Insulation abnormality analysis method and device based on intelligent fusion terminal, and equipment

Abstract

The application provides an insulation abnormality analysis method and device based on an intelligent fusion terminal, and belongs to the technical field of insulation analysis. The method comprises the following steps: acquiring monitoring data of each monitoring point in a target transformer area; each monitoring point comprises a plurality of full-quantity monitoring points and a plurality of simple monitoring points; for each monitoring point, determining a first analysis result corresponding to the monitoring point based on the monitoring data of the monitoring point and standard monitoring data corresponding to the monitoring point; marking each monitoring point with an abnormal first analysis result as an abnormal monitoring point; acquiring related monitoring points of each abnormal monitoring point, wherein when the monitoring point is a simple monitoring point, the related monitoring points of the monitoring point contain at least one full-quantity monitoring point and are marked as full-quantity related monitoring points; and performing an insulation abnormality analysis operation on each abnormal monitoring point to obtain a second analysis result corresponding to each abnormal monitoring point. The application can balance the data acquisition cost and the insulation analysis accuracy.

Description

Technical Field

[0001] This application belongs to the field of insulation analysis technology, and more specifically, relates to an insulation anomaly analysis method, device, and equipment based on an intelligent fusion terminal. Background Technology

[0002] The stability of the insulation status of a power distribution network is a core foundation for ensuring power supply reliability, equipment safety, and users' electricity rights. Real-time sensing, accurate analysis, and rapid location of the insulation status of the power distribution network are key requirements for the intelligent transformation of power distribution network operation and maintenance.

[0003] Because intelligent fusion terminals can collect and integrate monitoring data from various electrical monitoring devices in the distribution network, most existing technologies rely on intelligent fusion terminals for insulation anomaly analysis of the power grid. However, while using multi-dimensional electrical monitoring devices to collect data throughout the distribution network can ensure analysis accuracy, the high cost and complex installation and commissioning of these devices, coupled with cost reduction through the use of fewer-dimensional electrical monitoring devices, often results in low accuracy in insulation anomaly analysis due to insufficient data dimensions. Summary of the Invention

[0004] The purpose of this application is to provide an insulation anomaly analysis method, device, and equipment based on an intelligent fusion terminal, so as to balance data acquisition costs and insulation analysis accuracy.

[0005] A first aspect of this application provides an insulation anomaly analysis method based on a smart fusion terminal, comprising:

[0006] Acquire monitoring data from various monitoring points within the target area; each monitoring point includes: multiple full-scale monitoring points and multiple simplified monitoring points; the types of monitoring data from full-scale monitoring points are greater than those from simplified monitoring points;

[0007] For each monitoring point, the first analysis result corresponding to that monitoring point is determined based on the monitoring data of that monitoring point and the standard monitoring data corresponding to that monitoring point;

[0008] Mark each monitoring point whose first analysis result is abnormal as an abnormal monitoring point;

[0009] Obtain the relevant monitoring points for each anomaly monitoring point. When the monitoring point is a simple monitoring point, the relevant monitoring points of the monitoring point contain at least one full-scale relevant monitoring point.

[0010] For each abnormal monitoring point, in response to the fact that the abnormal monitoring point is a full monitoring point, the second analysis result of the full monitoring point is determined based on the monitoring data of the relevant monitoring points of the full monitoring point, the monitoring data of the full monitoring point, and the topological relationship between the full monitoring point and its corresponding relevant monitoring points;

[0011] For each abnormal monitoring point, in response to the fact that the abnormal monitoring point is a simplified monitoring point, the monitoring data of the simplified monitoring point is expanded based on the monitoring data of the full set of related monitoring points corresponding to the simplified monitoring point to obtain the full set of monitoring data of the simplified monitoring point; the second analysis result of the simplified monitoring point is determined based on the monitoring data of the related monitoring points corresponding to the simplified monitoring point, the full set of monitoring data of the simplified monitoring point, and the topological relationship between the simplified monitoring point and its corresponding related monitoring points.

[0012] A second aspect of this application provides an insulation anomaly analysis device based on a smart fusion terminal, comprising:

[0013] The first data acquisition module is used to acquire monitoring data from various monitoring points in the target area. Each monitoring point includes multiple full-scale monitoring points and multiple simplified monitoring points. The types of monitoring data from full-scale monitoring points are greater than the types of monitoring data from simplified monitoring points.

[0014] The first analysis module is used to determine the first analysis result for each monitoring point based on the monitoring data of that monitoring point and the standard monitoring data corresponding to that monitoring point.

[0015] The anomaly marking module is used to mark each monitoring point whose first analysis result is abnormal as an anomaly monitoring point;

[0016] The second data acquisition module is used to acquire the relevant monitoring points for each abnormal monitoring point. When the monitoring point is a simple monitoring point, the relevant monitoring points of the monitoring point contain at least one full-scale relevant monitoring point.

[0017] The second analysis module is used to, for each abnormal monitoring point, in response to the abnormal monitoring point being a full monitoring point, determine the second analysis result of the full monitoring point based on the monitoring data of the related monitoring points of the full monitoring point, the monitoring data of the full monitoring point, and the topological relationship between the full monitoring point and its corresponding related monitoring points; and, for each abnormal monitoring point, in response to the abnormal monitoring point being a simplified monitoring point, expand the monitoring data of the simplified monitoring point based on the monitoring data of the full related monitoring points corresponding to the simplified monitoring point to obtain the full monitoring data of the simplified monitoring point; and determine the second analysis result of the simplified monitoring point based on the monitoring data of the related monitoring points corresponding to the simplified monitoring point, the full monitoring data of the simplified monitoring point, and the topological relationship between the simplified monitoring point and its corresponding related monitoring points.

[0018] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the above-described insulation anomaly analysis method based on a smart fusion terminal.

[0019] In a fourth aspect of this application, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described insulation anomaly analysis method based on a smart fusion terminal.

[0020] The beneficial effects of the insulation anomaly analysis method, apparatus, and equipment based on intelligent fusion terminals provided in this application are as follows:

[0021] This application embodiment employs a hierarchical setup of full-scale monitoring points and simplified monitoring points. Simplified monitoring points significantly reduce the number of multi-dimensional monitoring devices required, lowering equipment procurement costs and installation / commissioning complexity. Simultaneously, addressing the issue of insufficient data dimensions at simplified monitoring points, data is expanded by associating them with corresponding full-scale related monitoring points. This eliminates the need for additional multi-dimensional equipment investment, allowing simplified monitoring points to obtain comprehensive data support, thus balancing data acquisition costs and insulation analysis accuracy. Regardless of whether the anomaly monitoring point is full-scale or simplified, this application embodiment analyzes the topological relationships between monitoring points. This ensures the analysis process has sufficient data sources and relies on the correlation logic of the power grid topology, avoiding biased judgments caused by isolated data. Ultimately, this significantly reduces misjudgments and omissions caused by insufficient data dimensions, improving the accuracy and reliability of insulation anomaly analysis results. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art 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.

