Intelligent connector data analysis method and system

Abstract

The invention relates to the technical field of data processing, in particular to a connector data intelligent analysis method and system. The method comprises the following steps: processing parameter items of each data point in a connector data set through a mean shift clustering algorithm to obtain a clustering center of a clustering cluster where each data point is located, and marking the data points in the same clustering cluster as the same kind of data points; acquiring a neighborhood of the data point by taking the data point as a center; obtaining the center directivity of the data point through the numerical value difference between each parameter item of the data point before and after each iteration and each parameter item of the corresponding clustering center; calculating the abnormal degree of the data points; through the comparison result of the abnormal degree of the data point and the preset threshold value, the analysis result of the connector data is obtained, and the accuracy of the obtained analysis result of the connector is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a connector data intelligent analysis method and system. Background Art

[0002] A connector is an electronic component that transmits and exchanges current or signals between electronic system devices. As a node, it is connected by plugging and unplugging a plug and a socket. For example, the USB (Universal Serial Bus) connector in the plug-and-socket connector is a standard interface between computers and peripheral devices. It can not only realize data transmission, but also provide power support for connected devices.

[0003] There is a lot of research and application content on USB connectors in the prior art. For example, the patent application document with publication number CN115934604A discloses a USB circuit, a USB controller switching method and a device. The USB circuit in this application can accurately and quickly determine the device with problems through the enumeration of USB devices and the interaction and influence of the power detection circuit, thereby saving the time of determining the problematic device and effectively improving the reliability of the USB interface.

[0004] The above-mentioned prior art can determine which device has a problem by setting up a USB circuit. However, if there is a problem with the quality of the connector itself, it will also affect the quality judgment result of the related connected device, reducing the accuracy of the quality judgment. In addition, in daily use, the quality of the connector also determines the service life of the related equipment.

[0005] Based on this, how to accurately obtain the quality inspection result of the connector is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0006] In order to solve the technical problem of how to accurately obtain the quality inspection results of connectors, the present invention provides a connector data intelligent analysis method and system.

[0007] In a first aspect, the present invention provides a connector data intelligent analysis method, which adopts the following technical solution: The parameter items of each data point in the connector data set are processed by the mean shift clustering algorithm to obtain the cluster center of each data point, and the data points in the same cluster are recorded as similar data points; the neighborhood of the data point is obtained with the data point as the center; the central directivity of the data point is obtained by the numerical difference between each parameter item of the data point and the corresponding parameter item of the cluster center before and after each iteration; ; is the abnormality degree of the ith data point, is the Euclidean distance between the ith data point and the corresponding cluster center, is the mean Euclidean distance between all data points in the cluster where the i-th data point is located and the cluster center, , are the i-th data point, the i-th data point in the neighborhood of the i-th data point, and The central directivity of similar data points, is the number of similar data points in the neighborhood of the i-th data point, is the relationship between the ith data point and its neighborhood The Euclidean distance between similar data points is is an exponential function with e as the base; the analysis result of the connector data is obtained by comparing the abnormality degree of the data point with the preset threshold.

[0008] When analyzing the connector data, the present invention clusters the data points in the connector data set through the mean shift clustering algorithm, and can accurately identify the abnormal data points that deviate from the cluster cluster, thereby accurately obtaining the connector with abnormal quality. In this process, the present invention takes into account that although some data points can be divided into cluster clusters, they do not always tend to the cluster center during the clustering process, and may be abnormal data points; based on this, the present invention accurately obtains the abnormal degree of each data point by obtaining the degree of tendency of each data point to the cluster center during the iteration process, and the difference in the Euclidean distance between the initial position of the data point and the cluster center, so that the connector data with abnormal quality can be accurately identified in the cluster cluster, reducing the impact of such abnormal data points on the detection results, and effectively improving the accuracy of the analysis results of the obtained connector.

[0009] According to a connector data intelligent analysis method provided by the present invention, the parameter items of each data point in the connector data set are processed by the mean shift clustering algorithm, which also includes: the values ​​of the parameter items corresponding to each connector are grouped into a data point, and the connector data set is obtained after preprocessing.

[0010] The present invention takes into account that there may be data missing in the originally collected connector data, and therefore improves the overall quality of the data through data preprocessing.

[0011] According to a connector data intelligent analysis method provided by the present invention, the parameter items of each data point in the connector data set are processed by a mean shift clustering algorithm to obtain the cluster center of the cluster cluster where each data point is located, including: calculating the weighted average value within a preset bandwidth of the data point by a Gaussian kernel function, and obtaining the mean shift vector of the data point according to the difference between the weighted average value and the data point; moving the data point in the direction of the corresponding mean shift vector; repeating the position movement step until a preset iteration termination condition is reached to obtain the cluster center corresponding to each data point.

[0012] The present invention obtains the clustering results of each data point in the connector data set through the mean shift clustering algorithm, so as to accurately identify abnormal data points that deviate from the clustering cluster.

