Methods, devices, and electronic equipment for determining the condition of cable equipment

Abstract

This application discloses a method, apparatus, and electronic device for determining the status of cable equipment. The method, applied in the field of electrical engineering, includes: calculating a first value for each target parameter based on parameter information of at least one target parameter; determining each fault type of the cable equipment and calculating a first weight for each fault type based on the first value of each target parameter; clustering the at least one target parameter and optimizing the first weight of each fault type based on the clustering results to obtain a second weight for each fault type; calculating a first matrix based on the first value of each target parameter; and determining the status of the cable equipment based on the first matrix and the second weight of each fault type. This application solves the problem in related technologies where the complexity and systematic nature of cable equipment lead to inaccurate evaluation results when assessing the status of cable equipment using preset index evaluation rules.

Description

Technical Field

[0001] This application relates to the field of electrical engineering, and more specifically, to a method, apparatus, and electronic device for determining the state of cable equipment. Background Technology

[0002] Currently, power cables have become the main artery for urban power transmission. High-voltage power cables are installed in underground passages (pipe galleries or tunnels), where the operating environment is usually quite harsh. Relying solely on manual maintenance is not only labor-intensive and inefficient, but also highly dangerous, especially when cable equipment malfunctions or the passage environment is abnormal, which can endanger the lives of on-site workers.

[0003] In existing technologies, cable tunnels are equipped with numerous online monitoring devices or track-mounted inspection robots. Track-mounted robots require track construction within the tunnel, but this method is costly, time-consuming, and prone to damaging the tunnel structure. Furthermore, the harsh environment poses significant risks to personnel. In addition, due to the complexity, uncertainty, ambiguity, and systematic nature of cable equipment, relying solely on a single evaluation index to assess its condition reduces the accuracy of the evaluation results.

[0004] There is currently no effective solution to the problem of inaccurate evaluation results caused by the complexity and systematic nature of cable equipment when evaluating the status of cable equipment using preset index evaluation rules in related technologies. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, and electronic device for determining the condition of cable equipment, in order to solve the problem in the related art where the evaluation results are inaccurate due to the complexity and systematic nature of cable equipment when evaluating the condition of cable equipment through preset index evaluation rules.

[0006] To achieve the above objectives, according to one aspect of this application, a method for determining the state of a cable device is provided. The method includes: calculating a first value for each target parameter based on parameter information of at least one target parameter, wherein the at least one target parameter includes parameters related to the cable device, and the first value characterizes the degree of damage to the cable device; determining each fault type of the cable device and calculating a first weight for each fault type based on the first value of each target parameter; clustering the at least one target parameter and optimizing the first weight of each fault type based on the clustering results to obtain a second weight for each fault type; calculating a first matrix based on the first value of each target parameter, and determining the state of the cable device based on the first matrix and the second weight of each fault type.

[0007] Further, calculating the first value of each target parameter based on the parameter information of at least one target parameter includes: collecting the operating data of the cable equipment, determining the at least one target parameter based on the operating data; determining the value range of each target parameter, wherein the value range includes at least one of the following: an upper limit and a lower limit; and calculating the first value of each target parameter based on the value range of each target parameter.

[0008] Further, each fault type of the cable equipment is determined, and a first weight for each fault type is calculated based on a first value of each target parameter, including: calculating the Spearman correlation coefficient between each target parameter and the fault type under each fault type; for each fault type, calculating the Spearman correlation coefficient corresponding to each target parameter to obtain a third weight for each target parameter under each fault type; calculating the first value of each target parameter and the third weight of each target parameter under each fault type to obtain a score for each fault type; and calculating the first weight for each fault type based on the score for each fault type.

[0009] Further, clustering the at least one target parameter and optimizing the first weight of each fault type based on the clustering results to obtain the second weight of each fault type includes: constructing a first graph, wherein the first graph includes nodes and edges, the nodes containing the target parameter and the fault type, and the edges representing the correlation between the target parameter and the fault type; calculating the edge betweenness number of each edge in the first graph, iteratively optimizing the first graph based on the edge betweenness number of each edge to obtain a second graph set; calculating the second value of each second graph in the second graph set, and determining a target graph based on the second value, wherein the second value is used to measure the compactness of the second graph; calculating the community contribution of each node based on the target graph, and optimizing the first weight of each fault type based on the community contribution to obtain the second weight of each fault type.

[0010] Further, calculating a first matrix based on the first value of each target parameter, and determining the state of the cable equipment based on the first matrix and the second weight of each fault type, includes: calculating the first value of each target parameter under each fault type to obtain the first matrix; dividing a preset value range into N intervals, and calculating a second matrix based on the N intervals, where N is the number of fault types; calculating the correlation degree of each fault type by combining the first matrix and the second matrix; calculating the correlation degree of each fault type and the second weight of each fault type to obtain a target score, and determining the state of the cable equipment based on the target score.

[0011] Further, the first value of each target parameter under each fault type is calculated to obtain the first matrix, including: calculating the first value of each target parameter under each fault type based on a preset algorithm to obtain the third value corresponding to each fault type, wherein the preset algorithm is determined according to a target interval, and the target interval is the interval to which the first value belongs within the N intervals; and determining the first matrix based on the third value corresponding to each fault type.

[0012] Further, the first matrix and the second matrix are calculated to obtain the correlation degree of each fault type, including: constructing a target expression, wherein the target expression is an expression that characterizes the correlation between the fault type and the target parameter based on set pair analysis, and the target expression includes at least the following parameters: similarity, difference, and opposition; substituting the first matrix and the second matrix into the target expression and calculating to obtain the correlation degree of each fault type.

[0013] Furthermore, after determining the state of the cable equipment based on the first matrix and the second weight of each fault type, the method further includes: determining the fault level of the cable equipment based on the target score, wherein the fault level is used to characterize the severity of the fault of the cable equipment; generating alarm information based on the at least one target parameter and the target score if the fault level is greater than a preset level; determining target measures based on the alarm information, and processing the cable equipment using the target measures.

[0014] To achieve the above objectives, according to another aspect of this application, a device for determining the state of cable equipment is provided. The device includes: a first calculation unit, configured to calculate a first value for each target parameter based on parameter information of at least one target parameter, wherein the at least one target parameter includes parameters related to the cable equipment, and the first value characterizes the degree of damage to the cable equipment; a second calculation unit, configured to determine each fault type of the cable equipment and calculate a first weight for each fault type based on the first value of each target parameter; a clustering unit, configured to cluster the at least one target parameter and optimize the first weight of each fault type based on the clustering results to obtain a second weight for each fault type; and a third calculation unit, configured to calculate a first matrix based on the first value of each target parameter and determine the state of the cable equipment based on the first matrix and the second weight of each fault type.

[0015] Further, the first calculation unit includes: a data acquisition subunit, used to acquire the operating data of the cable equipment and determine the at least one target parameter based on the operating data; a determination subunit, used to determine the value range of each target parameter, wherein the value range includes at least one of the following: an upper limit value and a lower limit value; and a first calculation subunit, used to calculate a first value of each target parameter according to the value range of each target parameter.

