A method and system for testing the instantaneous tolerance of a large-current terminal

Abstract

The present invention relates to the technical field of electrical performance testing, and particularly relates to a large-current terminal instantaneous tolerance testing method and system. Detection data of a terminal to be tested are respectively obtained when test currents of different intensities are applied to the terminal to be tested, including current data and associated electrical data; the detection data are segmented to obtain a plurality of current data segments and a plurality of associated electrical data segments; the current similarity between any two current data segments is obtained to obtain a current data connectivity graph, and the current data connectivity graph is split to obtain a number of initial split subgraphs; based on the correlation between the current data segments and the associated electrical data segments belonging to the same time period, the connection edges in the initial split subgraphs are adjusted and split again to obtain a number of target split subgraphs, thereby obtaining a clustering model. This clustering model is more relevant to the actual state of the terminal to be tested, and the accuracy of the clustering model is higher, thus improving the accuracy of the large-current terminal instantaneous tolerance testing.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical performance testing, and particularly relates to a method and system for testing the instantaneous tolerance of large-current terminals. Background Art

[0002] In the power system, a wiring terminal is a very common electrical component. Since it usually bears a large current, the wiring terminal can also be called a large-current terminal. The current tolerance performance of the large-current terminal is very important and affects the safe operation of the power system. Therefore, it is necessary to test the instantaneous tolerance of the large-current terminal during the production stage. The existing method for testing the instantaneous tolerance of large-current terminals is usually as follows: One or several large-current terminals are selected from the same batch as the terminals to be tested, and then currents of different intensities are input to the terminals to be tested. By changing the current intensity, different loads during the use process are simulated. The instantaneous large-current tolerance test of the terminals is used to verify the bearing capacity and safety of the terminals under sudden large-current conditions, so as to evaluate the production quality of the terminals.

[0003] The connected graph clustering algorithm is a relatively common clustering algorithm used to obtain a clustering model. The clustering model is obtained by clustering according to the actual detection data. Essentially, it is a classification model used to determine the quality of the detection object according to the classification situation in the clustering model. However, in the process of using the connected graph clustering algorithm to analyze the current data input into the large-current terminal to obtain a clustering model, the data involved is only the current data, and other relevant electrical parameters of the large-current terminal are not comprehensively analyzed, resulting in a low accuracy of the clustering model and affecting the accuracy of the instantaneous tolerance test of the large-current terminal. Summary of the Invention

[0004] In order to solve the technical problem of the low accuracy of the existing clustering model, the purpose of the present invention is to provide a method and system for testing the instantaneous tolerance of large-current terminals. The specific technical solutions adopted are as follows:

[0005] In the first aspect of the present invention, a method for testing the instantaneous tolerance of large-current terminals is provided, including:

[0006] Respectively obtain the detection data of the terminals to be tested when different intensities of test currents are input to the terminals to be tested. The detection data includes current data and associated electrical data;

[0007] Segment the detection data to obtain a plurality of current data segments and corresponding plurality of associated electrical data segments;

[0008] Obtain the current similarity between any two current data segments;

[0009] Based on the current similarity between any two current data segments, a current data connection graph is obtained, and based on the current similarity, the current data connection graph is split to obtain several initial split subgraphs;

[0010] Based on the relevant situations of the current data segments and the associated electrical data segments belonging to the same time period, the connection edges corresponding to any two time periods in the initial split subgraphs are adjusted, and multiple target split subgraphs are obtained by splitting again according to the connection edge adjustment results, thereby obtaining a clustering model.

[0011] In one embodiment, segmenting the detection data includes:

[0012] Obtain the extreme points in the detected current;

[0013] According to each extreme point, segment the current data to obtain multiple current data segments, and according to the time points of each extreme point, segment the associated electrical data to obtain multiple associated electrical data segments.

[0014] In one embodiment, obtaining a current data connection graph based on the current similarity between any two current data segments includes:

[0015] Taking each current data segment as each node, and connecting the corresponding two nodes based on the current similarity between any two current data segments to obtain a current data connection graph; wherein, the connection edge between two nodes is characterized by the current similarity between the corresponding two nodes.

[0016] In one embodiment, the associated electrical data includes voltage and temperature, and the associated electrical data segments include voltage data segments and temperature data segments;

[0017] The method for testing the instantaneous tolerance of large current terminals further includes: obtaining the similarity of associated electrical parameters between any two associated electrical data segments;

[0018] The similarity of associated electrical parameters between any two associated electrical data segments includes the voltage similarity between any two voltage data segments and the temperature similarity between any two temperature data segments.

