on the four power outage anomaly conduction dimensions—voltage, current, leakage current, and temperature—obtained in step S112, each feature in the core feature set of power outage anomaly conduction obtained in step S113 is categorized into its corresponding dimension. For example, features such as voltage sag amplitude and duration are categorized into the voltage anomaly feature subset; features such as overcurrent amplitude and current change rate are categorized into the current anomaly feature subset; features such as maximum leakage current value and its trend are categorized into the leakage current anomaly feature subset; and features such as temperature rise rate and maximum temperature value are categorized into the temperature anomaly feature subset. This yields a multi-dimensional anomaly feature set, with each subset corresponding to a conduction dimension, facilitating subsequent matching with the conduction path.

[0029] Step S1142: Collect information on trunk lines, branch lines, user access lines and load near-end conduction paths, record the node composition and connection relationship of each path, and form a complete conduction path list.

[0030] A detailed survey of the community's power distribution lines was conducted, collecting information on various transmission paths. Main lines run from the community's main distribution box to the distribution boxes in each building; branch lines run from the building distribution boxes to the unit distribution boxes; user access lines run from the unit distribution boxes to the smart meters in users' homes; and load-near-end lines run from the smart meters to various electrical appliances in users' homes. The nodes along each path were recorded, such as the main distribution box node, the distribution box node in Building 1, the distribution box node in Building 2, etc., as well as the connections between nodes, such as the outgoing line from the main distribution box connecting to the incoming line from the distribution box in Building 1, thus forming a complete list of transmission paths.

[0031] Step S1143: Match the voltage anomaly feature subset with the conduction paths of the main line and branch line, and match the current anomaly feature subset with the user access line and the near-end conduction path of the load to form a preliminary feature path matching result.

[0032] Based on the operating patterns and fault propagation characteristics of power distribution systems, voltage anomalies are typically caused by line-side faults and propagate along main lines and branch lines to the user side. Therefore, a subset of voltage anomaly characteristics is matched with the propagation paths of main lines and branch lines. Current anomalies are often related to load changes or line faults on the user side and are mainly propagated along user access lines and lines near the load. Therefore, a subset of current anomaly characteristics is matched with these paths. For example, when a voltage sag characteristic appears in the subset of voltage anomaly characteristics, the corresponding main lines and branch lines are found in the complete propagation path list to establish a preliminary correspondence between the voltage anomaly characteristic and these paths. Similarly, current anomaly characteristics are processed to form a preliminary matching result of the characteristic paths.

[0033] Step S1144: For a single abnormal feature under each dimension, determine the corresponding specific transmission path segment, path node and feature manifestation form to form a refined feature path matching result.

[0034] For each individual anomaly feature in the multi-dimensional anomaly feature set, its corresponding specific conduction path segment, path nodes, and characteristic manifestations along that path are further precisely determined. For example, in the voltage anomaly feature subset, there is a feature "the voltage of phase A of the main distribution box drops by 20% for 300ms". Through analysis, it is determined that the specific conduction path segment corresponding to this anomaly feature is the main line segment from the main distribution box to the distribution box of Building 1. The path nodes involved include the phase A outgoing node of the main distribution box and the phase A incoming node of the distribution box of Building 1. The characteristic manifestation is that the voltage amplitude gradually decreases with the conduction distance along this path segment. The above-mentioned refined analysis is performed on each individual anomaly feature to form a refined feature path matching result.

[0035] Step S1145: Identify the impact of anomalous features on the state of the transmission path, and the feedback effect of the transmission path parameters on the evolution of anomalous features, to form a two-way influence labeling result.

[0036] The impact of each anomalous feature on the state of its corresponding conduction path is analyzed. For example, voltage sags may cause changes in the operating state of equipment along the conduction path and variations in line losses. Simultaneously, parameters of the conduction path, such as line resistance, inductance, and capacitance, also have a feedback effect on the evolution of the anomalous feature; for instance, line resistance can further increase the magnitude of voltage sags. Through analysis of historical data and derivation of circuit principles, the aforementioned two-way influence relationship is identified, and the influence types (e.g., enhancing, weakening, neutral) are labeled, forming a two-way influence labeling result.

[0037] For example, step S11451: Extract single abnormal features from the set of multidimensional abnormal features, record the manifestation and intensity of each abnormal feature, and form single abnormal feature details.

[0038] From the set of anomaly features across different dimensions, such as the subset of voltage anomalies, extract individual anomaly features one by one, for example, "the voltage of phase B of the distribution box in Building 1 drops by 15% for 200ms". Record the manifestation of this anomaly feature, i.e., the voltage drop, and its intensity characteristics, including the drop magnitude of 15% and the duration of 200ms. Organize the above information into details of a single anomaly feature.

[0039] Step S11452: For the details of a single abnormal feature, select the corresponding conduction path segment from the complete conduction path list, analyze the changes in resistance, insulation performance and signal attenuation after the abnormal feature acts on the path segment, and form the impact data of the abnormality on the path.

[0040] Based on the anomaly type and related information in the details of a single anomaly feature, conduction path segments potentially affected by the anomaly feature are selected from the complete list of conduction paths. For example, for the anomaly feature "15% voltage dip in phase B of distribution box in Building 1," the branch line segments from distribution box in Building 1 to its downstream unit distribution box are selected. The effects of this voltage dip on the path segment are analyzed to determine whether the resistance changes due to temperature variations, whether the insulation performance is affected, and how the signal attenuation changes during conduction. These analysis results are recorded to form the anomaly's impact data on the path.

[0041] Step S11453: Analyze the conduction path parameter set, extract the conductor specifications, insulation parameters and connection tightness parameters of the corresponding conduction path segment, analyze the influence of the conductor specifications, insulation parameters and connection tightness parameters of the corresponding conduction path segment on the abnormal conduction rate, intensity attenuation and range diffusion, and form path feedback data on anomalies.

[0042] The conduction path parameter set contains detailed parameters for each conduction path segment, such as conductor specifications (cross-sectional area, material, copper or aluminum), insulation parameters (dielectric constant, breakdown voltage, etc.), and connection tightness parameters (tightening torque, etc.). For the conduction path segments determined in step S11452, conductor specifications, insulation parameters, and connection tightness parameters are extracted from the conduction path parameter set. The influence of these parameters on the abnormal conduction rate is analyzed. For example, a larger conductor cross-sectional area may result in a faster abnormal conduction rate; better insulation parameters may lead to a smaller attenuation of abnormal intensity; and better connection tightness may result in a slower spread of the abnormal range, thus forming feedback data on the path to the abnormality.

[0043] Step S11454: Differentiate between three types of bidirectional impact: enhancing, weakening, and neutral; determine the impact types of anomalies on paths and paths on anomalies; and form a bidirectional impact type judgment result.

[0044] Based on the impact data of the anomaly on the path obtained in step S11452 and the feedback data of the path on the anomaly obtained in step S11453, the type of bidirectional impact is determined. If the anomaly leads to a deterioration of the path state (e.g., increased resistance, decreased insulation performance), while the path parameters exacerbate the evolution of the anomaly (e.g., increased anomalous conduction rate, decreased intensity attenuation), it is an enhancing impact; if the anomaly has a small impact on the path state, and the path parameters significantly hinder the evolution of the anomaly, it is a weakening impact; if neither impact is significant, it is a neutral impact. This type determination is performed for each pair of anomalies and conduction path segments to form the bidirectional impact type determination result.

[0045] Step S11455: Combining the impact data of anomalies on the path and the feedback data of the path to the anomalies, label the specific manifestations and scope of each impact to form a two-way impact manifestation labeling result.

[0046] After determining the type of bidirectional impact, the specific manifestations of each impact are detailed by combining the data on the anomaly's influence on the path and the path's feedback data to the anomaly. For example, in an enhancing impact, the specific manifestation of the anomaly on the path might be "a 10% increase in path resistance," with an effect range of "the entire path segment"; the specific manifestation of the path on the anomaly might be "a 5% increase in anomaly conduction velocity," with an effect range of "from the point of anomaly occurrence to the end of the path." These specific manifestations and their effects are then labeled to form the bidirectional impact manifestation labeling results.

[0047] Step S11456: Construct a structure containing fields such as anomaly characteristics, corresponding path, impact of anomaly on path, path feedback to anomaly, impact type, and impact performance, as a bidirectional impact labeling framework.

[0048] Design a data structure containing multiple fields: anomaly characteristic, corresponding path, anomaly impact on path, path feedback to anomaly, impact type, and impact manifestation. The anomaly characteristic field records the specific anomaly characteristics; the corresponding path field records the propagation path segment corresponding to the anomaly characteristic; the anomaly impact on path field records the data on the impact of the anomaly on the path state; the path feedback to anomaly field records the feedback data of path parameters on the evolution of the anomaly characteristic; the impact type field records the type of bidirectional impact (enhancing, weakening, neutral); and the impact manifestation field records the specific manifestation and scope of the impact. This structure serves as a bidirectional impact labeling framework for organizing and storing related data.

[0049] Step S11457: Import the details of a single anomaly feature, the impact data of the anomaly on the path, the feedback data of the path to the anomaly, the judgment results of the two-way impact type, and the marking results of the two-way impact performance into the framework to form a preliminary two-way impact marking table.

[0050] The details of the single anomaly feature obtained in step S11451, the impact data of the anomaly on the path in step S11452, the feedback data of the path on the anomaly in step S11453, the judgment result of the bidirectional impact type in step S11454, and the marking result of the bidirectional impact performance in step S11455 are filled into the corresponding fields according to the field requirements of the bidirectional impact marking framework to form a preliminary bidirectional impact marking table.

[0051] Step S11458: Based on the power outage associated signal set, add data support for each mark in the merged bidirectional impact mark table to form a supplementary bidirectional impact mark table. Classify and arrange the supplementary bidirectional impact mark table according to the abnormal feature dimension, and output the bidirectional impact mark result.

[0052] Referring to the original signal data related to the anomaly and conduction path segment in the power outage-related signal set, such as voltage, current, and temperature signal records during the anomaly period, data support is added to each marker in the preliminary two-way influence marking table, explaining the source and calculation basis of the influence and feedback data. Then, the supplemented data is classified and arranged according to the dimension of the anomaly characteristic (voltage, current, leakage current, temperature) to form a supplemented two-way influence marking table, and finally, the two-way influence marking results are output.

[0053] Step S1146: Construct an association table structure containing fields such as dimension name, abnormal features, propagation path, path node, feature performance, and bidirectional impact, as the framework for the abnormal feature propagation path association table.

[0054] Construct a relational table structure containing fields such as dimension name, anomaly feature, conduction path, path node, feature manifestation, and bidirectional impact. The dimension name field identifies the power outage anomaly conduction dimension (voltage, current, leakage current, temperature) to which the record belongs; the anomaly feature field records the specific anomaly feature; the conduction path field records the corresponding conduction path segment; the path node field records the key nodes on the conduction path; the feature manifestation field records the manifestation of the anomaly feature on that path; and the bidirectional impact field references the bidirectional impact labeling result obtained in step S1145. This structure serves as the framework for the anomaly feature conduction path relational table.

[0055] Step S1147: Import the multidimensional anomaly feature set, the complete list of transmission paths, the refined matching results of feature paths, and the bidirectional influence labeling results into the framework to form a preliminary anomaly feature transmission path association table.

[0056] The multidimensional anomaly feature set obtained in step S1141, the complete transmission path list in step S1142, the feature path refinement matching results in step S1144, and the bidirectional influence labeling results in step S1145 are filled into the corresponding fields according to the field requirements of the anomaly feature transmission path association table framework. This ensures that each anomaly feature can be accurately associated with the corresponding transmission path, path node, feature performance, and bidirectional influence information, thus forming a preliminary anomaly feature transmission path association table.

[0057] Step S1148: Remove duplicate related items from the association table, merge duplicate descriptions of the same abnormal feature and the same path, maintain the uniqueness of the association relationship, and form a deduplicated association table.

[0058] The preliminary anomaly feature propagation path association table is examined to identify duplicate associations, i.e., duplicate records of the same anomaly feature and the same path. For example, errors in data acquisition or processing may cause the same anomaly feature and propagation path to be recorded multiple times. These duplicate associations are removed, and duplicate descriptions of the same anomaly feature and the same path are merged to ensure that each association is unique, forming a deduplicated association table.

[0059] Step S1149: Based on the power outage associated signal set, improve the missing feature details and path node information in the deduplicated association table to form a supplemented and improved association table, and classify and sort the supplemented and improved association table according to the dimension name to output the abnormal feature transmission path association table.

[0060] Referring to detailed data from the power outage-related signal set, missing feature details in the deduplicated correlation table are supplemented, such as the specific changes in abnormal features during transmission and their duration. Simultaneously, path node information is improved to ensure accurate recording of nodes on each transmission path segment. After supplementation and improvement, the correlation table is categorized and sorted according to dimension names (voltage, current, leakage current, temperature) to make the table structure clearer and easier for subsequent queries and use. The final output is an abnormal feature transmission path correlation table.

[0061] Step S115: Combine the abnormal feature propagation path association table to monitor the signal feature changes when the abnormality passes through each distribution node, record the strength evolution and impact range of the abnormality at the distribution node, and form a node abnormality propagation status record.

[0062] Using an anomaly propagation path association table, the propagation direction of the anomaly and the distribution nodes it passes through along the propagation path are determined. Monitoring equipment installed at each distribution node monitors the signal characteristic changes as the anomaly passes through that node in real time. For example, when a voltage anomaly passes through the main distribution box node, the change in voltage amplitude from normal to abnormal is recorded, as well as the voltage amplitude when the anomaly leaves the node. Simultaneously, the strength of the anomaly at that node is recorded, such as the magnitude of the voltage sag, and the extent of the anomaly's impact on surrounding equipment, such as whether it causes malfunctions in downstream users' electrical equipment. This information is recorded node by node, forming a node anomaly propagation status record.

