Comprehensive management system of operation and inspection equipment for operation and inspection of power grid

Abstract

The invention provides a comprehensive management system for operation and inspection equipment for power grid operation and inspection, which relates to the technical field of comprehensive management of equipment and comprises the steps of constructing an equipment node network, generating an equipment control link according to equipment control information, determining main path nodes and branch nodes, obtaining a branch link, sorting the main path nodes and the branch nodes according to a control sequence, and managing the branch link. Obtaining an operation inspection monitoring link; calculating an equipment anomaly coefficient of the operation inspection monitoring link, performing equipment anomaly judgment on the equipment nodes, obtaining equipment anomaly nodes, and generating an abnormal equipment link; calculating a node time sequence anomaly coefficient of the operation inspection monitoring link, performing node time sequence anomaly judgment on the equipment node, obtaining an abnormal time sequence node, and generating an abnormal time sequence link; and according to the abnormal equipment link and the abnormal time sequence link, generating a correlation abnormal link and a prediction abnormal range, and carrying out early warning and adjustment. The fault monitoring and management linkage and the fault processing efficiency of the power grid equipment network can be obviously improved.

Description

Technical Field

[0001] The present invention provides a comprehensive management system for operation and maintenance equipment used in power grid operation and maintenance, which relates to the technical field of comprehensive equipment management, and specifically relates to the technical field of comprehensive management of operation and maintenance equipment used in power grid operation and maintenance. Background Art

[0002] Under the background of the rapid development of modern industrial automation and the Internet of Things (IoT), the construction and monitoring of equipment networks have become key links to ensure the stable operation of the power grid operation and maintenance system, improve production efficiency, and reduce maintenance costs. The traditional monitoring and management systems for operation and maintenance equipment used in power grid operation and maintenance often rely on single or scattered monitoring means, making it difficult to comprehensively and real-time grasp the operation status and potential faults of equipment. The existing technologies for equipment monitoring and management in power grid operation and maintenance are difficult to conduct impact analysis and early warning adjustment on equipment fault correlation and time fault correlation, resulting in the inability to respond promptly to the reflection and expansion of accidents, causing serious consequences. Summary of the Invention

[0003] The present invention provides a comprehensive management system for operation and maintenance equipment used in power grid operation and maintenance to solve the problems that the traditional monitoring and management systems for operation and maintenance equipment used in power grid operation and maintenance often rely on single or scattered monitoring means, making it difficult to comprehensively and real-time grasp the operation status and potential faults of equipment, and the existing technologies for equipment monitoring and management in power grid operation and maintenance are difficult to conduct impact analysis and early warning adjustment on equipment fault correlation and time fault correlation, resulting in the inability to respond promptly to the reflection and expansion of accidents, causing serious consequences, etc.:

[0004] A comprehensive management system for operation and maintenance equipment used in power grid operation and maintenance proposed by the present invention, the system includes:

[0005] A monitoring link generation module, configured to construct an equipment node network, generate an equipment control link according to equipment control information, determine a main path node and a branch node, obtain a forked link, sort each main path node and branch node according to the control sequence, and obtain an operation and maintenance monitoring link;

[0006] An equipment anomaly analysis module, configured to calculate the equipment anomaly coefficient of the operation and maintenance monitoring link, perform equipment anomaly determination on the equipment nodes, obtain equipment anomaly nodes, and generate an anomaly equipment link;

[0007] A timing anomaly analysis module, configured to calculate the node timing anomaly coefficient of the operation and maintenance monitoring link, perform node timing anomaly determination on the equipment nodes, obtain anomaly timing nodes, and generate an anomaly timing link;

[0008] A comprehensive anomaly monitoring module, configured to generate an associated anomaly link and a predicted anomaly range according to the anomaly equipment link in combination with the anomaly timing link, and perform early warning and adjustment.

[0009] Further, the monitoring link generation module includes:

[0010] The device network composition module is used to obtain the device information of each operation and maintenance device and label the device nodes for each device information;

[0011] Obtain the device nodes corresponding to each device node label and form a device node network;

[0012] The control link generation module is used to obtain the device control information of each device node in the device node network and generate a device control link according to the device control information;

[0013] The link update module is used to obtain the device control data of each device node in the device control link, and determine the main path device and branch devices in the device control link according to the device control data;

[0014] Match the main path device for each branch device to obtain a bifurcation link;

[0015] Sort each device node on the main path and branch according to the controlled order to obtain a sorted link;

[0016] Update the device control link according to the sorted link to obtain an operation and maintenance monitoring link.

[0017] Furthermore, the device anomaly analysis module includes:

[0018] The device anomaly calculation module is used to obtain the device operation data of the device nodes in the operation and maintenance monitoring link and calculate the device anomaly coefficient according to the device operation data;

[0019] The device anomaly determination module is used to perform device anomaly determination on the device nodes according to the device anomaly coefficient to obtain a device determination result;

[0020] The device link generation module is used to obtain abnormal device nodes according to the device determination result, and generate an abnormal device link for multiple abnormal device nodes according to the controlled order;

[0021] The device risk acquisition module is used to obtain the normal device nodes in the abnormal device link, label the risk for the normal device nodes, and obtain device risk nodes.

[0022] Furthermore, the timing anomaly analysis module includes:

[0023] The timing anomaly calculation module is used to calculate the node timing anomaly coefficient according to the device operation data, the device anomaly coefficient and the device anomaly determination information;

[0024] The timing anomaly determination module is used to perform node timing anomaly determination on the device nodes according to the node timing anomaly coefficient to obtain a node timing determination result;

[0025] A timing link generation module, configured to obtain abnormal timing nodes according to the node timing determination result;

[0026] Obtain timing abnormal device nodes according to the abnormal timing nodes;

[0027] Generate an abnormal timing link by arranging multiple timing abnormal device nodes in the controlled order;

[0028] A timing risk acquisition module, configured to obtain normal device nodes in the abnormal timing link, perform risk marking on the normal device nodes, and obtain timing risk nodes.

[0029] Further, the comprehensive anomaly monitoring module includes:

[0030] An associated link generation module, configured to obtain overlapping device nodes of the abnormal device link and the abnormal timing link, and generate an associated abnormal link;

[0031] An abnormal range prediction module, configured to obtain non-overlapping device nodes of the abnormal device link and the abnormal timing link, and generate a predicted abnormal range;

[0032] An early warning adjustment module, configured to perform abnormal early warning on the associated abnormal link and adjust the data acquisition frequency for the predicted abnormal range.