[0019] In one embodiment, the process of obtaining the relevant situations of the current data segments and the associated electrical data segments belonging to the same time period includes:

[0020] Obtain the first correlation situation between the first current data segment and the first voltage data segment, and the second correlation situation between the first current data segment and the first temperature data segment; the first current data segment is the current data segment corresponding to the first time period, the first voltage data segment is the voltage data segment corresponding to the first time period, the first temperature data segment is the temperature data segment corresponding to the first time period, and the first time period is any time period.

[0021] In one embodiment, the obtaining process of the first correlation situation includes:

[0022] Obtain the first DTW (Dynamic Time Warping) distance between the first current data segment and the first voltage data segment, and the first covariance between the first current data segment and the first voltage data segment, and obtain the first correlation according to the first DTW distance and the first covariance; the first correlation is inversely proportional to the first DTW distance and directly proportional to the first covariance;

[0023] The obtaining process of the second correlation situation includes:

[0024] Obtain the second DTW distance between the first current data segment and the first temperature data segment, and the second covariance between the first current data segment and the first temperature data segment, and obtain the second correlation according to the second DTW distance and the second covariance; the second correlation is inversely proportional to the second DTW distance and directly proportional to the second covariance.

[0025] In one embodiment, adjusting the connection edges corresponding to any two time periods in the initial split sub - graph includes:

[0026] Obtain the first correlation and the second correlation corresponding to the second time period, where the second time period is another time period having a connection edge with the first time period;

[0027] Average the first correlation of the first current data segment and the first correlation of the second time period to obtain the first characteristic correlation; average the second correlation of the first current data segment and the second correlation of the second time period to obtain the second characteristic correlation;

[0028] Normalize the first characteristic correlation and the second characteristic correlation respectively to obtain the first voltage correction weight and the first temperature correction weight, and the sum of the first voltage correction weight and the first temperature correction weight is 1;

[0029] Based on the first voltage correction weight and the first temperature correction weight, perform weighted summation and normalization on the voltage similarity and temperature similarity corresponding to the first time period and the second time period to obtain the connection edge correction coefficient;

[0030] According to the connection edge correction coefficient, adjust the current similarity corresponding to the first time period and the second time period to obtain the adjusted current similarity.

[0031] In one embodiment, the large current terminal instantaneous tolerance test method further includes:

[0032] Obtain the number of target split subgraphs in the clustering model, the current data information entropy in each target split subgraph, and the current dispersion condition of each target split subgraph;

[0033] According to the number of target split subgraphs, the current data information entropy, and the current dispersion condition, obtain the instantaneous tolerance test result of the terminal to be tested.

[0034] In one embodiment, obtaining the instantaneous tolerance test result of the terminal to be tested according to the number of target split subgraphs, the current data information entropy, and the current dispersion condition includes:

[0035] Based on the number of target split subgraphs, the current data information entropy, and the current dispersion condition, obtain an instantaneous tolerance evaluation parameter of the terminal to be tested; the instantaneous tolerance evaluation parameter is inversely proportional to the number of target split subgraphs, the overall level of the current data information entropy, and the overall level of the current dispersion degree; the overall level of the current data information entropy is the average value of the current data information entropy of all the target split subgraphs; the overall level of the current dispersion degree is the average value of the current dispersion degrees of all the target split subgraphs; the current dispersion degree is equal to the average value of the distances between each node and the center of the corresponding target split subgraph in the corresponding target split subgraph;

[0036] Compare the instantaneous tolerance evaluation parameter with a first preset instantaneous tolerance evaluation threshold and a second preset instantaneous tolerance evaluation threshold, and determine the instantaneous tolerance test result of the terminal to be tested according to the magnitude relationship between the instantaneous tolerance evaluation parameter, the first preset instantaneous tolerance evaluation threshold, and the second preset instantaneous tolerance evaluation threshold; wherein, the first preset instantaneous tolerance evaluation threshold is greater than the second preset instantaneous tolerance evaluation threshold.

[0037] In a second aspect of the present invention, there is provided a large current terminal instantaneous tolerance test system, including: a memory and a processor; the memory is connected to the processor; the memory is used for storing program instructions; the processor is used for implementing the above-mentioned large current terminal instantaneous tolerance test method when the program instructions are executed.

