Power environment monitoring data visualization platform and abnormal diagnosis method

Abstract

The application relates to the technical field of power environment data monitoring, and discloses a power environment monitoring data visualization platform and an abnormality diagnosis method, which comprises the following steps: identifying abnormal data in power environment data; constructing a causal abnormality graph of the power environment data; exploring a fault path for each item of abnormal data based on the causal abnormality graph; calculating the fault confidence of each node in each fault path; screening and pruning each fault path based on the fault confidence of the node; obtaining a weight value of the node based on the causal abnormality graph, and calculating the comprehensive causal strength of each fault path based on the fault confidence and the weight value of the node; and determining an actual fault path and diagnosing a fault source based on the comprehensive causal strength. The application improves the accuracy of abnormality diagnosis results and the reliability of fault source positioning of a power environment monitoring system.

Description

Technical Field

[0001] This application relates to the technical field of power environment data monitoring, specifically to a power environment monitoring data visualization platform and anomaly diagnosis method. Background Technology

[0002] To achieve real-time status monitoring and risk early warning of power and environmental systems, the industry has widely adopted power and environmental monitoring systems. These systems utilize sensors, data acquisition devices, and data analysis platforms to continuously monitor various power supply parameters and environmental indicators, reflecting changes in the status of power supply equipment and its operating environment to a certain extent and assisting maintenance personnel in promptly identifying potential risks. However, most existing power and environmental monitoring systems remain at the level of threshold-based judgment or single-point indicator anomaly detection, and have some shortcomings.

[0003] Existing monitoring systems lack in-depth modeling of the complex interrelationships between equipment and the environment. The operating status of power equipment is highly coupled with environmental factors. Current technologies often monitor various parameters in isolation, lacking systematic causal logic reasoning, and cannot accurately determine the correlation and linkage between changes in different parameters, making it difficult to effectively identify the root causes of abnormal events.

[0004] Traditional fault diagnosis methods are mostly based on a single reasoning path or fixed diagnostic rules, lacking the ability for parallel reasoning with multiple paths and multiple results. In real-world systems, the same anomaly may be caused by multiple reasons, and there are complex causal transmission chains between different fault sources. A single reasoning conclusion is prone to misjudgment and cannot comprehensively and accurately reflect the true fault state of the system, thus limiting the accuracy and comprehensiveness of fault source localization.

[0005] For example, Chinese patent CN110046074B discloses a data center power environment monitoring system and method. This system performs power environment monitoring through a data acquisition module and a monitoring module. The acquisition module uses sensors deployed on it to collect data from the data center power system and sends the collected data to the monitoring module. The monitoring module receives the collected data, establishes a data analysis model, analyzes the data using the model, identifies any anomalies in the data center power environment system, and provides safety warnings. This achieves the goal of real-time monitoring of the equipment operation and environmental status of the data center power system, improving data storage security. Furthermore, the monitoring system can be viewed via an app or accessed anytime, anywhere via voice, enhancing its convenience. However, this technical solution still suffers from the problem mentioned in the background of this application: a lack of multi-path, multi-result parallel reasoning capabilities.

[0006] The information disclosed in this background section is intended only to enhance the understanding of the overall background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0007] The technical problem to be solved by this application is to overcome the defects of the prior art and provide a power environment monitoring data visualization platform and anomaly diagnosis method to improve the accuracy of anomaly diagnosis results and the reliability of fault source location in the power environment monitoring system.

[0008] To solve the above-mentioned technical problems, this application provides the following technical solution:

[0009] On the one hand, this application provides a method for diagnosing anomalies in power environment monitoring data, including the following steps:

[0010] Identify anomalous data in dynamic environmental data; construct a causal anomaly diagram of dynamic environmental data;

[0011] Based on the aforementioned causal anomaly diagram, explore fault paths for each abnormal data item;

[0012] Calculate the fault confidence of each node in each fault path, and perform linkage consistency verification and compensation correction on the fault confidence of the nodes;

[0013] Each faulty path is filtered and pruned based on the fault confidence of the node.

[0014] The weight values ​​of nodes are obtained based on the causal anomaly graph, and the comprehensive causal strength of each fault path is calculated based on the fault confidence and weight values ​​of the nodes.

[0015] The actual fault path is determined and the fault source is diagnosed based on the comprehensive causal strength.

[0016] As a preferred embodiment of the power environment monitoring data anomaly diagnosis method described in this application, the power environment data includes power data and environmental data; wherein, the power data includes the operating parameters of the power supply equipment in the power system; and the environmental data is the monitoring data of the working environment of the power supply equipment in the power system.