[0063] Step S116: Based on the node abnormal propagation status record, identify the line-side branch nodes, user-side access nodes, and load-near-end nodes that play a dominant role in the abnormal propagation rate and range, and form a list of key nodes for abnormal propagation.

[0064] Analyze the anomaly propagation status records at different nodes to identify those nodes that significantly affect the propagation rate and range of anomalies. For example, branch nodes on the line side, such as those from the main distribution box to individual buildings, affect the propagation rate of anomalies to different buildings; user-side access nodes, such as those from the unit distribution box to the user's home, determine whether anomalies can be successfully transmitted to the user side; and load-near-end nodes, such as those after the smart meter in the user's home, affect the extent to which anomalies affect specific loads. By comparing the changes in the anomaly propagation rate and the size of the impact range at different nodes, identify these dominant nodes and compile them into a list of key anomaly propagation nodes.

[0065] Step S117: Connect key nodes in series according to the abnormal conduction sequence, supplement the conduction duration and signal change details between each node, and form a preliminary power outage abnormal conduction trajectory.

[0066] Based on the chronological order of anomaly propagation, the nodes in the list of key anomaly propagation nodes are connected in series. For example, the anomaly first appears at the main distribution box node, then propagates to the distribution box node of Building 1, then to the distribution box node of Unit 1 in Building 1, and finally to the smart meter node in the user's home. During the series connection process, the propagation time between each node is supplemented, that is, the time it takes for the anomaly to propagate from one node to the next, as well as the details of signal changes during the propagation process, such as the gradual attenuation of voltage amplitude and the fluctuation of current, thus forming a preliminary power outage anomaly propagation trajectory.

[0067] Step S118: Combine the load operation signals in the power outage associated signal set, adjust the state description of the user-side end node in the abnormal transmission trajectory, and form a corrected power outage abnormal transmission trajectory.

[0068] The load operation signals in the power outage-related signal set reflect the operating status of various loads on the user side, such as the on / off status of equipment like air conditioners, refrigerators, and lighting, as well as their power consumption. When an anomaly propagates to the user-side end node, the load's operating status affects the anomaly's manifestation. For example, if a high-power motor is connected to the user-side end node, the motor's starting may exacerbate the current anomaly when the anomaly propagates to that node. By combining these load operation signals, the state description of the user-side end node in the initial power outage anomaly propagation trajectory is adjusted, such as by supplementing the influence of load type on the anomaly, resulting in a corrected power outage anomaly propagation trajectory.

[0069] Step S119: Based on the power outage associated signal set and combined with preset heavy load, high temperature and humidity scenario parameters, generate scenario supplementary conduction data, and fuse the corrected power outage abnormal conduction trajectory with the scenario supplementary conduction data to generate the power outage abnormal conduction trajectory containing multiple scenario conduction details.

[0070] Parameters for different scenarios such as heavy load, high temperature, and humidity are preset, including line load rate thresholds for heavy load scenarios, ambient temperature thresholds for high temperature scenarios, and humidity thresholds for humid scenarios. Data related to these scenarios is extracted from the power outage-related signal set, such as ambient temperature data during high-temperature periods in summer and load rate data during peak electricity consumption periods. This data is then combined with the preset parameters to generate supplementary conduction data for each scenario. This supplementary conduction data reflects the characteristics of abnormal conduction under different scenarios, such as changes in abnormal conduction rate due to increased line resistance in high-temperature environments. The corrected power outage abnormal conduction trajectory is then fused with the supplementary conduction data, adding conduction details for different scenarios to the trajectory, such as an increased abnormal conduction duration for a certain line segment in high-temperature scenarios. This generates a power outage abnormal conduction trajectory that includes conduction details from multiple scenarios.

[0071] Step S120: Based on the power outage anomaly propagation trajectory, and combined with the user-side load operation characteristics and distribution line parameters, construct a power outage risk correlation model for the user side of the low-voltage distribution network.

[0072] After obtaining the outage anomaly propagation trajectory, it is necessary to construct an outage risk correlation model by combining the user-side load operating characteristics and distribution line parameters. User-side load operating characteristics include load stability, startup characteristics, power consumption, and load type. For example, a user's load mainly consists of high-power appliances such as air conditioners and electric water heaters, which experience significant current fluctuations during startup. Distribution line parameters include conductor specifications, laying method, and insulation performance. For instance, some lines within the community use copper core conductors with a cross-sectional area of ​​10 square millimeters, laid in conduits. By analyzing the relationship between these characteristics and parameters and the outage anomaly propagation trajectory, a correlation model reflecting the outage risk can be constructed.

[0073] Step S121: Extract the abnormal transmission characteristics, key node information, and transmission patterns and node influence levels corresponding to different abnormal types from the power outage abnormal transmission trajectory to form an abnormal transmission core information set.

[0074] A thorough analysis of the abnormal power outage propagation trajectory is conducted to extract key information. Abnormal propagation characteristics include the type of abnormality (voltage sag, overcurrent, etc.), amplitude, duration, and propagation rate; key node information refers to the location and type of the key nodes identified in step S116; the propagation patterns corresponding to different abnormality types, such as voltage sags typically propagating rapidly along main lines, and overcurrents showing significant effects in user-connected lines; and the degree of node influence refers to the magnitude of each key node's impact during abnormal propagation, such as the main distribution box node having a high degree of influence on the abnormal propagation range. The above information is then compiled to form a core information set on abnormal propagation.

[0075] Step S122: Collect the stability, startup characteristics, power consumption and load type association features of the user-side load operation, and synchronously bind the access node location of the corresponding load to form a user-side load operation feature set.

[0076] The system collects operational data of user-side loads through smart meters and electricity monitoring devices in users' homes. Load stability characteristics can be reflected by the fluctuation of load power over a period of time; the smaller the fluctuation, the higher the stability. Starting characteristics include the magnitude of starting current and starting time, such as the large starting current generated when an air conditioner starts. Power consumption characteristics include the magnitude and trend of active and reactive power. Load type association characteristics include classifying loads into resistive loads (such as electric water heaters), inductive loads (such as motors), and capacitive loads. Simultaneously, the system records the location of the access node corresponding to each load. For example, if an air conditioner is connected to the smart meter in Unit 1, Room 101, Building 1, the above characteristics are bound to the access node location to form a set of user-side load operation characteristics.

[0077] Step S123: Collect parameters such as conductor specifications, laying method, insulation performance, branch structure, and connection tightness of the power distribution line to form a parameter set.

[0078] A comprehensive inspection and data collection process was conducted on the power distribution lines in the residential area. Conductor specifications included conductor material (copper or aluminum), cross-sectional area, and length; installation methods such as overhead, conduit installation, and direct burial; insulation performance was assessed by measuring parameters such as insulation resistance and dielectric loss; branch structure records included the number of branches and branch locations; and connection tightness parameters included tightening torque and contact resistance. These parameters were then compiled and summarized to form a power distribution line parameter set.

[0079] Step S124: Match the various features in the core information set of abnormal transmission with the user-side load operation feature set and the power distribution line parameter set one by one, establish the mutual influence relationship, and form a risk association mapping table.

[0080] The abnormal conduction characteristics and key node information in the core information set of abnormal conduction are matched one by one with the characteristics in the user-side load operation characteristic set and the distribution line parameter set. For example, the abnormal conduction characteristic of "voltage sag amplitude of 15%" is matched with the characteristic of "high-power motor starting" in the user-side load operation characteristic set to analyze the influence relationship between the two, such as the possibility that motor starting may cause voltage sag; the distribution line parameter of "line conductor cross-sectional area of ​​10 square millimeters" is matched with the "current overcurrent amplitude" in the abnormal conduction characteristics to analyze the impact of line cross-sectional area on current overcurrent. Through the above matching, the influence relationship between various characteristics is established, forming a risk association mapping table.

[0081] Step S125: Based on the risk association mapping table, divide the risk association into three levels: line-side parameter-driven, load characteristic-driven, and node status-driven, to form the risk association level division result.

[0082] Based on the strength of the influence relationships between various characteristics in the risk association mapping table, different risk association levels are defined. If a certain type of power outage risk is mainly caused by changes in distribution line parameters, such as leakage risk due to aging line insulation, it belongs to the line-side parameter-dominated level. If the risk is mainly determined by the characteristics of the user-side load, such as voltage sag risk due to frequent starting of high-power loads, it belongs to the load characteristic-dominated level. If the risk is mainly related to the state of critical nodes, such as temperature rise risk due to increased contact resistance caused by loose node connections, it belongs to the node state-dominated level. Through analysis of the risk association mapping table, the level to which each risk association belongs is determined, forming the risk association level classification result.

[0083] Step S126: Based on the risk association hierarchy classification results, build a multi-level risk association framework, set corresponding fields for abnormal transmission characteristics, load operation characteristics, power distribution line parameters and risk levels, and form a risk association model framework.

[0084] Based on the risk association hierarchy classification, a multi-level risk association framework is constructed. This framework is divided into a line-side parameter-dominated layer, a load characteristic-dominated layer, and a node status-dominated layer. Within each level, corresponding fields are set for anomaly propagation characteristics, load operation characteristics, distribution line parameters, and risk level. For example, in the line-side parameter-dominated layer, the anomaly propagation characteristic field records anomaly characteristics related to line parameters, the distribution line parameter field records the corresponding line parameters, and the risk level field indicates the degree of risk under this combination. Through this framework design, a risk association model framework is formed.

[0085] Step S127: Import the core information set of abnormal transmission, the user-side load operation feature set, the power distribution line parameter set and the risk association mapping table into the model framework to form a preliminary power outage risk association model.

[0086] The abnormal transmission core information set obtained in step S121, the user-side load operation feature set in step S122, the power distribution line parameter set in step S123, and the risk association mapping table in step S124 are imported into the corresponding levels and fields according to the field requirements of the risk association model framework, so that various types of data can establish a relationship within the framework, thereby forming a preliminary power outage risk association model.

[0087] Step S128: Combine historical power outage data on the user side of the low-voltage distribution network, adjust the influence weights of different levels of related factors on power outage risk, and obtain the adjusted power outage risk association model.

[0088] Data on past power outages at the user side of the low-voltage distribution network is collected, including historical outage data such as causes, affected areas, and losses. This historical data is analyzed to determine the relative importance of different levels of correlation factors (line-side parameters, load characteristics, node status) in actual power outage events, thereby adjusting their weights on outage risk. For example, if historical data shows a high proportion of outages caused by line insulation aging, the weight of the dominant level of line-side parameters is increased. Through these adjustments, an adjusted outage risk correlation model is obtained.

[0089] Step S1281: Collect the anomaly type, transmission path, load status, line parameters, impact range and handling results of historical power outage events on the user side of the low-voltage distribution network to form a historical power outage dataset.

[0090] By querying power outage records from the power supply bureau, user repair reports, and distribution system operation logs, detailed information on historical power outage events on the user side of the low-voltage distribution network is collected. Anomaly types include voltage dips, short-term interruptions, and leakage faults; the propagation path records the path of the outage anomaly from the point of occurrence to the user side; load status includes the load rate and main load types at the time of the outage; line parameters include the conductor specifications and insulation performance at the time of the outage; the affected area records the number of users and the affected region; and the handling results include the troubleshooting time and the measures taken. This information is then compiled into a historical power outage dataset.

[0091] Step S1282: Based on the risk association level classification results, filter line-side parameters, load characteristics and node status factors from the historical power outage dataset to form a historical association factor set.

[0092] Based on the risk association hierarchy classification results obtained in step S125, factors related to the line-side parameter-dominated, load characteristic-dominated, and node state-dominated hierarchies are selected from the historical power outage dataset. For example, line-side parameter factors include line insulation resistance and conductor cross-sectional area; load characteristic factors include load starting current and power factor; and node state factors include node contact resistance and temperature. The selected factors are then organized into a historical association factor set.

[0093] Step S1283: Based on the severity and scope of historical power outage events, analyze the influence ratio of each related factor in different scenarios to obtain the results of the factor influence degree analysis.

[0094] Historical power outage events were categorized according to severity (e.g., minor impact, moderate impact, severe impact) and scope of impact (e.g., single user, multiple users, entire region). For each category of power outage event, the influence percentage of each related factor in the process leading to the power outage event was analyzed. For example, in power outage events with severe impact and a large scope of impact, the influence percentage of line-side parameters may be relatively high; in minor power outage events affecting a single user, the influence percentage of load characteristics may be relatively high. Through the above analysis, the results of the factor influence degree analysis were obtained.

[0095] Step S1284: Based on the results of the factor influence degree analysis, assign initial weight values ​​to the correlation factors at different levels in the risk correlation model to form an initial weight allocation table.

[0096] Based on the analysis of the degree of influence of factors, initial weight values ​​are assigned to the correlation factors at the levels of line-side parameters, load characteristics, and node status in the risk correlation model. Factors with a higher percentage of influence are assigned larger weight values. For example, if the influence of line-side parameters accounts for 60% in a severe power outage event, a higher initial weight, such as 0.6, is assigned to the line-side parameter-dominant level; the load characteristics-dominant level accounts for 30% and is assigned a weight of 0.3; and the node status-dominant level accounts for 10% and is assigned a weight of 0.1. These weight values ​​are then compiled into an initial weight allocation table.