[0038] The present invention has the following beneficial effects: In addition to obtaining the current data of the terminal to be tested during the current test, other associated electrical data of the terminal to be tested are also obtained; the detection data is segmented, and the association between the data in any two data segments can be analyzed, so as to more deeply understand the change relationship between the current and the associated electrical data of the terminal to be tested during the test, thereby obtaining a current data connection graph and several initial split subgraphs obtained by splitting. Then, according to the association between different data within the data segment, the initial split subgraphs are adjusted and split again to obtain multiple target split subgraphs, thereby obtaining a corrected clustering model. Since the finally obtained clustering model is obtained based on the actual current data of the terminal to be tested and other associated electrical parameters, the obtained clustering model is more relevant to the actual state of the terminal to be tested, and the accuracy of the clustering model is higher, thereby improving the accuracy of the subsequent instantaneous tolerance test of the high-current terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flowchart of a method for testing the instantaneous tolerance of a high-current terminal provided by an embodiment of the present invention;

[0040] Figure 2 is a flowchart of the acquisition process of the first related situation provided by an embodiment of the present invention;

[0041] Figure 3 is a flowchart of the acquisition process of the second related situation provided by an embodiment of the present invention;

[0042] Figure 4 is a flowchart of adjusting the connection edges corresponding to any two time periods in the initial split subgraph provided by an embodiment of the present invention;

[0043] Figure 5 is a flowchart of the steps further included in a method for testing the instantaneous tolerance of a high-current terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following describes in detail the specific embodiments, structures, features, and effects of the present invention in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0046] This embodiment provides a method for testing the instantaneous tolerance of high-current terminals. The application scenario of this method for testing the instantaneous tolerance of high-current terminals is as follows: during the production stage of high-current terminals, one or a part of the terminals produced in a batch are selected as the test objects, which are defined as the terminals to be tested, and the test results are obtained by using the method for testing the instantaneous tolerance of high-current terminals provided in this embodiment. This embodiment takes the setting of one terminal to be tested as an example for illustration. When multiple terminals to be tested are set, the final test results are obtained by synthesizing the test results of all terminals to be tested. It should be understood that the test results of the terminals to be tested are used as the measurement results of all terminals in the same batch to achieve the instantaneous tolerance test during the production stage of high-current terminals.

[0047] As Figure 1 shown, this method for testing the instantaneous tolerance of high-current terminals includes the following steps:

[0048] Step 1: Obtain the detection data of the terminal to be tested when different intensities of test current are applied to the terminal to be tested. The detection data includes current data and associated electrical data.

[0049] Electrically connect the terminal to be tested to the test equipment. The test equipment includes a current generator that can output different intensities of test current to the test terminal. Among them, according to the expected maximum current capacity of the terminal to be tested, different test current levels are determined. In an exemplary embodiment, among the different intensities of test current, the minimum test current is set to 50% of the expected maximum tolerable current, and then it is increased by 10% each time to gradually increase the test current until it reaches 80% of the expected maximum tolerable current. Therefore, the different intensities of test current are: 50% of the expected maximum tolerable current, 60% of the expected maximum tolerable current, 70% of the expected maximum tolerable current, and 80% of the expected maximum tolerable current.

[0050] At the start of the test, the test equipment is started, and different intensities of test current are applied to the terminal to be tested respectively. Specifically, the test current is output in ascending order of current. In an exemplary embodiment, the test time for each current intensity is equal and continuous in time. Specifically: first, apply 50% of the expected maximum tolerable current, obtain the detection data of the terminal to be tested, after a preset time period, switch to 60% of the expected maximum tolerable current, obtain the detection data of the terminal to be tested, after the same preset time period, switch to 70% of the expected maximum tolerable current, obtain the detection data of the terminal to be tested, after the same preset time period, switch to 80% of the expected maximum tolerable current, obtain the detection data of the terminal to be tested, and end the test after the same preset time period.

[0051] The specific type of the test current can be flexibly set. To adapt to the actual operating conditions of the terminal, the test current can be set as a pulsed current or a rectangular current. This kind of current can make the current intensity in the terminal to be tested rise rapidly to the set value and continuously flow through the terminal within the specified time. Through this current simulation, the ability of the terminal to be tested to withstand current impact in a short time can be detected.

[0052] The detected data includes current data and associated electrical data. The current data can be detected by a current transformer electrically connected to the terminal to be tested. Among them, the associated electrical data is other electrical data related to the current data. In an exemplary embodiment, the associated electrical data includes voltage and temperature. Among them, the voltage is the voltage across the terminal to be tested after the current is applied, and the temperature is the temperature on the terminal to be tested after the current is applied. The voltage can be detected by a voltage transformer electrically connected to the terminal to be tested. The temperature can be detected by a temperature sensor electrically connected to the terminal to be tested.

[0053] The data sampling period can be flexibly set, such as 0.1 second. And for each sampling period, the current data, voltage data, and temperature data are collected once. Thus, each sampling period corresponds to a set of data: current data, voltage data, and temperature data. And the sampling moments of the current data, voltage data, and temperature data are the same. Further, the collected data can be preprocessed to delete abnormal noise values.

[0054] The detection time period corresponding to the detected data can be flexibly set. The detection time period contains a large number of sampling periods, so as to obtain a large amount of current data, as well as voltage data and temperature data.