[0017] The power supply equipment and its working environment are divided into zones according to functional areas or physical locations, and each type of power environment data in each zone is recorded as a separate power environment data item.

[0018] The identification of abnormal data in the dynamic environment data specifically includes:

[0019] Establish time series for each piece of dynamic environmental data; for any piece of dynamic environmental data, extract the latest m monitoring values ​​from the time series; calculate the rate of change and fluctuation of the dynamic environmental data based on the latest m monitoring values; m is a positive integer;

[0020] For each dynamic environment data point, set a reference value range, a change rate threshold, and a fluctuation level threshold.

[0021] If the dynamic environment data meets the following conditions: the rate of change is greater than the rate of change threshold, the degree of fluctuation is greater than the degree of fluctuation threshold, and at least n of the m monitored values ​​exceed at least one of the reference value ranges, then the dynamic environment data is abnormal data; n is a positive integer less than or equal to m.

[0022] As a preferred embodiment of the abnormal diagnosis method for power environment monitoring data described in this application, the causal anomaly graph includes nodes and directed edges; each node corresponds to a power environment data item; any directed edge is used to connect two nodes and points from the upstream node to the downstream node;

[0023] If any two nodes are connected by a directed edge, then the two nodes have an abnormal linkage relationship. Specifically, when the power environment data of the upstream node is abnormal, there is a probability that the power environment data of the downstream node will be abnormal, and the probability is equal to the edge weight of the directed edge.

[0024] Collect historical data, calculate the probability that any abnormality in a dynamic environmental data point will cause an abnormality in any other dynamic environmental data point besides itself, and assign weight values ​​to the corresponding directed edges in the causal anomaly graph.

[0025] As a preferred embodiment of the power environment monitoring data anomaly diagnosis method described in this application, the fault path includes nodes and directed edges in the causal anomaly graph; when exploring a fault path for any abnormal data, the node corresponding to the abnormal data is marked as the target node; any fault path has no cycles and no branches, and the target node has no downstream nodes;

[0026] The method for exploring fault paths is as follows: Starting from the target node, explore nodes in the direction of the directed edges in the causal anomaly graph; for each node explored, update the nodes and directed edges currently included in the fault path, and calculate the product of the weight values ​​of all directed edges currently included in the fault path; if the product of the weight values ​​is less than a preset minimum weight threshold, stop exploring and obtain a fault path.

[0027] As a preferred embodiment of the abnormal diagnosis method for power environment monitoring data described in this application, the method for calculating the fault confidence of each node in each fault path involves assigning an initial value to the fault confidence based on the time series of the power environment data corresponding to the node. Specifically, this includes: if the power environment data corresponding to the node is not abnormal data, then the fault confidence is 0; otherwise:

[0028] Calculate the mean of the latest m monitoring values ​​in the time series as the observed value of the node; obtain the rate of change and the degree of fluctuation calculated based on the latest m monitoring values; assign an initial value to the fault confidence based on any one of the following: the absolute value of the difference between the observed value and the midpoint of the reference value interval, the difference between the rate of change and the rate of change threshold, and the difference between the degree of fluctuation and the degree of fluctuation threshold.

[0029] The linked consistency verification of the fault confidence of the nodes includes abnormal direction verification and abnormal lag verification for each non-target node; if any non-target node fails the abnormal direction verification or abnormal lag verification, the confidence of the corresponding non-target node is set to 0.

[0030] As a preferred embodiment of the abnormal diagnosis method for power environment monitoring data described in this application, the abnormal direction verification specifically includes: extracting the difference between the observed value and the midpoint of the reference value interval for any non-target node and the adjacent downstream node in the fault path, and identifying the abnormal direction of the non-target node and the adjacent downstream node; the abnormal direction includes abnormal rise and abnormal fall.

[0031] Identify the abnormal linkage direction between a non-target node and its adjacent downstream node; the abnormal linkage direction includes positive linkage and negative linkage; wherein, positive linkage means that the abnormal direction between the non-target node and its adjacent downstream node is the same, and negative linkage means that the abnormal direction is opposite.

[0032] If the abnormal direction between the non-target node and its adjacent downstream node does not satisfy the abnormal linkage direction, then the non-target node fails the abnormal direction verification.