[0033] Further, the management method includes:

[0034] S1. Construct a device node network, generate a device control link according to device control information, determine main path nodes and branch nodes, obtain a bifurcation link, sort each main path node and branch node in the control order, and obtain an operation and inspection monitoring link;

[0035] S2. Calculate the device anomaly coefficient of the operation and inspection monitoring link, perform device anomaly determination on the device nodes, obtain device anomaly nodes, and generate an abnormal device link;

[0036] S3. Calculate the node timing anomaly coefficient of the operation and inspection monitoring link, perform node timing anomaly determination on the device nodes, obtain abnormal timing nodes, and generate an abnormal timing link;

[0037] S4. Generate an associated abnormal link and a predicted abnormal range according to the abnormal device link in combination with the abnormal timing link, and perform early warning and adjustment.

[0038] Further, the S1 includes:

[0039] Obtain the device information of each operation and inspection device, perform device node marking on each device information; obtain the device nodes corresponding to each device node marking, and form a device node network;

[0040] Obtain the device control information of each device node in the device node network, and generate a device control link according to the device control information;

[0041] Obtain the device control data of each device node in the device control link, and determine the main path device and branch device in the device control link according to the device control data;

[0042] Match the main path device for each branch device to obtain a bifurcation link;

[0043] Sort each device node on the main path and branch according to the controlled order to obtain a sorted link;

[0044] Update the device control link according to the sorted link to obtain an operation and maintenance monitoring link.

[0045] Further, the S2 includes:

[0046] Obtain the device operation data of the device node in the operation and maintenance monitoring link, and calculate the device anomaly coefficient according to the device operation data;

[0047] Perform device anomaly determination on the device node according to the device anomaly coefficient to obtain a device determination result;

[0048] Obtain the abnormal device nodes according to the device determination result, and generate an abnormal device link for multiple abnormal device nodes according to the controlled order;

[0049] Obtain the normal device nodes in the abnormal device link, and perform risk marking on the normal device nodes to obtain device risk nodes.

[0050] Further, the S3 includes:

[0051] Calculate the node timing anomaly coefficient according to the device operation data, device anomaly coefficient and device anomaly determination information;

[0052] Perform node timing anomaly determination on the device node according to the node timing anomaly coefficient to obtain a node timing determination result;

[0053] Obtain the abnormal timing nodes according to the node timing determination result;

[0054] Obtain the timing abnormal device nodes according to the abnormal timing nodes;

[0055] Generate an abnormal timing link for multiple timing abnormal device nodes according to the controlled order;

[0056] Obtain the normal device nodes in the abnormal timing link, and perform risk marking on the normal device nodes to obtain timing risk nodes.

[0057] Further, S4 includes:

[0058] Obtain the overlapping device nodes of the abnormal device link and the abnormal timing link, and generate an associated abnormal link;

[0059] Obtain the non-overlapping device nodes of the abnormal device link and the abnormal timing link, and generate a predicted abnormal range;

[0060] Conduct abnormal early warning for the associated abnormal link, and adjust the data acquisition frequency for the predicted abnormal range.

[0061] Advantages of the present invention: By automatically constructing a monitoring link and real-time analyzing device status data, the system can significantly improve the monitoring and management efficiency of power grid operation and maintenance equipment, reduce manual intervention and misjudgment. Combining device abnormal analysis and timing abnormal analysis, the system can accurately locate abnormal devices and abnormal time periods in the power grid, providing strong support for rapid response and effective handling. By predicting the abnormal range and sending out early warning signals in advance, the system can significantly enhance the early warning ability of the power grid, reduce the risk and loss of faults. The comprehensive and accurate monitoring data and early warning information provided by the system help operation and maintenance personnel make more scientific and reasonable operation and maintenance decisions, improving the overall operation and maintenance level of the power grid. The application of the system marks an important step in the intelligent and automated direction of power grid operation and maintenance, providing strong support for the construction and development of smart grids. Description of the Drawings

[0062] Figure 1 It is a schematic diagram of the management method of the operation and maintenance equipment comprehensive management system for power grid operation and maintenance;

[0063] Figure 2 It is a schematic diagram of the operation and maintenance monitoring link. Detailed Embodiments

[0064] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for explaining and illustrating the present invention, and are not used to limit the present invention.

[0065] In an embodiment of the present invention, a proposed operation and maintenance equipment comprehensive management system for power grid operation and maintenance, the system includes:

[0066] A monitoring link generation module, used to construct a device node network, generate a device control link according to device control information, determine the main road nodes and branch nodes, obtain the bifurcated link, sort each main road node and branch node according to the control order, and obtain the operation and maintenance monitoring link;

[0067] A device abnormal analysis module, used to calculate the device abnormal coefficient of the operation and maintenance monitoring link, conduct device abnormal determination on the device nodes, obtain the device abnormal nodes, and generate an abnormal device link;

[0068] The timing anomaly analysis module is used to calculate the node timing anomaly coefficient of the operation and maintenance monitoring link, determine the node timing anomaly of the device node, obtain the abnormal timing nodes, and generate the abnormal timing link.

[0069] The comprehensive anomaly monitoring module is used to generate the associated abnormal link and predict the abnormal range according to the abnormal device link and the abnormal timing link, and perform early warning and adjustment.

[0070] The working principle of the above technical solution is as follows: The monitoring link generation module constructs a device node network by collecting information such as the geographical location and functional attributes of each device in the power grid. These nodes represent various devices in the power grid, such as transformers, switches, lines, etc. Based on the device control information (such as control instructions, communication protocols, etc.), the system generates the control link between devices, clarifying the control relationship and dependency relationship between devices. The system identifies the main path nodes and branch nodes, obtains the bifurcated links, sorts the nodes according to the control sequence, and finally generates the operation and maintenance monitoring link. This step ensures that the monitoring link can accurately reflect the actual operation relationship and monitoring requirements between devices. The device anomaly analysis module calculates the device anomaly coefficient by monitoring the device operation state data (such as current, voltage, temperature, etc.), and evaluates whether the device is in an abnormal state. According to the anomaly coefficient threshold, the system determines the anomaly of the device node and identifies the abnormal device nodes. Integrate the abnormal device nodes and their related links to form an abnormal device link for subsequent analysis and processing. Analyze the change trend of the device node state data over time, calculate the node timing anomaly coefficient to identify the abnormal change of the device state in the time dimension. Similarly, based on the anomaly coefficient threshold, the system determines the timing anomaly of the device node and obtains the abnormal timing nodes. Integrate the abnormal timing nodes and their related links to form an abnormal timing link, providing a basis for analyzing the cause of device failures and predicting future trends. The comprehensive anomaly monitoring module combines the abnormal device link and the abnormal timing link to generate an associated abnormal link, revealing the internal connection between device anomalies and time changes. Based on the associated abnormal link and historical data, the system uses a prediction model to predict the possible abnormal range in the future, providing guidance for taking preventive measures in advance. The system issues a warning signal according to the prediction result and automatically or assist manually in adjusting or repairing the device to ensure the safe and stable operation of the power grid.