[0055] Step 2: Segment the detected data to obtain multiple current data segments and corresponding multiple associated electrical data segments.

[0056] Segment the detected data to obtain multiple current data segments and corresponding multiple associated electrical data segments. The specific segmentation method can be flexibly set. For example, the detection time period of the detected data is evenly divided into several time sub - segments, so as to obtain the current data segment of each time sub - segment and the corresponding associated electrical data segment.

[0057] In an exemplary embodiment, the test current is a pulsed current. Each intensity of the test current is a pulsed current corresponding to that intensity. Each intensity of the test current includes multiple pulsed currents in time sequence, and the duration of a single pulsed current can be flexibly set, such as 5 seconds. The pulsed current has changes from low to high and from high to low, that is, it has the characteristics of peak and valley values. Obtain the time-amplitude waveform curve of the detected current. Since the pulsed current has peak and valley values, each extreme value point in the time-amplitude waveform curve is obtained. The extreme value points include maximum values and minimum values. According to each extreme value point, the current data is segmented to obtain multiple current data segments. Specifically: the current data between two adjacent extreme value points is used as a current data segment, so as to obtain multiple current data segments. Then, according to the time points of each extreme value point, the voltage data is segmented. Specifically: the voltage data between the time points of any two extreme value points is used as a voltage data segment, so as to obtain multiple voltage data segments that correspond one-to-one with the current data segments; similarly, the temperature data between the time points of any two extreme value points is used as a temperature data segment, so as to obtain multiple temperature data segments that correspond one-to-one with the current data segments. Each current data segment, voltage data segment, and temperature data segment are in one-to-one correspondence in time sequence.

[0058] Step 3: Obtain the current similarity between any two current data segments.

[0059] In an exemplary embodiment, all the current data segments corresponding to the currents of all intensities are used as the data processing objects in this step, that is, the current similarity between any two current data segments among all the current data segments corresponding to the currents of all intensities is obtained.

[0060] The algorithm for calculating the current similarity can be flexibly set, such as cosine similarity, Pearson correlation coefficient, etc. In an exemplary embodiment, the DTW (Dynamic Time Warping) distance between any two current data segments is obtained, and then the DTW distance is negatively correlated and normalized to obtain the current similarity between any two current data segments. It should be understood that the negative correlation normalization in this embodiment can be , where n is the input data, is the exponential function with the natural constant e as the base.

[0061] In an exemplary embodiment, the correlation electrical parameter similarity between any two associated electrical data segments is also obtained. Specifically: the voltage similarity between any two voltage data segments is obtained in the above manner, and the temperature similarity between any two temperature data segments is obtained. Exemplarily: the DTW distance between any two voltage data segments is obtained, and then the DTW distance is negatively correlated and normalized to obtain the voltage similarity between any two voltage data segments; the DTW distance between any two temperature data segments is obtained, and then the DTW distance is negatively correlated and normalized to obtain the temperature similarity between any two temperature data segments.

[0062] Step 4: Based on the current similarity between all any two current data segments, a current data connection graph is obtained, and based on the current similarity, the current data connection graph is split to obtain several initial split subgraphs.

[0063] Based on the connected graph clustering algorithm, each current data segment is used as each node. Based on the current similarity between all any two current data segments, the corresponding two nodes are connected by edges, so as to realize the edge connection operation for all nodes, and a current data connection graph is obtained. Therefore, essentially, two nodes with a current similarity greater than 0 are connected by edges, and the connection edge between any two nodes is characterized by the current similarity between the corresponding two nodes.

[0064] Then, based on the current similarity, the nodes are preliminarily clustered, that is, the current data connection graph is split to obtain several initial split subgraphs, and each initial split subgraph is a clustering cluster. The connected graph clustering algorithm and the connected graph splitting both belong to existing algorithms and will not be elaborated here.

[0065] Step 5: Based on the relevant situation of the current data segment and the associated electrical data segment belonging to the same time period, the connection edges corresponding to any two time periods in the initial split subgraph are adjusted, and multiple target split subgraphs are obtained by splitting again according to the connection edge adjustment result, so as to obtain a clustering model.

[0066] In an exemplary embodiment, the first time period is set as any time period, the first current data segment is set as the current data segment corresponding to the first time period, the first voltage data segment is set as the voltage data segment corresponding to the first time period, and the first temperature data segment is set as the temperature data segment corresponding to the first time period.

[0067] The first relevant situation between the first current data segment and the first voltage data segment, and the second relevant situation between the first current data segment and the first temperature data segment are obtained.