[0033] As a preferred embodiment of the abnormal diagnosis method for power environment monitoring data described in this application, the abnormal lag verification specifically includes: extracting the time series corresponding to each node in the fault chain through a sliding window with a length of m monitoring values, and determining whether the power environment data is abnormal data after each sliding extraction; determining the time point at which each power environment data begins to show abnormality based on the judgment result after each sliding extraction, as the abnormal starting point of the corresponding node in the fault chain;

[0034] Calculate the time difference between the abnormal starting point of any non-target node and its adjacent downstream node in the fault path, and use it as the abnormal lag time of the corresponding non-target node; set a lag time interval for each directed edge in the causal anomaly graph; if the abnormal lag time of the non-target node is not located in the lag time interval of the directed edge between the non-target node and its adjacent downstream node, then the non-target node fails the abnormal lag verification.

[0035] As a preferred embodiment of the abnormal diagnosis method for power environment monitoring data described in this application, the step of screening and pruning each fault path specifically includes: after assigning an initial value to the fault confidence of each node in each fault path, performing a screening and pruning of the fault path once; after verifying the consistency of the fault confidence of each node, performing a second screening and pruning of the fault path a second time.

[0036] The methods for performing one or two filtering of faulty paths are as follows:

[0037] If, in any fault path, the fault confidence of all non-target nodes except the target node is 0, then the corresponding fault path is deleted.

[0038] If any fault path contains at least one non-target node with a fault confidence level of not 0, and at least one of the non-target nodes with a fault confidence level of not 0 has a non-target node with a confidence level of 0 with the target node, then the corresponding fault path is deleted.

[0039] The methods for performing one or two trims on faulty paths are as follows:

[0040] If any fault path contains at least one non-target node with a fault confidence of not 0, and there are no non-target nodes with a confidence of 0 between any non-target node with a fault confidence of not 0 and the target node, then the corresponding fault path is pruned, specifically including: deleting all non-target nodes with a fault confidence of 0 in the fault path.

[0041] As a preferred embodiment of the abnormal diagnosis method for power environment monitoring data described in this application, the weight value of any node is the weight value of the directed edge between the corresponding node and the adjacent downstream node in the corresponding fault path; the calculation of the comprehensive causal strength of any fault path based on the fault confidence and weight value of the node specifically includes: multiplying the fault confidence of each node by the corresponding weight value to obtain the weighted confidence of the node; and calculating the sum of the weighted confidence of all nodes in the fault path except the target node as the comprehensive causal strength.

[0042] The method for determining the actual fault path is as follows: sort the fault paths according to the magnitude of the comprehensive causal strength; select the fault path with the largest comprehensive causal strength as the actual fault path;

[0043] The method for diagnosing the fault source is as follows: the first node in the actual fault path is marked as the fault source; the first node is a node in the actual fault path that does not have an upstream node.

[0044] Secondly, this application provides a power environment monitoring data visualization platform, including an anomaly identification module, a causal modeling module, a causal reasoning module, a path evaluation module, and a visualization module; wherein:

[0045] The anomaly identification module is used to identify abnormal data in the power environment data;

[0046] The causal modeling module is used to construct a causal anomaly diagram of dynamic environmental data;

[0047] The causal reasoning module explores fault paths for each abnormal data item based on the causal anomaly graph, and filters and prunes each fault path;

[0048] The path evaluation module is used to calculate the comprehensive causal strength of each fault path, and to determine the actual fault path and diagnose the fault source based on the comprehensive causal strength.

[0049] The visualization module is used to visualize and display power environment data, cause-effect anomaly diagrams, actual fault paths, and fault sources.

[0050] Compared with the prior art, the beneficial effects achieved by this application are as follows:

[0051] This application constructs a causal anomaly diagram, designs a reasoning strategy with anomaly splitting capability, and combines a dynamic adjustment mechanism for power and environmental linkage consistency verification and node confidence. This enables multi-dimensional data fusion of power supply equipment and environmental factors, which can systematically reflect the complex linkage relationship between the power system and the environmental system, and improve the comprehensiveness and accuracy of anomaly analysis.

[0052] By using structured modeling of causal anomaly diagrams, the causal transmission logic between monitoring data is clearly expressed; by introducing a linkage consistency verification mechanism, the directionality and lag of node anomalies in the reasoning path are jointly verified to ensure that the diagnostic results have physical and logical rationality, and significantly reduce the risk of misjudgment and missed judgment.

[0053] The dynamic adjustment method for node confidence in this application comprehensively optimizes confidence assessment based on factors such as multi-path co-occurrence, regional focus, environmental anomaly assistance, and historical fault tendency, further improving the stability and reliability of anomaly location, helping to locate key investigation areas, and enhancing the practicality of fault diagnosis. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0055] Figure 1 A flowchart of the method for diagnosing abnormal power environment monitoring data provided in this application;

[0056] Figure 2 This is a schematic diagram of the structure of the power environment monitoring data visualization platform provided in this application. Detailed Implementation

[0057] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0058] Example 1

[0059] This embodiment describes a method for diagnosing anomalies in power environment monitoring data, referring to... Figure 1 The method includes the following steps: identifying anomalous data in the dynamic environment data;

[0060] The power environment data includes power data and environmental data; wherein, the power data includes the operating parameters of the power supply equipment in the power system; and the environmental data is the monitoring data of the working environment of the power supply equipment in the power system.