[0023] Figure 1 A schematic diagram of the architecture of an insulation anomaly analysis system based on an intelligent fusion terminal provided in an embodiment of this application;

[0024] Figure 2 A flowchart illustrating an insulation anomaly analysis method based on a smart fusion terminal, provided as an embodiment of this application;

[0025] Figure 3 This application provides a schematic flowchart of a method for determining a second analysis result according to an embodiment of the present application.

[0026] Figure 4 This application provides a schematic flowchart of a method for determining upstream and downstream analysis results according to an embodiment of the present application.

[0027] Figure 5 This application provides a schematic flowchart of a method for determining downstream analysis results according to an embodiment of the present application.

[0028] Figure 6 This is a structural block diagram of an insulation anomaly analysis device based on an intelligent fusion terminal provided in an embodiment of this application;

[0029] Figure 7 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0030] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0031] It is understood that in the embodiments of this application, data related to user information is involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0032] It should be noted that the terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0034] Please refer to Figure 1 , Figure 1This is a schematic diagram of the architecture of an insulation anomaly analysis system based on an intelligent fusion terminal, provided in one embodiment of this application. The system may include an intelligent fusion terminal and multiple monitoring points in the target distribution area. The intelligent fusion terminal communicates with each monitoring point. In this embodiment, the intelligent fusion terminal is an intelligent device on the power distribution network side of the power system, mainly used to realize the data acquisition, fusion, and processing of various monitoring points within the distribution area. Each monitoring point in the distribution area can monitor electrical and environmental parameters of its deployment area. Monitoring points can be divided into full-scale monitoring points and simplified monitoring points. Full-scale monitoring points collect more types of data than simplified monitoring points. For example, simplified monitoring points can monitor line voltage, line current, three-phase imbalance, and ambient temperature and humidity, while full-scale monitoring points can monitor all data types detected by simplified monitoring points, as well as electrical parameters such as line leakage current, insulation resistance value, partial discharge signal, zero-sequence current, and zero-sequence voltage.

[0035] In this embodiment, full-scale monitoring points can be deployed in core locations within the transformer substation, such as transformer outgoing terminals, the middle section of main lines, and intersections of important branch lines, covering the operational status of a large area of ​​the line. Simplified monitoring points can be deployed at the ends of branch lines, centralized user access points, and remote branch lines, filling the coverage gaps of the full-scale monitoring points and achieving full-area data collection within the transformer substation. More specifically, to ensure the accuracy of data monitoring and subsequent data complementarity, full-scale monitoring points and simplified monitoring points can be deployed alternately along the same line. The target transformer substation refers to the area where insulation anomaly analysis is to be performed; monitoring points can be divided into full-scale monitoring points and simplified monitoring points.

[0036] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating an insulation anomaly analysis method based on a smart fusion terminal, provided as an embodiment of this application. The method can be executed by the smart fusion terminal and may include steps S101-S105.

[0037] S101: Obtain monitoring data from each monitoring point in the target area.

[0038] In this embodiment, each monitoring point includes multiple full monitoring points and multiple simplified monitoring points. The intelligent fusion terminal can acquire monitoring data from each monitoring point in the target area at a preset frequency. During the acquisition process, the intelligent fusion terminal can receive the raw data uploaded by each monitoring point through wireless communication or wired transmission. At the same time, it also performs preliminary verification on the data, eliminating obviously invalid values ​​(such as data exceeding the physical range) and duplicate data to ensure the integrity and basic accuracy of the input data.

[0039] S102: For each monitoring point, determine the first analysis result corresponding to that monitoring point based on the monitoring data of that monitoring point and the standard monitoring data corresponding to that monitoring point.

[0040] In this embodiment, the standard monitoring data corresponding to the monitoring point can be set using the historical data of the monitoring point. Specifically, the first analysis result corresponding to the monitoring point can be determined by calculating the degree of deviation between the monitoring data of the monitoring point and the standard monitoring data corresponding to the monitoring point. For example, if the degree of deviation between the monitoring data of the monitoring point and the standard monitoring data corresponding to the monitoring point is greater than a preset deviation threshold, the first analysis result corresponding to the monitoring point is determined to be abnormal; if the degree of deviation between the monitoring data of the monitoring point and the standard monitoring data corresponding to the monitoring point is less than or equal to the preset deviation threshold, the first analysis result corresponding to the monitoring point is determined to be normal.

[0041] In this embodiment, since the monitoring data of the monitoring point contains multiple electrical parameters, the maximum value of the deviation among each electrical parameter can be used as the deviation between the monitoring data of that monitoring point and the standard monitoring data corresponding to that monitoring point. The deviation can be calculated as follows: Deviation = |Value of a certain electrical parameter in the monitoring data of that monitoring point - Value of the same electrical parameter in the standard monitoring data corresponding to that monitoring point| / Value of the same electrical parameter in the standard monitoring data corresponding to that monitoring point.

[0042] In this embodiment, a simplified monitoring point is used to monitor line voltage, line current, three-phase imbalance, and ambient temperature and humidity. A full-scale monitoring point, in addition to monitoring all data types monitored by the simplified monitoring point, can also monitor electrical parameters such as line leakage current, insulation resistance, partial discharge signal, zero-sequence current, and zero-sequence voltage. The first analysis result can be determined based on the following method:

[0043] Based on the monitoring data of the monitoring point, a comprehensive characteristic value is determined; based on the standard monitoring data of the monitoring point, a standard comprehensive characteristic value is determined; and based on the comprehensive characteristic value of the monitoring point and the standard comprehensive characteristic value of the monitoring point, a first analysis result is determined.

[0044] In this embodiment, if the monitoring point is a simplified monitoring point, the formula for calculating the comprehensive feature value corresponding to the simplified monitoring point can be:

[0045] ,in, This represents the fluctuation coefficient, used to characterize the fluctuation stability of voltage and current. This represents the first stability coefficient, used to characterize the stability of the three-phase imbalance. This represents the first correlation coefficient, used to characterize the degree of correlation between ambient temperature and voltage and current; These are the weighting coefficients.

[0046] ,in, For the voltage over i consecutive sampling cycles, Let i be the current for i consecutive sampling cycles. This represents the average voltage over i consecutive sampling periods. The average current over i consecutive sampling cycles is given. The smaller the sum of the percentage differences between the two values, the better. The closer it is to 1.

[0047] ,in, This indicates the three-phase imbalance during the first data collection cycle. This represents the three-phase imbalance in the i-th cycle. This represents the average three-phase imbalance over i periods. The smaller the change in three-phase imbalance, the better. The closer it is to 1.