[0016] Further, the second calculation unit includes: a second calculation subunit, used to calculate the Spearman correlation coefficient between each target parameter and the fault type under each fault type; a third calculation subunit, used to calculate the Spearman correlation coefficient corresponding to each target parameter for each fault type to obtain the third weight of each target parameter under each fault type; a fourth calculation subunit, used to calculate the first value of each target parameter and the third weight of each target parameter under each fault type to obtain the score of each fault type; and a fifth calculation subunit, used to calculate the first weight of each fault type based on the score of each fault type.

[0017] Further, the clustering unit includes: a construction subunit for constructing a first graph, wherein the first graph includes nodes and edges, the nodes contain the target parameter and the fault type, and the edges represent the correlation between the target parameter and the fault type; a sixth calculation subunit for calculating the edge betweenness number of each edge in the first graph, and iteratively optimizing the first graph based on the edge betweenness number of each edge to obtain a second graph set; a seventh calculation subunit for calculating a second value for each second graph in the second graph set, and determining a target graph based on the second value, wherein the second value is used to measure the compactness of the second graph; and an eighth calculation subunit for calculating the community contribution of each node based on the target graph, and optimizing a first weight for each fault type based on the community contribution to obtain a second weight for each fault type.

[0018] Further, the third calculation unit includes: a ninth calculation subunit, used to calculate the first value of each target parameter under each fault type to obtain the first matrix; a tenth calculation subunit, used to divide the preset value range into N intervals and calculate the second matrix based on the N intervals, where N is the number of fault types; an eleventh calculation subunit, used to calculate the first matrix and the second matrix to obtain the correlation degree of each fault type; and a twelfth calculation subunit, used to calculate the correlation degree of each fault type and the second weight of each fault type to obtain the target score and determine the status of the cable equipment based on the target score.

[0019] Further, the ninth calculation subunit includes: a first calculation module, used to calculate the first value of each target parameter under each fault type based on a preset algorithm to obtain the third value corresponding to each fault type, wherein the preset algorithm is determined according to a target interval, and the target interval is the interval to which the first value belongs within the N intervals; and a determination module, used to determine the first matrix based on the third value corresponding to each fault type.

[0020] Furthermore, the eleventh calculation subunit includes: a construction module for constructing a target expression, wherein the target expression is an expression characterizing the correlation between the fault type and the target parameter based on set pair analysis, and the target expression includes at least the following parameters: similarity, difference, and opposition; and a second calculation module for substituting the first matrix and the second matrix into the target expression and performing calculations to obtain the correlation degree of each fault type.

[0021] Furthermore, the apparatus further includes: a determining unit, configured to determine the fault level of the cable equipment based on the target score after determining the state of the cable equipment according to the first matrix and the second weight of each fault type, wherein the fault level is used to characterize the severity of the fault of the cable equipment; a generating unit, configured to generate alarm information based on the at least one target parameter and the target score if the fault level is greater than a preset level; and a processing unit, configured to determine target measures based on the alarm information and process the cable equipment using the target measures.

[0022] To achieve the above objectives, according to one aspect of this application, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method for determining the state of a cable device as described in any of the above claims, and the computer program, when executed by a processor, implements the steps of the method for determining the state of a cable device as described in various embodiments of this application.

[0023] To achieve the above objectives, according to one aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including stored computer instructions, wherein, when the computer instructions are executed by a processor, the method for determining the state of the cable device described in any one of the above claims is implemented.

[0024] To achieve the above objectives, according to one aspect of this application, an electronic device is provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method for determining the state of the cable device as described in any of the above.

[0025] This application employs the following steps: calculating a first value for each target parameter based on parameter information of at least one target parameter, wherein the at least one target parameter includes parameters related to cable equipment, and the first value is used to characterize the degree of damage to the cable equipment; determining each fault type of the cable equipment and calculating a first weight for each fault type based on the first value of each target parameter; clustering the at least one target parameter and optimizing the first weight of each fault type based on the clustering results to obtain a second weight for each fault type; calculating a first matrix based on the first value of each target parameter and determining the state of the cable equipment based on the first matrix and the second weight of each fault type. This solves the problem in related technologies where the complexity and systematic nature of cable equipment lead to inaccurate evaluation results when evaluating the state of cable equipment using preset index evaluation rules. By calculating the relative degradation of the target parameters, the problem of low evaluation accuracy caused by the relatively simple normalization calculation when normalizing massive multi-source heterogeneous data for evaluating the condition of cable equipment can be avoided, thus improving the accuracy of evaluating the condition of cable equipment. At the same time, by using the relative degradation of each target parameter under a single fault type to determine the first weight of the fault type, and then optimizing the second weight of the fault type based on the clustering algorithm, the accuracy of evaluating the condition of cable equipment is further improved. Attached Figure Description

[0026] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0027] Figure 1 This is a flowchart of a method for determining the state of cable equipment according to Embodiment 1 of this application;

[0028] Figure 2 This is a schematic diagram of an optional method for determining the state of cable equipment according to Embodiment 1 of this application;

[0029] Figure 3 This is a schematic diagram of a cable equipment status determination device according to Embodiment 2 of this application;

[0030] Figure 4 This is a schematic diagram of an electronic device for determining the state of a cable device according to Embodiment 5 of this application. Detailed Implementation

[0031] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0032] It should be noted that the user information (including but not limited to user device information, user personal information, collected data, used data, generated data, processed data, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, collected information, used information, generated information, processed information, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, and necessary confidentiality measures have been taken. These measures do not violate public order and good morals, and corresponding operation entry points are provided for users to choose to authorize or refuse. For example, this system has interfaces with relevant users or organizations. Before obtaining relevant information, a request to obtain the information needs to be sent to the aforementioned user or organization through the interface, and the relevant information is obtained only after receiving consent from the aforementioned user or organization.

[0033] It should be noted that this application provides users with a corresponding entry point for choosing to agree to or reject the automated decision-making results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0034] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying 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 data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] Example 1

[0037] The following describes this application in conjunction with the preferred implementation steps. Figure 1 This is a flowchart of a method for determining the state of cable equipment according to Embodiment 1 of this application, as follows: Figure 1 As shown, the method includes the following steps:

[0038] Step S101: Calculate a first value for each target parameter based on parameter information of at least one target parameter, wherein the at least one target parameter includes parameters related to the cable equipment, and the first value is used to characterize the degree of damage to the cable equipment.

[0039] In this first embodiment, to evaluate the condition of the cable equipment, it is necessary to collect relevant parameters of the cable equipment to obtain at least one target parameter mentioned above. Based on the parameter values ​​of the at least one target parameter, the relative degradation degree of each target parameter is calculated, i.e., the first value mentioned above. The relative degradation degree is an indicator used to assess the degree of performance degradation of the cable equipment.

[0040] In one alternative embodiment, when selecting at least one target parameter, the target parameter can be selected according to the principles of comprehensiveness, convenience, and hierarchy, such as temperature, current, voltage, humidity, air pollution, etc.

[0041] Step S102: Determine each fault type of the cable equipment and calculate the first weight of each fault type based on the first value of each target parameter.