[0068] Among them, as Figure 2 shown, the process of obtaining the first relevant situation includes:

[0069] Step 5-1: Obtain the first DTW distance between the first current data segment and the first voltage data segment, and the first covariance between the first current data segment and the first voltage data segment;

[0070] Step 5-2: Obtain the first correlation based on the first DTW distance and the first covariance; the first correlation is inversely proportional to the first DTW dynamic time warping distance and directly proportional to the first covariance.

[0071] In an exemplary embodiment, the relationship between the first correlation, the first DTW dynamic time warping distance, and the first covariance is specifically quantified through the following calculation formula of the first correlation:

[0072]

[0073] where is the first correlation corresponding to the first time period, I is the first current data segment, V is the first voltage data segment, is the first DTW distance between the first current data segment and the first voltage data segment, is the first covariance between the first current data segment and the first voltage data segment. norm is the normalization function.

[0074] According to the basic electrical principle, the changes in voltage and temperature are positively correlated with the change in current. For example, when the current is larger, the voltage is larger and the temperature is higher. Therefore, the following calculation formula can be used to normalize:

[0075]

[0076] where represents the standard deviation of the current data in the first current data segment, represents the standard deviation of the voltage data in the first voltage data segment.

[0077] represents the waveform change similarity between the first current data segment and the first voltage data segment, reducing the interference of data time delay on the similarity calculation result. The smaller the first DTW distance between the first current data segment and the first voltage data segment, the more similar the waveform changes between the two data segments, and the greater the correlation between the current and the voltage. The smaller the first DTW distance reflects the non-linear relationship between the first current data segment and the first voltage data segment; Essentially, it is the correlation coefficient after normalizing the first covariance, which reflects the correlation between the first current data segment and the first voltage data segment in terms of numerical values, and is used to characterize the linear relationship between current changes and voltage changes, that is, the change in current is proportional to the change in voltage. Therefore, the closer the current and voltage changes are, the better the terminal data of the corresponding node. The above two different calculation methods of correlation are used to consider the multi-dimensional relationship between data sequences.

[0078] Similarly, as Figure 3 shown, the process of obtaining the second correlation includes:

[0079] Step 5-3: Obtain the second DTW distance between the first current data segment and the first temperature data segment, and the second covariance between the first current data segment and the first temperature data segment;

[0080] Step 5-4: Obtain the second correlation based on the second DTW distance and the second covariance; the second correlation is inversely proportional to the second DTW distance and directly proportional to the second covariance.

[0081] In an exemplary embodiment, the relationship between the second correlation, the second DTW dynamic time warping distance, and the second covariance is specifically quantified through the following calculation formula of the second correlation:

[0082]

[0083] where is the second correlation corresponding to the first time period, T is the first temperature data segment, is the second DTW distance between the first current data segment and the first temperature data segment, is the second covariance between the first current data segment and the first temperature data segment.

[0084] By using the above process, the first correlation and the second correlation corresponding to each time period can be obtained.

[0085] Then, adjust the connecting edges corresponding to any two time periods in the initial split sub-graph, as Figure 4 shown, including:

[0086] Step 5-5: Obtain the first correlation and the second correlation corresponding to the second time period, where the second time period is another time period having a connecting edge with the first time period.

[0087] For any connecting edge in the initial split sub-graph, it corresponds to two nodes, that is, two time periods. Then, set the second time period as another time period having a connecting edge with the first time period, so there is a connecting edge between the first time period and the second time period.

[0088] By using the above steps 5-1 to 5-4, the first correlation and the second correlation corresponding to the second time period are obtained.

[0089] Step 5-6: Average the first correlation of the first current data segment and the first correlation of the second time period to obtain the first characteristic correlation; average the second correlation of the first current data segment and the second correlation of the second time period to obtain the second characteristic correlation.

[0090] The first correlation of the first current data segment is the correlation between the first current data segment and the first voltage data segment, and the first correlation of the second time period is the correlation between the second current data segment and the second voltage data segment. Then, averaging the first correlation of the first current data segment and the first correlation of the second time period, the first characteristic correlation between the first current data segment and the second current data segment is obtained, and the first characteristic correlation characterizes the correlation between the first current data segment and the second current data segment and the voltage data.

[0091] Similarly, the second correlation of the first current data segment is the correlation between the first current data segment and the first temperature data segment, and the second correlation of the second time period is the correlation between the second current data segment and the second temperature data segment. Then, averaging the second correlation of the first current data segment and the second correlation of the second time period, the second characteristic correlation between the first current data segment and the second current data segment is obtained, and the second characteristic correlation characterizes the correlation between the first current data segment and the second current data segment and the temperature data.

[0092] Step 5-7: Normalize the first characteristic correlation and the second characteristic correlation respectively to obtain the first voltage correction weight and the first temperature correction weight, and the sum of the first voltage correction weight and the first temperature correction weight is 1.