[0048] ,in This represents the average temperature over i periods. This represents the first correlation coefficient, used to characterize the correlation between voltage and temperature. This represents the second correlation coefficient, used to characterize the correlation between current and temperature. and It can be determined based on the historical data of this simple monitoring point, in order to For example, the ratio of voltage to temperature can be determined based on the average historical data of this simple monitoring point.

[0049] If the monitoring point is a full-monitoring point, then the formula for calculating the comprehensive characteristic value corresponding to the full-monitoring point can be:

[0050] ,in, This represents the second stability coefficient, used to characterize the stability of insulation parameters. This represents the second correlation coefficient, used to characterize the correlation between the zero-sequence parameter and the partial discharge signal. These are the weighting coefficients.

[0051] in, ,in, This represents the insulation resistance in the i-th cycle. This indicates the insulation resistance during the first cycle. This represents the average insulation resistance over i cycles. This represents the leakage current in the i-th cycle. This represents the leakage current in the first cycle. This represents the average leakage current over i cycles.

[0052] ,in, This represents the average value of the zero-sequence current. This represents the average value of the zero-sequence voltage. This represents the average value of the partial discharge signal. This indicates the maximum partial discharge value in the historical data of this full-scale monitoring point. Under normal circumstances, the proportion of zero-sequence parameters and the partial discharge signal are both extremely small. Close to 1.

[0053] In this embodiment, if or If the result is less than or equal to the standard composite characteristic value, the first analysis result is an anomaly. The standard composite characteristic value can be calculated based on historical data using the same formula as above.

[0054] In this embodiment, the weights for the weighted calculation can be determined based on experience or based on the type of line where the monitoring point is located. For example, if the line where the monitoring point is located is an overhead line, it is exposed to the outdoors and is greatly affected by wind, rain, vegetation, and temperature and humidity; voltage and current fluctuate frequently, and three-phase imbalance is prone to change; insulation faults are mostly caused by external interference, therefore... and The corresponding weight can be appropriately increased. If the monitoring point is located on a cable line, since it is generally buried or laid in a pipe, the environment is stable; voltage and current fluctuations are small, and the three phases are balanced; insulation aging and partial discharge are the main faults, and the zero-sequence parameter is obviously abnormal. Therefore, if this monitoring point is a full-quantity monitoring point, then the weight can be increased. and The corresponding weights are appropriately increased. It should be noted that the sum of all weights is 1. When the weight of a weighted item is increased, the weights of other weighted items can be reduced accordingly so that the total weight is 1.

[0055] S103: Mark each monitoring point whose first analysis result is abnormal as an abnormal monitoring point.

[0056] In this embodiment, the monitoring points whose first analysis results are abnormal, as determined in step S102, are marked as abnormal monitoring points, which can also be understood as monitoring points suspected of being abnormal obtained from preliminary screening.

[0057] S104: Obtain the relevant monitoring points for each abnormal monitoring point. When the monitoring point is a simple monitoring point, the relevant monitoring points of the monitoring point contain at least one full-quantity relevant monitoring point.

[0058] In this embodiment, relevant monitoring points for each monitoring point can be preset in advance. For example, relevant personnel can set them based on experience and the topological relationship of each monitoring point in the line. More specifically, for a monitoring point, its upstream and downstream monitoring points can be identified as relevant monitoring points, as can monitoring points belonging to the same line. To ensure the accuracy of subsequent data expansion, when deploying monitoring points, it should be ensured that at least one full-scale monitoring point exists among the relevant monitoring points of the simple monitoring point, which can be marked as a full-scale relevant monitoring point.

[0059] In this embodiment, once the abnormal monitoring point is determined, the relevant monitoring points for each abnormal monitoring point can be determined from the preset relevant monitoring points of each monitoring point. The preset relevant monitoring points of each monitoring point can be stored in tabular form.

[0060] S105: For each abnormal monitoring point, in response to the fact that the abnormal monitoring point is a full monitoring point, the second analysis result of the full monitoring point is determined based on the monitoring data of the relevant monitoring points of the full monitoring point, the monitoring data of the full monitoring point, and the topological relationship between the full monitoring point and its corresponding relevant monitoring points.

[0061] S106: For each abnormal monitoring point, in response to the fact that the abnormal monitoring point is a simplified monitoring point, the monitoring data of the simplified monitoring point is expanded based on the monitoring data of the full set of related monitoring points corresponding to the simplified monitoring point to obtain the full set of monitoring data of the simplified monitoring point; the second analysis result of the simplified monitoring point is determined based on the monitoring data of the related monitoring points corresponding to the simplified monitoring point, the full set of monitoring data of the simplified monitoring point, and the topological relationship between the simplified monitoring point and its corresponding related monitoring points.

[0062] In this embodiment, the insulation anomaly analysis operation refers to a secondary, precise analysis process performed on the anomaly monitoring point. Its core is to verify the existence of the anomaly by combining more relevant data. The second analysis result refers to the final judgment result. Topological relationships describe the line connection structure between monitoring points, such as A being upstream of B, C and D being on the same branch line, reflecting the physical connection logic of the power system.

[0063] In this embodiment, since the full monitoring point itself has already collected a wealth of data, there is no need to supplement the data. The anomaly of the full monitoring point can be determined directly by combining its own monitoring data, the monitoring data of related monitoring points, and the topological relationship.

[0064] In this embodiment, since the basic data collected by the simplified monitoring point is insufficient for detailed insulation anomaly analysis, the data needs to be expanded before analysis. The data expansion step can utilize monitoring data from all relevant monitoring points to infer the missing data from the simplified monitoring point. For example, by using the correlation between voltage and insulation resistance of all monitoring points and combining it with the voltage data of the simplified monitoring point, its insulation resistance can be calculated. After obtaining the complete data for the simplified monitoring point, it is then combined with the monitoring data and topological relationships of its related monitoring points (using the same logic as the full monitoring point) to determine whether the anomaly at the simplified monitoring point is real, and the second analysis result is output.

[0065] As can be seen from the above, the embodiments of this application, through a hierarchical setup of full-scale monitoring points and simplified monitoring points, significantly reduce the number of multi-dimensional monitoring devices required, lowering equipment procurement costs and installation and commissioning complexity. Simultaneously, addressing the issue of insufficient data dimensions at simplified monitoring points, data is expanded by associating them with corresponding full-scale related monitoring points. This eliminates the need for additional multi-dimensional equipment investment, allowing simplified monitoring points to obtain full data support, thus balancing data acquisition costs and insulation analysis accuracy. In these embodiments, regardless of whether the anomaly monitoring point is full-scale or simplified, the analysis is performed based on the topological relationships between monitoring points. This ensures the analysis process has sufficient data sources and relies on the correlation logic of the power grid topology, avoiding biased judgments caused by isolated data. Ultimately, this significantly reduces misjudgments and omissions caused by insufficient data dimensions, improving the accuracy and reliability of insulation anomaly analysis results.