[0097] Step S1285: Substitute the historical correlation factor set and the initial weight allocation table into the preliminary power outage risk correlation model, compare the model output results with the actual historical power outage situation, and form the weight verification results.

[0098] The data of each correlation factor in the historical correlation factor set and the weight values ​​in the initial weight allocation table are substituted into the preliminary power outage risk correlation model. The model then assesses the risk of historical power outage events and outputs the assessment results. The risk level, possible causes of power outages, and other results output by the model are compared with the actual situation of historical power outage events to analyze the accuracy of the model's predictions. If the model output deviates significantly from the actual situation, it indicates that the initial weight allocation is unreasonable. Weight verification results are generated, and the correlation factors with large deviations and their corresponding weights are recorded.

[0099] Step S1286: For the correlation factors with significant deviations in the weight verification results, adjust their weight values ​​and the weight ratios between different factors to form a corrected weight allocation table.

[0100] Based on the weight validation results, identify the factors that cause significant deviations between the model output and actual conditions. Analyze the causes of these deviations, adjust the weight values ​​of these factors, and adjust the weight ratios between different factors to improve the model's accuracy. For example, if the model underestimates the impact of load characteristics on a certain type of power outage event, leading to prediction bias, increase the weight value of the load characteristic-dominant level and correspondingly decrease the weights of other levels. After adjustment, a revised weight allocation table is generated.

[0101] Step S1287: Based on the scene adaptation rule set, verify the adaptation effect of the modified weight allocation table in various scenes and form scene adaptation verification results.

[0102] The scenario adaptation rule set includes features and rules for different electricity consumption scenarios (such as peak residential electricity consumption, industrial electricity consumption periods, and holidays). The revised weight allocation table is applied to the preliminary power outage risk association model, and the model is validated under various scenarios to observe its prediction accuracy and adaptation effect. For example, in the peak residential electricity consumption scenario, the model is validated to see if it can accurately assess the risk of power outages caused by excessive load. Based on the validation results, scenario adaptation validation results are generated.

[0103] Step S1288: Based on the scenario adaptation verification results, fine-tune the weights in the corrected weight allocation table to generate the final weight allocation table.

[0104] Based on the scenario adaptation verification results, if the model's adaptation performance is still unsatisfactory in certain scenarios, the weights in the revised weight allocation table are further fine-tuned. For example, in industrial power consumption scenarios, the influence of line-side parameters may be greater, requiring an appropriate increase in their weights. After fine-tuning, the final weight allocation table is generated.

[0105] Step S1289: Import the final weight allocation table into the preliminary power outage risk association model, replace the initial weight parameters, and form a power outage risk association model after weight update.

[0106] The weight values ​​in the final weight allocation table are imported into the preliminary power outage risk association model, replacing the original initial weight parameters. This allows the model to more accurately reflect the impact of different related factors on power outage risk, forming a power outage risk association model with updated weights.

[0107] Step S12810: In the power outage risk association model after weight update, associate the basis for weight adjustment and the applicable scenario identifier, and output the adjusted power outage risk association model.

[0108] In the updated power outage risk correlation model, an adjustment basis is added for each weight parameter, explaining which historical data and analysis results were used to determine the weight. Simultaneously, an applicable scenario identifier is added to the model, indicating its applicability in different scenarios such as residential and industrial power consumption. After completing these correlations and identifiers, the adjusted power outage risk correlation model is output.

[0109] Step S129: Configure corresponding association judgment rules for the adjusted power outage risk association model for different load types and line parameter combinations to form a scenario adaptation rule set.

[0110] Different load types (such as residential loads, industrial loads, and commercial loads) and line parameter combinations (such as different conductor specifications and laying methods) correspond to different outage risk modes. For example, frequent motor starts in industrial loads may lead to a higher risk of voltage sags; large-section line conductors have a lower overcurrent risk under the same load. For these different combinations, corresponding association judgment rules are configured for the adjusted outage risk association model. For example, when industrial loads are combined with small-section lines, the overcurrent risk judgment threshold is increased. These rules are then compiled into a scenario-adaptive rule set.

[0111] Step S1210: The adjusted power outage risk association model is fused with the scenario adaptation rule set to obtain the power outage risk association model.

[0112] The adjusted power outage risk association model is organically integrated with the scenario adaptation rule set, enabling the model to automatically apply corresponding association judgment rules based on different load types and line parameter combinations, thereby more accurately assessing power outage risks. For example, when the model detects that the current scenario is an industrial load and the line conductor cross-sectional area is small, it automatically invokes the corresponding overcurrent risk judgment rule. The final power outage risk association model is obtained after integration.

[0113] Step S130: Based on the power outage risk association model, generate power outage collaborative intervention parameters that link the user-side monitoring and control unit and the line-side control equipment according to the risk factors determined by the power outage risk association model.

[0114] By utilizing the established power outage risk correlation model, the operational data of the current low-voltage distribution network user side is analyzed to identify the main risk factors leading to power outages, such as line overcurrent and voltage sag. Based on these risk factors, corresponding intervention strategies are formulated, linking user-side monitoring and control units (such as smart meters and residual current devices) and line-side control equipment (such as line switches and capacitors) to generate coordinated intervention parameters, such as the capacity of user-side load shedding and the operating time of line-side switches.

[0115] Step S131: Analyze the power outage risk association model, extract the risk association level, abnormal transmission characteristics and corresponding influencing factors, locate the dominant factors and scope of influence of the current power outage risk, and form the risk model analysis results.

[0116] The power outage risk correlation model is analyzed to extract the risk correlation levels (line-side parameter-driven, load characteristic-driven, node state-driven), abnormal conduction characteristics (such as voltage sag amplitude and current overcurrent value), and corresponding influencing factors (such as line resistance and load power). By analyzing this information, it is determined which level of factor is dominant to the current power outage risk and the potential scope of the risk, such as whether it affects users in a specific building or the entire community, thus forming the risk model analysis results.

[0117] Step S132: Based on the impact range in the risk model analysis results, locate the load monitoring unit, leakage current measurement and control unit and node status monitoring unit covering the impact range to form a user-side measurement and control unit list.

[0118] Based on the impact range in the risk model analysis results, determine the user-side area that needs to be monitored. Within this area, identify all load monitoring units (such as smart meters, used to monitor user load power), leakage current monitoring units (such as leakage current protectors, used to monitor leakage current), and node status monitoring units (such as temperature sensors installed in distribution boxes, used to monitor node temperature). Compile the location, model, communication address, and other information of the above units to form a user-side monitoring unit list.

[0119] Step S133: Based on the dominant factors of the line-side parameters in the risk model analysis results, locate the corresponding line switching equipment, load regulating equipment and fault isolation equipment, and form a list of line-side control equipment.

[0120] If the risk model analysis results show that the current power outage risk is dominated by line-side parameters, such as line overcurrent caused by excessive line load, then it is necessary to locate the corresponding line switching equipment (such as circuit breakers in the main distribution box), load regulating equipment (such as capacitor banks, used for reactive power compensation to adjust the load), and fault isolation equipment (such as sectionalizing switches, used to isolate faulty line sections). The location, model, control method, and other information of the above equipment should be compiled into a list of line-side control equipment.

[0121] Step S134: Connect the user-side measurement and control unit list with the line-side control equipment list, set the signal transmission path and collaborative action logic between each unit and equipment, and form a collaborative intervention link table.

[0122] Connect the devices and units in the user-side monitoring and control unit list and the line-side control equipment list to determine the signal transmission paths between them, such as data transmission via power line carrier communication or wireless communication. Simultaneously, set up coordinated action logic, such as sending a regulation command to the line-side load regulation equipment when the user-side load monitoring unit detects that the load exceeds a threshold, or sending a tripping command to the line-side switching equipment when the leakage current monitoring and control unit detects leakage current. Record the above signal transmission paths and coordinated action logic to form a coordinated intervention link table.

[0123] Step S135: Based on the risk model analysis results, design action plans for load shedding, line segmentation isolation, parameter adjustment and fault location for different risk-dominant factors, forming a set of intervention action plans.

[0124] Based on the different dominant risk factors identified in the risk model analysis, corresponding intervention action plans are designed. For risks dominated by load characteristics, such as excessive load on the user side, a load shedding plan is designed to determine the type and capacity of loads to be shelded. For risks dominated by line-side parameters, such as line faults, a line segmentation isolation plan is designed to determine the isolated line segments and the sequence of switch actions. For risks dominated by node status, such as excessively high node temperature, a parameter adjustment plan is designed, such as reducing the load downstream of the node. Simultaneously, a fault location action plan is designed to determine the fault location by analyzing data from each monitoring unit. These plans are then compiled into an intervention action plan set.

[0125] Step S136: Match the various actions in the intervention action plan set with the corresponding equipment in the user-side measurement and control unit list and the line-side control equipment list to determine the executing entity and action requirements, and form an action equipment matching table.

[0126] For each type of action in the intervention action scheme set, such as load shedding, identify the equipment capable of performing the action from the user-side measurement and control unit list and the line-side control equipment list. For example, load shedding can be performed by a smart circuit breaker on the user side. Identify the smart circuit breaker as the executing entity and clarify the action requirements, such as shedding time and shedding capacity. Match the action, the executing entity, and the action requirements to form an action-equipment matching table.

[0127] Step S137: Based on the collaborative intervention link table and the action device matching table, determine the execution sequence, action interval, and timing connection conditions of various intervention actions, and generate an intervention action timing table.

[0128] Referring to the signal transmission paths and collaborative action logic in the collaborative intervention link table, as well as the execution entities and action requirements in the action device matching table, the execution sequence of various intervention actions is determined. For example, fault location is performed first, followed by line segmentation isolation, and finally load adjustment. Simultaneously, the interval time between actions is set, such as executing line isolation 5 seconds after fault location is completed, and the timing connection conditions are clarified, such as the next action only starting after the previous action is completed and feedback is successful. The above information is then compiled into an intervention action sequence table.

[0129] Step S1371: Extract the signal transmission path and duration from the collaborative intervention link table, record the signal transmission time between different device units, and form signal transmission timing data.

[0130] In the collaborative intervention link table, each signal transmission path has a corresponding transmission method and estimated time. For example, the user-side monitoring and control unit sends a signal to the line-side control equipment via power line carrier communication, with a transmission time of approximately 100ms. By extracting the signal transmission paths and durations between the different equipment units and recording the signal transmission time for each path, signal transmission timing data is formed.

[0131] Step S1372: Parse the action device matching table to determine the execution device, action complexity, and required preparation time for each intervention action, forming basic information for action execution.

[0132] The action device matching table is parsed to identify the corresponding executing device for each intervention action, such as a load disconnection action being performed by a smart circuit breaker. The complexity of the actions is analyzed; for example, simple switching actions have low complexity, while complex parameter adjustment actions have high complexity. Based on the performance of the executing device and the complexity of the action, the preparation time required for each intervention action is determined; for example, the preparation time for the smart circuit breaker to open is approximately 50ms. The above information is then compiled into the basic information for action execution.

[0133] Step S1373: Based on the severity of risk in the risk model analysis results, prioritize four types of intervention actions: emergency response, fault isolation, parameter adjustment, and monitoring feedback, and form an action priority classification result.

[0134] Based on the severity of risks assessed in the risk model analysis, intervention actions are categorized into different priorities. Emergency response actions have the highest priority and are used to handle severe risks that may immediately lead to power outages, such as leakage faults. Fault isolation actions have the next highest priority and are used to isolate faulty areas to prevent the risk from spreading. Parameter adjustment actions have a lower priority and are used to optimize system operating parameters and reduce potential risks. Monitoring and feedback actions have the lowest priority and are used to monitor the system status after the intervention actions are executed. This priority classification clarifies the execution order and priority of each type of action.

[0135] Step S1374: Sort the various intervention actions according to the priority order of emergency response, fault isolation, parameter adjustment and monitoring feedback to obtain a preliminary action sequence.

[0136] The intervention actions in the intervention action plan set are ordered from highest to lowest priority: emergency response, fault isolation, parameter adjustment, and monitoring feedback. For example, the emergency response to leakage faults is arranged first, followed by line fault isolation, then load parameter adjustment, and finally system status monitoring and feedback. This ordering yields a preliminary action sequence.

[0137] Step S1375: Based on the signal transmission timing data and the basic information of action execution, set the interval duration for adjacent actions in the preliminary action timing sequence to obtain the timing sequence after interval adjustment.

[0138] Based on the signal transmission time between different device units in the signal transmission timing data, and the preparation time and execution time of each action in the action execution basic information, the required interval between adjacent actions is calculated. For example, after the previous action is completed, it is necessary to wait for the signal to be transmitted to the next execution device, plus the preparation time of that device, before the next action can begin. Adding the above interval to the initial action timing sequence yields the interval-adjusted timing sequence.

[0139] Step S1376: Based on the collaborative intervention link table, adjust the time sequence to mark intervention actions that can be executed in parallel, forming a parallel optimized time sequence.

[0140] The collaborative intervention link table may contain some independent signal transmission paths and intervention actions that can be executed in parallel to improve intervention efficiency. For example, load shedding can be performed in parallel while performing line segmentation isolation. Based on the information in the collaborative intervention link table, these parallel intervention actions are identified, marked in the interval-adjusted timing sequence, and the timing arrangement is adjusted to form a parallel optimized timing sequence.

[0141] Step S1377: Set the connection triggering conditions between adjacent actions, including the completion signal of the previous action, the equipment status feedback signal and the parameter compliance signal, to form the action connection condition marking result.