[0071] The technical effects of the above technical solution are as follows: By automatically constructing a monitoring link and analyzing device status data in real time, the system can significantly improve the monitoring efficiency of power grid operation and maintenance equipment, reduce manual intervention and misjudgment. Combining equipment anomaly analysis and time-series anomaly analysis, the system can accurately locate abnormal equipment and abnormal time periods in the power grid, providing strong support for rapid response and effective handling. By predicting the scope of anomalies and sending early warning signals in advance, the system can significantly enhance the early warning ability of the power grid, reducing the risk and losses caused by faults. The comprehensive and accurate monitoring data and early warning information provided by the system help operation and maintenance personnel make more scientific and reasonable operation and maintenance decisions, improving the overall operation and maintenance level of the power grid. The application of the system marks an important step in the intelligent and automated development of power grid operation and maintenance, providing strong support for the construction and development of smart grids.

[0072] In one embodiment of the present invention, the monitoring link generation module includes:

[0073] The device network composition module is used to obtain the device information of each operation and maintenance device and label the device nodes for each device information; the device information includes information such as the device name, usage, and characteristics of the operation and maintenance device.

[0074] Obtain the device nodes corresponding to each device node label and form a device node network;

[0075] The control link generation module is used to obtain the device control information of each device node in the device node network and generate a device control link according to the device control information;

[0076] The link update module is used to obtain the device control data of each device node in the device control link, and determine the main path device and branch devices in the device control link according to the device control data; the main path device is the device that all branch devices need to communicate with, and the branch devices are the devices other than the main path device (similar to the trunk and branches).

[0077] Match each branch device with the main path device to obtain a forked link;

[0078] Obtain the control connection relationship between each branch device and each main path device, match the branch devices with the main path devices that have a control connection relationship, and adjust them according to the device control order to obtain the forked link corresponding to the main path device;

[0079] Sort each device node on the main path and branch according to the controlled order to obtain a sorted link;

[0080] Update the device control link according to the sorted link to obtain an operation and maintenance monitoring link.

[0081] The working principle of the above technical solution is as follows: The system first collects detailed information of each operation and maintenance device in the power grid, including device name, usage, characteristics, etc. These information are sorted out, and a unique device node label is assigned to each device for subsequent management and identification. The device nodes corresponding to all device node labels are organized to form a device node network. This network reflects the physical connections and logical relationships among the devices in the power grid. The system obtains the device control information of each device node in the device node network, and these information describe how the devices control and communicate with each other. Based on the device control information, the system generates device control links, that is, the control relationships and paths among the devices. Through the analysis of device control data, the system determines which devices are main path devices (i.e., devices to which all branch devices need to be communicatively connected), and which devices are branch devices (i.e., devices other than the main path devices). For each branch device, the system performs matching of main path devices to find all possible connection points of the main path devices. Based on these connection points, the system generates branch links, that is, the control paths for how the branch devices branch out from the main path devices. The system further analyzes the control connection relationships between each branch device and each main path device to ensure that each branch device is correctly matched with its corresponding main path device. Each device node of the main path and branch is sorted according to the device control sequence (such as start sequence, shutdown sequence, etc.) to generate a sorted link. The system updates the original device control link according to the sorted link to obtain the final operation and maintenance monitoring link. This link not only reflects the control relationships among the devices, but also clarifies the sequence and priority of device monitoring.

[0082] The technical effects of the above technical solution are as follows: By automatically collecting and sorting out device information, the system can quickly construct a device node network and generate detailed device control links and operation and maintenance monitoring links, significantly improving the management efficiency of power grid operation and maintenance. Labeling and sorting each device node ensure that no important device is missed during the monitoring process, and at the same time, monitoring is carried out in the correct sequence, improving the accuracy and reliability of monitoring. By identifying main path devices and branch devices, the system can reasonably allocate monitoring resources, prioritize key devices and key links, and achieve optimal allocation and utilization of resources. Once an abnormality or fault occurs in the operation and maintenance monitoring link, the system can quickly locate the problem and initiate the corresponding emergency response mechanism, shortening the fault handling time and improving the safety and stability of the power grid. The system provides data support and decision-making basis for intelligent operation and maintenance. Through integration with other intelligent systems, it can further realize the automation, intelligence and high efficiency of power grid operation and maintenance.

[0083] In an embodiment of the present invention, the device anomaly analysis module includes:

[0084] The device anomaly calculation module is used to obtain the device operation data of the device nodes in the operation and inspection monitoring link, and calculate the device anomaly coefficient according to the device operation data;

[0085] The calculation formula of the device anomaly coefficient is as follows:

[0086]

[0087] Where BX is the device anomaly coefficient, z is the total number of types of operation data, Y xi is the actual monitoring data of the i-th type of operation data, Y ui is the preset threshold of the monitoring data of the i-th type of operation data, IF i is the judgment condition coefficient of the i-th type of operation data. When (Y xi -Y ui ) is greater than 0, IF i is 1. When (Y xi -Y ui ) is less than or equal to 0, IF i is 0. When calculating, first eliminate the unit and then calculate the data;

[0088] The device anomaly determination module is used to perform device anomaly determination on the device nodes according to the device anomaly coefficient to obtain the device determination result;

[0089] Compare the device anomaly coefficient with the preset device anomaly threshold. When the device anomaly coefficient is greater than the preset device anomaly threshold, the corresponding device node is determined as an abnormal device node; otherwise, it is a normal device node.

[0090] The device link generation module is used to obtain the abnormal device nodes according to the device determination result, and generate an abnormal device link by arranging multiple abnormal device nodes in the controlled order;

[0091] The device risk acquisition module is used to obtain the normal device nodes in the abnormal device link, and perform risk marking on the normal device nodes to obtain device risk nodes.