[0061] The preferred power data in this embodiment includes, but is not limited to, voltage data, current data, power factor, load level, battery pack internal resistance, and battery temperature; the preferred environmental data in this embodiment includes, but is not limited to, ambient temperature, ambient humidity, air conditioning operating status, water immersion monitoring information, and smoke alarm information.

[0062] The power supply equipment and its working environment are divided into zones according to functional areas or physical locations, and each type of power environment data in each zone is recorded as a separate power environment data item.

[0063] In this embodiment, the power supply equipment is preferably partitioned according to functional areas, such as dividing the power supply equipment into power cabinet area, UPS equipment area, and battery pack area. In this embodiment, the working environment of the power supply equipment is also preferably partitioned according to physical location, such as the first equipment room partition and the second equipment room partition corresponding to the independent physical equipment room where the power supply equipment is located. Through the above partitioning, the physical area to which each power environment data belongs is divided and classified, which facilitates the subsequent modeling of causal relationships between power environment data in different areas and across areas.

[0064] The identification of abnormal data in the dynamic environment data specifically includes:

[0065] Set a monitoring cycle; at the beginning of each monitoring cycle, collect each dynamic environment data synchronously;

[0066] Establish time series for each piece of dynamic environmental data, and align the time series of each piece of dynamic environmental data with timestamps;

[0067] For any dynamic environment data, extract the latest m monitoring values ​​from the time series; calculate the rate of change and the degree of fluctuation of the dynamic environment data based on the latest m monitoring values; m is a positive integer; in this embodiment, the rate of change is preferably the average slope of the dynamic environment data, and the degree of fluctuation is the standard deviation of the dynamic environment data.

[0068] For each dynamic environment data point, set a reference value range, a change rate threshold, and a fluctuation level threshold.

[0069] If the dynamic environment data meets the following conditions: the rate of change is greater than the rate of change threshold, the degree of fluctuation is greater than the degree of fluctuation threshold, and at least n of the m monitored values ​​exceed at least one of the reference value ranges, then the dynamic environment data is abnormal data; n is a positive integer less than or equal to m.

[0070] In this embodiment, reference value ranges, rate of change thresholds, and fluctuation thresholds can be set for each power environment data item based on the statistical distribution of equipment technical specifications and historical data.

[0071] Construct a causal anomaly diagram of dynamic environmental data;

[0072] The causal anomaly graph includes nodes and directed edges; each node corresponds to a dynamic environment data point; each directed edge connects two nodes and points from the upstream node to the downstream node.

[0073] If any two nodes are connected by a directed edge, then the two nodes have an abnormal linkage relationship. Specifically, when the power environment data of the upstream node is abnormal, there is a probability that the power environment data of the downstream node will be abnormal, and the probability is equal to the edge weight of the directed edge.

[0074] Collect historical data, calculate the probability that any abnormality in a dynamic environmental data point will cause an abnormality in any other dynamic environmental data point besides itself, and assign weight values ​​to the corresponding directed edges in the causal anomaly graph.

[0075] Based on the aforementioned causal anomaly diagram, explore fault paths for each abnormal data item;

[0076] The fault path includes nodes and directed edges in the causal anomaly graph; when exploring a fault path for any abnormal data, the node corresponding to the abnormal data is marked as the target node; any fault path is acyclic and has no branches, and the target node has no downstream nodes;

[0077] The method for exploring fault paths is as follows: Starting from the target node, explore nodes in the direction of the directed edges in the causal anomaly graph; for each node explored, update the nodes and directed edges currently included in the fault path, and calculate the product of the weight values ​​of all directed edges currently included in the fault path; if the product of the weight values ​​is less than a preset minimum weight threshold, stop exploring and obtain a fault path.

[0078] In this embodiment, the process of exploring fault paths involves tracing multiple paths along the directed edges in the inverse graph to find a set of upstream nodes that may lead to the current anomaly. Each fault path represents a possible causal chain of anomalous data. The minimum weight threshold is used to limit the lower limit of the confidence of fault paths, control the effectiveness of the inference fault paths, and avoid the presence of a large number of low-confidence, weakly correlated nodes in the fault paths.