[0142] Set connection trigger conditions for adjacent actions in the parallel optimized timing sequence. For example, the next action can only start executing after the previous action has completed and sent an execution completion signal; or the next action is triggered only when the device status feedback signal shows that the device is ready, or the parameter compliance signal shows that the relevant parameters meet the action requirements. Mark the above trigger conditions to form the action connection condition marking result.

[0143] Step S1378: Construct a time sequence table framework that includes fields such as action sequence number, action name, execution device, priority, execution duration, interval duration, and connection conditions to form an intervention action time sequence table framework.

[0144] Construct a sequence table framework containing fields such as action number, action name, execution device, priority, execution duration, interval duration, and connection condition. The action number identifies the order of actions; the action name describes the content of the action; the execution device indicates the entity performing the action; the priority reflects the importance of the action; the execution duration is the time required for the action to complete from start to finish; the interval duration is the waiting time between actions; and the connection condition is the triggering condition for the action to begin execution. This framework serves as the intervention action sequence table framework.

[0145] Step S1379: Import the parallel optimized time sequence and action connection condition labeling results into the framework to form a preliminary intervention action time sequence table.

[0146] The action information in the parallel optimized time sequence, such as action number, name, execution device, priority, execution duration, interval duration, etc., as well as the connection conditions in the action connection condition marking results, are filled into the corresponding positions according to the field requirements of the intervention action time sequence table framework to form a preliminary intervention action time sequence table.

[0147] Step S13710: Check whether there are any conflicts in the sequence of actions, intervals and durations in the preliminary intervention action sequence table, correct the conflicting parts, and output the intervention action sequence table.

[0148] The initial intervention action sequence table is reviewed to check whether the action order meets priority requirements, whether the interval between actions is sufficient, whether the execution time is reasonable, and whether there are any issues such as overlapping or conflicting action times. For example, scheduling two high-priority actions to be executed at the same time can cause resource conflicts. Conflicts are corrected by adjusting the action order or intervals to ensure the sequence table's rationality and feasibility, ultimately outputting the intervention action sequence table.

[0149] Step S138: Combining the correlation parameters in the power outage risk correlation model, set the execution range, duration and triggering conditions of each intervention action to form an intervention action parameter set.

[0150] Referring to the correlation parameters in the power outage risk correlation model, such as the line load rate threshold and the allowable voltage fluctuation range, specific execution parameters are set for each intervention action. For example, the execution magnitude of the load shedding action is to shed a 10kW load for a duration of 30 minutes, triggered when the line load rate exceeds 80%; the execution magnitude of the parameter adjustment action is to increase the capacitor's input capacity by 5kvar for a duration of 1 hour, triggered when the power factor is below 0.9. These execution magnitudes, durations, and trigger conditions are then compiled into an intervention action parameter set.

[0151] Step S139: Integrate the intervention action plan set, action equipment matching table, intervention action sequence table and intervention action parameter set to form a collaborative intervention strategy and output preliminary power outage collaborative intervention parameters.

[0152] By integrating the intervention action plan set, action equipment matching table, intervention action sequence table, and intervention action parameter set, and comprehensively considering the action plan, executing equipment, timing arrangement, and action parameters, a complete collaborative intervention strategy is formed. This collaborative intervention strategy clarifies which intervention actions should be taken under different power outage risk conditions, which equipment should perform them, in what order they should be performed, and the specific parameters of the actions. Based on this collaborative intervention strategy, preliminary power outage collaborative intervention parameters are output.

[0153] Step S1310: Combine historical collaborative intervention data to adjust the intervention action parameters and timing to generate the power outage collaborative intervention parameters.

[0154] Collect historical data on past coordinated interventions, including the effectiveness of intervention actions, the rationality of parameter settings, and the optimality of timing. Analyze this historical data to identify potential problems in the initial power outage coordinated intervention parameters, such as insufficient execution of a certain intervention action leading to incomplete risk elimination, or unreasonable timing causing intervention delays. Based on the analysis results, adjust the intervention action parameters (such as execution magnitude and duration) and timing (such as action intervals and execution order) to generate the final power outage coordinated intervention parameters.

[0155] Step S140: Based on the outage collaborative intervention parameters and combined with user-side power distribution scenario data, generate frequent outage monitoring and control commands applicable to the user side of the low-voltage power distribution network.

[0156] After obtaining the power outage coordinated intervention parameters, and combining them with user-side power distribution scenario data, such as user electricity usage habits, power distribution topology, and current power load levels, the coordinated intervention parameters are transformed into specific, executable frequent power outage monitoring and control commands. For example, based on the load distribution in a certain area of ​​the user-side power distribution scenario, the load shedding parameters are specified as shedding specific electrical loads of a few households in that area.

[0157] Step S141: Extract the intervention action plan, execution equipment, action sequence and action parameters from the power outage collaborative intervention parameters as the core element set of collaborative intervention.

[0158] The parameters for coordinated power outage intervention are analyzed to extract key elements, including intervention action plans (such as specific plans for load shedding and line isolation), execution equipment (such as smart circuit breakers and capacitors), action sequence (the execution order and timing of each action), and action parameters (such as shedding capacity and adjustment range). These elements are integrated to form a core set of coordinated intervention elements, which serves as the basis for generating measurement and control commands.

[0159] Step S142: Collect user-side power distribution topology, load distribution, electricity consumption time characteristics, line laying environment and safe operation requirements to form a user-side power distribution scenario dataset.

[0160] By surveying and collecting data from the user-side power distribution system, the following information is obtained: power distribution topology, such as line connection methods and node locations; load distribution, such as load types and power levels in different areas; electricity consumption characteristics, such as peak and off-peak hours; line laying environment, such as whether the lines are in high-temperature or humid environments; and safe operation requirements, such as safe voltage and current ranges. This data is then compiled into a user-side power distribution scenario dataset.

[0161] Step S143: Compare the set of core elements for collaborative intervention with the user-side power distribution scenario dataset, and adjust the intervention actions and parameters according to the comparison results to form a scenario-adaptive intervention plan.

[0162] The intervention action plan, execution equipment, action sequence, and action parameters, which are the core elements of collaborative intervention, are compared with data such as the power distribution topology and load distribution in the user-side power distribution scenario dataset. For example, based on the load distribution, it is determined whether the load shedding targets in the original intervention action plan are reasonable; based on the line laying environment, the action parameters are adjusted to adapt to the impact of the environment on line performance. Based on the comparison results, the intervention actions and parameters are adjusted to form a scenario-adaptive intervention plan suitable for the current user-side power distribution scenario.

[0163] Step S144: Based on the scenario-adaptive intervention scheme, divide the measurement and control commands into four categories: load regulation, line isolation, fault location, parameter monitoring and emergency response, and form the measurement and control command type classification result.

[0164] Based on the nature and purpose of the intervention actions in the scenario-adaptive intervention scheme, they are classified into different types of measurement and control commands. Load regulation commands are used to adjust the load on the user side, such as disconnecting or connecting loads; line isolation commands are used to isolate faulty line segments, such as disconnecting line switches; fault location commands are used to instruct fault detection equipment to locate the fault; parameter monitoring commands are used to monitor the operating parameters of the power distribution system, such as voltage, current, and temperature; emergency response commands are used to deal with emergencies, such as emergency tripping in the event of a leakage fault. This results in the classification of measurement and control command types.

[0165] Step S145: For each type of measurement and control instruction, build an instruction framework that includes fields such as instruction identifier, execution subject, action parameters, timing requirements, feedback standards, and safety constraints to form a measurement and control instruction framework set.

[0166] Command frameworks were established for four categories of measurement and control commands: load regulation, line isolation, fault location, parameter monitoring, and emergency response. Each framework includes fields such as command identifier (for unique identification of the command), executing entity (the device or unit executing the command), action parameters (such as the disconnection capacity for load regulation, the switch number for line isolation), timing requirements (time limits for command execution), feedback criteria (the content and format of information to be fed back after command execution), and safety constraints (safety conditions that must be met when executing the command, such as operator permissions and equipment status requirements). These frameworks are then organized to form a measurement and control command framework set.

[0167] Step S146: Import the corresponding information from the scenario adaptation intervention plan into the measurement and control instruction framework set, determine the specific content and execution requirements of each measurement and control instruction, and form a preliminary set of frequent power outage measurement and control instructions.

[0168] Based on the intervention actions, execution devices, action parameters, and timing arrangements in the scenario-adaptive intervention plan, these are imported into the corresponding fields of the measurement and control instruction framework set. For example, information such as the execution device, disconnection capacity, and execution time of the load control action is filled into the load control instruction framework to determine the specific content and execution requirements of each measurement and control instruction, such as "Instruction Identifier: FZ-001; Execution Subject: Smart Circuit Breaker of Unit 1, Building 1; Action Parameter: Disconnect 5kW of load; Timing Requirement: Execute at 10:00; Feedback Standard: Return to open state after execution; Safety Constraint: Confirm that there is no short-circuit fault in the line." This forms a preliminary set of frequent power outage measurement and control instructions.

[0169] Step S147: Based on the intervention action sequence table, add collaborative logic relationships between instructions to the initial frequent power outage monitoring and control instruction set to generate a collaborative monitoring and control instruction set.

[0170] Referring to the execution order, interval, and connection conditions of each action in the intervention action sequence table, collaborative logical relationships are added between instructions in the initial frequent power outage monitoring and control instruction set. For example, instruction B can only be executed after instruction A is completed; instructions C and D can be executed in parallel; when instruction E fails, instruction F is triggered. By adding these collaborative logical relationships, the instruction set forms an organic whole, ensuring the coordinated execution of intervention actions and generating a collaborative monitoring and control instruction set.

[0171] Step S1471: Analyze the intervention action sequence table, extract the action number, execution duration, interval duration and connection conditions, determine the temporal relationship between instructions, and form instruction temporal relationship data.

[0172] The intervention action sequence table is parsed to extract the action number, execution duration of each action, interval duration between actions, and connection conditions. Based on this information, the temporal relationships between different instructions are determined, such as instruction 1 being executed before instruction 2, with a 5-second interval between them, and the execution of instruction 2 requiring the completion of instruction 1 as a connection condition. These temporal relationships are then organized into instruction temporal association data.

[0173] Step S1472: Decompose the initial frequent power outage monitoring and control instruction set according to instruction type and executing device to form a device-specific instruction subset, and output the sub-device instruction set.

[0174] The initial set of frequent power outage monitoring and control commands is divided according to command type (such as load regulation, line isolation, etc.) and executing device. For example, all load regulation commands executed by the intelligent circuit breaker in Building 1 are grouped into one subset, and line isolation commands executed by the main distribution box switch are grouped into another subset. This forms device-specific command subsets, outputting device-specific command sets to facilitate different devices receiving and executing their respective commands.

[0175] Step S1473: Based on the instruction timing association data, mark the corresponding action number and timing position for each instruction in the sub-device instruction set to form a timing identifier instruction set.

[0176] Based on the action sequence number and timing relationship in the instruction timing association data, each instruction in the sub-device instruction set is labeled with its corresponding action sequence number and its timing position in the entire intervention process, such as the 3rd instruction executed. In this way, each instruction has a clear timing identifier, forming a timing-identified instruction set.

[0177] Step S1474: For instructions with a connection relationship, add a logical association that triggers the execution of the subsequent instruction after the completion of the preceding instruction, forming a set of triggering associated instructions.

[0178] For instructions that have a sequential relationship in the instruction timing association data, such as instruction B needing instruction A to be executed before it can be executed, add the corresponding logical association to the timing identifier instruction set. For example, add "Trigger condition: Instruction A execution completion signal" to the description of instruction B to form a trigger association instruction set, ensuring that instructions are executed in the correct order.

[0179] Step S1475: For instructions that can be executed in parallel, mark their parallel execution conditions and corresponding device coordination logic to form a parallel associated instruction set.

[0180] For instructions that can be executed in parallel, based on the triggering associated instruction set, their parallel execution conditions are marked, such as "execute simultaneously with instruction C," along with the corresponding device coordination logic, such as how different devices can start execution synchronously and how resources can be coordinated. This forms a parallel associated instruction set, improving the execution efficiency of intervention actions.

[0181] Step S1476: Set the adjustment plan and alternative actions for subsequent instructions when a certain instruction fails to execute, forming an exception handling instruction set.

[0182] Considering the possibility of instruction execution failures, a follow-up instruction adjustment plan and alternative action are defined for each instruction in case of failure. For example, when instruction A fails, the execution of instruction B is canceled, and instruction D is executed instead as an alternative action. These exception handling plans are compiled into an exception handling instruction set.

[0183] Step S1477: Integrate the timing identifier instruction set, trigger association instruction set, parallel association instruction set, and exception handling instruction set, retain the core instruction content, and supplement the collaborative association description to form the initial collaborative measurement and control instruction.

[0184] The information from the timing identifier instruction set, trigger association instruction set, parallel association instruction set, and exception handling instruction set is integrated, retaining the core content of each instruction, such as instruction identifier, execution subject, and action parameters, and supplementing the collaborative association description, such as the timing relationship between instructions, triggering conditions, parallel logic, and exception handling scheme, to form the initial collaborative measurement and control instructions.

[0185] Step S1478: Redundancy removal is performed on the coordination descriptions in the initial coordinated measurement and control instructions to form a simplified coordinated measurement and control instruction set.

[0186] The collaborative association descriptions in the initial collaborative measurement and control commands are examined, and duplicate or redundant content is removed to ensure that the descriptions are concise and clear. For example, if multiple commands have the same trigger condition description, only one instance needs to be retained. After redundancy removal, a simplified collaborative measurement and control command set is formed.