[0093] Specifically: Calculate the sum value of the first characteristic correlation and the second characteristic correlation, and this sum value is defined as the characteristic correlation sum value. Then, take the ratio of the first characteristic correlation to the characteristic correlation sum value as the first voltage correction weight, and take the ratio of the second characteristic correlation to the characteristic correlation sum value as the second voltage correction weight.

[0094] Step 5-8: Based on the first voltage correction weight and the first temperature correction weight, perform weighted summation on the voltage similarity and temperature similarity corresponding to the first time period and the second time period, and normalize to obtain the connection edge correction coefficient.

[0095] Specifically, the following calculation formula is used to obtain the connection edge correction coefficient corresponding to the first time period and the second time period:

[0096]

[0097] Among them, Represents the connection edge correction coefficient corresponding to the first time period and the second time period; Represents the voltage similarity corresponding to the first time period and the second time period; Represents the temperature similarity corresponding to the first time period and the second time period; Represents the first voltage correction weight; Represents the first temperature correction weight. The normalization in this embodiment can be carried out in the following general manner: .

[0098] By adopting the above process, the connection edge correction coefficient corresponding to any two time periods can be obtained, that is, the connection edge correction coefficient corresponding to any two nodes.

[0099] Step 5-9: Adjust the current similarity corresponding to the first time period and the second time period according to the connection edge correction coefficient to obtain the adjusted current similarity.

[0100] Adjust the current similarity corresponding to the first time period and the second time period according to the connection edge correction coefficient corresponding to the first time period and the second time period to obtain the adjusted current similarity. Specifically, multiply the connection edge correction coefficient corresponding to the first time period and the second time period by the current similarity corresponding to the first time period and the second time period, and the product obtained is the adjusted current similarity. Thus, the adjusted current similarity corresponding to any two time periods is obtained.

[0101] The adjusted current similarity characterizes the possibility that the nodes between the same split subgraphs of the corresponding two nodes in the current data connection graph belong to the same clustering cluster.

[0102] Finally, split again according to the adjusted current similarity corresponding to any two time periods to obtain multiple target split subgraphs, thereby obtaining the clustering model. Therefore, according to the above process, the connection edges between any two nodes are weighted and adjusted to obtain the weighted connection edges, and then re-split according to the weighted results to obtain several target split subgraphs. Each target split subgraph represents a clustering cluster, and the clustering model is obtained. The clustering model is used for subsequent terminal instantaneous tolerance tests.

[0103] In an exemplary embodiment, as Figure 5 shown, the large current terminal instantaneous tolerance test method further includes:

[0104] Step 6: Obtain the number of target split subgraphs in the clustering model, the current data information entropy in each target split subgraph, and the current dispersion situation in each target split subgraph.

[0105] The instantaneous tolerance of a terminal refers to the quantitative evaluation of the terminal's ability to adapt to external disturbances, load changes, or faults under specific working conditions. Cluster analysis is performed on the test results of the terminal at different current intensities, and the instantaneous tolerance of the terminal is evaluated by combining the number of target split subgraphs in the clustering results, the characteristics of each target split subgraph, etc. Therefore, the number of target split subgraphs in the clustering model, the information entropy of the current data in each target split subgraph, and the current dispersion of each target split subgraph are obtained.

[0106] The larger the number of target split subgraphs, the greater the difference in the response states of the terminal to various current intensity changes, indicating that the working state of the terminal is more unstable and the tolerance is lower.

[0107] Since each target split subgraph contains multiple nodes, and each node contains current data for multiple sampling periods, the information entropy of the current data contained in each target split subgraph is calculated. The information entropy characterizes the complexity of the current data in the corresponding target split subgraph. The smaller the information entropy of the current data, the less complex the current data in the target split subgraph, the more stable the terminal can operate, and the stronger the tolerance; on the contrary, the larger the information entropy of the current data, the more unstable the working state of the terminal, and the lower the tolerance corresponding to the target split subgraph with a higher complexity.

[0108] For any target split subgraph, the current dispersion of the target split subgraph is represented by the degree of current dispersion. The process of obtaining the degree of current dispersion is as follows: obtain the center of the target split subgraph, and obtain the distance between each node in the target split subgraph and the center of the target split subgraph, so as to obtain the average value of the distances between all nodes in the target split subgraph and the center of the target split subgraph. This average value is the degree of current dispersion of the target split subgraph. The smaller the degree of current dispersion, the closer the nodes in the target split subgraph are, indicating that within the cluster corresponding to the target split subgraph, the change in the performance of the terminal is smaller, the more stable the terminal can operate, and the stronger the tolerance.