[0042] In this first embodiment, to evaluate the condition of the cable equipment, it is necessary to define the fault types of the cable equipment and calculate the initial weight of each fault type based on the relative deterioration of each target parameter, i.e., the first weight of each fault type mentioned above. The fault types of the cable equipment can be set according to the actual situation, for example, they can be set as good, normal, warning, abnormal, and severe.

[0043] Step S103: Cluster at least one target parameter, and optimize the first weight of each fault type based on the clustering results to obtain the second weight of each fault type.

[0044] In this first embodiment, in order to more accurately evaluate the status of cable equipment, it is necessary to apply the GN clustering algorithm (Girvan-Newman algorithm, a community detection algorithm based on graph theory) to calculate the community contribution of the target parameters in each fault type. Then, the first weight in each fault type is optimized according to the community contribution of the target parameters to obtain the second weight of each fault type.

[0045] Step S104: Calculate the first matrix based on the first value of each target parameter, and determine the state of the cable equipment based on the first matrix and the second weight of each fault type.

[0046] In this first embodiment, in order to evaluate the status of the cable equipment, it is necessary to calculate the similarity and difference inverse evaluation matrix based on the first value of each target parameter, i.e., the first matrix mentioned above. Then, based on the first value of each target parameter and the second weight of each fault type, the set pair analysis correlation degree of each fault type is calculated, and the comprehensive correlation degree of the overall operating status of the cable equipment is calculated based on the calculation results to obtain the final target score of the cable equipment. The status of the cable equipment is determined based on the target score.

[0047] In summary, the cable equipment status determination method provided in Embodiment 1 of this application calculates a first value for each target parameter based on parameter information of at least one target parameter, wherein the at least one target parameter includes parameters related to the cable equipment, and the first value is used to characterize the degree of damage to the cable equipment; determines each fault type of the cable equipment, and calculates a first weight for each fault type based on the first value of each target parameter; clusters the at least one target parameter, and optimizes the first weight of each fault type based on the clustering results to obtain a second weight for each fault type; calculates a first matrix based on the first value of each target parameter, and determines the status of the cable equipment based on the first matrix and the second weight of each fault type. This method solves the problem in related technologies where the evaluation results are inaccurate due to the complexity and systematic nature of the cable equipment when evaluating the status of cable equipment using preset index evaluation rules. By calculating the relative degradation of the target parameters, the problem of low evaluation accuracy caused by the relatively simple normalization calculation when normalizing massive multi-source heterogeneous data for evaluating the condition of cable equipment can be avoided, thus improving the accuracy of evaluating the condition of cable equipment. At the same time, by using the relative degradation of each target parameter under a single fault type to determine the first weight of the fault type, and then optimizing the second weight of the fault type based on the clustering algorithm, the accuracy of evaluating the condition of cable equipment is further improved.

[0048] Optionally, in the method for determining the state of cable equipment provided in Embodiment 1 of this application, calculating the first value of each target parameter based on the parameter information of at least one target parameter includes: collecting operating data of the cable equipment, determining at least one target parameter based on the operating data; determining the value range of each target parameter, wherein the value range includes at least one of the following: upper limit of value, lower limit of value; and calculating the first value of each target parameter based on the value range of each target parameter.

[0049] In this first embodiment, in order to assess the aging status of the cable equipment, the relative deterioration degree of each target parameter can be calculated using the value range of each target parameter, i.e., the first value mentioned above.

[0050] In one optional embodiment, inspection data of the cable equipment, i.e., the operating data of the cable equipment mentioned above, can be collected by an inspection robot to determine each target parameter. Then, based on past experience, the upper or lower limit of the value of each target parameter, i.e., the value range mentioned above, is determined. When the target parameter has an upper limit value 'a', the calculation function for the relative degradation degree can be as shown in Formula 1.

[0051]

[0052] Where x represents the value of the target parameter, a represents the upper limit of the target parameter's value, and μ(x) represents the relative degradation degree of the target parameter, i.e., the first value mentioned above. When the target parameter has a lower limit value b, the calculation function for the relative degradation degree can be shown in Formula 2.

[0053]

[0054] Where x represents the value of the target parameter, b represents the lower limit of the target parameter value, and μ(x) represents the relative degradation of the target parameter, i.e. the first value mentioned above.

[0055] Optionally, in the method for determining the state of cable equipment provided in Embodiment 1 of this application, each fault type of the cable equipment is determined, and a first weight of each fault type is calculated based on a first value of each target parameter, including: calculating the Spearman correlation coefficient between each target parameter and the fault type under each fault type; calculating the Spearman correlation coefficient corresponding to each target parameter for each fault type to obtain a third weight of each target parameter under each fault type; calculating the first value of each target parameter and the third weight of each target parameter under each fault type to obtain a score for each fault type; and calculating the first weight of each fault type based on the score of each fault type.

[0056] In this first embodiment, in order to accurately evaluate and calculate the initial weight of each fault type, i.e. the first weight mentioned above, the Spearman correlation coefficient between the target parameter and the fault type can be calculated, and then the first weight of each fault type can be calculated based on the Spearman correlation coefficient.

[0057] In an optional embodiment, the formula for calculating the Spearman correlation coefficient between the target parameter and the fault type can be as shown in Formula 3.

[0058]

[0059] Where, d iLet d be the difference in rank between the i-th data pairs. For each target parameter, rank its parameter values ​​according to size, and replace the original parameter values ​​with the rank corresponding to the target parameter values. For each pair of data points (Xi, Yi), calculate their rank difference d. i =rank(Xi) - rank(Yi), where n represents the sample size of the target parameters under the same fault type, and ρ represents the Spearman correlation coefficient between the target parameters and the fault type. The initial weights of the target parameters, i.e., the aforementioned third weights, can be calculated based on the Spearman correlation coefficients of each target parameter under the same fault type. The calculation method is shown in Formula 4.

[0060]

[0061] Where Wm is the initial weight of the m-th target parameter under the same fault type, that is, the third weight of each target parameter mentioned above, ρ m Let be the Spearman correlation coefficient of the m-th target parameter, and n be the number of target parameters for this fault type. The calculation method for the score of each fault type, based on the first value of each target parameter and the third weight of each target parameter for each fault type, is shown in Formula 5.

[0062]

[0063] In the formula, y k The score is for the k-th fault type, where n is the number of target parameters for that fault type, and W is the score for the k-th fault type. i μ represents the third weight of the i-th objective parameter under the k-th fault type. i (x) represents the Spearman correlation coefficient of the i-th objective parameter under the k-th fault type. The initial weights (i.e., the first weights of the fault types mentioned above) for each fault type in the cable equipment condition assessment are calculated based on the fault type scores, as shown in Formula 6.

[0064]

[0065] In the formula, ω k Let z be the first weight of the k-th fault type, z be the number of fault types, and y be the weight of the k-th fault type. i y represents the score for the i-th fault type. k The score for the k-th fault type.