[0187] Step S1479: Simulate the instruction execution process, check and correct the coordination logic conflicts, and form a verified coordinated measurement and control instruction set.

[0188] By simulating the execution flow of collaborative measurement and control commands, conflicts in the collaborative logic between commands are checked, such as timing discrepancies or trigger condition conflicts. For example, if two commands are simultaneously set to execute only after the other has completed, a deadlock may occur. Conflicts are corrected to ensure smooth command execution, resulting in a verified collaborative measurement and control command set.

[0189] Step S14710: Sort the verified collaborative measurement and control instruction set according to the action timing table, and output the collaborative measurement and control instruction set.

[0190] According to the execution order of the actions in the intervention action sequence table, the instructions in the verified collaborative measurement and control instruction set are sorted so that the order of the instructions is consistent with the execution order of the actions, so that the execution unit can receive and execute the instructions in sequence and finally output the collaborative measurement and control instruction set.

[0191] Step S148: Based on the instruction receiving requirements of the user-side measurement and control unit and the line-side control equipment, the format of the collaborative measurement and control instruction set is converted to form a format-adapted measurement and control instruction set.

[0192] Different user-side measurement and control units and line-side control equipment may have different command reception format requirements, such as communication protocols, data formats, and command encoding. Based on these requirements, the commands in the collaborative measurement and control command set are format-converted, such as converting the command content from text format to binary format conforming to the Modbus protocol, to ensure that the commands can be correctly received and parsed by the corresponding equipment, thus forming a format-adapted measurement and control command set.

[0193] Step S149: Perform redundancy removal processing on the format-adapted measurement and control instruction set to form an optimized frequent power outage measurement and control instruction set. Sort the optimized frequent power outage measurement and control instruction set according to instruction type and execution sequence, and output frequent power outage measurement and control instructions applicable to the user side of the low-voltage distribution network.

[0194] After verifying the format compatibility of the monitoring and control instruction set, duplicate or similar instructions are removed to avoid redundant execution and resource waste. For example, two instructions that require the same device to perform the same action at the same time can be merged into one instruction. This redundancy removal process results in an optimized frequent power outage monitoring and control instruction set. Then, the optimized instruction set is sorted according to instruction type (load regulation, line isolation, etc.) and execution sequence to ensure a clear instruction structure and execution order. The final output is a frequent power outage monitoring and control instruction for use on the user side of the low-voltage distribution network.

[0195] Step S150: Send the frequent power outage monitoring and control command to the corresponding execution unit and collect the power distribution operation status data after the command is executed.

[0196] The generated frequent power outage monitoring and control commands are sent to the corresponding user-side monitoring and control units and line-side control equipment via the communication network. Upon receiving the commands, the execution units perform the corresponding actions as required. After the commands are executed, monitoring equipment collects operating status data of the power distribution system, such as voltage, current, power, and temperature, to evaluate the effectiveness of the command execution.

[0197] Step S210: Perform vulnerability analysis on the risk transmission path of the power outage risk association model, and extract the correlation edges between the abnormal transmission features and the distribution line parameters in the power outage risk association model. The correlation edges are the connection paths where there is an influence relationship between the abnormal transmission features and the distribution line parameters.

[0198] In this embodiment, the power outage risk association model constructed in step S120 is used as the processing object. This power outage risk association model contains a set of abnormal conduction features and a set of distribution line parameters. Each abnormal conduction feature in the abnormal conduction feature set is associated with one or more distribution line parameters in the distribution line parameter set through a connection path. First, all established connection paths in the power outage risk association model are traversed, and the name of the abnormal conduction feature at the beginning and the name of the distribution line parameter at the end of each connection path are read one by one. For example, if the beginning of a connection path is the abnormal conduction feature "voltage sag recovery time" and the end is the distribution line parameter "line joint contact resistance value", then this connection path is recorded as an associated edge, and the association direction of this associated edge is recorded as from the abnormal conduction feature to the distribution line parameter. All the associated edges obtained by traversal are summarized to form a risk conduction associated edge set. Each associated edge in the risk conduction associated edge set uniquely corresponds to a pair of influence relationship connection paths between abnormal conduction features and distribution line parameters.

[0199] Step S211: Perform vulnerability quantification on each of the associated edges, and calculate the product of the frequency of activation of the associated edge in historical power outage events and the aging rate of the parameters of the distribution line connected to the associated edge, as the vulnerability value of the associated edge.

[0200] From the historical power outage dataset formed in step S1281, extract the records of abnormal conduction features and abnormal distribution line parameters involved in each historical power outage event. Compare each associated edge with the records of each historical power outage event one by one. If, in a certain historical power outage event, the abnormal conduction feature connected to the associated edge is recorded as occurring and the distribution line parameter connected to the associated edge is recorded as exceeding the normal value range, then mark the associated edge as activated once in that historical power outage event. Count the total number of times each associated edge is activated in all historical power outage events, and divide the total number of activations by the total number of historical power outage events to obtain the activation frequency of the associated edge. At the same time, extract the aging rate parameter of the distribution line parameter connected to each associated edge from the distribution line parameter set formed in step S123. The aging rate parameter is the amount of numerical decay or numerical drift of the distribution line parameter caused by material aging or environmental corrosion per unit operating time. For example, the aging rate of the line insulation resistance is a decrease of 0.5 megohms per year. The activation frequency of each associated edge is multiplied by the aging rate of the power distribution line parameters connected to that associated edge, and the resulting product is used as the vulnerability value of that associated edge.

[0201] Step S212: Map the vulnerability values ​​of all associated edges in the power outage risk association model to the distribution topology corresponding to the power outage anomaly propagation trajectory to generate a vulnerability heat map. The vulnerability heat map indicates the vulnerability values ​​of each line segment and each distribution node in the distribution topology.

[0202] Obtain the distribution topology data corresponding to the power outage anomaly propagation trajectory generated in step S110. This distribution topology data includes the spatial orientation of all line segments and the geographical location of all distribution nodes within the distribution system. The vulnerability value of each associated edge calculated in step S211 is spatially matched according to the actual line segment or distribution node corresponding to the distribution line parameter connected to that associated edge, assigning the vulnerability value of each associated edge to the line segment or distribution node where the distribution line parameter connected to that associated edge is located. If the same line segment or distribution node is mapped by multiple associated edges and assigned multiple vulnerability values, the sum of the vulnerability values ​​of all associated edges on that line segment or distribution node is taken as the comprehensive vulnerability value of that line segment or distribution node. Using the power distribution topology as a base map, the overall vulnerability values ​​of each line segment and each distribution node are visualized and rendered in the form of a thermal distribution. The line segments and distribution nodes with higher overall vulnerability values ​​are rendered in darker colors, while the line segments and distribution nodes with lower overall vulnerability values ​​are rendered in lighter colors, generating a vulnerability thermal distribution map. This vulnerability thermal distribution map directly indicates the level of vulnerability of each line segment and each distribution node in the power distribution topology through the depth of color.

[0203] Step S213: Based on the vulnerability thermal distribution map, identify line sections with vulnerability values ​​exceeding the vulnerability threshold as high-vulnerability sections, and identify distribution nodes with vulnerability values ​​exceeding the vulnerability threshold as high-vulnerability nodes, thus forming a list of critical vulnerable parts.

[0204] On the vulnerability heat map generated in step S212, a unified vulnerability threshold is pre-set as the classification standard. This vulnerability threshold is set as a critical reference value for the comprehensive vulnerability value based on the historical operation and maintenance experience of the power distribution system. The comprehensive vulnerability value of each line segment in the vulnerability heat map is read sequentially, and compared with the vulnerability threshold. Line segments with comprehensive vulnerability values ​​exceeding the threshold are marked as high-vulnerability segments, and the segment identifier, start and end node names, and comprehensive vulnerability value of each high-vulnerability segment are recorded. The comprehensive vulnerability value of each distribution node in the vulnerability heat map is read sequentially, and compared with the vulnerability threshold. Distribution nodes with comprehensive vulnerability values ​​exceeding the threshold are marked as high-vulnerability nodes, and the node identifier, node location description, and comprehensive vulnerability value of each high-vulnerability node are recorded. All high-vulnerability segments and all high-vulnerability nodes are summarized to form a list of critical vulnerable locations. The list of critical vulnerable locations includes sub-lists of high-vulnerability segments and high-vulnerability nodes.

[0205] Step S214: Perform line parameter sensitivity disturbance simulation on the highly vulnerable section, modify the distribution line parameter values ​​of the highly vulnerable section one by one in the power outage risk association model, observe the change in the power outage risk level output by the power outage risk association model after each modification, and record the distribution line parameter value that causes the power outage risk level to jump as the parameter critical failure value of the highly vulnerable section.

[0206] Each highly vulnerable section is selected from the list of vulnerable critical parts as the current processing object. The corresponding distribution line parameter item for this highly vulnerable section is located in the power outage risk association model constructed in step S120, and the current actual value of this distribution line parameter item is recorded as the initial baseline value for disturbance simulation. The distribution line parameter value of the current highly vulnerable section is modified successively by decreasing and increasing steps according to a preset disturbance step size. After each modification, the power outage risk association model is rerun, so that the power outage risk association model recalculates and outputs a new power outage risk level based on the modified distribution line parameter value. The power outage risk level output after each modification is compared with the power outage risk level under the initial baseline value, and the change range of the power outage risk level corresponding to each modification is calculated. The distribution line parameter modification value corresponding to the first occurrence of a jump in the power outage risk level from a preset risk level to an adjacent higher risk level is recorded as the parameter critical failure value of this highly vulnerable section. The above perturbation simulation process is performed on each highly vulnerable section to obtain the critical failure value of the parameters for each highly vulnerable section.

[0207] Step S215: Perform node state degradation extrapolation on the highly vulnerable node, fit the node state degradation trend curve according to the historical state data of the highly vulnerable node, extrapolate along the node state degradation trend curve to the node failure critical point, and take the extrapolation time to reach the node failure critical point as the remaining effective runtime of the highly vulnerable node.

[0208] Each highly vulnerable node is selected from the list of vulnerable critical components as the current processing object. The historical state data sequence of the highly vulnerable node within its historical operating period is extracted from the node abnormal conduction state record formed in step S115. This historical state data includes at least one of the following: contact temperature historical record sequence, contact resistance historical record sequence, and signal attenuation historical record sequence. A mathematical curve fitting is performed on the historical state data sequence with the time axis as the horizontal axis and the state quantity values ​​in the historical state data sequence as the vertical axis to obtain the node state degradation trend curve of the highly vulnerable node. The critical failure standard value of the corresponding type of state quantity for the highly vulnerable node is obtained from the power distribution system operation and maintenance specifications. The node state degradation trend curve is extended outward along the time axis until the state quantity value on the extrapolated curve reaches the critical failure standard value. The time point corresponding to this position is taken as the node failure critical point. The time span between the current time point and the node failure critical point is calculated, and this time span is taken as the remaining effective operating time of the highly vulnerable node. The above degradation simulation process is performed on each highly vulnerable node to obtain the remaining effective runtime of each highly vulnerable node.

[0209] Step S216: Generate vulnerability prevention and intervention parameters based on the critical failure value of the parameters and the remaining effective operating time. The vulnerability prevention and intervention parameters are used to trigger preventive maintenance actions on the high-vulnerability section or the high-vulnerability node before the power line parameter value of the high-vulnerability section approaches the critical failure value of the parameters or before the operating time of the high-vulnerability node approaches the remaining effective operating time.

[0210] The critical failure value of the parameter for each highly vulnerable section obtained in step S214 is compared with the current distribution line parameter value of that highly vulnerable section. The numerical difference between the current distribution line parameter value and the critical failure value is calculated. This numerical difference is divided by the aging rate of the corresponding distribution line parameter of the highly vulnerable section to obtain the remaining parameter approximation time for that highly vulnerable section. The remaining effective running time of each highly vulnerable node obtained in step S215 is compared with the preset prevention lead time. The difference between the remaining effective running time and the prevention lead time is calculated as the prevention trigger countdown for that highly vulnerable node. For each highly vulnerable section, vulnerability prevention intervention parameters are generated, including section identifier, critical failure value, current distribution line parameter value, remaining parameter approximation time, and recommended preventive maintenance action type. For each highly vulnerable node, vulnerability prevention intervention parameters are generated, including node identifier, critical failure standard value of state quantity, current state quantity value, remaining effective running time, prevention trigger countdown, and recommended preventive maintenance action type. The vulnerability prevention and intervention parameters are used to trigger preventive maintenance actions on high-vulnerability sections or high-vulnerability nodes to maintenance personnel or automatic maintenance systems before the parameter values ​​of distribution lines in high-vulnerability sections approach the critical failure value or before the operating time of high-vulnerability nodes approaches the remaining effective operating time.

[0211] Step S217: The vulnerability prevention intervention parameters are fused with the power outage collaborative intervention parameters to generate collaborative enhancement intervention parameters that include preventive maintenance actions and real-time intervention actions.

[0212] Obtain the outage coordinated intervention parameters generated in step S130. These parameters include real-time intervention action plans, real-time intervention execution equipment, and real-time intervention action sequence for any existing outage risk. Sort the vulnerability prevention intervention parameters generated in step S216 according to the urgency of the preventive maintenance action trigger time, placing the vulnerability prevention intervention parameter with the shortest preventive trigger countdown at the top. Merge the sorted vulnerability prevention intervention parameters with the real-time intervention actions in the outage coordinated intervention parameters. For cases where the trigger time of the preventive maintenance action overlaps with the execution time of the real-time intervention action, determine the action priority according to the power distribution system operation safety rules, placing actions with higher priority at the beginning of the execution sequence and actions with lower priority at the end, while adjusting the interval between adjacent actions to avoid action conflicts. After merging, generate coordinated enhanced intervention parameters. These parameters simultaneously include the trigger conditions, maintenance execution equipment, and maintenance action parameters for preventive maintenance actions, and the execution conditions, intervention execution equipment, and intervention action parameters for real-time intervention actions, clearly defining the execution order and connection relationship between preventive maintenance actions and real-time intervention actions.

