A fast fault location method for multi-energy systems

Abstract

The present invention relates to the field of fault location technology, specifically to a fast fault location method for a multi-energy system, comprising the following steps: based on energy conversion equipment and transmission paths in the multi-energy system, updating an energy flow graph, adjusting energy nodes and path parameters, mapping energy flow states, and traversing the graph using a Dijkstra algorithm to obtain an energy flow mapping diagram. In the present invention, the Dijkstra algorithm provides fast and accurate route optimization when mapping fault paths, reduces the time delay of fault diagnosis, and the application of a random forest algorithm optimizes the in-depth analysis of fault causes, allows for rapid processing of complex data and analysis of fault causes, ensures accurate acquisition of fault source path information and node indicators, effectively shortens fault recovery time, reduces maintenance costs, and combines historical and real-time data analysis to optimize the ability of preventive maintenance and accurate prediction of faults, thereby improving the overall stability and reliability of the energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of fault location, and in particular to a rapid fault location method for a multi-energy system. Background Art

[0002] The field of fault location technology focuses on discovering and diagnosing faults or anomalies in energy systems and determining their specific locations. It is widely used in power networks, communication systems, traffic control and various industrial automation systems. The key is to reduce the operation interruption time and maintenance costs caused by faults. Effective fault location can quickly and accurately identify the fault point, thereby quickly restoring normal operation and ensuring the stability and reliability of the system.

[0003] Among them, the rapid fault location method of multi-energy systems is a technology that quickly identifies and locates faults. Its main purpose is to improve the operational efficiency and reliability of energy systems. Especially in multi-energy environments, accurate and rapid fault diagnosis is crucial to prevent energy supply interruptions and system performance degradation. The source of the fault is located by analyzing energy flows and system feedback, thereby achieving effective management and maintenance of complex energy networks.

[0004] Traditional methods rely on simple fault analysis algorithms, which cannot meet the needs of accurate diagnosis in a dynamic and interconnected multi-energy environment, resulting in inaccurate positioning of the fault point, leading to subsequent diagnostic errors, affecting energy supply and system performance. Existing technologies are also insufficient in real-time data processing capabilities, making it difficult to effectively respond to rapidly changing fault conditions, increasing the difficulty and economic cost of system maintenance, and causing a significant increase in maintenance costs when faults occur frequently or the system is large in scale, which affects the long-term stable operation and reliability of the energy system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a rapid fault location method for a multi-energy system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for rapid fault location of a multi-energy system, comprising the following steps:

[0007] S1: Based on the energy conversion equipment and transmission paths in the multi-energy system, the energy flow diagram is updated, the energy nodes and path parameters are adjusted, the energy flow state is mapped, and the diagram is traversed using the Dijkstra algorithm to obtain the energy flow mapping diagram;

[0008] S2: Based on the energy flow map, identify abnormal flow monitoring nodes and transmission paths in the diagram, analyze network traffic and transmission efficiency, identify areas with large deviations, determine the optimal path and cause of the fault, and obtain fault source path information;

[0009] S3: Based on the fault source path information, prioritize the fault nodes, select high-priority fault nodes for data collection and analysis, and use the random forest algorithm to locate key fault nodes to obtain fault node indicators;

[0010] S4: Based on the fault node indicator, compare the historical fault data and the current maintenance record, identify the fault mode and potential cause, and perform fault judgment and analysis to obtain a fault location log.

[0011] The present invention is improved in that the step of mapping the energy flow state is specifically as follows:

[0012] S111: collecting current data of energy conversion devices and transmission paths in the multi-energy system, adjusting energy node and path parameters, and generating an adjusted parameter data set;

[0013] S112: Based on the adjusted parameter data set, using the formula:

[0014]

[0015] Compute Node To Node Energy Flow ,in, Is a node The output power, is the distance between nodes, is the inter-node resistance, and is the adjustment factor used to optimize energy transfer efficiency;

[0016] S113: Based on the energy flow, energy loss and flow efficiency are evaluated, and the accuracy of the evaluation results is verified to obtain an energy flow state diagram.