[0066] In one embodiment of this application, the topological relationship between the full-scale monitoring point and its corresponding related monitoring points includes: upstream and downstream topological relationship and same-line topological relationship;

[0067] refer to Figure 3 Based on the monitoring data of relevant monitoring points of the full-scale monitoring point, the monitoring data of the full-scale monitoring point, and the topological relationship between the full-scale monitoring point and its corresponding relevant monitoring points, the second analysis result of the full-scale monitoring point is determined, including:

[0068] S1: For each relevant monitoring point, if the topological relationship between the total number of monitoring points and the relevant monitoring point is an upstream-downstream topological relationship, mark it as an upstream-downstream monitoring point; if the topological relationship between the total number of monitoring points and the relevant monitoring point is a same-line topological relationship, mark it as a same-line monitoring point.

[0069] S2: Determine the upstream and downstream analysis results based on the monitoring data of the full monitoring points and the monitoring data of each upstream and downstream monitoring point;

[0070] S3: Determine the analysis results for the same route based on the monitoring data of the full monitoring points and the monitoring data of each monitoring point along the same route;

[0071] S4: Determine the second analysis result for the full monitoring point based on the upstream and downstream analysis results and the same route analysis results.

[0072] In this embodiment, upstream and downstream topology refers to the line connection relationship determined by the direction of current flow. Electrical energy is transmitted from the power source to the user end, with the line closer to the power source being upstream and the line farther from the power source being downstream. For example, the monitoring point at the transformer's output end is upstream of the monitoring point on its downstream branch line, and the monitoring point at the user's side at the end of the branch line is downstream of the monitoring point in the middle of the branch line. Same-line topology refers to monitoring points located on the same complete line, but without a clear upstream / downstream subordinate relationship. For example, two units in the same building connected to the same branch line have their monitoring points on the same line.

[0073] In this embodiment, based on the classified monitoring points, it is possible to analyze whether the anomaly is a true insulation anomaly from two dimensions: upstream and downstream analysis and same-line analysis.

[0074] Upstream and downstream analysis refers to comparing the data from the full monitoring point with those from upstream and downstream monitoring points to see if the anomaly conforms to the transmission pattern of the line. For example, if an anomaly occurs first at the upstream monitoring point, and the anomaly signal is transmitted to the anomaly point according to a pattern with distance (upstream and downstream distance), it is likely a real anomaly; if there is no anomaly upstream and no signs of impact downstream, it is likely a false alarm.

[0075] Line-wide analysis refers to comparing the data from the full monitoring point with the data from other monitoring points along the same line to determine whether the anomaly is a common problem across the entire line. For example, if other monitoring points along the same line also show a similar decrease in insulation parameters, it may indicate overall line aging; if only that point is abnormal while other points along the same line are normal, it may indicate a localized fault at that point, such as insulation damage.

[0076] In this embodiment, the upstream and downstream analysis results, the same-line analysis results, and the second analysis results can all be essentially a number between 0 and 1, that is, the probability of the monitoring point being abnormal.

[0077] Specifically, in one embodiment of this application, each upstream and downstream monitoring point includes an upstream monitoring point and a downstream monitoring point;

[0078] refer to Figure 4 Based on the monitoring data from all monitoring points and the monitoring data from various upstream and downstream monitoring points, the upstream and downstream analysis results are determined, including:

[0079] S201: Determine the target time when abnormal data appears at the full monitoring point, and whether abnormal data appears in the monitoring data of the upstream monitoring point;

[0080] S202: If no abnormal data is found in the monitoring data of the upstream monitoring point, the preset analysis result will be determined as the upstream analysis result;

[0081] S203: If abnormal data appears in the monitoring data of the upstream monitoring point, determine the first abnormal moment when the abnormal data appears at the upstream monitoring point;

[0082] S204: Determine the standard abnormal time based on the first abnormal time and the distance between the upstream monitoring point and the full monitoring point;

[0083] S205: Determine the upstream analysis results based on the standard anomaly time and the target anomaly time;

[0084] S206: Determine the downstream analysis results based on the monitoring data from downstream monitoring points and the monitoring data from all monitoring points;

[0085] S207: Determine the upstream and downstream analysis results based on the upstream and downstream analysis results.

[0086] In this embodiment, abnormal data can refer to the electrical parameter with the largest deviation in the aforementioned steps of determining whether each first analysis result is abnormal. The target abnormal time is the specific time when abnormal data first appears at the full monitoring point, serving as the time benchmark for judging abnormal propagation. The first abnormal time refers to the specific time when abnormal data first appears at the upstream monitoring point. The standard abnormal time refers to the theoretical time when the full monitoring point should exhibit an abnormality if the abnormality propagates from upstream to the full monitoring point, calculated based on the abnormal time of the upstream monitoring point and the distance between the upstream monitoring point and the full monitoring point. It should be noted that the distance between the upstream monitoring point and the full monitoring point specifically refers to the line length.

[0087] In this embodiment, if there is no abnormal data at the upstream monitoring point, it means that the abnormality is most likely not caused by upstream transmission. Therefore, a low probability value can be preset as the preset analysis result, such as 0.1 or 0.2.

[0088] If there is abnormal data at the upstream monitoring point, it is necessary to further verify whether the anomaly was transmitted from upstream to that point. The closer the standard anomaly time and the target anomaly time are, the more likely the anomaly is to be transmitted from upstream, and the higher the probability of the upstream analysis result. For example, the upstream analysis result can be determined by the following formula: ,in, This refers to the upstream analysis results, representing the probability of an anomaly. A higher value indicates that the anomaly is more likely to be caused by upstream transmission. It is a natural constant. This is an adjustment coefficient, and it must be greater than 0. The specific value can be set based on experience or through multiple verifications. The absolute time difference between the target anomaly time and the standard anomaly time is used in the calculation process. Only substitute numerical values.

[0089] refer to Figure 5In one embodiment of this application, determining the downstream analysis result based on the monitoring data from downstream monitoring points and the monitoring data from all monitoring points includes:

[0090] S2061: Based on the degree of deviation of abnormal data in the monitoring data of the full monitoring point and the distance between the full monitoring point and each downstream monitoring point, determine the standard deviation of each downstream monitoring point;

[0091] S2062: For each downstream monitoring point, the analysis result of the downstream monitoring point is determined based on the target deviation degree of abnormal data in the monitoring data of the downstream monitoring point and the standard deviation degree corresponding to the downstream monitoring point.

[0092] S2063: Determine the downstream analysis results based on the analysis results of each downstream monitoring point.

[0093] In this embodiment, the standard deviation refers to the theoretically expected abnormal deviation value of each downstream monitoring point, calculated based on the abnormal deviation of all monitoring points and the line distance between the full monitoring point and the downstream monitoring point. For example, it can be calculated as follows: ,in This represents the standard deviation of downstream monitoring points, which is also the theoretically expected abnormal deviation value. This represents the degree of abnormal deviation at all monitoring points, i.e., the deviation between the actual monitoring data at that point and the standard data. This is the line distance between the full-scale monitoring point and the downstream monitoring point. The attenuation coefficient can be preset based on the line type. For example, the signal attenuation of cable lines is slow, while that of overhead lines is greatly affected by the environment and attenuates quickly. Therefore, the signal attenuation coefficient in cable lines is smaller than that in overhead lines. The specific value can be set based on experience or multiple verifications.