[0066] Optionally, in the method for determining the state of cable equipment provided in Embodiment 1 of this application, clustering at least one target parameter and optimizing the first weight of each fault type based on the clustering results to obtain the second weight of each fault type includes: constructing a first graph, wherein the first graph includes nodes and edges, nodes contain target parameters and fault types, and edges represent the correlation between target parameters and fault types; calculating the edge betweenness number of each edge in the first graph, iteratively optimizing the first graph based on the edge betweenness number of each edge to obtain a set of second graphs; calculating the second value of each second graph in the set of second graphs, and determining the target graph based on the second value, wherein the second value is used to measure the compactness of the second graph; calculating the community contribution of each node based on the target graph, and optimizing the first weight of each fault type based on the community contribution to obtain the second weight of each fault type.

[0067] In this first embodiment, in order to accurately evaluate the condition of the cable equipment, the target parameters of the cable equipment can be clustered to determine the target parameters that are more closely related to the fault type, and the second weight of the fault type can be optimized, so as to evaluate the condition of the cable equipment more accurately based on the second weight of the fault type.

[0068] In an optional embodiment, a graph structure can be constructed based on the relationship between the target parameter and the fault type, i.e., the first graph mentioned above, wherein the nodes contain the target parameter and the fault type. When there is a relationship between the fault type and the target parameter, an edge is constructed between the fault type and the target parameter. For example, if the target parameter A1 belongs to the fault type B1, then an edge is constructed between the target parameter A1 and the fault type B1.

[0069] Then, an adjacency matrix is ​​constructed based on the first graph to store the correlation between target parameters and fault types. The betweenness number of each edge is calculated, which represents the importance of that edge in the shortest paths among all possible source nodes. The first graph is iteratively optimized based on the betweenness number of each edge, and the graphs obtained in the iteration process form the second graph set. For each iteration, the edge with the largest betweenness number in the graph is found and deleted until all edges are deleted, resulting in a series of subgraphs, i.e., the second graph set mentioned above, where each subgraph represents a community. After each iteration, the modularity of the currently generated graph (i.e., the second value mentioned above) is calculated until no new communities can be generated. The second graph with the largest modularity in all generated second graph sets is found, which is the optimal clustering result and the target graph mentioned above.

[0070] The modularity function measures the quality of community partitioning in a graph structure. It divides the nodes in a graph into communities and calculates the ratio between the density of nodes within a community and the connectivity between nodes in different communities. The formula for calculating modularity is shown in Equation 7.

[0071]

[0072] In the formula, A ij This refers to whether an edge or its weight exists between node i and node j in the graph (the value is an element of the adjacency matrix), ∑ i,j∈S (A ij The number of edges within community S is twice the total number of edges. k represents twice the expected number of edges in community S under random partitioning. i and k j These are the degrees of node i and node j, respectively, δ(S) i ,S j ) is an indicator function that takes a value of 1 when node i and node j belong to the same community, and 0 otherwise. m is the total weight or total number of edges in the graph. e The number of edges within a community, O e S refers to the number of external edges of a community. i and S j These are the community tags to which nodes i and j belong, respectively.

[0073] Finally, the community contribution of each node is calculated based on the optimal optimization result target graph. The formula for calculating the community contribution is shown in Formula 8.

[0074]

[0075] In the formula, β is the weighted resolution coefficient, which is taken as e e (e is the natural logarithm), k is the k-th objective parameter community, N is the number of objective parameter communities, w i Let n be the first weight for the i-th objective parameter, and n be the number of objective parameters in the objective parameter community. The second weight for each fault type is determined based on the community contribution of each node.

[0076] By iteratively eliminating intermediaries, multiple communities are eventually formed. The community with the highest density is identified based on modularity, resulting in the optimal clustering result. This allows for the calculation of community contribution based on several target parameters with varying degrees of correlation. The community contribution is then incorporated into the fault type weight optimization process, increasing the weight of strongly correlated communities and decreasing the weight of weakly correlated communities. This improves the accuracy of fault type weights and further enhances the accuracy of cable equipment evaluation results.

[0077] Optionally, in the method for determining the state of cable equipment provided in Embodiment 1 of this application, a first matrix is ​​calculated based on the first value of each target parameter, and the state of the cable equipment is determined based on the first matrix and the second weight of each fault type, including: calculating the first value of each target parameter under each fault type to obtain a first matrix; dividing a preset value range into N intervals, and calculating a second matrix based on the N intervals, where N is the number of fault types; calculating the first matrix and the second matrix to obtain the correlation degree of each fault type; calculating the correlation degree of each fault type and the second weight of each fault type to obtain a target score, and determining the state of the cable equipment based on the target score.

[0078] In this first embodiment, in order to accurately assess the status of the cable equipment, the target score can be calculated based on the optimized second weight of the fault type and the similarity and difference in the evaluation matrix (i.e., the first matrix mentioned above), thereby determining the status of the cable equipment based on the branch target score.

[0079] In an optional embodiment, the similarity / difference inverse evaluation matrix for each target parameter, i.e., the first matrix mentioned above, can be obtained by calculating the relative deterioration degree of the target parameter. The correlation difference is divided into several sub-intervals with equal step sizes using an equal division method. The number of sub-intervals is the same as the number of fault levels, all equal to l mentioned above. The l coefficients in the similarity / difference inverse coefficient matrix consist of an opposition degree of -1, a similarity degree of 1, and the remaining l-2 differences. The calculation method for the l-2 differences among the l coefficients can be as shown in Formula 9.

[0080]

[0081] In this embodiment, the fault level can be set to 5 levels, i.e., l = 5, and the differences of l-2 are 0.5, 0, and -0.5 respectively. Therefore, the similarity-dissimilarity inverse coefficient matrix E = [1 0.5 0 -0.5 -1]. T By calculating the similarity-dissimilarity inverse evaluation matrix and the similarity-dissimilarity inverse coefficient matrix, the correlation degree of each fault type is obtained. To conduct an overall condition evaluation of the cable equipment, the correlation degree of each fault type can be calculated to obtain the comprehensive correlation degree of the cable equipment. Its matrix form is shown in Formula 10.

[0082]

[0083] In the formula, W is the weight coefficient matrix for z fault types, and R t Let E be the similarity-dissimilarity inverse evaluation matrix (i.e., the first matrix mentioned above), and let E be the similarity-dissimilarity inverse coefficient matrix (i.e., the second matrix mentioned above). The result μ is calculated based on the comprehensive connectivity of the overall operating status of the cable equipment. t This leads to the final cable equipment condition score, calculated as score P = μt ×100, where P is the target score mentioned above. Finally, the status of the cable equipment is determined based on the target score. The correspondence between the target score P and the status of the cable equipment is shown in Table 1.

[0084] Table 1

[0085] Status Level Status Name Specific meaning S1 good It is operating well and in an orderly and normal state, with no signs of malfunction. S2 generally The system is operating normally, with slight performance degradation and no signs of malfunction. S3 Notice The operating status needs attention; there is a minor fault, but it does not affect power production and transmission. S4 abnormal The equipment is in poor condition and poses significant safety hazards; maintenance and repair work must be carried out as soon as possible. S5 serious The system is nearing failure and has experienced a major malfunction, requiring immediate repairs.

[0086] Optionally, in the method for determining the state of cable equipment provided in Embodiment 1 of this application, the first value of each target parameter under each fault type is calculated to obtain a first matrix, including: calculating the first value of each target parameter under each fault type based on a preset algorithm to obtain a third value corresponding to each fault type, wherein the preset algorithm is determined based on a target interval, and the target interval is the interval to which the first value belongs within N intervals; and determining the first matrix based on the third value corresponding to each fault type.