[0213] For example, the method may further include: step S218: performing multi-source abnormal coupling effect analysis on the abnormal power outage conduction trajectory, extracting multiple abnormal conduction events that overlap in the time dimension or are adjacent in the spatial dimension in the abnormal power outage conduction trajectory, forming an abnormal conduction event cluster.

[0214] Extract all abnormal conduction events contained in the power outage abnormal conduction trajectory generated in step S110. Each abnormal conduction event carries information about its abnormal start time, abnormal end time, and abnormal conduction spatial path. Arrange all abnormal conduction events along a timeline according to their abnormal start and end times. Group abnormal conduction events with overlapping time intervals on the timeline, and group those with overlapping time intervals exceeding the overlap time threshold into the same time overlap group. Simultaneously, perform spatial adjacency analysis on all abnormal conduction events according to their abnormal conduction spatial paths, grouping those with shared line sections or shared distribution nodes along their abnormal conduction spatial paths into the same spatial adjacency group. Extract abnormal conduction events that belong to both the same time overlap group and the same spatial adjacency group, and combine them into an abnormal conduction event cluster. Finally, group all abnormal conduction events that meet both the time overlap and spatial adjacency conditions into their respective abnormal conduction event clusters, forming an abnormal conduction event cluster set.

[0215] Step S219: Label each abnormal conduction event in the abnormal conduction event cluster with an abnormal type. The abnormal types include voltage sag abnormal type, current overcurrent abnormal type, leakage current surge abnormal type, and temperature surge abnormal type, to obtain an abnormal type labeled event cluster.

[0216] Extract the type definition of each abnormal conduction dimension from the power outage abnormality conduction dimension division result formed in step S112. The abnormality type corresponding to the voltage abnormality conduction dimension is voltage sag abnormality type, the abnormality type corresponding to the current abnormality conduction dimension is current overcurrent abnormality type, the abnormality type corresponding to the leakage current abnormality conduction dimension is leakage current surge abnormality type, and the abnormality type corresponding to the temperature abnormality conduction dimension is temperature surge abnormality type. For each abnormal conduction event cluster in the abnormal conduction event cluster set obtained in step S218, traverse each abnormal conduction event within the abnormal conduction event cluster, and determine the abnormal conduction dimension to which it belongs based on the abnormality feature description of the abnormal conduction event. For example, an abnormal conduction event with an abnormality feature description of a short-term sudden drop in voltage amplitude is determined to be a voltage sag abnormality type, an abnormal conduction event with an abnormality feature description of a sudden surge in current amplitude is determined to be a current overcurrent abnormality type, an abnormal conduction event with an abnormality feature description of a sudden increase in zero-sequence current is determined to be a leakage current surge abnormality type, and an abnormal conduction event with an abnormality feature description of a rapid rise in contact temperature is determined to be a temperature surge abnormality type. The identified anomaly types are labeled onto the corresponding anomaly propagation events, so that each anomaly propagation event carries an anomaly type label. After labeling, an anomaly type labeled event cluster is obtained.

[0217] Step S220: Determine the coupling effect of any two abnormal transmission events of different abnormal types in the abnormal type labeled event cluster. If the time overlap of the two abnormal transmission events exceeds the time overlap threshold and the spatial overlap of the two abnormal transmission events in the transmission path exceeds the spatial overlap threshold, then it is determined that the two abnormal transmission events have a coupling effect relationship, forming an abnormal coupling relationship pair set.

[0218] In the anomaly type labeled event cluster obtained in step S219, any two anomaly propagation events with different anomaly type labels are selected to form an anomaly event pair to be determined. The start and end times of the first and second anomaly propagation events in the anomaly event pair to be determined are extracted. The length of the overlapping time interval between the two anomaly propagation events on the time axis is calculated. The length of the overlapping time interval is divided by the duration of the shorter anomaly propagation event in the two anomaly propagation events to obtain the time overlap. The spatial propagation paths of the first and second anomaly propagation events in the anomaly event pair to be determined are extracted. The spatial overlap length of the two anomaly propagation paths is calculated. The spatial overlap length is divided by the total length of the shorter path in the two anomaly propagation paths to obtain the spatial overlap. The time overlap is compared with a preset time overlap threshold, and the spatial overlap is compared with a preset spatial overlap threshold. If both the time overlap and spatial overlap exceed the time overlap threshold and the spatial overlap threshold, it is determined that the two anomaly propagation events in the anomaly event pair to be determined have a coupling relationship. The above judgment process is performed on all possible pairs of abnormal events to be judged, and the abnormal event pairs that are judged to have a coupling relationship are summarized to form a set of abnormal coupling relationship pairs.

[0219] Step S221: Quantify the coupling strength of each abnormal coupling pair in the set of abnormal coupling pairs, and calculate the weighted sum of the abnormal amplitude product of the two abnormal propagation events in the abnormal coupling pair and the time overlap, as the coupling strength value of the abnormal coupling pair.

[0220] From the set of abnormal coupling pairs obtained in step S220, each abnormal coupling pair is selected as the current processing object. The abnormal amplitudes of the first and second abnormal conduction events in the abnormal coupling pair are extracted from the power outage abnormal conduction trajectory. The abnormal amplitude is the maximum deviation of the abnormal characteristic of the abnormal conduction event from the normal reference value. For example, the abnormal amplitude of a voltage sag anomaly is the absolute value of the difference between the lowest sag voltage and the rated voltage; the abnormal amplitude of an overcurrent anomaly is the absolute value of the difference between the peak overcurrent current and the rated current. The abnormal amplitudes of the first and second abnormal conduction events are multiplied to obtain the abnormal amplitude product. The time overlap of the abnormal coupling pair is extracted, and the abnormal amplitude product and the time overlap are weighted and summed according to preset weighting coefficients. The preset weighting coefficients include a first weighting coefficient for the abnormal amplitude product and a second weighting coefficient for the time overlap, and the sum of the first and second weighting coefficients is one. The result of the weighted summation is used as the coupling strength value of the abnormal coupling pair. The above quantization process is performed on each pair of anomalous coupling relationships to obtain the coupling strength value of each pair of anomalous coupling relationships.

[0221] Step S222: Divide the set of abnormal coupling pairs into a strongly coupled abnormal pair subset and a weakly coupled abnormal pair subset according to the coupling strength value. The strongly coupled abnormal pair subset contains abnormal coupling pairs whose coupling strength value exceeds the coupling strength threshold, and the weakly coupled abnormal pair subset contains abnormal coupling pairs whose coupling strength value does not exceed the coupling strength threshold.

[0222] A unified coupling strength threshold is set as the dividing line between strong and weak coupling. This threshold is determined based on statistical analysis of historical coupling fault cases in the power distribution system. The coupling strength value of each abnormal coupling pair in the abnormal coupling pair set obtained in step S221 is read one by one and compared with the coupling strength threshold. Abnormal coupling pairs with coupling strength values ​​exceeding the threshold are classified into a strongly coupled abnormal pair subset. Each pair in this strongly coupled abnormal pair subset has a high coupling strength, indicating a significant mutual influence between the two abnormal propagation events. Abnormal coupling pairs with coupling strength values ​​not exceeding the threshold are classified into a weakly coupled abnormal pair subset. Each pair in this weakly coupled abnormal pair subset has a low coupling strength, indicating a relatively weak mutual influence between the two abnormal propagation events. After the classification, strongly coupled abnormal pair subsets and weakly coupled abnormal pair subsets are obtained.

[0223] Step S223: Perform coupling amplification effect deduction on each pair of strongly coupled abnormal relationships in the subset of strongly coupled abnormal relationships. Simultaneously trigger two abnormal propagation events of the strongly coupled abnormal relationship pair in the power outage risk association model. Record the superimposed power outage risk level output by the model and compare it with the sum of the independent power outage risk levels when each abnormal propagation event is triggered individually. Take the incremental part of the superimposed power outage risk level exceeding the sum of the independent power outage risk levels as the coupling amplification effect of the strongly coupled abnormal relationship pair.

[0224] The power outage risk correlation model constructed in step S120 is set as the basic model for inference. Each pair of strongly coupled abnormal relationships is selected one by one from the subset of strongly coupled abnormal relationship pairs as the current inference object. First, a separate triggering inference is performed. In the power outage risk correlation model, only the first abnormal propagation event in the current strongly coupled abnormal relationship pair is triggered, while keeping other related factors in a normal baseline state. The first independent power outage risk level output by the power outage risk correlation model is recorded. Then, in the power outage risk correlation model, only the second abnormal propagation event in the current strongly coupled abnormal relationship pair is triggered, while keeping other related factors in a normal baseline state. The second independent power outage risk level output by the power outage risk correlation model is recorded. The first independent power outage risk level and the second independent independent power outage risk level are added together to obtain the sum of the independent power outage risk levels. Then, a simultaneous triggering inference is performed. In the power outage risk correlation model, both abnormal propagation events in the current strongly coupled abnormal relationship pair are triggered simultaneously, while keeping other related factors in a normal baseline state. The superimposed power outage risk level output by the power outage risk correlation model is recorded. The superimposed power outage risk level is compared with the sum of the independent power outage risk levels. The difference between the superimposed power outage risk level and the sum of the independent power outage risk levels is calculated, and this difference is taken as the coupling amplification effect of the strongly coupled anomaly relationship pair. The above deduction process is performed for each strongly coupled anomaly relationship pair to obtain the coupling amplification effect of each pair.

[0225] Step S224: Perform coupling risk correction on the power outage risk association model according to the coupling amplification effect quantity, add a coupling effect compensation term to the power outage risk association model, the coupling effect compensation term takes the joint occurrence of the two abnormal transmission characteristics of the strongly coupled abnormal relationship pair as the trigger condition, and takes the coupling amplification effect quantity as the risk level compensation range, to obtain the coupled enhanced power outage risk association model.

[0226] The power outage risk association model constructed in step S120 is structurally expanded by adding a coupling effect compensation term node to the risk level calculation output of the model. From the coupling amplification effect of each pair of strongly coupled anomaly relationships obtained in step S223, the joint occurrence condition of the two anomaly propagation features corresponding to that pair is extracted. This joint occurrence condition is set as the trigger condition for the coupling effect compensation term, and the coupling amplification effect is set as the risk level compensation magnitude when the trigger condition is met. The trigger conditions and risk level compensation magnitudes corresponding to all strongly coupled anomaly relationships are added one by one to the compensation rule table of the coupling effect compensation term node. When the power outage risk association model detects that two anomaly propagation features corresponding to any trigger condition in the compensation rule table appear simultaneously in the input data, the risk level compensation magnitude corresponding to that trigger condition is automatically added to the original power outage risk level output by the model, resulting in the coupled-corrected power outage risk level. The power outage risk association model after completing the addition of the coupling effect compensation term node and the configuration of the compensation rules is the coupled-enhanced power outage risk association model.

[0227] Step S225: Regenerate the power outage collaborative intervention parameters using the coupled enhanced power outage risk association model.

[0228] The original power outage risk association model used in step S130 is replaced with the coupled enhanced power outage risk association model obtained in step S224, and the complete process from steps S131 to S1310 is re-executed. First, the risk association level, abnormal conduction characteristics, corresponding influencing factors, and the triggering state of the coupling effect compensation item are extracted from the coupled enhanced power outage risk association model to locate the dominant coupling factor and the scope of coupling influence of the current power outage risk. Then, based on the dominant coupling factor and the scope of coupling influence, the user-side monitoring and control units and line-side control equipment are repositioned, and the user-side monitoring and control unit list and the line-side control equipment list are updated. Next, the intervention action plan is redesigned, adding coupling decoupling intervention actions for the coupling effect compensation item based on the original intervention action plan. For example, when a voltage sag anomaly and a current overcurrent anomaly are detected simultaneously and trigger the coupling effect compensation item, a composite intervention action is added to simultaneously disconnect the load causing the voltage sag and limit the current overcurrent. Finally, a new power outage collaborative intervention parameter is generated that integrates coupling and decoupling intervention actions. This collaborative intervention parameter can simultaneously address single abnormal conduction events and coupled abnormal conduction events, achieving more comprehensive collaborative intervention for power outage risks.

[0229] For example, the method may further include: step S226: performing instruction execution reliability prediction on the frequent power outage measurement and control instructions, extracting the execution subject identifier, action type and action parameters of each measurement and control instruction in the frequent power outage measurement and control instructions, and obtaining the historical instruction execution success rate of the execution device corresponding to the execution subject identifier, wherein the historical instruction execution success rate is the ratio of the number of times the execution device successfully executes instructions to the total number of received instructions within a preset historical period.

[0230] The complete content of each monitoring and control command generated in step S140 is read one by one. Each monitoring and control command includes a command identifier field, an execution subject identifier field, an action type field, and an action parameter field. The unique identifier of the executing device is extracted from the execution subject identifier field of each monitoring and control command; the action type corresponding to the monitoring and control command is extracted from the action type field; and the specific value of the action parameter is extracted from the action parameter field. Simultaneously, the command execution records of the executing device corresponding to each execution subject identifier within a preset historical period are obtained from the power distribution system equipment operation log. The preset historical period is the most recent complete operating cycle. The command execution record includes the total number of commands received by the executing device within that period and the number of commands successfully executed. The historical command execution success rate of the executing device is obtained by dividing the number of successfully executed commands by the total number of commands received. The execution subject identifier, action type, action parameter, and corresponding historical command execution success rate of each monitoring and control command are stored in a correlated manner.