[0092] The working principle of the above technical solution is as follows: The system first obtains the device operation data of each device node from the operation and maintenance monitoring link. This data may include real-time or historical data such as current, voltage, temperature, vibration, etc., reflecting the operation status of the device. For each device node, the system calculates the device anomaly coefficient based on its device operation data. The system compares the calculated device anomaly coefficient with a preset threshold to determine whether the device is abnormal. If the anomaly coefficient exceeds the threshold, the device is determined to be an abnormal device. According to the device determination result, the system obtains all the device nodes determined to be abnormal. Then, in the controlled order of these devices in the operation and maintenance monitoring link (i.e., their positions in the control link or start / stop order), these abnormal device nodes are connected to generate an abnormal device link. In the abnormal device link, in addition to the abnormal device nodes, there may also be normal device nodes connected to the abnormal devices. Although these devices do not currently show abnormalities, due to their direct connection or dependency on the abnormal devices, they may face potential risks. The system will mark these normal device nodes with risks and label them as device risk nodes. In this way, the operation and maintenance personnel can focus on these devices to prevent potential problems from occurring.

[0093] The technical effect of the above technical solution is: By real-time monitoring and calculating the device anomaly coefficient, the system can detect potential problems in advance before the device appears serious faults and achieve early warning. Through the in the formula, the anomaly coefficient of the device relative to can be calculated. When (Y xi -Y ui ) is greater than 0, IF is 1, [(Y xi -Y ui )*IF]=(Y xi -Y ui ). When (Y xi -Y ui ) is less than or equal to 0, IF is 0, [(Y xi -Y ui)*IF]=0; This helps the operation and maintenance personnel to take measures in a timely manner to avoid the expansion of faults and the aggravation of losses. The system can accurately locate the abnormal device and its position in the operation and inspection monitoring link, providing accurate fault information for the operation and maintenance personnel. This helps to shorten the fault troubleshooting time and improve the fault handling efficiency. By marking the risk of normal device nodes, the system can identify in advance the devices that may be affected by abnormal devices and take corresponding preventive measures. This helps to reduce the overall operation risk of the power grid and improve the stability and reliability of the power grid. The entire abnormal detection and risk marking process is based on device operation data, realizing data-driven decision support. This makes the operation and maintenance decisions more scientific, reasonable and effective. The application of this process marks an important step in the intelligent direction of power grid operation and inspection. Through automated and intelligent means, the efficiency and accuracy of power grid operation and inspection are improved, and the cost and risk of manual intervention are reduced.

[0094] In one embodiment of the present invention, the timing anomaly analysis module includes:

[0095] A timing anomaly calculation module, configured to calculate a node timing anomaly coefficient according to the device operation data, the device anomaly coefficient, and the device anomaly determination information;

[0096] The calculation formula of the node timing anomaly coefficient is:

[0097]

[0098] where SX is the anomaly coefficient of the specified time node in the timing, b is the total number of devices at the specified time node in the timing, IF a is the time node anomaly judgment coefficient of the a-th device, BX a is the device anomaly coefficient of the a-th device at the specific time node, BX y is the preset device anomaly threshold at the specific time node. When BX a > BX y then, IF a = 1. When BX a ≤ BX y then, IF a = 0;

[0099] A timing anomaly determination module, configured to perform node timing anomaly determination on the device node according to the node timing anomaly coefficient to obtain a node timing determination result;

[0100] The node timing anomaly coefficient is the anomaly coefficient of each time node of each timing. It is used to determine the correlation of device anomalies at the same time node and the degree of anomaly at this time node.

[0101] A timing link generation module, configured to obtain abnormal timing nodes according to the node timing determination result;

[0102] Obtain the timing abnormal device node according to the abnormal timing node;

[0103] Generate an abnormal timing link by arranging multiple timing abnormal device nodes in the controlled order;

[0104] A timing risk acquisition module, configured to acquire normal device nodes in the abnormal timing link, perform risk marking on the normal device nodes, and obtain timing risk nodes.

[0105] The working principle of the above technical solution is as follows: The system first calculates the timing abnormal coefficient of each device node by combining device operation data, device abnormal coefficient, and device abnormal determination information. This coefficient aims to reflect the comprehensive abnormal coefficient of all device nodes at a certain time in the timing, aiming to reflect the device abnormal degree and correlation at the same time. Compare the calculated node timing abnormal coefficient with a preset threshold to determine whether there is a timing abnormality in the device node. If the timing abnormal coefficient exceeds the threshold, it is considered that there is a timing abnormality in the device node. According to the node timing determination result, the system filters out all nodes determined to be timing abnormal, that is, abnormal timing nodes. Map these abnormal timing nodes to specific devices to obtain timing abnormal device nodes. These devices exhibit abnormal behavior in timing, which may indicate an impending fault or performance degradation. Connect multiple timing abnormal device nodes in the controlled order in the operation and inspection monitoring link to generate an abnormal timing link. This link reveals the propagation path and potential influence range of timing abnormalities in power grid devices. In the abnormal timing link, in addition to timing abnormal device nodes, there may also be normal device nodes directly or indirectly connected to these abnormal devices. Although these devices do not currently exhibit timing abnormalities, due to their positional or functional relevance, they may face potential risks. The system will perform timing risk marking on these normal device nodes and mark them as timing risk nodes. In this way, operation and maintenance personnel can more comprehensively understand the potential risks in the power grid and take corresponding preventive measures.

[0106] The technical effect of the above technical solution is: By calculating the node timing abnormal coefficient and making a determination, the system can deeply understand the abnormal behavior of the device state changing over time, providing strong support for fault prediction and preventive maintenance. When IF in the formula aThe larger it is, the greater the anomaly coefficient of the time node. Conversely, the smaller it is. Mapping the time-series anomaly nodes to specific devices and generating the anomaly time-series link helps to accurately identify potential risk points and risk propagation paths in the power grid, providing a scientific basis for operation and maintenance decisions. Based on time-series anomaly detection and risk annotation, the system can issue early warning signals in advance and guide operation and maintenance personnel to take intervention measures in a timely manner to prevent the occurrence or expansion of faults and ensure the safe and stable operation of the power grid. By accurately identifying time-series anomaly devices and risk nodes, the system can guide the optimal allocation of operation and maintenance resources, invest limited resources in the places where they are most needed, and improve the efficiency and effectiveness of operation and maintenance. The application of this process further improves the intelligent level of power grid operation and inspection, and realizes the comprehensive, real-time and accurate monitoring and evaluation of the state of power grid equipment through automated and intelligent means.

[0107] In one embodiment of the present invention, the comprehensive anomaly monitoring module includes:

[0108] The associated link generation module is used to obtain the overlapping device nodes of the abnormal device link and the abnormal time-series link, and generate an associated abnormal link;

[0109] The abnormal range prediction module is used to obtain the non-overlapping device nodes of the abnormal device link and the abnormal time-series link (i.e., device risk nodes and time-series risk nodes), and generate a predicted abnormal range;

[0110] The early warning adjustment module is used to perform abnormal early warning on the associated abnormal link and adjust the data collection frequency for the predicted abnormal range. The adjustment of the data collection frequency includes multiplying or reducing by a multiple.