[0109] Step 7: Obtain the instantaneous tolerance test result of the terminal to be tested according to the number of target split subgraphs, the information entropy of the current data, and the current dispersion.

[0110] The overall level of the information entropy of the current data is calculated according to the information entropy of the current data of each target split subgraph. The overall level of the information entropy of the current data is the average value of the information entropy of the current data of all target split subgraphs.

[0111] The overall level of the degree of current dispersion is calculated according to the degree of current dispersion of each target split subgraph. The overall level of the degree of current dispersion is the average value of the degree of current dispersion of all target split subgraphs.

[0112] According to the number of target split sub - graphs, the information entropy of current data, and the current dispersion situation, an instantaneous tolerance evaluation parameter of the terminal to be tested is obtained. The instantaneous tolerance evaluation parameter is inversely proportional to the number of target split sub - graphs, the overall level of the information entropy of current data, and the overall level of current dispersion degree. As a specific implementation, the following calculation formula is given:

[0113]

[0114] Among them, Q is the instantaneous tolerance evaluation parameter, K is the number of target split sub - graphs, H is the overall level of the information entropy of current data, and D is the overall level of current dispersion degree.

[0115] A first preset instantaneous tolerance evaluation threshold and a second preset instantaneous tolerance evaluation threshold are preset. The first preset instantaneous tolerance evaluation threshold is greater than the second preset instantaneous tolerance evaluation threshold. Both the first preset instantaneous tolerance evaluation threshold and the second preset instantaneous tolerance evaluation threshold are preset values greater than 0 and less than 1. For example, the first preset instantaneous tolerance evaluation threshold is 0.7, and the second preset instantaneous tolerance evaluation threshold is 0.3.

[0116] Compare the instantaneous tolerance evaluation parameter Q with the first preset instantaneous tolerance evaluation threshold and the second preset instantaneous tolerance evaluation threshold: If it is greater than the first preset instantaneous tolerance evaluation threshold, it is determined that the instantaneous tolerance of the terminal to be tested is good; If it is less than or equal to the first preset instantaneous tolerance evaluation threshold and greater than or equal to the second preset instantaneous tolerance evaluation threshold, it is determined that the instantaneous tolerance of the terminal to be tested is average; If it is less than the second preset instantaneous tolerance evaluation threshold, it is determined that the instantaneous tolerance of the terminal to be tested is poor.

[0117] It should be understood that the specific values of the first preset instantaneous tolerance evaluation threshold and the second preset instantaneous tolerance evaluation threshold are set according to actual judgment needs. Moreover, the implementer can also set more thresholds according to actual needs to achieve more refined judgment.

[0118] Take the obtained instantaneous tolerance test result of the terminal to be tested as the test result of all terminals in this batch.

[0119] This embodiment also provides a large - current terminal instantaneous tolerance test system, including: a memory and a processor; The memory is connected to the processor, and the memory is used to store program instructions; The processor is used to implement the steps in the above - mentioned large - current terminal instantaneous tolerance test method embodiment when the program instructions are executed.

[0120] In an exemplary embodiment, the present invention provides a computer - readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the steps in the above - mentioned large - current terminal instantaneous tolerance test method embodiment.

[0121] It should be noted that the above-mentioned sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0122] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for testing the transient tolerance of high current terminals, characterized in that: include: Respectively acquiring detection data of the terminal to be tested when a test current of different strengths is passed through the terminal to be tested, the detection data including current data and associated electrical data; Segmenting the detection data to obtain a plurality of current data segments and a corresponding plurality of associated electrical data segments; Obtaining the current similarity between any two current data segments; Based on the current similarity between any two current data segments, a current data connectivity graph is obtained, and based on the current similarity, the current data connectivity graph is split to obtain a plurality of initial split subgraphs; Based on the correlation between the current data segments and the associated electrical data segments belonging to the same time period, the connection edges corresponding to any two time periods in the initial split subgraph are adjusted, and multiple target split subgraphs are split again according to the connection edge adjustment results, thereby obtaining a clustering model; The high current terminal transient tolerance test method also includes: Obtaining the number of the target split subgraphs in the clustering model, the information entropy of the current data in each target split subgraph, and the current dispersion of each target split subgraph; The transient tolerance test result of the terminal to be tested is obtained according to the number of the target split subgraphs, the current data information entropy and the current dispersion.

2. The method for testing the transient tolerance of high current terminals according to claim 1, characterized in that: Segmenting the detection data includes: Obtaining the extreme value point in the detection current; The current data is segmented according to each extreme point to obtain a plurality of current data segments, and the associated electrical data is segmented according to the time point of each extreme point to obtain a plurality of associated electrical data segments.