[0094] In this embodiment, the analysis result of a downstream monitoring point can be determined based on the target deviation degree of abnormal data in the monitoring data of that downstream monitoring point and the standard deviation degree corresponding to that downstream monitoring point. The analysis result of the downstream monitoring point can still be expressed in probabilistic form; the smaller the difference between the target deviation degree and the standard deviation degree, the higher the probability value in the analysis result of the downstream monitoring point. The specific determination process will not be elaborated in this embodiment, and those skilled in the art can set it themselves. In this embodiment, the analysis results of each downstream monitoring point can be weighted to obtain the downstream analysis result. Similarly, the upstream and downstream analysis results can be weighted to obtain the upstream and downstream analysis results. The weights of each weighting item in the weighting calculation can be the same.

[0095] As can be seen from the above, this embodiment of the application subdivides the topological relationship between the full-scale monitoring points and their related monitoring points into upstream and downstream topological relationships and intra-line topological relationships, and performs anomaly analysis based on these two relationships respectively. Through upstream and downstream analysis, it is possible to determine whether the anomaly conforms to the line conduction law. For example, based on the anomaly situation and anomaly conduction time of the upstream monitoring point, it can be determined whether the anomaly of the full-scale monitoring points is caused by upstream conduction. Through intra-line analysis, it is possible to determine whether the anomaly is a common problem of the entire line or a local fault. For example, by comparing data from other monitoring points on the same line, it can be determined whether it is overall line aging or insulation layer damage at a certain point. This method of analysis from two different dimensions makes the insulation anomaly analysis more comprehensive and accurate, effectively avoiding misjudgments and omissions that may be caused by single-dimensional analysis. This embodiment also considers the influence of line type on signal attenuation when calculating the standard deviation of downstream monitoring points, and sets different attenuation coefficients. Cable lines have slow signal attenuation, while overhead lines are greatly affected by the environment and attenuate quickly. Therefore, the signal attenuation coefficient in cable lines is smaller than that in overhead lines, which can adapt to different types of lines, improving the universality and accuracy of the analysis. Regardless of the line environment, it can accurately determine the insulation anomaly situation.

[0096] In one embodiment of this application, considering that the insulation state of the same line should tend to be consistent, if the insulation coefficient of a single point is small compared with the overall insulation coefficient of the line, it indicates that the anomaly is a common problem of the line (real); if the difference is large, it may be a local false alarm. Therefore, the analysis results of the same line can be determined based on the following method:

[0097] The insulation coefficient of the first line is determined based on the monitoring data from all monitoring points.

[0098] The insulation coefficient of the second line is determined based on the monitoring data from each monitoring point on the same line.

[0099] The analysis results for the same line are determined based on the insulation coefficient of the first line and the insulation coefficient of the second line.

[0100] In this embodiment, the line insulation coefficient is a core indicator reflecting the insulation status, which can be obtained based on the ratio of leakage current to ground voltage. The monitoring data of the full-scale monitoring points contain leakage current and ground voltage. Each monitoring point on the same line as the full-scale monitoring point can be a full-scale monitoring point or a simplified monitoring point. However, if the monitoring data of a simplified monitoring point does not contain leakage current and / or ground voltage, it can be obtained by expanding the data based on the monitoring data of the corresponding full-scale related monitoring points. The specific expansion method is detailed later.

[0101] In this embodiment, if there are multiple monitoring points along the same line, the insulation coefficient of the line can be determined separately based on the monitoring data corresponding to each relevant monitoring point, and then a weighted sum can be performed. Since the data from the simplified relevant monitoring points is obtained through expansion, the reliability of the line insulation coefficient calculated based on the expanded data is relatively lower than the reliability of the line insulation coefficient calculated based on the original data from all monitoring points. Therefore, the weighted calculation weight of the simplified relevant monitoring points is less than the weighted calculation weight of the all relevant monitoring points in the weighted calculation.

[0102] In this embodiment, the smaller the difference between the insulation coefficient of the first line and the insulation coefficient of the second line, the greater the probability value in the analysis results of the same line. The specific determination process will not be described in this embodiment, and those skilled in the art can set it themselves.

[0103] As can be seen from the above, this embodiment is based on the principle that the insulation state of the same line tends to be consistent. It determines the nature of the anomaly by comparing the insulation coefficient difference between a single monitoring point (full-scale monitoring point) and the overall line. If the insulation coefficient of a single point differs little from the overall line insulation coefficient, it indicates that the anomaly is a common problem of the line and is a genuine anomaly; a large difference may indicate a local false alarm. This judgment method directly targets the core characteristics of the insulation state of the same line, accurately distinguishing between genuine anomalies and false alarms, avoiding ineffective maintenance due to local interference or misjudgment, and improving the accuracy of anomaly judgment. In this embodiment, when there are multiple monitoring points on the same line, a weighted summation method is used to determine the line insulation coefficient, taking into account the reliability differences of different data sources. Since the data from the simplified related monitoring points is obtained through expansion, its reliability is relatively lower than that of the line insulation coefficient calculated based on the original data from the full-scale monitoring points. Therefore, it is given a smaller weight in the weighted calculation. This weighting method allows the final line insulation coefficient to more accurately reflect the actual situation, avoiding calculation deviations caused by different data reliability and improving the accuracy of the analysis results.

[0104] In one embodiment of this application, the monitoring data from the simplified monitoring point is of the type of simplified monitoring data; the monitoring data from the full monitoring point includes both simplified monitoring data and additional monitoring data.

[0105] Based on the monitoring data of the full set of related monitoring points corresponding to this simplified monitoring point, the monitoring data of this simplified monitoring point is expanded to obtain the full monitoring data of this simplified monitoring point, including:

[0106] For each fully correlated monitoring point of the simplified monitoring point, determine the mapping relationship between the simplified monitoring data and the additional monitoring data of the fully correlated monitoring point to obtain the mapping relationship of the fully correlated monitoring point;

[0107] Based on the mapping relationship of each full-scale related monitoring point and the simplified monitoring data of the simplified monitoring point, determine the additional monitoring data of the simplified monitoring point;

[0108] The simplified monitoring data and additional monitoring data of this simplified monitoring point are integrated to obtain the full monitoring data of this simplified monitoring point.

[0109] In this embodiment, simplified monitoring data refers to the basic data that a simplified monitoring point can only collect, which cannot directly reflect the insulation status. Both simplified and full-scale monitoring points can monitor this data. Additional monitoring data refers to data unique to full-scale monitoring points that can directly reflect the insulation status, such as insulation resistance, leakage current, or voltage to ground.