[0111] The working principle of the above technical solution is as follows: The system first compares the abnormal device link and the abnormal timing link to find the overlapping device nodes between the two. These nodes are identified as abnormal in the device anomaly determination and also show timing anomalies in the timing anomaly determination, so they are considered highly correlated anomaly points. Connect these overlapping device nodes to generate an associated anomaly link. This link reveals the direct connection between device anomalies and timing anomalies and is the most critical anomaly area in the power grid. In addition to the overlapping device nodes in the associated anomaly link, the system also pays attention to the non-overlapping device nodes that only appear in the abnormal device link or the abnormal timing link. Although these nodes do not show both device anomalies and timing anomalies at the same time, due to their positional or functional relevance, they may become abnormal in the future. These non-overlapping device nodes (including device risk nodes and timing risk nodes) are included in the predicted anomaly range. This range provides an overview of the potential anomaly areas in the power grid and helps the operation and maintenance personnel make preparations in advance. For the associated anomaly link, the system immediately issues an anomaly warning signal to notify the operation and maintenance personnel to pay attention to this area and prepare to take emergency measures. For the predicted anomaly range, the system dynamically adjusts the data collection frequency according to the risk level and the results of the prediction model. For areas with higher risks, the data collection frequency is increased by multiples to more intensively monitor the changes in the device status; for areas with lower risks or relatively stable areas, the data collection frequency is appropriately reduced to save resources.

[0112] The technical effects of the above technical solution are as follows: By generating an associated anomaly link and issuing an anomaly warning, the system can accurately locate the high-risk areas in the power grid, guide the operation and maintenance personnel to respond quickly, and reduce the possibility of faults occurring and expanding. Combining device anomalies and timing anomalies to generate a predicted anomaly range helps to more comprehensively evaluate the potential risks in the power grid. This comprehensive analysis method improves the accuracy and reliability of the prediction. Dynamically adjusting the data collection frequency according to the predicted anomaly range realizes the optimal allocation of monitoring resources. Increasing the data collection frequency in areas with higher risks can detect potential problems earlier; reducing the data collection frequency in areas with lower risks can save resources and reduce the burden of data processing. Through automated and intelligent means for anomaly warning and data collection frequency adjustment, the system can significantly improve the operation and maintenance efficiency. The operation and maintenance personnel can focus more on high-risk areas and key issues, reducing unnecessary inspections and interventions. The application of this process helps to enhance the stability and reliability of the power grid. By timely discovering and responding to potential risks, the system can reduce the occurrence of faults and power outage time and improve the overall operation level of the power grid.

[0113] In one embodiment of the present invention, the management method includes:

[0114] S1. Construct a device node network, generate a device control link according to device control information, determine the main path nodes and branch nodes, obtain the bifurcated link, sort each main path node and branch node according to the control order, and obtain the operation and maintenance monitoring link;

[0115] S2. Calculate the device anomaly coefficient of the operation and maintenance monitoring link, conduct device anomaly determination on the device nodes, obtain the device anomaly nodes, and generate the anomaly device link;

[0116] S3. Calculate the node timing anomaly coefficient of the operation and maintenance monitoring link, conduct node timing anomaly determination on the device nodes, obtain the anomaly timing nodes, and generate the anomaly timing link;

[0117] S4. Generate an associated anomaly link and a predicted anomaly range according to the anomaly device link in combination with the anomaly timing link, and conduct early warning and adjustment.

[0118] The working principle of the above technical solution is as follows: By collecting information such as the geographical location and functional attributes of each device in the power grid, a device node network is constructed. These nodes represent various devices in the power grid, such as transformers, switches, lines, etc. Based on device control information (such as control instructions, communication protocols, etc.), the system generates a control link between devices, clarifying the control relationship and dependency relationship between devices. The system identifies the main path nodes and branch nodes, obtains the bifurcated link, sorts the nodes according to the control order, and finally generates the operation and maintenance monitoring link. This step ensures that the monitoring link can accurately reflect the actual operation relationship and monitoring requirements between devices. The device anomaly analysis module calculates the device anomaly coefficient by monitoring the device operation state data (such as current, voltage, temperature, etc.), and evaluates whether the device is in an abnormal state. According to the anomaly coefficient threshold, the system conducts anomaly determination on the device nodes and identifies the abnormal device nodes. Integrate the abnormal device nodes and their related links to form an abnormal device link for subsequent analysis and processing. Analyze the change trend of the device node state data over time, calculate the node timing anomaly coefficient to identify the abnormal change of the device state in the time dimension. Similarly, based on the anomaly coefficient threshold, the system conducts timing anomaly determination on the device nodes and obtains the anomaly timing nodes. Integrate the anomaly timing nodes and their related links to form an anomaly timing link, providing a basis for analyzing the cause of device failure and predicting future trends. The comprehensive anomaly monitoring module combines the anomaly device link and the anomaly timing link to generate an associated anomaly link, revealing the internal connection between device anomalies and time changes. Based on the associated anomaly link and historical data, the system uses a prediction model to predict the possible future anomaly range, providing guidance for taking preventive measures in advance. The system issues an early warning signal according to the prediction result and automatically or assist manually in device adjustment or maintenance to ensure the safe and stable operation of the power grid.

[0119] The technical effects of the above technical solution are as follows: By automatically constructing a monitoring link and analyzing the device status data in real time, the system can significantly improve the monitoring efficiency of power grid operation and maintenance equipment, reduce manual intervention and misjudgment. Combining equipment anomaly analysis and time-series anomaly analysis, the system can accurately locate abnormal equipment and abnormal time periods in the power grid, providing strong support for rapid response and effective handling. By predicting the abnormal range and sending early warning signals in advance, the system can significantly enhance the early warning ability of the power grid, reducing the risk and losses caused by faults. The comprehensive and accurate monitoring data and early warning information provided by the system help operation and maintenance personnel make more scientific and reasonable operation and maintenance decisions, improving the overall operation and maintenance level of the power grid. The application of the system marks an important step towards the intelligent and automated direction of power grid operation and maintenance, providing strong support for the construction and development of smart grids.

[0120] In one embodiment of the present invention, S1 includes:

[0121] Obtain the device information of each operation and maintenance device, and perform device node annotation on each device information; the device information includes information such as the device name, usage, and characteristics of the operation and maintenance device.