[0017] The present invention is improved in that the step of traversing the graph is specifically as follows:

[0018] S121: Based on the Dijkstra algorithm, according to the connection parameters and energy transmission characteristics between the nodes, the total weight of each path is calculated, and the optimal path between the nodes is determined to generate an optimal path analysis result;

[0019] S122: Optimize the optimal path analysis result using the formula:

[0020]

[0021] Get an energy flow map, where is the optimal energy consumption, Indicates The energy consumption of each path, Is the path to The resistance, is the adjustment factor of the path impedance, is the number of paths.

[0022] The present invention is improved in that the steps of identifying the abnormal flow monitoring node and the transmission path are specifically as follows:

[0023] S211: Quantitatively analyze the energy flow map, measure the real-time energy flow of nodes and connection paths, and compare with historical flow data to generate a preliminary abnormal monitoring list;

[0024] S212: Based on the preliminary abnormal monitoring list, the formula is used:

[0025]

[0026] Quantify the abnormal degree of each node and path, and obtain a list of abnormal nodes and paths, where is the deviation index, is the currently monitored energy flow, is the historical average energy flow of the corresponding node or path, is the standard deviation of the historical data.

[0027] The present invention is improved in that the step of acquiring the fault source path information is specifically as follows:

[0028] S221: Analyze the network traffic and transmission efficiency, identify areas with large deviations, including energy consumption rate and transmission efficiency of computing nodes and connection paths, identify areas where energy consumption or flow efficiency deviations exceed the standard, and generate a list of areas with large deviations;

[0029] S222: Based on the list of regions with large deviations, the formula is used:

[0030]

[0031] Analyze the fault severity of each area and generate fault source path information, where: is the failure index, It is The energy flow of each node or path, is the corresponding expected energy value, It is The standard deviation of historical data, is the total number of nodes or paths.

[0032] The present invention is improved in that the step of performing priority sorting is specifically as follows:

[0033] S311: Based on the fault source path information, extract key performance indicators of each fault node, including the number of fault occurrences and the scope of fault impact, and establish a performance evaluation data set;

[0034] S312: Based on the performance evaluation data set, according to the severity and impact scope of the fault, the formula is used:

[0035]

[0036] Calculate the priority scores of the nodes, sort the nodes according to their priorities, and obtain the priority sorting data, where: Representation Node Total rating, Indicates the number of failures. Indicates the scope of influence, is the normalization factor.

[0037] The present invention is improved in that the steps of locating the key fault node are specifically as follows:

[0038] S321: Collect key data of the faulty node, including operation logs, performance indicators, and historical fault records, to obtain a faulty node data set;

[0039] S322: Based on the fault node data set, the key fault nodes are located using the random forest algorithm, using the formula:

[0040]

[0041] Get the fault node index, where Indicates a faulty node The risk score of Is a node In Features The observed value on It is a feature The weight of is the number of nodes, is the number of features.

[0042] The present invention is improved in that the steps of obtaining the fault location log are specifically as follows:

[0043] S411: Based on the fault node indicator, matching is performed with historical fault data, key performance indicators are extracted through data synchronization and preliminary screening, and fault data comparison results are generated;

[0044] S412: Based on the fault data comparison result, the formula is used:

[0045]

[0046] Calculate the average deviation of the failure data ,in, Representative Fault data points, is the average value of historical data, is the total number of data points;

[0047] S413: Based on the average deviation, identify abnormal patterns and potential fault causes, and integrate fault types, occurrence times, and potential causes to obtain a fault location log.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are:

[0049] In the present invention, the Dijkstra algorithm is used to provide fast and accurate route optimization when mapping fault paths, reducing the time delay of fault diagnosis. The application of the random forest algorithm optimizes the in-depth analysis of fault causes, allowing rapid processing of complex data and analysis of fault causes, ensuring accurate acquisition of fault source path information and node indicators, effectively shortening fault recovery time, and reducing maintenance costs. Combined with historical and real-time data analysis, it also optimizes preventive maintenance and the ability to accurately predict faults, thereby improving the overall stability and reliability of the energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flowchart of a rapid fault location method for a multi-energy system is proposed for the present invention;

[0051] Figure 2 A flow chart for mapping the energy flow state in the present invention;

[0052] Figure 3 A flowchart for traversing a graph in the present invention;

[0053] Figure 4 It is a flow chart for identifying abnormal flow monitoring nodes and transmission paths in the present invention;

[0054] Figure 5 This is a flow chart for obtaining fault source path information in the present invention;