[0110] In this embodiment, the mapping relationship of the full-scale correlation monitoring points refers to the fixed correlation pattern between the simplified monitoring data and the additional monitoring data in a single full-scale monitoring point, which can be obtained through data fitting.

[0111] In this embodiment, additional monitoring data for the simplified monitoring point is determined based on the mapping relationship of each full-scale correlation monitoring point and the simplified monitoring data of the simplified monitoring point, including:

[0112] For each fully relevant monitoring point, the target additional monitoring data for that simplified monitoring point is determined based on the mapping relationship of that fully relevant monitoring point and the simplified monitoring data of that simplified monitoring point.

[0113] The additional monitoring data of each target at the simplified monitoring point are weighted and calculated to obtain the additional monitoring data of the simplified monitoring point; wherein, the weight corresponding to the additional monitoring data of each target is determined based on the distance between the full-scale related monitoring point corresponding to the additional monitoring data of the target and the simplified monitoring point.

[0114] In this embodiment, the target additional monitoring data refers to the possible additional data of a single full-related monitoring point, which is calculated separately by combining its own simple-additional data mapping relationship with the simple data of the simple monitoring point.

[0115] In this embodiment, for each fully correlated monitoring point, its own simplified monitoring data and additional monitoring data are analyzed to find the correlation pattern between the two, i.e., the mapping relationship, which can be determined through linear fitting and simple functional relationships. The existing simplified monitoring data of the simplified monitoring point is substituted into the mapping relationship of all fully correlated monitoring points to obtain multiple target additional monitoring data, which are then weighted and fused. The closer the fully correlated monitoring points are, the higher their weight and the stronger their data correlation. Finally, the original simplified monitoring data of the simplified monitoring point is integrated with the inferred additional monitoring data to obtain the full monitoring data for that simplified monitoring point.

[0116] In this embodiment, the weight corresponding to the additional monitoring data of each target is negatively correlated with the distance between the full correlation monitoring point and the simplified monitoring point corresponding to the additional monitoring data of the target. The greater the distance, the lower the weight. This can be determined by a simple linear formula or mapping relationship. The intercept and slope of the linear formula can be set based on multiple experiments and preferences.

[0117] In this embodiment, after obtaining the full monitoring data of the simplified monitoring point, it can be regarded as the full monitoring point. That is, the process of determining the second analysis result of the simplified monitoring point is the same as the process of determining the analysis result of the full monitoring point in the previous embodiment, so it will not be described again in this embodiment.

[0118] As can be seen from the above, the embodiments of this application utilize the data characteristics of simplified monitoring points and full-scale monitoring points. Through data expansion methods, the simplified monitoring data of simplified monitoring points is expanded into full-scale monitoring data. Although simplified monitoring points can only collect basic data, by associating them with relevant full-scale monitoring points, additional monitoring data that directly reflects the insulation status and was originally missing can be obtained, such as insulation resistance, leakage current, or voltage to ground. This fully taps the data potential of existing monitoring equipment, making simplified monitoring points functionally equivalent to full-scale monitoring points, and improving the data integrity and comprehensiveness of the entire monitoring system.

[0119] Corresponding to the insulation anomaly analysis method based on intelligent fusion terminals in the above embodiments, Figure 6 This is a structural block diagram of an insulation anomaly analysis device based on a smart fusion terminal, provided as an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 6 The insulation anomaly analysis device 60 based on the intelligent fusion terminal includes: a first data acquisition module 61, a first analysis module 62, an anomaly marking module 63, a second data acquisition module 64, and a second analysis module 65.

[0120] The first data acquisition module 61 is used to acquire monitoring data from each monitoring point in the target area; each monitoring point includes: multiple full monitoring points and multiple simplified monitoring points; the types of monitoring data from full monitoring points are more than the types of monitoring data from simplified monitoring points;

[0121] The first analysis module 62 is used to determine the first analysis result corresponding to each monitoring point based on the monitoring data of that monitoring point and the standard monitoring data corresponding to that monitoring point.

[0122] The anomaly marking module 63 is used to mark each monitoring point whose first analysis result is abnormal as an abnormal monitoring point;

[0123] The second data acquisition module 64 is used to acquire the relevant monitoring points for each abnormal monitoring point. When the monitoring point is a simple monitoring point, the relevant monitoring points of the monitoring point contain at least one full monitoring point and are marked as full relevant monitoring points.

[0124] The second analysis module 65 is configured to, for each abnormal monitoring point, in response to the abnormal monitoring point being a full monitoring point, determine a second analysis result for the full monitoring point based on the monitoring data of the relevant monitoring points of the full monitoring point, the monitoring data of the full monitoring point, and the topological relationship between the full monitoring point and its corresponding relevant monitoring points; and, for each abnormal monitoring point, in response to the abnormal monitoring point being a simplified monitoring point, expand the monitoring data of the simplified monitoring point based on the monitoring data of the full relevant monitoring points corresponding to the simplified monitoring point to obtain the full monitoring data of the simplified monitoring point; and determine a second analysis result for the simplified monitoring point based on the monitoring data of the relevant monitoring points corresponding to the simplified monitoring point, the full monitoring data of the simplified monitoring point, and the topological relationship between the simplified monitoring point and its corresponding relevant monitoring points.

[0125] In one embodiment of this application, the topological relationship between the full-scale monitoring point and its corresponding related monitoring points includes: upstream and downstream topological relationship and same-line topological relationship;

[0126] The second analysis module 65 is specifically used to mark each relevant monitoring point as an upstream or downstream monitoring point if the topological relationship between the total number of monitoring points and the relevant monitoring point is an upstream or downstream topological relationship; and to mark it as a same-line monitoring point if the topological relationship between the total number of monitoring points and the relevant monitoring point is a same-line topological relationship.

[0127] The upstream and downstream analysis results are determined based on the monitoring data from all monitoring points and the monitoring data from each upstream and downstream monitoring point.

[0128] The analysis results for the same route are determined based on the monitoring data from all monitoring points and the monitoring data from each monitoring point along the same route.

[0129] The second analysis result for this full-scale monitoring point was determined based on the upstream and downstream analysis results and the same route analysis results.

[0130] In one embodiment of this application, each upstream and downstream monitoring point includes an upstream monitoring point and a downstream monitoring point;

[0131] The second analysis module 65 is also specifically used to determine the target abnormal time when abnormal data appears at the full monitoring point, and whether abnormal data appears in the monitoring data of the upstream monitoring point.

[0132] If no abnormal data is found in the monitoring data of the upstream monitoring points, the preset analysis result will be determined as the upstream analysis result.

[0133] If abnormal data is found in the monitoring data of the upstream monitoring point, the first abnormal moment when the abnormal data appears at the upstream monitoring point is determined.