[0122] Obtain the device nodes corresponding to each device node annotation, and form a device node network;

[0123] Obtain the device control information of each device node in the device node network, and generate a device control link according to the device control information;

[0124] Obtain the device control data of each device node in the device control link, and determine the main path device and branch devices in the device control link according to the device control data; the main path device is the device to which all branch devices need to communicate and connect, and the branch devices are the devices other than the main path device (similar to the trunk and branches).

[0125] Match each branch device with the main path device to obtain a bifurcated link;

[0126] Obtain the control connection relationship between each branch device and each main path device, match the branch devices with the main path device that have a control connection relationship, and adjust them according to the device control order to obtain the bifurcated link corresponding to the main path device;

[0127] Sort each device node on the main path and branches according to the controlled order to obtain a sorted link;

[0128] Update the device control link according to the sorted link to obtain an operation and maintenance monitoring link.

[0129] The working principle of the above technical solution is as follows: The system first collects detailed information of each operation and maintenance device in the power grid, including device name, usage, characteristics, etc. These information are sorted out, and a unique device node label is assigned to each device for subsequent management and identification. The device nodes corresponding to all device node labels are organized to form a device node network. This network reflects the physical connections and logical relationships among devices in the power grid. The system obtains the device control information of each device node in the device node network, and these information describe how devices control and communicate with each other. Based on the device control information, the system generates device control links, that is, the control relationships and paths among devices. Through the analysis of device control data, the system determines which devices are main path devices (i.e., devices that all branch devices need to communicate with), and which devices are branch devices (i.e., devices other than main path devices). For each branch device, the system performs matching of main path devices to find all possible connection points of main path devices. Based on these connection points, the system generates bifurcation links, that is, the control paths for how branch devices branch out from main path devices. The system further analyzes the control connection relationships between each branch device and each main path device to ensure that each branch device is correctly matched with its corresponding main path device. Each device node of the main path and branch is sorted according to the device control sequence (such as start sequence, shutdown sequence, etc.) to generate a sorted link. The system updates the original device control link according to the sorted link to obtain the final operation and maintenance monitoring link. This link not only reflects the control relationships among devices, but also clarifies the sequence and priority of device monitoring.

[0130] The technical effects of the above technical solution are as follows: By automatically collecting and sorting device information, the system can quickly construct a device node network, generate detailed device control links and operation and maintenance monitoring links, significantly improving the management efficiency of power grid operation and maintenance. Labeling and sorting each device node ensure that no important device is missed during the monitoring process, and monitoring is carried out in the correct order, improving the accuracy and reliability of monitoring. By identifying main path devices and branch devices, the system can reasonably allocate monitoring resources, prioritize key devices and key links, and achieve optimized allocation and utilization of resources. Once an abnormality or fault occurs in the operation and maintenance monitoring link, the system can quickly locate the problem and initiate the corresponding emergency response mechanism, shortening the fault handling time and improving the safety and stability of the power grid. The system provides data support and decision-making basis for intelligent operation and maintenance. Through integration with other intelligent systems, it can further realize the automation, intelligence and high efficiency of power grid operation and maintenance.

[0131] In an embodiment of the present invention, S2 includes:

[0132] Obtain the device operation data of the device nodes of the operation and maintenance monitoring link, and calculate the device abnormality coefficient according to the device operation data;

[0133] The calculation formula for the device anomaly coefficient is as follows:

[0134]

[0135] Among them, BX is the device anomaly coefficient, z is the total number of types of operation data, Y xi is the actual monitored data of the i-th type of operation data, Y ui is the preset threshold of the monitored data of the i-th type of operation data, IF is the judgment condition coefficient. When (Y xi - Y ui ) is greater than 0, IF is 1. When (Y xi - Y ui ) is less than 0, IF is 0. When calculating, first cancel the units and then calculate the data;

[0136] Perform device anomaly determination on the device nodes according to the device anomaly coefficient to obtain the device determination result;

[0137] Obtain the abnormal device nodes according to the device determination result, and generate an abnormal device link by arranging multiple abnormal device nodes in the controlled order;

[0138] Obtain the normal device nodes in the abnormal device link, and perform risk marking on the normal device nodes to obtain device risk nodes.

[0139] The working principle of the above technical solution is as follows: The system first obtains the device operation data of each device node from the operation and maintenance monitoring link. These data may include real-time or historical data such as current, voltage, temperature, vibration, etc., which reflect the operation status of the device. For each device node, the system calculates the device anomaly coefficient based on its device operation data. The system compares the calculated device anomaly coefficient with a preset threshold to determine whether the device is abnormal. If the anomaly coefficient exceeds the threshold, the device is determined to be an abnormal device. According to the device determination result, the system obtains all the device nodes determined to be abnormal. Then, in the controlled order of these devices in the operation and maintenance monitoring link (i.e., their positions in the control link or the start / stop order), these abnormal device nodes are connected to generate an abnormal device link. In the abnormal device link, in addition to the abnormal device nodes, there may also be normal device nodes connected to the abnormal devices. Although these devices do not currently show abnormalities, due to their direct connection or dependency on the abnormal devices, they may face potential risks. The system will perform risk marking on these normal device nodes and mark them as device risk nodes. In this way, the operation and maintenance personnel can focus on these devices and prevent potential problems from occurring.

[0140] The technical effects of the above technical solution are as follows: By real-time monitoring and calculating the device anomaly coefficient, the system can detect potential problems in advance before the device has a serious failure, achieving early warning. This helps the operation and maintenance personnel take timely measures to avoid the expansion of the failure and the aggravation of losses. The system can accurately locate the abnormal device and its position in the operation and inspection monitoring link, providing accurate fault information for the operation and maintenance personnel. This helps shorten the fault troubleshooting time and improve the fault handling efficiency. By marking the risk of normal device nodes, the system can identify in advance the devices that may be affected by abnormal devices and take corresponding preventive measures. This helps reduce the overall operation risk of the power grid and improve the stability and reliability of the power grid. The entire anomaly detection and risk marking process is based on device operation data, realizing data-driven decision support. This makes the operation and maintenance decision-making more scientific, reasonable and effective. The application of this process marks an important step in the intelligent direction of power grid operation and inspection. Through automated and intelligent means, the efficiency and accuracy of power grid operation and inspection are improved, and the cost and risk of manual intervention are reduced.