[0231] Step S227: Perform an instruction execution condition compliance check on each measurement and control instruction, obtain the current operating status information of the execution device corresponding to the execution subject identifier, the current operating status information includes the device communication link status, device power supply status and device execution mechanism status, determine whether the execution device currently has the conditions to execute the measurement and control instruction based on the current operating status information, and mark the measurement and control instructions that do not have the execution conditions as condition missing instructions.

[0232] Through the power distribution system equipment status monitoring interface, the current operating status information of the execution equipment corresponding to each execution entity identifier is obtained in real time. The current operating status information includes equipment communication link status information, equipment power supply status information, and equipment actuator status information. The equipment communication link status information indicates whether the communication link between the execution equipment and the control center is unobstructed; the equipment power supply status information indicates whether the power supply to the execution equipment is normal; and the equipment actuator status information indicates whether the mechanical execution components of the execution equipment are in an operable state. For each measurement and control command extracted in step S226, the execution entity identifier of the measurement and control command is read, and the current operating status information corresponding to the execution entity identifier is found. The equipment communication link status, equipment power supply status, and equipment actuator status are checked in sequence to see if they are unobstructed, normal, and operable. If all three checks are normal, the execution equipment is determined to have the conditions to execute the measurement and control command. If any check is abnormal, the execution equipment is determined to not have the conditions to execute the measurement and control command, the measurement and control command is marked as a condition-deficient command, and the specific reason for the condition deficiency is recorded.

[0233] Step S228: Perform inter-instruction dependency verification on each measurement and control instruction. Based on the timing connection conditions and triggering dependency relationships between each measurement and control instruction in the frequent power outage measurement and control instructions, check whether there is a situation where subsequent measurement and control instructions that depend on the preceding instruction cannot be triggered because the preceding instruction is marked as a condition-missing instruction. Mark the measurement and control instructions that cannot be triggered due to the condition-missing preceding instruction as dependency blocking instructions.

[0234] Extract the timing connection conditions and trigger dependencies between each measurement and control instruction from the collaborative measurement and control instruction set generated in step S147. The timing connection conditions describe the execution order of the measurement and control instructions, and the trigger dependencies describe that the triggering of a subsequent instruction must be based on the completion of a preceding instruction. For each measurement and control instruction in the frequent power outage measurement and control instructions generated in step S140, check whether the trigger condition of the measurement and control instruction references the execution completion signal of another measurement and control instruction as a trigger prerequisite. If it references the execution completion signal of a preceding instruction, then establish a dependency association between the measurement and control instruction and that preceding instruction. Compare the condition-missing instructions marked in step S227 with the preceding instructions in all dependency associations. If the preceding instruction that a measurement and control instruction depends on has been marked as a condition-missing instruction, then the trigger condition of the measurement and control instruction will never be met because the preceding instruction cannot be completed. Mark the measurement and control instruction as a dependency blocking instruction, and record the instruction identifier of the preceding instruction that is the direct cause of blocking the dependency blocking instruction.

[0235] Step S229: Generate an instruction alternative for the missing condition instruction. Search for alternative execution devices that can perform the same action type as the missing condition instruction in the user-side measurement and control unit list and the line-side control equipment list. If an alternative execution device exists, replace the execution subject identifier of the missing condition instruction with the identifier of the alternative execution device and generate an alternative execution instruction. If no alternative execution device exists, generate an instruction execution abnormality alarm.

[0236] Extract all available devices and their supported action types from the user-side measurement and control unit list formed in step S132 and the line-side control equipment list formed in step S133. For each condition-missing instruction marked in step S227, extract the action type and action parameters of the condition-missing instruction, and search the list of available devices and their supported action types for other available devices that support the action type besides the original execution subject identifier. If at least one alternative available device supporting the action type is found, select the device with the largest load margin from the alternative available devices as the alternative execution device, replace the original execution subject identifier of the condition-missing instruction with the identifier of the alternative execution device, keep the action type and action parameters unchanged, generate the alternative execution instruction, and mark the instruction as an alternative execution instruction and the reason for substitution in the alternative execution instruction. If no alternative available device supporting the action type is found, the alternative execution instruction cannot be generated, and an instruction execution anomaly alarm is generated. This instruction execution anomaly alarm includes the instruction identifier of the condition-missing instruction, the original execution subject identifier, the reason for the condition-missing condition, and a description of no alternative device.

[0237] Step S230: Generate a timing reconstruction scheme for the dependent blocking instruction. Under the premise of ensuring that the overall goal of the intervention remains unchanged, adjust the dependency relationship between the dependent blocking instruction and its preceding instructions, and change the triggering condition of the dependent blocking instruction from depending on the execution completion signal of the condition missing instruction to depending on the manual confirmation signal or the preset timeout signal, and generate a timing reconstruction instruction.

[0238] For each dependent blocking instruction marked in step S228, the preceding instruction identifier referenced in the trigger condition of the dependent blocking instruction is extracted. It is confirmed that this preceding instruction identifier matches the instruction identifier of a condition-missing instruction marked in step S227, meaning the dependent blocking reason for the instruction is that the preceding condition-missing instruction cannot be executed. While ensuring that the overall objective of the power outage coordinated intervention parameters generated in step S130 remains unchanged, the trigger condition of the dependent blocking instruction is redesigned. The trigger condition is changed from the completion signal of the dependent condition-missing instruction to a manual confirmation signal from the power distribution system maintenance personnel, or to a preset timeout signal. This preset timeout signal is automatically triggered when the preset waiting timeout period, calculated from the originally planned execution time of the condition-missing instruction specified in the intervention action sequence table, is reached. After modifying the trigger condition, the other contents of the dependent blocking instruction remain unchanged. A timing reconstruction instruction is generated, and the instruction is marked as a timing reconstruction instruction, the reconstruction reason, and a new trigger condition description.

[0239] Step S231: Integrate the alternative execution instruction and the timing reconstruction instruction into the frequent power outage measurement and control instruction, replace the original condition missing instruction and dependency blocking instruction, and generate a frequent power outage measurement and control instruction with enhanced instruction execution reliability.

[0240] In the frequent power outage monitoring and control instruction set generated in step S140, the original monitoring and control instructions marked as condition-missing instructions are located. The corresponding alternative execution instructions or instruction execution anomaly alarms generated in step S229 are inserted into the corresponding positions in the frequent power outage monitoring and control instruction set. If an alternative execution instruction is generated, the original condition-missing instruction is replaced; if an instruction execution anomaly alarm is generated, an alarm label is added after the original condition-missing instruction, and the execution of that instruction is suspended. In the frequent power outage monitoring and control instruction set, the original monitoring and control instructions marked as dependency blocking instructions are located. The corresponding timing reconstruction instructions generated in step S230 replace the original dependency blocking instructions. Simultaneously, based on changes in the execution subject of the alternative execution instructions and changes in the triggering conditions of the timing reconstruction instructions, the timing connection relationships between the instructions in the frequent power outage monitoring and control instruction set are readjusted to ensure that the replaced instruction set has no timing logic conflicts. After integration, a frequent power outage monitoring and control instruction set with enhanced execution reliability is generated. This instruction set has pre-emptively eliminated the risk of execution failure due to abnormal equipment status and dependency blocking between instructions before execution.

[0241] Combination Figure 2 Content, Figure 2This is a schematic diagram of the main control interface of the intelligent monitoring and control system for frequent power outages applied to the user side of a low-voltage distribution network, provided in an embodiment of the present invention. It illustrates the functional module division and operation entry points of each step of the method in the system console, corresponding to the complete processing flow of steps S110 to S150 and the extended processing steps S210 to S231 in the embodiment. The top of the interface is a global status bar displaying the system logo "PS" and the title "Intelligent Monitoring and Control System for Frequent Power Outages V3.2". The right side displays real-time status data: "1,245" indicates the number of currently online monitoring nodes, "8" indicates the number of abnormal propagation events recorded today, and "3" indicates the number of currently activated intervention strategies. The left side contains a navigation menu and a real-time status area, including navigation buttons for functional modules such as "Conduction Trajectory Tracking", "Risk Model Management", "Collaborative Intervention Center", "Historical Instruction Library", "Equipment List Management", "Data Analysis Dashboard" and "System Settings". Below the navigation menu is a tree diagram of "Active Conduction Early Warning", which highlights the abnormal path that is currently being conducted with an orange animated dashed line, and marks the end with "Risk Level: Medium - Voltage Sag Conduction in Progress". This area corresponds to the real-time status monitoring after generating the abnormal power outage conduction trajectory in steps S110 to S119 of the embodiment. The central core area is divided into two parts: the upper part is the "Visualization of Power Outage Anomaly Transmission Trajectory" area, with a light blue grid background. Different icon colors are used to distinguish "line-side nodes," "branch nodes," and "user nodes," and a dynamically flowing "signal flow" is displayed to indicate the direction of anomaly transmission. At the same time, a detailed information panel of "Node Anomaly Transmission Status Record" is labeled. This area corresponds to the step of generating and visualizing the power outage anomaly transmission trajectory in steps S110 to S119 of the embodiment. The lower part is the "Generation of Collaborative Intervention Parameters" area, which displays information such as "risk-dominant factors," "intervention action plan," "user-side execution unit," "line-side control equipment," "action parameters," and "time series / delay" in tabular form. This area corresponds to the step of generating power outage collaborative intervention parameters in steps S131 to S1310 of the embodiment. The right-hand control area includes a ring chart of "Risk Control Model Analysis Results" (showing that line-side parameters dominate 45%, load characteristics dominate 35%, and node status dominates 20%), an equipment list control panel (containing collapsible panels for "User-Side Measurement and Control Unit List" and "Line-Side Control Equipment List"), and an "Instruction Issuance Control Console" (featuring a large green "Issue Measurement and Control Instruction Sequence" execution button). This area corresponds to the steps S140 to S150 in the embodiment where frequent power outage measurement and control instructions are generated and issued for execution. The bottom information bar displays "Scenario: 17:00:00 - Residential Area - Peak Electricity Consumption Hour - Ring Network Topology - Critical Load: Electric Vehicle Charging Pile," corresponding to the user-side power distribution scenario data collected in step S142 of the embodiment. Figure 2The console interface shown provides a fully visualized operation platform for the method of this invention, from anomaly monitoring, trajectory analysis, risk modeling to the issuance of collaborative intervention commands. Operators can use this interface to monitor the power outage anomaly propagation process in real time, view the risk model analysis results, configure collaborative intervention parameters, and issue measurement and control commands, thereby realizing intelligent, visualized, and interactive measurement and control management of frequent power outages on the user side of low-voltage distribution networks.

[0242] In one exemplary embodiment, a smart monitoring and control system for frequent power outages applied to the user side of a low-voltage distribution network is provided. This system can be a terminal, server, etc., and its internal structure diagram can be as follows: Figure 2 As shown, this intelligent monitoring and control system for frequent power outages applied to the user side of a low-voltage distribution network includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, near-field communication, or other technologies. When the computer program is executed by the processor, it implements an intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the casing of a smart monitoring and control system for frequent power outages on the user side of a low-voltage distribution network. It can also be an external keyboard, touchpad, or mouse, etc.

[0243] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for intelligent monitoring and control of frequent power outages applied to the user side of a low-voltage distribution network, characterized in that, The method includes: Collect operation-related signals of user-side power distribution nodes, and based on the operation-related signals, analyze the transmission path and state changes of anomalies from the line side to the user side, and generate the power outage anomaly transmission trajectory of the user side of the low-voltage power distribution network. Based on the aforementioned power outage anomaly propagation trajectory, and combined with the user-side load operation characteristics and distribution line parameters, a power outage risk correlation model for the user side of the low-voltage distribution network is constructed. Based on the power outage risk correlation model, and according to the risk factors determined by the power outage risk correlation model, power outage coordinated intervention parameters are generated to link the user-side monitoring and control unit and the line-side control equipment. Based on outage collaborative intervention parameters and combined with user-side power distribution scenario data, frequent outage monitoring and control commands are generated for use on the user side of low-voltage power distribution networks. Frequent power outage monitoring and control commands are sent to the corresponding execution units, and power distribution operation status data are collected after the commands are executed.