[0079] Calculate the fault confidence of each node in each fault path, and perform linkage consistency verification and compensation correction on the fault confidence of the nodes;

[0080] The method for calculating the fault confidence of each node in each fault path involves assigning an initial value to the fault confidence based on the time series of the power environment data corresponding to the node. Specifically, this includes: if the power environment data corresponding to the node is not abnormal data, then the fault confidence is 0; otherwise:

[0081] Calculate the mean of the latest m monitoring values ​​in the time series as the observed value of the node; obtain the rate of change and the degree of fluctuation calculated based on the latest m monitoring values; assign an initial value to the fault confidence based on any one of the following: the absolute value of the difference between the observed value and the midpoint of the reference value interval, the difference between the rate of change and the rate of change threshold, and the difference between the degree of fluctuation and the degree of fluctuation threshold.

[0082] In this embodiment, the larger any of the above differences (or their absolute values) is, the larger the initial value assigned to the fault confidence level is.

[0083] The linked consistency verification of the fault confidence of the nodes includes abnormal direction verification and abnormal lag verification for each non-target node; if any non-target node fails the abnormal direction verification or abnormal lag verification, the confidence of the corresponding non-target node is set to 0.

[0084] Simple data anomalies do not equate to the validity of the causal logic of the fault path. Only when the direction of the abnormal linkage between adjacent nodes in the fault path matches the abnormal lag time can the fault confidence of a node in the fault path be valid.

[0085] The abnormal direction verification specifically includes: extracting the difference between the observed value and the midpoint of the reference value interval for any non-target node and the adjacent downstream node in the fault path, and identifying the abnormal direction of the non-target node and the adjacent downstream node; the abnormal direction includes abnormal rise and abnormal fall.

[0086] In this embodiment, if the difference between the observed value of a non-target node or its adjacent downstream node and the midpoint of the reference value interval is greater than 0, then the abnormal direction of the corresponding node is abnormal upward; otherwise, the abnormal direction is abnormal downward.

[0087] Identify the abnormal linkage direction between non-target nodes and adjacent downstream nodes; the abnormal linkage direction includes positive linkage and negative linkage; wherein, positive linkage means that the abnormal direction between the non-target node and the adjacent downstream node is the same, and negative linkage means that the abnormal direction is opposite; for example, an increase in UPS load leading to an increase in current is a positive linkage; an increase in current leakage leading to a decrease in terminal voltage is a negative linkage.

[0088] If the abnormal direction between the non-target node and its adjacent downstream node does not satisfy the abnormal linkage direction, then the non-target node fails the abnormal direction verification.

[0089] Anomaly direction verification can eliminate fault path nodes with obvious logical errors, ensuring the overall physical logic reliability of the fault path.

[0090] The abnormal lag verification specifically includes: extracting the time series corresponding to each node in the fault chain through a sliding window of length m monitoring values, and determining whether the power environment data is abnormal after each sliding extraction; determining the time point at which each power environment data begins to show abnormality based on the judgment result after each sliding extraction, as the abnormal starting point of the corresponding node in the fault chain;

[0091] For example, if a sliding cut is performed along the time series from newest to oldest, and the dynamic environment data is determined to be abnormal after the first few sliding cuts, and the dynamic environment data is determined to be non-abnormal from a certain sliding cut, then the time point corresponding to the midpoint of the window of that sliding cut is the time point when the dynamic environment data begins to show abnormality.

[0092] Calculate the time difference between the abnormal starting point of any non-target node and its adjacent downstream node in the fault path, and use it as the abnormal lag time of the corresponding non-target node; set a lag time interval for each directed edge in the causal anomaly graph; if the abnormal lag time of the non-target node is not located in the lag time interval of the directed edge between the non-target node and its adjacent downstream node, then the non-target node fails the abnormal lag verification.

[0093] Each directed edge has a lag time interval set empirically, representing the time difference between when an anomaly occurs in the upstream node and when it causes an anomaly in the downstream node. For example, after a UPS overload, the battery temperature rise typically has a lag of several minutes; after humidity increases, the insulation performance deteriorates and requires a certain accumulation time. If the anomaly in the downstream node occurs earlier than or much later than the anomaly in the upstream node, it is considered that the anomaly in the upstream node has not been transmitted to the downstream node. By verifying the anomaly lag, causal logic errors in fault paths caused by monitoring system errors and noise interference can be prevented, and erroneously explored fault paths can be eliminated.