[0141] In one embodiment of the present invention, S3 includes:

[0142] Calculate the node time-series anomaly coefficient according to the device operation data, the device anomaly coefficient and the device anomaly determination information;

[0143] The calculation formula of the node time-series anomaly coefficient is:

[0144]

[0145] Where SX is the anomaly coefficient of the specified time node in the time series, b is the total number of devices at the specified time node in the time series, IF a is the time node anomaly judgment coefficient of the a-th device, BX a is the device anomaly coefficient of the a-th device at a specific time node, BX y is the preset device anomaly threshold at a specific time node. When BX a > BX y , IF a = 1. When BX a ≤ BX y , IF a = 0;

[0146] Perform node time-series anomaly determination on the device node according to the node time-series anomaly coefficient to obtain the node time-series determination result;

[0147] Obtain the abnormal time-series node according to the node time-series determination result;

[0148] Obtain the time-series abnormal device node according to the abnormal time-series node;

[0149] Generate an abnormal timing link for multiple timing abnormal device nodes according to the controlled sequence.

[0150] Obtain the normal device nodes in the abnormal timing link, perform risk annotation on the normal device nodes, and obtain timing risk nodes.

[0151] The working principle of the above technical solution is as follows: The system first calculates the timing abnormal coefficient of each device node by combining device operation data, device abnormal coefficient, and device abnormal determination information. This coefficient aims to reflect the comprehensive abnormal coefficient of all device nodes at a certain time in the timing, aiming to reflect the device abnormal degree and correlation at the same time. Compare the calculated node timing abnormal coefficient with a preset threshold to determine whether there is a timing abnormality in the device node. If the timing abnormal coefficient exceeds the threshold, it is considered that there is a timing abnormality in the device node. According to the node timing determination result, the system filters out all nodes determined to be timing abnormal, that is, abnormal timing nodes. Map these abnormal timing nodes to specific devices to obtain timing abnormal device nodes. These devices show abnormal behavior in timing, which may indicate an impending fault or performance degradation. Connect multiple timing abnormal device nodes in the controlled sequence in the operation and inspection monitoring link to generate an abnormal timing link. This link reveals the propagation path and potential influence range of timing abnormalities in power grid devices. In the abnormal timing link, in addition to timing abnormal device nodes, there may also be normal device nodes directly or indirectly connected to these abnormal devices. Although these devices do not currently show timing abnormalities, due to their positional or functional relevance, they may face potential risks. The system will perform timing risk annotation on these normal device nodes and mark them as timing risk nodes. In this way, operation and maintenance personnel can more comprehensively understand the potential risks in the power grid and take corresponding preventive measures.

[0152] The technical effects of the above technical solution are as follows: By calculating the node timing abnormal coefficient and making a determination, the system can deeply understand the abnormal behavior of the device state changing over time, providing strong support for fault prediction and preventive maintenance. Mapping the timing abnormal nodes to specific devices and generating an abnormal timing link helps to accurately identify potential risk points and risk propagation paths in the power grid, providing a scientific basis for operation and maintenance decision-making. Based on timing abnormal detection and risk annotation, the system can issue early warning signals in advance and guide operation and maintenance personnel to take intervention measures in a timely manner to prevent the occurrence or expansion of faults and ensure the safe and stable operation of the power grid. By accurately identifying timing abnormal devices and risk nodes, the system can guide the optimal allocation of operation and maintenance resources, invest limited resources in the places where they are most needed, and improve the efficiency and effect of operation and maintenance. The application of this process further improves the intelligent level of power grid operation and inspection, and realizes the comprehensive, real-time, and accurate monitoring and evaluation of the state of power grid devices through automated and intelligent means.

[0153] In one embodiment of the present invention, S4 includes:

[0154] Obtain the overlapping device nodes of the abnormal device link and the abnormal timing link, and generate an associated abnormal link;

[0155] Obtain the non-overlapping device nodes of the abnormal device link and the abnormal timing link (i.e., device risk nodes and timing risk nodes), and generate a predicted abnormal range;

[0156] Perform abnormal early warning on the associated abnormal link, and adjust the data acquisition frequency for the predicted abnormal range. The adjustment of the data acquisition frequency includes increasing or decreasing by multiples.

[0157] The working principle of the above technical solution is as follows: The system first compares the abnormal device link and the abnormal timing link to find the overlapping device nodes between the two. These nodes are identified as abnormal in the device anomaly determination and also show timing anomalies in the timing anomaly determination. Therefore, they are considered highly associated abnormal points. Connect these overlapping device nodes to generate an associated abnormal link. This link reveals the direct connection between device anomalies and timing anomalies and is the most critical abnormal area in the power grid. In addition to the overlapping device nodes in the associated abnormal link, the system also focuses on the non-overlapping device nodes that only appear in the abnormal device link or the abnormal timing link. Although these nodes do not show both device anomalies and timing anomalies at the same time, due to their positional or functional relevance, they may become abnormal in the future. Include these non-overlapping device nodes (including device risk nodes and timing risk nodes) in the predicted abnormal range. This range provides an overview of the potential abnormal areas in the power grid, helping the operation and maintenance personnel to make preparations in advance. For the associated abnormal link, the system immediately issues an abnormal early warning signal to notify the operation and maintenance personnel to pay attention to this area and be prepared to take emergency measures. For the predicted abnormal range, the system dynamically adjusts the data acquisition frequency according to the risk level and the results of the prediction model. For areas with higher risks, increase the data acquisition frequency by multiples to more intensively monitor the changes in the device status; for areas with lower risks or relatively stable areas, appropriately reduce the data acquisition frequency to save resources.

[0158] The technical effects of the above technical solution are as follows: By generating associated abnormal links and issuing abnormal warnings, the system can accurately locate high-risk areas in the power grid and guide maintenance personnel to respond quickly, reducing the possibility of faults occurring and expanding. Combining equipment anomalies and timing anomalies to generate a predicted abnormal range helps to more comprehensively evaluate potential risks in the power grid. This comprehensive analysis method improves the accuracy and reliability of predictions. Dynamically adjusting the data collection frequency according to the predicted abnormal range realizes the optimal allocation of monitoring resources. Increasing the data collection frequency in high-risk areas can detect potential problems earlier; reducing the data collection frequency in low-risk areas can save resources and reduce the burden of data processing. Through automated and intelligent means for abnormal warning and data collection frequency adjustment, the system can significantly improve the maintenance efficiency. Maintenance personnel can focus more on high-risk areas and key issues, reducing unnecessary inspections and interventions. The application of this process helps to enhance the stability and reliability of the power grid. By timely detecting and responding to potential risks, the system can reduce the occurrence of faults and power outage time, improving the overall operation level of the power grid.