[0087] In an optional embodiment, the interval length of the five fault levels can be divided into 0.2 using the equal division method. The membership functions corresponding to the five fault levels are f1(x), f2(x), f3(x), f4(x), and f5(x), respectively. The method for calculating the third value corresponding to each fault type can be as shown in Formulas 11 to 15.

[0088]

[0089]

[0090] Where x is the relative degradation degree of each target parameter (i.e., the first value mentioned above). The similarity and difference in the inverse evaluation matrix R corresponding to each fault type of the target parameter can be expressed as: R = [f1(x)f2(x)f3(x)f4(x)f5(x)], which is the first matrix mentioned above.

[0091] Optionally, in the method for determining the state of cable equipment provided in Embodiment 1 of this application, the first matrix and the second matrix are calculated to obtain the correlation degree of each fault type, including: constructing a target expression, wherein the target expression is an expression that characterizes the correlation between fault types and target parameters based on set pair analysis, and the target expression includes at least the following parameters: similarity, difference, and opposition; substituting the first matrix and the second matrix into the target expression and performing calculations to obtain the correlation degree of each fault type.

[0092] In an alternative embodiment, set pair analysis can be applied to handle deterministic and uncertain relationships between two sets. The relationship between set pairs is measured by the degree of association, which can be expressed as shown in Formula 16.

[0093] μ=a+bi+cj(XVI)

[0094] In the formula, a represents the similarity between the two sets, b represents the dissimilarity between the two sets, c represents the opposition between the two sets, and i and j represent elements in the similarity-dissimilarity inverse coefficient matrix. Further subdividing the dissimilarity b yields the corresponding multivariate connection degree. In this first embodiment, the value of l is 5, and the connection degree can be further expressed as shown in Formula 17:

[0095] μ=r1+r2i1+r3i2+…+r l-1 i l-2 +r l j(17)

[0096] Wherein, the parameter matrix [r1r2…r l-1 r l For each fault type, the similarity and difference inverse evaluation matrix of the target parameters is given by matrix E = [1i1…i...]. l-2 [j] is the similarity and dissimilarity inverse coefficient matrix E, μ is the correlation degree of a certain fault type, and r i Let be the contribution of the i-th objective parameter to this fault type.

[0097] Optionally, in the method for determining the state of cable equipment provided in Embodiment 1 of this application, after determining the state of the cable equipment based on the first matrix and the second weight of each fault type, the method further includes: determining the fault level of the cable equipment based on the target score, wherein the fault level is used to characterize the severity of the fault of the cable equipment; generating alarm information based on at least one target parameter and the target score when the fault level is greater than a preset level; determining target measures based on the alarm information, and using the target measures to process the cable equipment.

[0098] In this first embodiment, to ensure the normal operation of the cable equipment, the fault level of the cable equipment can be determined based on the calculated target score. If the fault level is greater than a preset level, all target parameters and target scores generate alarm information and determine target measures, which are then used to process the cable equipment. For example, if the target score falls within the "good" range in Table 1, it is determined that the cable equipment does not require maintenance. If the target score falls within the "severe" or "abnormal" range in Table 1, it is determined that the cable equipment requires alarm information and target measures, and maintenance is performed on the cable equipment according to the target measures.

[0099] Optionally, in this first embodiment, the process for evaluating the condition of cable equipment can be as follows: Figure 2As shown. First, based on the inspection data from the substation inspection robot, the relative degradation degree of key parameters for evaluating the cable equipment status (i.e., the target parameters mentioned above) is calculated. The Spearman correlation coefficient between the key parameters and fault types is calculated to obtain the weight of each key parameter (i.e., the first weight of the target parameters mentioned above). Then, based on the first weight of each key parameter, the score of each fault type is calculated, and it is determined whether to calculate the scores of all fault types. If not, the calculation continues. If all fault types are scored, the initial weight of each fault type is calculated based on the scores (i.e., the first weight of the fault type mentioned above), and the similarity / dissimilarity inverse evaluation matrix is ​​calculated based on the relative degradation degree of each key parameter. Second, a clustering algorithm is applied to calculate the optimal clustering result (i.e., the community group mentioned above) of the key parameters for each fault type, and the community contribution is calculated to optimize the initial weight of the fault type, obtaining the second weight of the fault type mentioned above. Based on the similarity / dissimilarity inverse evaluation matrix and the second weight of the fault type (i.e., the community group mentioned above), the initial weight of the fault type is optimized, obtaining the second weight of the fault type mentioned above. Figure 2 The weight optimization results are used to calculate the set pair analysis correlation degree of each fault type (i.e., the correlation degree mentioned above) and the comprehensive correlation degree of the cable equipment operating status, and then the status score (i.e., the target score mentioned above) is obtained.

[0100] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0101] Example 2

[0102] Embodiment 2 of this application also provides a device for determining the state of cable equipment. It should be noted that the device for determining the state of cable equipment in Embodiment 2 of this application can be used to execute the method for determining the state of cable equipment provided in Embodiment 1 of this application. The device for determining the state of cable equipment provided in Embodiment 2 of this application will be described below.

[0103] Figure 3 This is a schematic diagram of a cable equipment status determination device according to Embodiment 2 of this application. Figure 3 As shown, the device includes: a first computing unit 301, a second computing unit 302, a clustering unit 303, and a third computing unit 304.

[0104] Specifically, the first calculation unit 301 is used to calculate a first value of each target parameter based on parameter information of at least one target parameter, wherein the at least one target parameter includes parameters related to the cable equipment, and the first value is used to characterize the degree of damage to the cable equipment.

[0105] The second calculation unit 302 is used to determine each fault type of the cable equipment and calculate the first weight of each fault type based on the first value of each target parameter.

[0106] Clustering unit 303 is used to cluster at least one target parameter and optimize the first weight of each fault type based on the clustering results to obtain the second weight of each fault type.

[0107] The third calculation unit 304 is used to calculate the first matrix based on the first value of each target parameter, and to determine the state of the cable equipment based on the first matrix and the second weight of each fault type.

[0108] The cable equipment status determination device provided in Embodiment 2 of this application includes a first calculation unit 301 that calculates a first value for each target parameter based on parameter information of at least one target parameter, wherein the at least one target parameter includes parameters related to the cable equipment, and the first value is used to characterize the degree of damage to the cable equipment; a second calculation unit 302 that determines each fault type of the cable equipment and calculates a first weight for each fault type based on the first value of each target parameter; a clustering unit 303 that clusters the at least one target parameter and optimizes the first weight of each fault type based on the clustering results to obtain a second weight for each fault type; and a third calculation unit 304 that calculates a first matrix based on the first value of each target parameter and determines the status of the cable equipment based on the first matrix and the second weight of each fault type. This solves the problem in related technologies where the evaluation results are inaccurate due to the complexity and systematic nature of the cable equipment when evaluating the status of cable equipment using preset index evaluation rules. By calculating the relative degradation of the target parameters, the problem of low evaluation accuracy caused by the relatively simple normalization calculation when normalizing massive multi-source heterogeneous data for evaluating the condition of cable equipment can be avoided, thus improving the accuracy of evaluating the condition of cable equipment. At the same time, by using the relative degradation of each target parameter under a single fault type to determine the first weight of the fault type, and then optimizing the second weight of the fault type based on the clustering algorithm, the accuracy of evaluating the condition of cable equipment is further improved.