2. The intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network according to claim 1, characterized in that, The process involves collecting operational correlation signals from user-side distribution nodes, analyzing the transmission path and state changes of anomalies from the line side to the user side based on these signals, and generating a power outage anomaly transmission trajectory for the user side of the low-voltage distribution network, including: Collect voltage, current, leakage current, temperature and load operation signals of the user-side power distribution nodes, and synchronously record the location and operation scenario of the power distribution nodes corresponding to each signal to form a set of power outage related signals on the user side of the low-voltage power distribution network. Based on the power outage associated signal set, multiple power outage anomaly conduction dimensions are divided to form a power outage anomaly conduction dimension division result. The multiple power outage anomaly conduction dimensions include power outage anomaly conduction dimensions of voltage, current, leakage current and temperature. For each type of signal in the power outage-related signal set, extract the features reflecting abnormal conduction under each conduction dimension to form a core feature set of abnormal conduction of power outages; Based on the results of the power outage anomaly transmission dimension classification, the correspondence between anomaly characteristics of different dimensions and transmission paths from the line side to the user side is established, forming an anomaly characteristic transmission path association table; By combining the abnormal feature propagation path association table, the signal feature changes when the abnormal passes through each distribution node are monitored, the strength evolution and impact range of the abnormal at the distribution node are recorded, and a node abnormal propagation status record is formed. Based on the node anomaly propagation status records, identify the line-side branch nodes, user-side access nodes, and load-near-end nodes that play a dominant role in the anomaly propagation rate and range, and form a list of key nodes for anomaly propagation. Connect key nodes in the sequence of abnormal transmission, supplement the transmission duration and signal change details between each node, and form a preliminary abnormal power outage transmission trajectory. By combining the load operation signals in the power outage-related signal set, the state description of the end node on the user side in the abnormal transmission trajectory is adjusted to form a corrected power outage abnormal transmission trajectory. Based on the power outage associated signal set and combined with preset heavy load, high temperature and humidity scenario parameters, scenario supplementary conduction data is generated. The corrected power outage abnormal conduction trajectory is fused with the scenario supplementary conduction data to generate the power outage abnormal conduction trajectory containing conduction details of multiple scenarios.

3. The intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network according to claim 1, characterized in that, Based on the power outage anomaly propagation trajectory, and combined with the user-side load operation characteristics and distribution line parameters, a power outage risk correlation model for the user side of the low-voltage distribution network is constructed, including: From the power outage anomaly propagation trajectory, extract the anomaly propagation features, key node information, and propagation patterns and node impact degrees corresponding to different anomaly types to form an anomaly propagation core information set. Collect the stability, startup characteristics, power consumption, and load type association features of the user-side load operation, and synchronously bind the access node location of the corresponding load to form a user-side load operation feature set; Collect parameters such as conductor specifications, laying method, insulation performance, branch structure, and connection tightness of the power distribution line to form a parameter set; The various features in the core information set of abnormal transmission are matched one by one with the user-side load operation feature set and the power distribution line parameter set to establish the mutual influence relationship and form a risk association mapping table. Based on the risk association mapping table, three risk association levels are divided into line-side parameter-driven, load characteristic-driven, and node status-driven levels, forming the risk association level division result. Based on the risk association hierarchy classification results, a multi-level risk association framework is built, and corresponding fields for anomaly transmission characteristics, load operation characteristics, power distribution line parameters and risk levels are set to form a risk association model framework. The core information set of abnormal transmission, the user-side load operation feature set, the power distribution line parameter set and the risk association mapping table are imported into the model framework to form a preliminary power outage risk association model. By combining historical power outage data from the user side of the low-voltage distribution network, the influence weights of different levels of related factors on power outage risk are adjusted to obtain the adjusted power outage risk correlation model. For different load types and line parameter combinations, corresponding association judgment rules are configured for the adjusted power outage risk association model to form a scenario adaptation rule set; The adjusted power outage risk association model is fused with the scenario adaptation rule set to obtain the power outage risk association model.

4. The intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network according to claim 1, characterized in that, The method, combining the aforementioned power outage risk correlation model, generates power outage coordinated intervention parameters for the user-side monitoring and control unit and the line-side control equipment based on the risk factors determined by the power outage risk correlation model, including: The power outage risk correlation model is analyzed to extract the risk correlation level, abnormal transmission characteristics and corresponding influencing factors, locate the dominant factors and scope of influence of the current power outage risk, and form the risk model analysis results; Based on the impact range in the risk model analysis results, the load monitoring unit, leakage current measurement and control unit, and node status monitoring unit covering the impact range are located to form a user-side measurement and control unit list; Based on the dominant factors of line-side parameters in the risk model analysis results, the corresponding line switching equipment, load regulating equipment and fault isolation equipment are located to form a list of line-side control equipment. Connect the user-side measurement and control unit list with the line-side control equipment list, set the signal transmission path and collaborative action logic between each unit and equipment, and form a collaborative intervention link table; Based on the risk model analysis results, action plans for load shedding, line segmentation isolation, parameter adjustment and fault location are designed for different risk-dominant factors, forming a set of intervention action plans. The various actions in the intervention action plan set are matched with the corresponding equipment in the user-side measurement and control unit list and the line-side control equipment list to determine the executing subject and action requirements, and an action equipment matching table is formed. Based on the collaborative intervention link table and the action equipment matching table, the execution sequence, action interval and timing connection conditions of various intervention actions are determined, and an intervention action timing table is generated. Based on the correlation parameters in the power outage risk correlation model, the execution range, duration and triggering conditions of each intervention action are set to form an intervention action parameter set; The system integrates the intervention action plan set, action equipment matching table, intervention action sequence table and intervention action parameter set to form a collaborative intervention strategy and outputs preliminary power outage collaborative intervention parameters. By combining historical collaborative intervention data, the intervention action parameters and timing are adjusted to generate the power outage collaborative intervention parameters.

5. The intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network according to claim 1, characterized in that, The method, based on outage coordination intervention parameters and combined with user-side power distribution scenario data, generates frequent outage monitoring and control commands applicable to the user side of the low-voltage distribution network, including: From the power outage coordinated intervention parameters, the intervention action plan, execution equipment, action sequence and action parameters are extracted as the core element set of coordinated intervention. Collect user-side power distribution topology, load distribution, electricity consumption time characteristics, line laying environment and safety operation requirements to form a user-side power distribution scenario dataset. The core elements of collaborative intervention are compared with the user-side power distribution scenario dataset. Based on the comparison results, the intervention actions and parameters are adjusted to form a scenario-adaptive intervention plan. Based on the scenario-adaptive intervention scheme, four types of measurement and control commands are classified: load regulation, line isolation, fault location, parameter monitoring, and emergency response, resulting in a classification of measurement and control command types. For each type of measurement and control command, an command framework is built that includes fields for command identifier, execution subject, action parameters, timing requirements, feedback standards, and safety constraints, forming a measurement and control command framework set; Import the corresponding information from the scenario adaptation intervention plan into the measurement and control command framework set, determine the specific content and execution requirements of each measurement and control command, and form a preliminary set of frequent power outage measurement and control commands; Based on the intervention action sequence table, add collaborative logic relationships between instructions to the initial frequent power outage monitoring and control instruction set to generate a collaborative monitoring and control instruction set; Based on the instruction receiving requirements of the user-side measurement and control unit and the line-side control equipment, the collaborative measurement and control instruction set is format-converted to form a format-adapted measurement and control instruction set; After the format is adapted, the measurement and control instruction set is deredundant to form an optimized frequent power outage measurement and control instruction set. The optimized frequent power outage measurement and control instruction set is sorted according to instruction type and execution sequence, and the frequent power outage measurement and control instructions applied to the user side of the low-voltage distribution network are output.

6. The intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network according to claim 2, characterized in that, Based on the results of the power outage anomaly propagation dimension classification, a correspondence between different dimensions of anomaly characteristics and the propagation path from the line side to the user side is established, forming an anomaly characteristic propagation path association table, including: Based on the results of the power outage anomaly propagation dimension, the core feature set of power outage anomaly propagation is classified into the corresponding dimension, forming a subset of voltage, current, leakage current and temperature anomaly features, and outputting a dimension-specific anomaly feature set. Collect information on trunk lines, branch lines, user access lines and load near-end conduction paths, record the node composition and connection relationship of each path, and form a complete list of conduction paths; The voltage anomaly feature subset is matched with the conduction path of the main line and branch line, and the current anomaly feature subset is matched with the user access line and the near-end conduction path of the load to form a preliminary feature path matching result; For a single abnormal feature under each dimension, the corresponding specific transmission path segment, path node and feature manifestation form are determined to form a refined feature path matching result. Identify the impact of anomalous features on the state of the transmission path, and the feedback effect of the transmission path parameters on the evolution of anomalous features, to form a two-way influence labeling result; Construct an association table structure containing fields such as dimension name, abnormal features, propagation path, path node, feature performance, and bidirectional impact, as the framework for the association table of abnormal feature propagation path; Import the multidimensional anomaly feature set, complete list of transmission paths, refined matching results of feature paths, and bidirectional influence marking results into the framework to form a preliminary anomaly feature transmission path association table. Remove duplicate related items from the association table, merge duplicate descriptions of the same abnormal feature and the same path, maintain the uniqueness of the association relationship, and form a deduplicated association table; Based on the power outage associated signal set, the missing feature details and path node information in the deduplicated association table are improved to form a supplemented and improved association table. The supplemented and improved association table is then classified and sorted according to the dimension name, and an abnormal feature transmission path association table is output.

7. The intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network according to claim 3, characterized in that, The process involves combining historical power outage data from the user side of the low-voltage distribution network, adjusting the weights of different levels of related factors on power outage risk, and obtaining an adjusted power outage risk correlation model, including: Collect the anomaly types, propagation paths, load status, line parameters, impact range, and handling results of historical power outage events on the user side of the low-voltage distribution network to form a historical power outage dataset; Based on the risk association hierarchy classification results, line-side parameters, load characteristics and node status factors are selected from the historical power outage dataset to form a historical association factor set. Based on the severity and scope of historical power outages, the influence ratio of each related factor under different scenarios is analyzed to obtain the results of the factor influence degree analysis. Based on the results of the factor influence degree analysis, initial weight values ​​are assigned to the associated factors at different levels in the risk association model, forming an initial weight allocation table; Substitute the historical set of related factors and the initial weight allocation table into the preliminary power outage risk association model, compare the model output with the actual historical power outage situation, and form the weight verification results. For the correlation factors with significant deviations in the weight verification results, adjust their weight values ​​and the weight ratios between different factors to form a revised weight allocation table. Based on the scenario adaptation rule set, the adaptation effect of the revised weight allocation table in various scenarios is verified, and scenario adaptation verification results are generated. Based on the scenario adaptation verification results, the weights in the revised weight allocation table are fine-tuned to generate the final weight allocation table. Import the final weight allocation table into the preliminary power outage risk association model, replace the initial weight parameters, and form a power outage risk association model after weight update. After the weight update, the power outage risk association model is associated with the basis for weight adjustment and the applicable scenario identifier, and the adjusted power outage risk association model is output.

8. The intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network according to claim 4, characterized in that, Based on the collaborative intervention link table and the action device matching table, the execution order, action intervals, and timing connection conditions of various intervention actions are determined, and an intervention action timing table is generated, including: Extract the signal transmission path and duration from the collaborative intervention link table, record the signal transmission time between different device units, and form signal transmission time sequence data. The action device matching table is analyzed to determine the execution device, action complexity, and preparation time required for each intervention action, thus forming the basic information for action execution; Based on the severity of risks in the risk model analysis results, four types of intervention actions are prioritized: emergency response, fault isolation, parameter adjustment, and monitoring feedback, resulting in action priority classification results. The various intervention actions are sorted according to the priority order of emergency response, fault isolation, parameter adjustment and monitoring feedback to obtain a preliminary action sequence. Based on signal transmission timing data and basic information of action execution, an interval duration is set for adjacent actions in the initial action timing sequence to obtain the timing sequence after interval adjustment. Based on the collaborative intervention link table, the time sequence is adjusted to mark intervention actions that can be executed in parallel, forming a parallel optimized time sequence; Set the connection trigger conditions between adjacent actions, including the completion signal of the previous action, the equipment status feedback signal and the parameter compliance signal, and form the action connection condition marking result; Construct a time sequence table framework that includes fields such as action sequence number, action name, execution device, priority, execution duration, interval duration, and connection conditions to form an intervention action time sequence table framework; The parallel optimized time sequence and action connection condition labeling results are imported into the framework to form a preliminary intervention action time sequence table; Check for conflicts in the sequence of actions, intervals, and durations in the preliminary intervention action sequence table, correct any conflicts, and output the intervention action sequence table.

9. The intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network according to claim 5, characterized in that, Based on the intervention action sequence table, collaborative logic relationships between instructions are added to the initial frequent power outage monitoring and control instruction set to generate a collaborative monitoring and control instruction set, including: Analyze the intervention action sequence table, extract the action number, execution duration, interval duration and connection conditions, determine the temporal relationship between instructions, and form instruction temporal relationship data; The initial set of frequent power outage monitoring and control instructions is broken down according to instruction type and executing device to form a device-specific instruction subset, and the device-specific instruction set is output. Based on instruction timing association data, each instruction in the sub-device instruction set is labeled with the corresponding action number and timing position to form a timing identifier instruction set; For instructions that have a connection relationship, add a logical association that triggers the execution of the subsequent instruction after the completion of the preceding instruction, forming a set of triggering related instructions; For instructions that can be executed in parallel, their parallel execution conditions and corresponding device coordination logic are marked to form a set of parallel associated instructions; Define the adjustment plan and alternative actions for subsequent instructions when a certain instruction fails to execute, forming an exception handling instruction set; The system integrates the timing identifier instruction set, trigger association instruction set, parallel association instruction set, and exception handling instruction set, retains the core instruction content, and supplements the collaborative association description to form the initial collaborative measurement and control instruction; Redundancy is removed from the coordination descriptions in the initial coordinated measurement and control commands to form a simplified coordinated measurement and control command set; Simulate the instruction execution process, check and correct any conflicts in the coordination logic, and form a verified coordinated measurement and control instruction set; The verified collaborative measurement and control instruction set is sorted according to the action timing table, and the collaborative measurement and control instruction set is output.

10. A smart monitoring and control system for frequent power outages applied to the user side of a low-voltage distribution network, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the intelligent monitoring and control method for frequent power outages applied to the user side of a low-voltage distribution network as described in any one of claims 1 to 9 by executing the machine-executable instructions.

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