[0055] Figure 6 A flowchart for prioritizing in the present invention;

[0056] Figure 7 This is a flowchart for locating key fault nodes in the present invention;

[0057] Figure 8 This is a flow chart for obtaining fault location logs in the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0060] Example

[0061] See also Figure 1 The present invention provides a technical solution: a method for quickly locating faults in a multi-energy system, comprising the following steps:

[0062] S1: Based on the energy conversion equipment and transmission paths in the multi-energy system, the energy flow diagram is updated, the energy nodes and path parameters are adjusted, the energy flow state is mapped, and the diagram is traversed using the Dijkstra algorithm to obtain the energy flow mapping diagram;

[0063] S2: Based on the energy flow mapping diagram, identify the abnormal flow monitoring nodes and transmission paths in the diagram, analyze the network traffic and transmission efficiency, identify the area with large deviations, determine the optimal path and cause of the fault, and obtain the fault source path information;

[0064] S3: Based on the fault source path information, prioritize the fault nodes, select high-priority fault nodes for data collection and analysis, and use the random forest algorithm to locate the key fault nodes to obtain the fault node indicators;

[0065] S4: Based on the fault node indicators, compare the historical fault data and current maintenance records, identify the fault mode and potential causes, and perform fault judgment and analysis to obtain the fault location log.

[0066] The energy flow map includes traffic distribution, connection strength, and energy consumption indicators. The fault source path information includes abnormal nodes, abnormal strength, and critical paths. The fault node indicators include node fault tolerance, fault response time, and repair priority. The fault location log includes fault type, impact range, and historical comparative analysis information.

[0067] See also Figure 2, the steps for mapping the energy flow state are as follows:

[0068] S111: collecting current data of energy conversion devices and transmission paths in the multi-energy system, adjusting energy node and path parameters, and generating an adjusted parameter data set;

[0069] Collect current energy conversion equipment and transmission path data to prepare for updating and optimizing the flow diagram of the energy system, including extracting energy flow and equipment performance data from field monitoring equipment and system logs. The parameters of each device and path, such as power, resistance and connection type, are recorded and checked for completeness and accuracy through data verification to ensure that the collected data accurately reflects the actual energy flow. This data will directly affect the accurate establishment of the energy model and parameter adjustment in the subsequent steps, and generate an adjusted energy parameter list.

[0070] S112: Based on the adjusted parameter data set, use the formula:

[0071]

[0072] Compute Node To Node Energy Flow ,in, Is a node The output power, is the distance between nodes, is the inter-node resistance, and is the adjustment factor used to optimize energy transfer efficiency;

[0073] The following data was collected:

[0074] node Output power kilowatt, rice, Ohm, adjustment factor and , the calculation process is:

[0075]

[0076]

[0077]

[0078]

[0079] This result shows that the node To Node The energy flow is about 116 kilowatts, which shows that after parameter optimization, the energy transmission efficiency between nodes is high and the energy loss is effectively controlled, which helps to improve the energy efficiency of the overall network.

[0080] S113: Based on the energy flow, perform energy loss and flow efficiency evaluation, verify the accuracy of the evaluation result, and obtain an energy flow state diagram;

[0081] Based on energy flow, a comprehensive assessment of energy loss and flow efficiency is conducted, including the application of statistical analysis and model verification techniques to analyze the data in the model diagram. During the analysis process, focus is placed on energy loss and flow efficiency indicators to ensure that the indicators meet the requirements. Multiple rounds of iterative optimization are used to accurately adjust the diagram to significantly improve the overall performance of the energy system and obtain an energy flow status diagram.

[0082] See also Figure 3 , the steps to traverse the graph are as follows:

[0083] S121: Based on the Dijkstra algorithm, according to the connection parameters and energy transmission characteristics between nodes, the total weight of each path is calculated, and the optimal path between nodes is determined to generate the optimal path analysis result;

[0084] Based on the Dijkstra algorithm, the energy flow graph is traversed to determine the shortest path between nodes, involving all nodes and connections in the graph, where nodes represent various conversion devices or transmission points in the energy system, and connections represent energy transmission paths. Starting from the starting point, the shortest path to each node is gradually expanded, and the total weight of each path is calculated, involving the energy loss and transmission efficiency of each connection. By examining the weight, the shortest path analysis results from each node to other nodes are generated, in preparation for further optimization, and preliminary path analysis results are generated.