[0094] The above-mentioned linkage consistency verification mechanism can effectively distinguish between simple independent parameter anomalies and linkage anomalies that truly conform to the causal logic of the system; thus ensuring that the final output diagnostic results are not only based on the data anomalies themselves, but also on the logical causal linkage between the data, thereby improving the accuracy and interpretability of the diagnosis.

[0095] The fault confidence of each node is compensated and corrected, specifically including:

[0096] If any node exists in more than one fault path, the fault confidence of the corresponding node is increased, and the more times the node appears in different fault paths, the greater the increase in fault confidence.

[0097] The same node appears simultaneously in multiple independently inferred fault paths. Although these paths are different, they all use this node as a key relay. This indicates that the node has a significant impact on the dynamic environment system. Based on this, the confidence level of this node can be appropriately increased to enhance the consistency and credibility of the overall anomaly explanation.

[0098] If the power environment data corresponding to at least M nodes all correspond to the same partition of the power supply equipment and its working environment, then increase the fault confidence of all nodes in the corresponding partition.

[0099] If, in multiple fault paths, the abnormal nodes are concentrated in a certain functional area or physical location, such as most paths involving a UPS system or a certain battery pack, it indicates that the overall risk level of that area is high. Appropriately increasing the fault confidence of all relevant nodes in that area can help locate key areas for investigation and improve the practicality of fault diagnosis.

[0100] Each faulty path is filtered and pruned based on the fault confidence of the node.

[0101] The process of filtering and pruning each fault path specifically includes: after assigning an initial value to the fault confidence of each node in each fault path, performing a first filtering and pruning of the fault path; and after verifying the consistency of the fault confidence of each node, performing a second filtering and pruning of the fault path.

[0102] The methods for performing one or two filtering of faulty paths are as follows:

[0103] If, in any fault path, the fault confidence of all non-target nodes except the target node is 0, then the corresponding fault path is deleted.

[0104] If any fault path contains at least one non-target node with a fault confidence level of not 0, and at least one of the non-target nodes with a fault confidence level of not 0 has a non-target node with a confidence level of 0 with the target node, then the corresponding fault path is deleted.

[0105] The methods for performing one or two trims on faulty paths are as follows:

[0106] If any fault path contains at least one non-target node with a fault confidence of not 0, and there are no non-target nodes with a confidence of 0 between any non-target node with a fault confidence of not 0 and the target node, then the corresponding fault path is pruned, specifically including: deleting all non-target nodes with a fault confidence of 0 in the fault path.

[0107] The weight values ​​of nodes are obtained based on the causal anomaly graph, and the comprehensive causal strength of each fault path is calculated based on the fault confidence and weight values ​​of the nodes.

[0108] The weight value of any node is the weight value of the directed edge between the corresponding node and the adjacent downstream node in the corresponding fault path; the calculation of the comprehensive causal strength of any fault path based on the fault confidence and weight value of the node specifically includes: multiplying the fault confidence of each node by the corresponding weight value to obtain the weighted confidence of the node; and calculating the sum of the weighted confidence of all nodes in the fault path except the target node as the comprehensive causal strength.

[0109] The actual fault path is determined and the fault source is diagnosed based on the comprehensive causal strength.

[0110] The method for determining the actual fault path is as follows: sort the fault paths according to the magnitude of the comprehensive causal strength; select the fault path with the largest comprehensive causal strength as the actual fault path;

[0111] The method for diagnosing the fault source is as follows: the first node in the actual fault path is marked as the fault source; the first node is a node in the actual fault path that does not have an upstream node.

[0112] Example 2

[0113] This embodiment is the second embodiment of this application; it is based on the same inventive concept as Embodiment 1, and refers to... Figure 2 This embodiment introduces a power environment monitoring data visualization platform, including an anomaly identification module, a causal modeling module, a causal reasoning module, a path evaluation module, and a visualization module; wherein:

[0114] The anomaly identification module is used to identify abnormal data in the power environment data. This module monitors the power data of the power supply equipment and the environmental data of the working environment in real time, and combines dynamic abnormal intervals to accurately mark the abnormal status of various parameters, ensuring the effectiveness and timeliness of the data source.

[0115] The causal modeling module is used to construct a causal anomaly graph of power environment data. Based on the equipment operation logic, environmental linkage mechanism and historical data relationship, this module establishes a directed graph structure that reflects the causal logic of the data, providing a complete data association foundation for subsequent fault reasoning and path analysis.

[0116] The causal reasoning module explores fault paths for each abnormal data item based on the causal anomaly graph, and filters and prunes each fault path. This module discovers multiple potential fault causal chains through reverse search and dynamically filters out low-confidence or unreasonable redundant paths, thereby improving reasoning efficiency and accuracy.