3. The method for testing the transient tolerance of high current terminals according to claim 1, characterized in that: Based on the current similarity between any two current data segments, a current data connectivity graph is obtained, including: Each current data segment is taken as each node, and the corresponding two nodes are connected by edges based on the current similarity of any two current data segments to obtain a current data connectivity graph; wherein the connection edge between two nodes is characterized by the current similarity between the corresponding two nodes.

4. The method for testing the transient tolerance of high current terminals according to claim 1, wherein: The associated electrical data includes voltage and temperature, and the associated electrical data segment includes a voltage data segment and a temperature data segment; The high current terminal transient tolerance test method further includes: obtaining the similarity of associated electrical parameters of any two associated electrical data segments; The similarity of the associated electrical parameters of any two associated electrical data segments includes the voltage similarity of any two voltage data segments and the temperature similarity of any two temperature data segments.

5. The method for testing the transient tolerance of high current terminals according to claim 4, characterized in that: The process of obtaining the relevant information of the current data segment and the associated electrical data segment belonging to the same time period includes: Obtain a first correlation between a first current data segment and a first voltage data segment, and a second correlation between a first current data segment and a first temperature data segment; the first current data segment is a current data segment corresponding to a first time period, the first voltage data segment is a voltage data segment corresponding to the first time period, the first temperature data segment is a temperature data segment corresponding to the first time period, and the first time period is any time period.

6. The method for testing the transient tolerance of high current terminals according to claim 5, characterized in that: The process of obtaining the first relevant situation includes: Acquire a first DTW dynamic time normalization distance between a first current data segment and a first voltage data segment, and a first covariance between the first current data segment and the first voltage data segment, and obtain a first correlation according to the first DTW dynamic time normalization distance and the first covariance; the first correlation is inversely proportional to the first DTW dynamic time normalization distance and is proportional to the first covariance; The process of obtaining the second relevant situation includes: Obtain a second DTW dynamic time normalization distance between the first current data segment and the first temperature data segment, and a second covariance between the first current data segment and the first temperature data segment, and obtain a second correlation based on the second DTW dynamic time normalization distance and the second covariance; the second correlation is inversely proportional to the second DTW dynamic time normalization distance and directly proportional to the second covariance.

7. The method for testing the transient tolerance of high current terminals according to claim 6, characterized in that: Adjust the connection edges between any two time periods corresponding to the initial split subgraph, including: Acquire a first correlation and a second correlation corresponding to a second time period, where the second time period is another time period having a connection edge with the first time period; A first correlation of the first current data segment and a first correlation of the second time period are averaged to obtain a first characteristic correlation; a second correlation of the first current data segment and a second correlation of the second time period are averaged to obtain a second characteristic correlation; Normalizing the first characteristic correlation and the second characteristic correlation respectively to obtain a first voltage correction weight and a first temperature correction weight, wherein the sum of the first voltage correction weight and the first temperature correction weight is 1; Based on the first voltage correction weight and the first temperature correction weight, weighted sum is performed on the voltage similarity and the temperature similarity corresponding to the first time period and the second time period, and normalized to obtain a connection edge correction coefficient; According to the connection edge correction coefficient, the current similarity corresponding to the first time period and the second time period is adjusted to obtain an adjusted current similarity.

8. The method for testing the transient tolerance of high current terminals according to claim 1, wherein: Obtaining a transient tolerance test result of the terminal to be tested according to the number of the target split subgraphs, the current data information entropy, and the current dispersion, including: Based on the number of the target split subgraphs, the current data information entropy and the current dispersion, the transient tolerance evaluation parameter of the terminal to be tested is obtained; the transient tolerance evaluation parameter is inversely proportional to the number of the target split subgraphs, the overall level of the current data information entropy and the overall level of the current dispersion; the overall level of the current data information entropy is the average value of the current data information entropy of all the target split subgraphs; the overall level of the current dispersion is the average value of the current dispersion of all the target split subgraphs; the current dispersion is equal to the average value of the distance between each node and the center of the corresponding target split subgraph in the corresponding target split subgraph; The transient tolerance evaluation parameter is compared with a first preset transient tolerance evaluation threshold and a second preset transient tolerance evaluation threshold, and the transient tolerance test result of the terminal to be tested is determined according to the magnitude relationship between the transient tolerance evaluation parameter and the first preset transient tolerance evaluation threshold and the second preset transient tolerance evaluation threshold; wherein the first preset transient tolerance evaluation threshold is greater than the second preset transient tolerance evaluation threshold.

9. A high current terminal transient tolerance test system, characterized in that it includes: Memory and processor; The memory is connected to the processor; The memory is used to store program instructions; The processor is used to implement the high-current terminal transient tolerance test method according to any one of claims 1 to 8 when the program instructions are executed.

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