[0109] Optionally, in the cable equipment status determination device provided in Embodiment 2 of this application, the first calculation unit 301 mentioned above includes: a collection subunit, used to collect operating data of the cable equipment and determine at least one target parameter based on the operating data; a determination subunit, used to determine the value range of each target parameter, wherein the value range includes at least one of the following: upper limit of value and lower limit of value; and a first calculation subunit, used to calculate a first value of each target parameter according to the value range of each target parameter.

[0110] Optionally, in the cable equipment status determination device provided in Embodiment 2 of this application, the second calculation unit 302 includes: a second calculation subunit for calculating the Spearman correlation coefficient between each target parameter and the fault type under each fault type; a third calculation subunit for calculating the Spearman correlation coefficient corresponding to each target parameter for each fault type to obtain the third weight of each target parameter under each fault type; a fourth calculation subunit for calculating the first value of each target parameter and the third weight of each target parameter under each fault type to obtain the score of each fault type; and a fifth calculation subunit for calculating the first weight of each fault type based on the score of each fault type.

[0111] Optionally, in the cable equipment status determination device provided in Embodiment 2 of this application, the clustering unit 303 includes: a construction subunit for constructing a first graph, wherein the first graph includes nodes and edges, nodes contain target parameters and fault types, and edges represent the correlation between target parameters and fault types; a sixth calculation subunit for calculating the edge betweenness number of each edge in the first graph, and iteratively optimizing the first graph based on the edge betweenness number of each edge to obtain a second graph set; a seventh calculation subunit for calculating a second value of each second graph in the second graph set, and determining a target graph based on the second value, wherein the second value is used to measure the compactness of the second graph; and an eighth calculation subunit for calculating the community contribution of each node based on the target graph, and optimizing the first weight of each fault type based on the community contribution to obtain a second weight of each fault type.

[0112] Optionally, in the cable equipment status determination device provided in Embodiment 2 of this application, the third calculation unit 304 mentioned above includes: a ninth calculation subunit, used to calculate the first value of each target parameter under each fault type to obtain a first matrix; a tenth calculation subunit, used to divide the preset value range into N intervals and calculate a second matrix based on the N intervals, where N is the number of fault types; an eleventh calculation subunit, used to calculate the first matrix and the second matrix to obtain the correlation degree of each fault type; and a twelfth calculation subunit, used to calculate the correlation degree of each fault type and the second weight of each fault type to obtain a target score, and determine the status of the cable equipment based on the target score.

[0113] Optionally, in the cable equipment status determination device provided in Embodiment 2 of this application, the aforementioned ninth calculation subunit includes: a first calculation module, used to calculate the first value of each target parameter under each fault type based on a preset algorithm to obtain the third value corresponding to each fault type, wherein the preset algorithm is determined according to a target interval, and the target interval is the interval to which the first value belongs within N intervals; and a determination module, used to determine a first matrix based on the third value corresponding to each fault type.

[0114] Optionally, in the cable equipment status determination device provided in Embodiment 2 of this application, the eleventh calculation subunit mentioned above includes: a construction module for constructing a target expression, wherein the target expression is an expression that characterizes the correlation between fault types and target parameters based on set pair analysis, and the target expression includes at least the following parameters: similarity, difference, and opposition; and a second calculation module for substituting the first matrix and the second matrix into the target expression and performing calculations to obtain the correlation degree of each fault type.

[0115] Optionally, in the cable equipment status determination device provided in Embodiment 2 of this application, the device further includes: a determination unit, configured to determine the fault level of the cable equipment based on a target score after determining the status of the cable equipment based on a first matrix and a second weight for each fault type, wherein the fault level is used to characterize the severity of the fault of the cable equipment; a generation unit, configured to generate alarm information based on at least one target parameter and a target score when the fault level is greater than a preset level; and a processing unit, configured to determine target measures based on the alarm information and process the cable equipment using the target measures.

[0116] The device for determining the state of the cable equipment includes a processor and a memory. The first calculation unit 301, the second calculation unit 302, the clustering unit 303, and the third calculation unit 304 mentioned above are all stored in the memory as program units. The processor executes the program units stored in the memory to realize the corresponding functions.

[0117] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can improve the accuracy of cable equipment status assessments.

[0118] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0119] Embodiment 3 of this application provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements a method for determining the state of a cable device.

[0120] Embodiment 4 of this application provides a processor for running a program, wherein the program executes a method for determining the state of a cable device during runtime.

[0121] like Figure 4 As shown, Embodiment 5 of this application provides an electronic device. The device includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: calculating a first value for each target parameter based on parameter information of at least one target parameter, wherein the at least one target parameter includes parameters related to cable equipment, and the first value is used to characterize the degree of damage to the cable equipment; determining each fault type of the cable equipment and calculating a first weight for each fault type based on the first value of each target parameter; clustering the at least one target parameter and optimizing the first weight of each fault type based on the clustering results to obtain a second weight for each fault type; calculating a first matrix based on the first value of each target parameter and determining the state of the cable equipment based on the first matrix and the second weight of each fault type.

[0122] When the processor executes the program, it also performs the following steps: calculating the first value of each target parameter based on the parameter information of at least one target parameter, including: collecting the operating data of the cable equipment, determining at least one target parameter based on the operating data; determining the value range of each target parameter, wherein the value range includes at least one of the following: upper limit of value, lower limit of value; and calculating the first value of each target parameter based on the value range of each target parameter.

[0123] When the processor executes the program, it also performs the following steps: determining each fault type of the cable equipment and calculating the first weight of each fault type based on the first value of each target parameter, including: calculating the Spearman correlation coefficient between each target parameter and the fault type under each fault type; for each fault type, calculating the Spearman correlation coefficient corresponding to each target parameter to obtain the third weight of each target parameter under each fault type; calculating the first value of each target parameter and the third weight of each target parameter under each fault type to obtain the score of each fault type; and calculating the first weight of each fault type based on the score of each fault type.

[0124] When the processor executes the program, it also performs the following steps: clustering at least one target parameter, and optimizing the first weight of each fault type based on the clustering results to obtain the second weight of each fault type, including: constructing a first graph, wherein the first graph includes nodes and edges, nodes contain target parameters and fault types, and edges represent the relationship between target parameters and fault types; calculating the edge betweenness number of each edge in the first graph, and iteratively optimizing the first graph based on the edge betweenness number of each edge to obtain a set of second graphs; calculating the second value of each second graph in the set of second graphs, and determining the target graph based on the second value, wherein the second value is used to measure the compactness of the second graph; calculating the community contribution of each node based on the target graph, and optimizing the first weight of each fault type based on the community contribution to obtain the second weight of each fault type.