[0134] The standard anomaly time is determined based on the first anomaly time and the distance between the upstream monitoring point and the full-scale monitoring point.

[0135] The upstream analysis results are determined based on the standard anomaly time and the target anomaly time.

[0136] The downstream analysis results are determined based on the monitoring data from downstream monitoring points and the monitoring data from all monitoring points.

[0137] The upstream and downstream analysis results are determined based on the upstream and downstream analysis results.

[0138] In one embodiment of this application, the second analysis module 65 is further used to determine the standard deviation of each downstream monitoring point based on the degree of deviation of abnormal data in the monitoring data of the full monitoring point and the distance between the full monitoring point and each downstream monitoring point.

[0139] For each downstream monitoring point, the analysis results for that downstream monitoring point are determined based on the target deviation of the abnormal data in the monitoring data of that downstream monitoring point and the standard deviation of the corresponding downstream monitoring point.

[0140] The downstream analysis results are determined based on the analysis results of each downstream monitoring point.

[0141] In one embodiment of this application, the second analysis module 65 is further configured to determine the first line insulation coefficient based on the monitoring data of the full monitoring points;

[0142] The insulation coefficient of the second line is determined based on the monitoring data from each monitoring point on the same line.

[0143] The analysis results for the same line are determined based on the insulation coefficient of the first line and the insulation coefficient of the second line.

[0144] In one embodiment of this application, the monitoring data from the simplified monitoring point is of the type of simplified monitoring data; the monitoring data from the full monitoring point includes both simplified monitoring data and additional monitoring data.

[0145] The second analysis module 65 is further used to determine the mapping relationship between the simplified monitoring data and the additional monitoring data of each full-related monitoring point for the simplified monitoring point, and to obtain the mapping relationship of the full-related monitoring point.

[0146] Based on the mapping relationship of each full-scale related monitoring point and the simplified monitoring data of the simplified monitoring point, determine the additional monitoring data of the simplified monitoring point;

[0147] The simplified monitoring data and additional monitoring data of this simplified monitoring point are integrated to obtain the full monitoring data of this simplified monitoring point.

[0148] In one embodiment of this application, the second analysis module 65 is further configured to determine the target additional monitoring data of each full-related monitoring point based on the mapping relationship of the full-related monitoring points and the simplified monitoring data of the simplified monitoring points.

[0149] The additional monitoring data of each target at the simplified monitoring point are weighted and calculated to obtain the additional monitoring data of the simplified monitoring point; wherein, the weight corresponding to the additional monitoring data of each target is determined based on the distance between the full-scale related monitoring point corresponding to the additional monitoring data of the target and the simplified monitoring point.

[0150] In one embodiment of this application, the first analysis module 62 is specifically used to determine the first analysis result corresponding to the monitoring point as abnormal in response to the deviation between the monitoring data of the monitoring point and the standard monitoring data corresponding to the monitoring point being greater than a preset deviation threshold.

[0151] See Figure 7 , Figure 7 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 7 The electronic device 700 in this embodiment may include one or more processors 701, one or more input devices 702, one or more output devices 703, and one or more memories 704. The processors 701, input devices 702, output devices 703, and memories 704 communicate with each other via a communication bus 705. The memories 704 store computer programs, including program instructions. The processors 701 execute the program instructions stored in the memories 704. Specifically, the processors 701 are configured to invoke the program instructions to perform the functions of each module / unit in the above-described device embodiments, for example... Figure 6 The functions of the first data acquisition module 61, the first analysis module 62, the anomaly marking module 63, the second data acquisition module 64, and the second analysis module 65 are shown.

[0152] It should be understood that, in the embodiments of this application, the processor 701 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0153] Input device 702 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 703 may include a display (LCD, etc.), a speaker, etc.

[0154] The memory 704 may include read-only memory and random access memory, and provides instructions and data to the processor 701. A portion of the memory 704 may also include non-volatile random access memory. For example, the memory 704 may also store device type information.

[0155] In specific implementations, the processor 701, input device 702, and output device 703 described in the embodiments of this application can execute the implementation method described in the insulation anomaly analysis method based on intelligent fusion terminal provided in the embodiments of this application, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.

[0156] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0157] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0158] Those skilled in the art will recognize that the modules / units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0159] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules, units, or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or modules / units, or it may be an electrical, mechanical, or other form of connection.

[0161] The modules / units described as separate components may or may not be physically separate. Similarly, the components shown as modules / units may or may not be physical modules / units; they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0162] Furthermore, the functional modules / units in the various embodiments of this application can be integrated into one processing module / unit, or each module / unit can exist physically separately, or two or more modules / units can be integrated into one module / unit. The integrated modules / units described above can be implemented in hardware or in the form of software functional modules / units.

[0163] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An insulation abnormality analysis method based on an intelligent fusion terminal, characterized by, The method comprises the following steps: acquiring monitoring data of each monitoring point in a target substation area; the monitoring points comprise a plurality of full-quantity monitoring points and a plurality of simple monitoring points; the types of the monitoring data of the full-quantity monitoring points are more than the types of the monitoring data of the simple monitoring points; for each monitoring point, determining a first analysis result corresponding to the monitoring point based on the monitoring data of the monitoring point and standard monitoring data corresponding to the monitoring point; labeling each monitoring point with an abnormal first analysis result as an abnormal monitoring point; acquiring related monitoring points of each abnormal monitoring point, wherein when the monitoring point is a simple monitoring point, the related monitoring points of the monitoring point comprise at least one full-quantity related monitoring point; for each abnormal monitoring point, in response to the abnormal monitoring point being a full-quantity monitoring point, determining a second analysis result of the full-quantity monitoring point based on the monitoring data of the related monitoring points of the full-quantity monitoring point, the monitoring data of the full-quantity monitoring point and a topological relationship between the full-quantity monitoring point and the related monitoring points corresponding to the full-quantity monitoring point; for each abnormal monitoring point, in response to the abnormal monitoring point being a simple monitoring point, expanding the monitoring data of the simple monitoring point based on the monitoring data of the full-quantity related monitoring points corresponding to the simple monitoring point to obtain full-quantity monitoring data of the simple monitoring point; and determining a second analysis result of the simple monitoring point based on the monitoring data of the related monitoring points corresponding to the simple monitoring point, the full-quantity monitoring data of the simple monitoring point and a topological relationship between the simple monitoring point and the related monitoring points corresponding to the simple monitoring point.