[0085] S122: Optimize the optimal path analysis result using the formula:

[0086]

[0087] Get an energy flow map, where is the optimal energy consumption, Indicates The energy consumption of each path, Is the path to The resistance, is the adjustment factor of the path impedance, which is used to measure the effect of resistance on energy loss. is the number of paths;

[0088] The monitored data are as follows:

[0089] kilowatt / hour, ohm, , there are three paths, then the calculation process is:

[0090]

[0091]

[0092]

[0093] The results show that the optimized path has lower energy loss, reaching 119 kilowatt-hours, which significantly improves the efficiency of energy transmission and reduces the energy consumption of the overall network.

[0094] See also Figure 4 ,The specific steps for identifying abnormal flow monitoring nodes and ,transmission paths are as follows:

[0095] S211: Quantitatively analyze the energy flow map, measure the real-time energy flow of nodes and connection paths, and compare with historical flow data to generate a preliminary abnormal monitoring list;

[0096] The energy flow map is quantitatively analyzed. Data collection technology is used to measure the real-time energy flow of each node and its connecting paths, and compared with the archived historical flow data. Through comparative analysis, nodes and paths that deviate significantly from normal operating parameters are identified. Nodes and paths that show abnormally high or abnormally low flow are used as abnormal candidates to generate a preliminary abnormal monitoring list. The list is based on statistical analysis of multiple flow readings, which takes into account the uniqueness and operating environment of each node and path to ensure the relevance of the results.

[0097] S212: Based on the preliminary abnormal monitoring list, use the formula:

[0098]

[0099] Quantify the abnormal degree of each node and path, and obtain a list of abnormal nodes and paths, where is the deviation index, is the currently monitored energy flow, is the historical average energy flow of the corresponding node or path, is the standard deviation of the historical data;

[0100] The following data was collected:

[0101] kilowatt, kilowatt, Kilowatts, calculated as follows:

[0102]

[0103]

[0104] The result shows that the energy flow deviation index of this node is 1.0. If the threshold is 0.5, then 1.0>0.5, and this node can be classified as a marginal anomaly, which requires further monitoring or investigation to determine whether there is an ongoing problem or occasional fluctuation.

[0105] See also Figure 5 , the specific steps for obtaining the fault source path information are:

[0106] S221: Analyze network traffic and transmission efficiency, identify areas with large deviations, including energy consumption rate and transmission efficiency of computing nodes and connection paths, identify areas where energy consumption or flow efficiency deviations exceed the standard, and generate a list of areas with large deviations;

[0107] Conduct in-depth analysis of the identified abnormal flow monitoring nodes and transmission paths. To this end, collect real-time data of each node and its connection path, including energy consumption rate and transmission efficiency, compare with the performance benchmark, perform trend analysis and deviation calculation on the energy consumption and efficiency data, determine which nodes and paths deviate from the normal operating range, and ensure the accuracy and reliability of the results. Generate a list of areas with large deviations, which will directly affect the next step of fault analysis.

[0108] S222: Based on the list of areas with large deviations, the formula is used:

[0109]

[0110] Analyze the fault severity of each area and generate fault source path information, where: is the failure index, It is The energy flow of each node or path, is the corresponding expected energy value, It is The standard deviation of historical data, is the total number of nodes or paths;

[0111] There are three nodes, and their current energy values ​​are:

[0112] , , ;

[0113] The expected energy values ​​are:

[0114] , , ;

[0115] The standard deviations are:

[0116] , , ;

[0117] The calculation process is as follows:

[0118]

[0119]

[0120]

[0121] The fault index in the overall network is 2.07. If the baseline value is 1, the results show that there are obvious energy flow anomalies, which require further investigation and maintenance measures.

[0122] See also Figure 6 , the steps for prioritization are as follows:

[0123] S311: based on the fault source path information, extract key performance indicators of each fault node, including the number of fault occurrences and the fault impact range, and establish a performance evaluation data set;

[0124] The key performance indicators of each faulty node are extracted from the comprehensive fault source path information. The process involves analyzing the statistical data of the fault frequency and impact range, classifying and analyzing the data to ensure that the characteristics and problems of each node can be accurately identified. By comparing the data of different nodes, the influence and criticality of each node in the entire system are evaluated. The data will be used to evaluate the performance of the node and form an initial performance evaluation data set.