[0117] The path evaluation module is used to calculate the comprehensive causal strength of each fault path, and to determine the actual fault path and diagnose the fault source based on the comprehensive causal strength.

[0118] The visualization module is used to visualize power environment data, cause-effect diagrams, actual fault paths, and fault sources, making it easier for maintenance personnel to quickly understand the system status, trace the abnormal transmission path, and improve the efficiency of fault diagnosis and handling.

[0119] The specific functions of each module described above are explained in the relevant content of the power environment monitoring data anomaly diagnosis method described in Example 1, and will not be repeated here.

[0120] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of protection of this application, and these forms are all within the protection scope of this application.

Claims

1. A method for diagnosing anomalies in power environment monitoring data, characterized in that: Includes the following steps: Identify anomalous data in dynamic environmental data; construct a causal anomaly diagram of dynamic environmental data; Based on the aforementioned causal anomaly diagram, explore fault paths for each abnormal data item; Calculate the fault confidence of each node in each fault path, and perform linkage consistency verification and compensation correction on the fault confidence of the nodes; Each faulty path is filtered and pruned based on the fault confidence of the node. The weight values ​​of nodes are obtained based on the causal anomaly graph, and the comprehensive causal strength of each fault path is calculated based on the fault confidence and weight values ​​of the nodes. Based on the comprehensive causal strength, the actual fault path is determined and the fault source is diagnosed; The identification of abnormal data in the dynamic environment data specifically includes: Establish time series for each piece of dynamic environmental data; for any piece of dynamic environmental data, extract the latest m monitoring values ​​from the time series; calculate the rate of change and fluctuation of the dynamic environmental data based on the latest m monitoring values; m is a positive integer; For each dynamic environment data point, set a reference value range, a change rate threshold, and a fluctuation level threshold. If the dynamic environment data meets the following conditions: the rate of change is greater than the rate of change threshold, the degree of fluctuation is greater than the degree of fluctuation threshold, and at least n of the m monitored values ​​exceed at least one of the reference value ranges, then the dynamic environment data is abnormal data; n is a positive integer less than or equal to m. The method for calculating the fault confidence of each node in each fault path involves assigning an initial value to the fault confidence based on the time series of the power environment data corresponding to the node. Specifically, this includes: if the power environment data corresponding to the node is not abnormal data, then the fault confidence is 0; otherwise: Calculate the mean of the latest m monitoring values ​​in the time series as the observed value of the node; obtain the rate of change and the degree of fluctuation calculated based on the latest m monitoring values; assign an initial value to the fault confidence based on any one of the following: the absolute value of the difference between the observed value and the midpoint of the reference value interval, the difference between the rate of change and the rate of change threshold, and the difference between the degree of fluctuation and the degree of fluctuation threshold. The linked consistency verification of the fault confidence of the nodes includes abnormal direction verification and abnormal lag verification for each non-target node; if any non-target node fails the abnormal direction verification or abnormal lag verification, the confidence of the corresponding non-target node is set to 0.

2. The method for diagnosing abnormal power environment monitoring data as described in claim 1, characterized in that: The power environment data includes power data and environmental data; wherein, the power data includes the operating parameters of the power supply equipment in the power system; and the environmental data is the monitoring data of the working environment of the power supply equipment in the power system. The power supply equipment and its working environment are divided into zones according to functional areas or physical locations, and each type of power environment data in each zone is recorded as a separate power environment data item.

3. The method for diagnosing abnormal power environment monitoring data as described in claim 2, characterized in that: The causal anomaly graph includes nodes and directed edges; each node corresponds to a dynamic environment data point; each directed edge connects two nodes and points from the upstream node to the downstream node. If any two nodes are connected by a directed edge, then the two nodes have an abnormal linkage relationship. Specifically, when the power environment data of the upstream node is abnormal, there is a probability that the power environment data of the downstream node will be abnormal, and the probability is equal to the edge weight of the directed edge. Collect historical data, calculate the probability that any abnormality in a dynamic environmental data point will cause an abnormality in any other dynamic environmental data point besides itself, and assign weight values ​​to the corresponding directed edges in the causal anomaly graph.

4. The method for diagnosing abnormal power environment monitoring data as described in claim 3, characterized in that: The fault path includes nodes and directed edges in the causal anomaly graph; when exploring a fault path for any abnormal data, the node corresponding to the abnormal data is marked as the target node; any fault path is acyclic and has no branches, and the target node has no downstream nodes; The method for exploring fault paths is as follows: Starting from the target node, explore nodes in the direction of the directed edges in the causal anomaly graph; for each node explored, update the nodes and directed edges currently included in the fault path, and calculate the product of the weight values ​​of all directed edges currently included in the fault path; if the product of the weight values ​​is less than a preset minimum weight threshold, stop exploring and obtain a fault path.