[0125] When the processor executes the program, it also performs the following steps: calculating a first matrix based on the first value of each target parameter, and determining the state of the cable equipment based on the first matrix and the second weight of each fault type, including: calculating the first value of each target parameter under each fault type to obtain the first matrix; dividing the preset value range into N intervals, and calculating a second matrix based on the N intervals, where N is the number of fault types; calculating the first matrix and the second matrix to obtain the correlation degree of each fault type; calculating the correlation degree of each fault type and the second weight of each fault type to obtain the target score, and determining the state of the cable equipment based on the target score.

[0126] When the processor executes the program, it also performs the following steps: calculates the first value of each target parameter under each fault type to obtain a first matrix, including: calculating the first value of each target parameter under each fault type based on a preset algorithm to obtain a third value corresponding to each fault type, wherein the preset algorithm is determined based on the target interval, and the target interval is the interval to which the first value belongs within N intervals; and determines the first matrix based on the third value corresponding to each fault type.

[0127] When the processor executes the program, it also performs the following steps: calculating the first matrix and the second matrix to obtain the correlation degree of each fault type, including: constructing a target expression, wherein the target expression is an expression that characterizes the correlation between fault types and target parameters based on set pair analysis, and the target expression includes at least the following parameters: similarity, difference, and opposition; substituting the first matrix and the second matrix into the target expression and performing calculations to obtain the correlation degree of each fault type.

[0128] When the processor executes the program, it also performs the following steps: after determining the state of the cable equipment based on the first matrix and the second weight of each fault type, the above method further includes: determining the fault level of the cable equipment based on the target score, wherein the fault level is used to characterize the severity of the fault of the cable equipment; if the fault level is greater than the preset level, generating alarm information based on at least one target parameter and the target score; determining target measures based on the alarm information, and using the target measures to process the cable equipment.

[0129] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0130] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: calculating a first value for each target parameter based on parameter information of at least one target parameter, wherein the at least one target parameter includes parameters related to the cable equipment, and the first value is used to characterize the degree of damage to the cable equipment; determining each fault type of the cable equipment, and calculating a first weight for each fault type based on the first value of each target parameter; clustering the at least one target parameter, and optimizing the first weight of each fault type based on the clustering results to obtain a second weight for each fault type; calculating a first matrix based on the first value of each target parameter, and determining the state of the cable equipment based on the first matrix and the second weight of each fault type.

[0131] When executed on a data processing device, it is also suitable to execute an initialization program with the following method steps: calculating a first value for each target parameter based on parameter information of at least one target parameter, including: collecting operating data of the cable equipment, determining at least one target parameter based on the operating data; determining the value range of each target parameter, wherein the value range includes at least one of the following: an upper limit of value and a lower limit of value; and calculating a first value for each target parameter based on the value range of each target parameter.

[0132] When executed on a data processing device, it is also suitable to execute an initialization program with the following method steps: determining each fault type of the cable equipment and calculating a first weight for each fault type based on a first value of each target parameter, including: calculating the Spearman correlation coefficient between each target parameter and the fault type under each fault type; for each fault type, calculating the Spearman correlation coefficient corresponding to each target parameter to obtain a third weight for each target parameter under each fault type; calculating the first value of each target parameter and the third weight of each target parameter under each fault type to obtain a score for each fault type; and calculating the first weight for each fault type based on the score for each fault type.

[0133] When executed on a data processing device, it is also suitable to execute an initialization procedure with the following steps: clustering at least one target parameter and optimizing a first weight for each fault type based on the clustering results to obtain a second weight for each fault type, including: constructing a first graph, wherein the first graph includes nodes and edges, nodes containing target parameters and fault types, and edges representing the association between target parameters and fault types; calculating the edge betweenness number of each edge in the first graph, iteratively optimizing the first graph based on the edge betweenness number of each edge to obtain a set of second graphs; calculating a second value for each second graph in the set of second graphs, and determining a target graph based on the second value, wherein the second value is used to measure the compactness of the second graph; calculating the community contribution of each node based on the target graph, and optimizing the first weight for each fault type based on the community contribution to obtain a second weight for each fault type.

[0134] When executed on a data processing device, it is also suitable to execute an initialization program with the following steps: calculating a first matrix based on a first value of each target parameter, and determining the state of the cable equipment based on the first matrix and a second weight of each fault type, including: calculating the first value of each target parameter under each fault type to obtain a first matrix; dividing a preset value range into N intervals, and calculating a second matrix based on the N intervals, where N is the number of fault types; calculating the first matrix and the second matrix to obtain the correlation degree of each fault type; calculating the correlation degree of each fault type and the second weight of each fault type to obtain a target score, and determining the state of the cable equipment based on the target score.

[0135] When executed on a data processing device, it is also suitable to execute an initialization program with the following steps: calculating the first value of each target parameter under each fault type to obtain a first matrix, including: calculating the first value of each target parameter under each fault type based on a preset algorithm to obtain a third value corresponding to each fault type, wherein the preset algorithm is determined according to a target interval, the target interval being the interval to which the first value belongs within N intervals; and determining the first matrix based on the third value corresponding to each fault type.

[0136] When executed on a data processing device, it is also suitable to execute an initialization program with the following method steps: calculating the first matrix and the second matrix to obtain the correlation degree of each fault type, including: constructing a target expression, wherein the target expression is an expression that characterizes the correlation between fault types and target parameters based on set pair analysis, and the target expression includes at least the following parameters: identity degree, difference degree, and opposition degree; substituting the first matrix and the second matrix into the target expression and calculating to obtain the correlation degree of each fault type.

[0137] When executed on a data processing device, it is also suitable to execute an initialization procedure with the following steps: after determining the state of the cable equipment based on a first matrix and a second weight for each fault type, the above method further includes: determining the fault level of the cable equipment based on a target score, wherein the fault level is used to characterize the severity of the fault in the cable equipment; generating alarm information based on at least one target parameter and a target score when the fault level is greater than a preset level; determining target measures based on the alarm information, and using the target measures to process the cable equipment.

[0138] 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.

[0139] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0140] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0141] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0142] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0143] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0144] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0145] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0146] 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.

[0147] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining the state of cable equipment, characterized in that, include: A first value for each target parameter is calculated based on parameter information of at least one target parameter, wherein the at least one target parameter includes parameters related to the cable equipment, and the first value is used to characterize the degree of damage to the cable equipment; Each fault type of the cable equipment is determined, and a first weight for each fault type is calculated based on a first value of each target parameter; Cluster the at least one target parameter, and optimize the first weight of each fault type based on the clustering results to obtain the second weight of each fault type; A first matrix is ​​calculated based on the first value of each target parameter, and the state of the cable equipment is determined based on the first matrix and the second weight of each fault type. Specifically, the at least one target parameter is clustered, and the first weight of each fault type is optimized based on the clustering results to obtain the second weight of each fault type, including: Construct a first graph, wherein the first graph includes nodes and edges, the nodes contain the target parameters and the fault types, and the edges represent the correlation between the target parameters and the fault types; Calculate the edge betweenness number of each edge in the first graph, and iteratively optimize the first graph based on the edge betweenness number of each edge to obtain the second graph set; Calculate a second value for each second graph in the second graph set, and determine the target graph based on the second value, wherein the second value is used to measure the density of the second graphs; The community contribution of each node is calculated based on the target graph, and the first weight of each fault type is optimized based on the community contribution to obtain the second weight of each fault type. Calculating a first matrix based on a first value for each target parameter, and determining the state of the cable equipment based on the first matrix and a second weight for each fault type, including: The first matrix is ​​obtained by calculating the first value of each target parameter under each fault type; The preset value range is divided into N intervals, and a second matrix is ​​calculated based on the N intervals, where N is the number of fault types; The correlation degree of each fault type is obtained by calculating the first matrix and the second matrix; The correlation degree and the second weight of each fault type are calculated to obtain the target score, and the status of the cable equipment is determined based on the target score.