[0125] S312: Based on the performance evaluation data set, according to the severity and impact of the fault, the formula is used:

[0126]

[0127] Calculate the priority scores of the nodes, sort the nodes according to their priorities, and obtain the priority sorting data, where: Representation Node Total rating, Indicates the number of failures. Indicates the scope of influence, is the normalization factor;

[0128] Number of failures of node i during the observation period , impact range , normalization factor , the calculation steps are as follows:

[0129] Calculate the influence range inside the square root:

[0130]

[0131] Apply the formula to calculate the node score:

[0132]

[0133] The result shows that the comprehensive score of node i is 14.29, which reflects the priority of the node in the fault management system. A high score indicates that the handling of node failures should be given priority.

[0134] See also Figure 7 ,The specific steps for locating key fault nodes are:

[0135] S321: Collect key data of the faulty node, including operation logs, performance indicators, and historical fault records, to obtain a faulty node data set;

[0136] Collecting key data from high-priority fault nodes involves data integration and preprocessing to ensure that the data input into the model is complete and reliable, including extracting data from the monitoring system, cleaning and formatting the data, ensuring that all data meets the input requirements of the random forest algorithm, and then performing preliminary statistical analysis on the data to identify anomalies or deviations in order to adjust subsequent data processing strategies and generate a complete fault node data set.

[0137] S322: Based on the fault node data set, the random forest algorithm is used to locate the key fault nodes, using the formula:

[0138]

[0139] Get the fault node index, where Indicates a faulty node The risk score of Is a node In Features The observed value on It is a feature The weight of is the number of nodes, is the number of features;

[0140] The following data was collected:

[0141] Failure data point of node i The value is [10, 20, 30], is [0.2, 0.5, 0.3], then the sum of squares of each term is calculated as:

[0142]

[0143] The calculation process of the fault score is:

[0144]

[0145] The result shows that the failure score of node i is 0.561, which indicates that its failure risk is medium to low, which helps to arrange maintenance or more in-depth detection measures in a targeted manner.

[0146] See also Figure 8 , the specific steps for obtaining fault location logs are as follows:

[0147] S411: Based on the fault node indicators, match with the historical fault data, extract key performance indicators through data synchronization and preliminary screening, and generate fault data comparison results;

[0148] The collected data of the current faulty node is processed by an automated script to obtain a preliminary analysis report of key performance indicators. This data synchronization includes real-time verification and correlation analysis to ensure that the information obtained reflects the current status of the node. The filtered data is matched and compared with the historical fault database to identify any abnormalities or known fault modes, generate fault data comparison results, and provide data support for subsequent steps.

[0149] S412: Based on the fault data comparison results, the formula is used:

[0150]

[0151] Calculate the average deviation of the failure data ,in, Representative Fault data points, is the average value of historical data, is the total number of data points;

[0152] There is a node with the average historical data under normal circumstances is 100, the data point of the current faulty node They are 120, 130 and 110 respectively, with a total of data points, The calculation of is as follows:

[0153]

[0154]

[0155]

[0156] This result shows that the data from the failed node shows a deviation of 20 units above the historical average, indicating that the node has obvious performance issues.

[0157] S413: Based on the average deviation, identify abnormal patterns and potential fault causes, and integrate the fault type, occurrence time and potential cause to obtain a fault location log;

[0158] Using the identified abnormal patterns and fault causes, a fault location log is established to record the fault type, occurrence time, cause and recommended solutions. The log can provide an operational guide to make maintenance response faster and more accurate. The generated fault location log ensures that maintenance measures can be executed quickly and effectively, thereby greatly improving the reliability and safety of the energy system.