2. The insulation abnormality analysis method based on the smart fusion terminal according to claim 1, characterized by, the topological relationship between the full-quantity monitoring point and the related monitoring points corresponding to the full-quantity monitoring point comprises an upstream-downstream topological relationship and a same-line topological relationship; the determination of the second analysis result of the full-quantity monitoring point based on the monitoring data of the related monitoring points of the full-quantity monitoring point, the monitoring data of the full-quantity monitoring point and the topological relationship between the full-quantity monitoring point and the related monitoring points corresponding to the full-quantity monitoring point comprises: for each related monitoring point, in response to the topological relationship between the full-quantity monitoring point and the related monitoring point being an upstream-downstream topological relationship, labeling the related monitoring point as an upstream-downstream monitoring point; and in response to the topological relationship between the full-quantity monitoring point and the related monitoring point being a same-line topological relationship, labeling the related monitoring point as a same-line monitoring point; determining an upstream-downstream analysis result based on the monitoring data of the full-quantity monitoring point and the monitoring data of each upstream-downstream monitoring point; determining a same-line analysis result based on the monitoring data of the full-quantity monitoring point and the monitoring data of each same-line monitoring point; determining the second analysis result of the full-quantity monitoring point based on the upstream-downstream analysis result and the same-line analysis result.

3. The insulation abnormality analysis method based on the smart fusion terminal according to claim 2, characterized by, each upstream-downstream monitoring point comprises an upstream monitoring point and a downstream monitoring point; the determination of the upstream-downstream analysis result based on the monitoring data of the full-quantity monitoring point and the monitoring data of each upstream-downstream monitoring point comprises: determining a target abnormal moment at which the full-quantity monitoring point appears abnormal data, and whether abnormal data appears in the monitoring data of the upstream monitoring point; if no abnormal data appears in the monitoring data of the upstream monitoring point, determining a preset analysis result as an upstream analysis result; if abnormal data appears in the monitoring data of the upstream monitoring point, determining a first abnormal moment at which the upstream monitoring point appears the abnormal data; determine a standard abnormal time based on the first abnormal time and a distance between the upstream monitoring point and the total quantity monitoring point; determine an upstream analysis result based on the standard abnormal time and the target abnormal time; determine a downstream analysis result based on monitoring data of the downstream monitoring point and monitoring data of the total quantity monitoring point; determine the upstream and downstream analysis result based on the upstream analysis result and the downstream analysis result.

4. The insulation abnormality analysis method based on the smart fusion terminal according to claim 3, characterized by, The determination of the downstream analysis result based on the monitoring data of the downstream monitoring point and the monitoring data of the total quantity monitoring point comprises: determine a standard deviation corresponding to each downstream monitoring point based on a deviation of abnormal data in the monitoring data of the total quantity monitoring point and a distance between the total quantity monitoring point and each downstream monitoring point; determine an analysis result of each downstream monitoring point based on a target deviation of abnormal data in the monitoring data of the downstream monitoring point and the standard deviation corresponding to the downstream monitoring point; determine the downstream analysis result based on the analysis result of each downstream monitoring point.

5. The smart convergence terminal-based insulation abnormality analysis method of claim 2, wherein, The determination of the same-line analysis result based on the monitoring data of the total quantity monitoring point and the monitoring data of each same-line monitoring point comprises: determine a first line insulation coefficient based on the monitoring data of the total quantity monitoring point; determine a second line insulation coefficient based on the monitoring data of each same-line monitoring point; determine the same-line analysis result based on the first line insulation coefficient and the second line insulation coefficient. 6.The smart fusion terminal based insulation abnormality analysis method of claim 1, wherein, The type of the monitoring data of the simple monitoring point is simple monitoring data, and the type of the monitoring data of the total quantity monitoring point comprises simple monitoring data and additional monitoring data. The expansion of the monitoring data of the simple monitoring point based on the monitoring data of the total quantity related monitoring point corresponding to the simple monitoring point to obtain the total quantity monitoring data of the simple monitoring point comprises: determine a mapping relationship between the simple monitoring data and the additional monitoring data of each total quantity related monitoring point corresponding to the simple monitoring point to obtain the mapping relationship of the total quantity related monitoring point; determine the additional monitoring data of the simple monitoring point based on the mapping relationship of each total quantity related monitoring point and the simple monitoring data of the simple monitoring point; integrate the simple monitoring data and the additional monitoring data of the simple monitoring point to obtain the total quantity monitoring data of the simple monitoring point.

7. The smart convergence terminal-based insulation abnormality analysis method of claim 6, wherein, The determination of the additional monitoring data of the simple monitoring point based on the mapping relationship of each total quantity related monitoring point and the simple monitoring data of the simple monitoring point comprises: determine target additional monitoring data of the simple monitoring point based on the mapping relationship of each total quantity related monitoring point and the simple monitoring data of the simple monitoring point; perform weighted calculation on each target additional monitoring data of the simple monitoring point to obtain the additional monitoring data of the simple monitoring point; wherein a weight corresponding to each target additional monitoring data is determined based on a distance between the total quantity related monitoring point corresponding to the target additional monitoring data and the simple monitoring point.

8. The smart convergence terminal-based insulation abnormality analysis method of claim 1, wherein, The determination of the first analysis result corresponding to the monitoring point based on the monitoring data of the monitoring point and the standard monitoring data corresponding to the monitoring point comprises: In response to the deviation of the monitoring data of the monitoring point from the standard monitoring data corresponding to the monitoring point being greater than a preset deviation threshold, the first analysis result corresponding to the monitoring point is determined as abnormal.

9. An insulation abnormality analysis apparatus based on an intelligent fusion terminal, characterized by The method comprises the steps of: a first data acquisition module configured to acquire monitoring data of each monitoring point in a target area; the monitoring points comprise a plurality of full-quantity monitoring points and a plurality of simple monitoring points; the types of the monitoring data of the full-quantity monitoring points are more than the types of the monitoring data of the simple monitoring points; a first analysis module configured to determine, for each monitoring point, a first analysis result corresponding to the monitoring point based on the monitoring data of the monitoring point and the standard monitoring data corresponding to the monitoring point; an abnormal marking module configured to mark each monitoring point with an abnormal first analysis result as an abnormal monitoring point; a second data acquisition module configured to acquire related monitoring points of each abnormal monitoring point; when the monitoring point is a simple monitoring point, the related monitoring points of the monitoring point contain at least one full-quantity related monitoring point; a second analysis module configured to, for each abnormal monitoring point, determine a second analysis result of the full-quantity monitoring point based on the monitoring data of the related monitoring points of the full-quantity monitoring point, the monitoring data of the full-quantity monitoring point, and the topological relationship between the full-quantity monitoring point and the related monitoring points corresponding to the full-quantity monitoring point, in response to the abnormal monitoring point being a full-quantity monitoring point; and, for each abnormal monitoring point, based on the monitoring data of the full-quantity related monitoring points corresponding to the simple monitoring point, expand the monitoring data of the simple monitoring point to obtain full-quantity monitoring data of the simple monitoring point, in response to the abnormal monitoring point being a simple monitoring point; determine a second analysis result of the simple monitoring point based on the monitoring data of the related monitoring points corresponding to the simple monitoring point, the full-quantity monitoring data of the simple monitoring point, and the topological relationship between the simple monitoring point and the related monitoring points corresponding to the simple monitoring point.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

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