5. The method for diagnosing abnormal power environment monitoring data as described in claim 4, characterized in that: The abnormal direction verification specifically includes: extracting the difference between the observed value and the midpoint of the reference value interval for any non-target node and the adjacent downstream node in the fault path, and identifying the abnormal direction of the non-target node and the adjacent downstream node; the abnormal direction includes abnormal rise and abnormal fall. Identify the abnormal linkage direction between a non-target node and its adjacent downstream node; the abnormal linkage direction includes positive linkage and negative linkage; wherein, positive linkage means that the abnormal direction between the non-target node and its adjacent downstream node is the same, and negative linkage means that the abnormal direction is opposite. If the abnormal direction between the non-target node and its adjacent downstream node does not satisfy the abnormal linkage direction, then the non-target node fails the abnormal direction verification.

6. The method for diagnosing abnormal power environment monitoring data as described in claim 5, characterized in that: The abnormal lag verification specifically includes: extracting the time series corresponding to each node in the fault chain through a sliding window of length m monitoring values, and determining whether the power environment data is abnormal after each sliding extraction; determining the time point at which each power environment data begins to show abnormality based on the judgment result after each sliding extraction, as the abnormal starting point of the corresponding node in the fault chain; Calculate the time difference between the abnormal starting point of any non-target node and its adjacent downstream node in the fault path, and use it as the abnormal lag time of the corresponding non-target node; set a lag time interval for each directed edge in the causal anomaly graph; if the abnormal lag time of the non-target node is not located in the lag time interval of the directed edge between the non-target node and its adjacent downstream node, then the non-target node fails the abnormal lag verification.

7. The method for diagnosing abnormal power environment monitoring data as described in claim 6, characterized in that: The process of filtering and pruning each fault path specifically includes: after assigning an initial value to the fault confidence of each node in each fault path, performing a first filtering and pruning of the fault path; and after verifying the consistency of the fault confidence of each node, performing a second filtering and pruning of the fault path. The methods for performing one or two filtering of faulty paths are as follows: If, in any fault path, the fault confidence of all non-target nodes except the target node is 0, then the corresponding fault path is deleted. If any fault path contains at least one non-target node with a fault confidence of not 0, and at least one of the non-target nodes with a fault confidence of not 0 in the fault path has a non-target node with a confidence of 0 between it and the target node, then the corresponding fault path is deleted. The methods for performing one or two trims on faulty paths are as follows: If any fault path contains at least one non-target node with a fault confidence of not 0, and there are no non-target nodes with a confidence of 0 between any non-target node with a fault confidence of not 0 and the target node, then the corresponding fault path is pruned, specifically including: deleting all non-target nodes with a fault confidence of 0 in the fault path.

8. The method for diagnosing abnormal power environment monitoring data as described in claim 7, characterized in that: The weight of any node is the weight of the directed edge between the corresponding node and the adjacent downstream node in the corresponding fault path. The calculation of the comprehensive causal strength of any fault path based on the fault confidence and weight value of nodes specifically includes: multiplying the fault confidence of each node by the corresponding weight value to obtain the weighted confidence of the node; and calculating the sum of the weighted confidence of all nodes in the fault path except the target node as the comprehensive causal strength. The method for determining the actual fault path is as follows: sort the fault paths according to the magnitude of the comprehensive causal strength; select the fault path with the largest comprehensive causal strength as the actual fault path; The method for diagnosing the fault source is as follows: the first node in the actual fault path is marked as the fault source; the first node is a node in the actual fault path that does not have an upstream node.

9. A power environment monitoring data visualization platform, used to implement the power environment monitoring data anomaly diagnosis method as described in any one of claims 1-8, characterized in that: It includes an anomaly detection module, a causal modeling module, a causal reasoning module, a path evaluation module, and a visualization module; among which: The anomaly identification module is used to identify abnormal data in the power environment data; The causal modeling module is used to construct a causal anomaly diagram of dynamic environmental data; The causal reasoning module explores fault paths for each abnormal data item based on the causal anomaly graph, and filters and prunes each fault path; The path evaluation module is used to calculate the comprehensive causal strength of each fault path, and to determine the actual fault path and diagnose the fault source based on the comprehensive causal strength. The visualization module is used to visualize and display power environment data, cause-effect anomaly diagrams, actual fault paths, and fault sources.

Images