[0159] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for rapid fault location of a multi-energy system, characterized in that: The following steps are involved: Based on the energy conversion equipment and transmission paths in the multi-energy system, the energy flow diagram is updated, the energy nodes and path parameters are adjusted, the energy flow state is mapped, and the diagram is traversed using the Dijkstra algorithm to obtain the energy flow mapping diagram; Based on the energy flow map, identify abnormal flow monitoring nodes and transmission paths in the diagram, perform network traffic and transmission efficiency analysis, identify areas with large deviations, determine the optimal path and cause of the fault, and obtain fault source path information; Perform the network flow and transmission efficiency analysis, identify areas with large deviations, including energy consumption rate and transmission efficiency of computing nodes and connection paths, identify areas where energy consumption or flow efficiency deviations exceed the standard, and generate a list of areas with large deviations; Based on the fault source path information, the fault nodes are prioritized, high-priority fault nodes are selected for data collection and analysis, and the key fault nodes are located using a random forest algorithm to obtain fault node indicators; Based on the fault node indicators, historical fault data and current maintenance records are compared to identify fault modes and potential causes, and fault judgment and analysis are performed to obtain a fault location log; The steps of traversing the graph are specifically as follows: Based on the Dijkstra algorithm, according to the connection parameters and energy transmission characteristics between nodes, the total weight of each path is calculated, and the optimal path between nodes is determined to generate the optimal path analysis result; The optimal path analysis result is optimized using the formula: Get an energy flow map, where is the optimal energy consumption, Indicates The energy consumption of each path, Is the path to The resistance, is the adjustment factor of the path impedance, is the number of paths.

2. The rapid fault location method of a multi-energy system according to claim 1, characterized in that: The steps of mapping the energy flow state are specifically as follows: Collect current data of energy conversion equipment and transmission paths in the multi-energy system, adjust energy node and path parameters, and generate adjusted parameter data sets; Based on the adjusted parameter data set, the formula is adopted: Compute Node To Node Energy Flow ,in, Is a node The output power, is the distance between nodes, is the inter-node resistance, and is the adjustment factor used to optimize energy transfer efficiency; Based on the energy flow, energy loss and flow efficiency are evaluated, and the accuracy of the evaluation results is verified to obtain an energy flow state diagram.

3. The rapid fault location method of a multi-energy system according to claim 1, characterized in that: The steps of identifying the abnormal flow monitoring nodes and transmission paths are specifically as follows: Quantitatively analyzing the energy flow map, measuring the real-time energy flow of nodes and connection paths, and comparing with historical flow data to generate a preliminary abnormal monitoring list; Based on the preliminary abnormal monitoring list, the formula is used: Quantify the abnormal degree of each node and path, and obtain a list of abnormal nodes and paths, where is the deviation index, is the currently monitored energy flow, is the historical average energy flow of the corresponding node or path, is the standard deviation of the historical data.

4. The rapid fault location method of a multi-energy system according to claim 1, characterized in that: The steps for obtaining the fault source path information are specifically as follows: Based on the list of areas with large deviations, the formula is used: Analyze the fault severity of each area and generate fault source path information, where: is the failure index, It is The energy flow of each node or path, is the corresponding expected energy value, It is The standard deviation of historical data, is the total number of nodes or paths.

5. The rapid fault location method of a multi-energy system according to claim 1, characterized in that: The steps of prioritizing are specifically as follows: Based on the fault source path information, key performance indicators of each fault node are extracted, including the number of fault occurrences and the scope of fault impact, to establish a performance evaluation data set; Based on the performance evaluation data set, according to the severity and impact range of the fault, the formula is used: Calculate the priority scores of the nodes, sort the nodes according to their priorities, and obtain the priority sorting data, where: Representation Node Total rating, Indicates the number of failures. Indicates the scope of influence, is the normalization factor.

6. The rapid fault location method of a multi-energy system according to claim 1, characterized in that: The steps for locating the key fault node are specifically as follows: Collect key data of the faulty node, including operation logs, performance indicators, and historical fault records, to obtain the faulty node data set; Based on the fault node data set, the random forest algorithm is used to locate the key fault nodes, using the formula: Get the fault node index, where Indicates a faulty node The risk score of Is a node In Features The observed value on It is a feature The weight of is the number of nodes, is the number of features.

7. The rapid fault location method of a multi-energy system according to claim 1, characterized in that: The steps for obtaining the fault location log are specifically as follows: Based on the fault node indicators, the indicators are matched with historical fault data, key performance indicators are extracted through data synchronization and preliminary screening, and fault data comparison results are generated; Based on the fault data comparison results, the formula is used: Calculate the average deviation of the failure data ,in, Representative Fault data points, is the average value of historical data, is the total number of data points; Based on the average deviation, abnormal patterns and potential fault causes are identified, and the fault type, occurrence time and potential cause are integrated to obtain a fault location log.

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