[0159] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A comprehensive management system for power grid operation and inspection equipment, characterized in that: The system comprises: The monitoring link generation module is used to build a device node network, generate a device control link according to the device control information, determine the main road node and the branch road node, obtain the bifurcated link, sort the main road node and the branch road node according to the control order, and obtain the operation and inspection monitoring link; The equipment anomaly analysis module is used to calculate the equipment anomaly coefficient of the operation and inspection monitoring link, determine the equipment anomaly of the equipment node, obtain the equipment anomaly node, and generate the abnormal equipment link; The timing anomaly analysis module is used to calculate the node timing anomaly coefficient of the operation and inspection monitoring link, determine the node timing anomaly of the equipment node, obtain the abnormal timing node, and generate the abnormal timing link; The comprehensive abnormality monitoring module is used to generate associated abnormal links and predict the abnormal range according to the abnormal device link combined with the abnormal timing link, and perform early warning and adjustment.

2. According to claim 1, a comprehensive management system for power grid operation and inspection equipment, characterized in that: The monitoring link generation module includes: The equipment network composition module is used to obtain the equipment information of each operation and inspection equipment and mark the equipment node for each equipment information; Get the device node corresponding to each device node label to form a device node network; A control link generation module, used to obtain device control information of each device node in the device node network, and generate a device control link according to the device control information; A link update module, used to obtain device control data of each device node of the device control link, and determine a main device and a branch device in the device control link according to the device control data; Match each branch device with the main device to obtain the bifurcated link; Sort each device node of the main circuit and the branch circuit according to the controlled order to obtain the sorting link; The equipment control link is updated according to the sequencing link to obtain the operation and inspection monitoring link.

3. According to claim 1, a comprehensive management system for power grid operation and inspection equipment, characterized in that: The device abnormality analysis module includes: An equipment anomaly calculation module is used to obtain equipment operation data of equipment nodes in the operation and inspection monitoring link, and calculate the equipment anomaly coefficient according to the equipment operation data; An equipment abnormality determination module is used to perform equipment abnormality determination on the equipment node according to the equipment abnormality coefficient to obtain an equipment determination result; A device link generation module is used to obtain abnormal device nodes according to the device determination result, and generate abnormal device links for multiple abnormal device nodes in a controlled order; The device risk acquisition module is used to acquire normal device nodes in the abnormal device link, perform risk marking on the normal device nodes, and acquire device risk nodes.

4. According to claim 1, a comprehensive management system for power grid operation and inspection equipment, characterized in that: The timing anomaly analysis module includes: A timing anomaly calculation module, used to calculate a node timing anomaly coefficient according to the equipment operation data, the equipment anomaly coefficient and the equipment anomaly determination information; A timing anomaly determination module is used to perform node timing anomaly determination on a device node according to the node timing anomaly coefficient to obtain a node timing determination result; A timing link generation module, used for obtaining abnormal timing nodes according to the node timing determination result; According to the abnormal timing node, obtaining the timing abnormal device node; Generate an abnormal timing link by controlling multiple timing abnormal device nodes in the order in which they are controlled; The timing risk acquisition module is used to acquire normal device nodes in the abnormal timing link, perform risk marking on the normal device nodes, and obtain timing risk nodes.

5. According to claim 1, a comprehensive management system for power grid operation and inspection equipment, characterized in that: The comprehensive abnormality monitoring module includes: An associated link generation module is used to obtain overlapping device nodes of abnormal device links and abnormal timing links, and generate associated abnormal links; An abnormal range prediction module is used to obtain non-overlapping device nodes of abnormal device links and abnormal timing links to generate a predicted abnormal range; The early warning adjustment module is used to issue abnormal early warnings for associated abnormal links and adjust the data collection frequency for the predicted abnormal range.

6. A management method for implementing the integrated management system for power grid operation and inspection equipment according to claim 1, characterized in that: The management method comprises: S1. Build a device node network, generate a device control link according to the device control information, determine the main road node and the branch road node, obtain the bifurcated link, sort the main road node and the branch road node according to the control order, and obtain the operation and inspection monitoring link; S2. Calculate the equipment abnormality coefficient of the operation and inspection monitoring link, determine the equipment abnormality of the equipment node, obtain the equipment abnormal node, and generate the abnormal equipment link; S3. Calculate the node timing anomaly coefficient of the operation and inspection monitoring link, determine the node timing anomaly of the equipment node, obtain the abnormal timing node, and generate the abnormal timing link; S4. Generate associated abnormal links and predict abnormal ranges based on the abnormal device links combined with abnormal timing links, and perform early warning and adjustment.

7. The management method of the integrated management system for power grid operation and inspection equipment according to claim 6 is characterized in that: The S1 includes: Obtain the equipment information of each operation and inspection equipment, and mark the equipment node for each equipment information; Get the device node corresponding to each device node label to form a device node network; Acquire device control information of each device node in the device node network, and generate a device control link according to the device control information; Acquire device control data of each device node of the device control link, and determine a main device and a branch device in the device control link according to the device control data; Match each branch device with the main device to obtain the bifurcated link; Sort each device node of the main circuit and the branch circuit according to the controlled order to obtain the sorting link; The equipment control link is updated according to the sequencing link to obtain the operation and inspection monitoring link.

8. The management method of the integrated management system for power grid operation and inspection equipment according to claim 6 is characterized in that: The S2 includes: Obtaining equipment operation data of equipment nodes in the operation and inspection monitoring link, and calculating equipment abnormality coefficients according to the equipment operation data; Performing device abnormality determination on the device node according to the device abnormality coefficient to obtain a device determination result; Acquire abnormal device nodes according to the device determination result, and generate abnormal device links according to the controlled order of multiple abnormal device nodes; Normal device nodes in the abnormal device link are obtained, and risk labels are performed on the normal device nodes to obtain device risk nodes.

9. The management method of the integrated management system for power grid operation and inspection equipment according to claim 6 is characterized in that: The S3 includes: Calculate the node timing anomaly coefficient based on the equipment operation data, the equipment anomaly coefficient and the equipment anomaly determination information; Perform node timing anomaly determination on the device node according to the node timing anomaly coefficient to obtain a node timing determination result; According to the node timing determination result, obtaining the abnormal timing node; According to the abnormal timing node, obtaining the timing abnormal device node; Generate an abnormal timing link by controlling multiple timing abnormal device nodes in the order in which they are controlled; Normal device nodes in the abnormal timing link are obtained, and risk labels are performed on the normal device nodes to obtain timing risk nodes.

10. The management method of the integrated management system for power grid operation and inspection equipment according to claim 6, characterized in that: The S4 includes: Obtain overlapping device nodes of abnormal device links and abnormal timing links, and generate associated abnormal links; Obtain non-overlapping device nodes of abnormal device links and abnormal timing links, and generate a predicted abnormal range; Issue abnormal warnings for associated abnormal links and adjust the data collection frequency for the predicted abnormal range.

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