2. The method according to claim 1, characterized in that, Calculate the first value of each target parameter based on parameter information of at least one target parameter, including: Collect the operating data of the cable equipment, and determine the at least one target parameter based on the operating data; Determine the value range for each target parameter, wherein the value range includes at least one of the following: upper limit and lower limit; Calculate the first value of each target parameter based on its range.

3. The method according to claim 1, characterized in that, Determine each fault type of the cable equipment, and calculate a first weight for each fault type based on a first value of each target parameter, including: Calculate the Spearman correlation coefficient between each target parameter and the fault type for each fault type; For each fault type, the Spearman correlation coefficient corresponding to each target parameter is calculated to obtain the third weight of each target parameter under each fault type; The score for each fault type is obtained by calculating the first value of each target parameter and the third weight of each target parameter under each fault type; Calculate the first weight for each fault type based on the score for each fault type.

4. The method according to claim 1, characterized in that, The first matrix is ​​obtained by calculating the first value of each target parameter under each fault type, including: The first value of each target parameter under each fault type is calculated based on a preset algorithm to obtain the third value corresponding to each fault type. The preset algorithm is determined based on a target interval, which is the interval to which the first value belongs among the N intervals. The first matrix is ​​determined based on the third value corresponding to each fault type.

5. The method according to claim 1, characterized in that, The correlation degree of each fault type is calculated by performing calculations on the first matrix and the second matrix, including: Construct a target expression, wherein the target expression is an expression that characterizes the relationship between the fault type and the target parameter based on set pair analysis, and the target expression includes at least the following parameters: similarity, difference, and opposition; Substitute the first matrix and the second matrix into the target expression and perform calculations to obtain the correlation degree for each fault type.

6. The method according to claim 1, characterized in that, After determining the state of the cable equipment based on the first matrix and the second weight of each fault type, the method further includes: The fault level of the cable equipment is determined based on the target score, wherein the fault level is used to characterize the severity of the fault of the cable equipment; If the fault level is greater than a preset level, an alarm message is generated based on at least one target parameter and the target score; Based on the alarm information, a target measure is determined, and the target measure is used to process the cable equipment.

7. A device for determining the state of cable equipment, characterized in that, include: The first calculation unit is used to calculate a first value for each target parameter based on parameter information of at least one target parameter, wherein the at least one target parameter includes parameters related to the cable equipment, and the first value is used to characterize the degree of damage to the cable equipment; The second calculation unit is used to determine each fault type of the cable equipment and calculate the first weight of each fault type based on the first value of each target parameter. A clustering unit is used to cluster the at least one target parameter and optimize the first weight of each fault type based on the clustering results to obtain the second weight of each fault type. The third calculation unit is used to calculate a first matrix based on a first value of each target parameter, and to determine the state of the cable equipment based on the first matrix and a second weight of each fault type. The clustering units include: Construct a sub-unit for constructing a first graph, wherein the first graph includes nodes and edges, the nodes contain the target parameters and the fault types, and the edges represent the correlation between the target parameters and the fault types; The sixth calculation subunit is used to calculate the edge betweenness of each edge in the first graph, and to iteratively optimize the first graph based on the edge betweenness of each edge to obtain the second graph set; The seventh calculation subunit is used to calculate the second value of each second graph in the second graph set and determine the target graph based on the second value, wherein the second value is used to measure the density of the second graph; The eighth calculation subunit is used to calculate the community contribution of each node based on the target graph, and optimize the first weight of each fault type based on the community contribution to obtain the second weight of each fault type. The third calculation unit includes: The ninth calculation subunit is used to calculate the first value of each target parameter under each fault type to obtain the first matrix; The tenth calculation subunit is used to divide the preset value range into N intervals and calculate the second matrix based on the N intervals, where N is the number of fault types; The eleventh calculation subunit is used to calculate the first matrix and the second matrix to obtain the correlation degree of each fault type; The twelfth calculation subunit is used to calculate the correlation degree of each fault type and the second weight of each fault type to obtain the target score, and determine the status of the cable equipment based on the target score.

8. The apparatus according to claim 7, characterized in that, The first computing unit includes: A data acquisition subunit is used to acquire the operating data of the cable equipment and determine the at least one target parameter based on the operating data. A sub-unit is defined to determine the value range of each target parameter, wherein the value range includes at least one of the following: upper limit of value and lower limit of value; The first calculation subunit is used to calculate the first value of each target parameter based on the value range of each target parameter.

9. The apparatus according to claim 7, characterized in that, The second calculation unit includes: The second calculation subunit is used to calculate the Spearman correlation coefficient between each target parameter and the fault type under each fault type; The third calculation subunit is used to calculate the Spearman correlation coefficient for each target parameter for each fault type, and obtain the third weight of each target parameter under each fault type. The fourth calculation subunit is used to calculate the first value of each target parameter and the third weight of each target parameter under each fault type to obtain the score of each fault type; The fifth calculation subunit is used to calculate the first weight of each fault type based on the score of each fault type.

10. The apparatus according to claim 7, characterized in that, The ninth computational subunit includes: The first calculation module is used to calculate the first value of each target parameter under each fault type based on a preset algorithm to obtain the third value corresponding to each fault type. The preset algorithm is determined based on a target interval, which is the interval to which the first value belongs among the N intervals. The determination module is used to determine the first matrix based on the third value corresponding to each fault type.

11. The apparatus according to claim 7, characterized in that, The eleventh calculation subunit includes: A construction module is used to construct a target expression, wherein the target expression is an expression that characterizes the relationship between the fault type and the target parameter based on set pair analysis, and the target expression includes at least the following parameters: similarity, difference, and opposition. The second calculation module is used to substitute the first matrix and the second matrix into the target expression and perform calculations to obtain the correlation degree of each fault type.

12. The apparatus according to claim 7, characterized in that, The device further includes: A determining unit is configured to determine the fault level of the cable equipment based on the target score after determining the state of the cable equipment according to the first matrix and the second weight of each fault type, wherein the fault level is used to characterize the severity of the fault of the cable equipment; The generation unit is configured to generate alarm information based on at least one target parameter and the target score when the fault level is greater than a preset level. The processing unit is used to determine the target measures based on the alarm information and to process the cable equipment using the target measures.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes stored computer instructions, wherein, when the computer instructions are executed by a processor, the method for determining the state of the cable equipment as described in any one of claims 1 to 6 is implemented.

14. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method for determining the state of a cable device as described in any one of claims 1 to 6.

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