[0013] According to a connector data intelligent analysis method provided by the present invention, the parameter items of each data point in the connector data set are processed by a mean shift clustering algorithm to obtain the cluster center of the cluster where each data point is located, and also includes: eliminating data points that have no corresponding cluster as abnormal data points.

[0014] According to a connector data intelligent analysis method provided by the present invention, obtaining the neighborhood of the data point with the data point as the center includes: presetting the neighborhood radius of the data point; and obtaining other data points within the neighborhood radius of the data point as the neighborhood of the data point.

[0015] According to a connector data intelligent analysis method provided by the present invention, the central directivity of the data point satisfies the relationship: ; is the central directivity of the ith data point, is the number of iterations of the mean shift clustering algorithm, is the number of parameter items in the data point, is the weight of the jth parameter item, , are the i-th data point The value of the jth parameter item before and after the iteration, is the value of the jth parameter item of the cluster center corresponding to the ith data point.

[0016] The present invention provides an accurate method for calculating the central directivity of data points. By analyzing the degree of deviation between the data point and the cluster center parameter item before and after iteration, the displacement difference between the data point and the cluster center before and after iteration is obtained, so that the degree of tendency of each data point to the cluster center can be accurately obtained.

[0017] According to a connector data intelligent analysis method provided by the present invention, a parameter item weight acquisition method includes: obtaining the weight of each parameter item by using a hierarchical analysis method.

[0018] According to a method for intelligent analysis of connector data provided by the present invention, the analysis result of the connector data is obtained by comparing the abnormality degree of the data point with a preset threshold, including: if the abnormality degree of the data point is greater than the preset threshold, the connector analysis result corresponding to the data point is abnormal; otherwise, the connector analysis result corresponding to the data point is normal.

[0019] According to a connector data intelligent analysis method provided by the present invention, the analysis result of the connector data is obtained, and then the following steps are further included: in response to the connector analysis result corresponding to the data point being abnormal, the abnormal connector and the value of its corresponding parameter item are marked and stored in an abnormal database.

[0020] The present invention takes into account the importance of connector data analysis to daily life and production, and therefore creates a separate database for abnormal data to facilitate subsequent data analysis.

[0021] In a second aspect, the present invention provides a connector data intelligent analysis system, which adopts the following technical solution: A connector data intelligent analysis system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned connector data intelligent analysis method is implemented.

[0022] By adopting the above technical solution, the above-mentioned connector data intelligent analysis method is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is made according to the memory and the processor for easy use.

[0023] The present invention has the following technical effects: Based on the above technical solution, when analyzing the data of the connector, the present invention clusters the data points in the connector data set through the mean shift clustering algorithm, and can accurately identify the abnormal data points that deviate from the cluster cluster, thereby accurately obtaining the connector with abnormal quality. In this process, the present invention takes into account that although some data points can be divided into cluster clusters, they do not tend to the cluster center during the clustering process; based on this, the present invention accurately obtains the degree of abnormality of each data point by obtaining the degree of tendency of each data point to the cluster center during the iteration process, and the difference in the Euclidean distance between the initial position of the data point and the cluster center, so that the connector data with abnormal quality can be accurately identified in the cluster cluster, reducing the impact of such abnormal data points on the detection results, and effectively improving the accuracy of the analysis results of the obtained connector. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0025] Figure 1 A schematic diagram of a flow chart of a connector data intelligent analysis method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0028] A connector is an electronic component that transmits and exchanges current or signals between electronic system devices. As a node, it is connected by plugging and unplugging a plug and a socket. For example, the USB connector in the plug-and-socket connector is a standard interface between computers and peripheral devices. It can not only realize data transmission, but also provide power support for connected devices.

[0029] If there are problems with the quality of the connector itself, it will affect the quality judgment results and daily use of related connection devices. However, there are many types of connectors and many types of related parameter items for evaluating connector quality. If the abnormal data in each parameter item is directly identified through threshold judgment, a normal connector may be identified as abnormal, thereby reducing the accuracy of abnormal identification.

[0030] The mean shift clustering algorithm is a density-based non-parametric clustering algorithm that iteratively updates the position of data points by obtaining the fastest direction of increasing data point density, so that data points move to areas with higher density until they converge to the local maximum density. In this way, the algorithm can automatically discover the cluster structure of the data without pre-setting the number of clusters, thereby accurately identifying abnormal data points that do not belong to any cluster.

[0031] Based on this, an embodiment of the present invention discloses a method for intelligent analysis of connector data. The method processes connector data by a mean shift clustering algorithm, so that connectors with poor quality can be accurately obtained from a large number of connector data.

[0032] For details, please refer to Figure 1 As shown, Figure 1 A flow chart of a method for intelligent analysis of connector data provided by an embodiment of the present invention, the method specifically comprises the following steps.

[0033] S1: Get the parameter items corresponding to each data point in the connector data set.

[0034] The parameter items corresponding to each data point may be the physical size, temperature resistance data, moisture resistance data, shock resistance, and service life of the connector, etc., which may be set according to actual needs.

[0035] It should be noted that the physical size of the connector determines the volume and shape of the connector, which in turn affects its plugging and unplugging force and transmission performance; the operating temperature range of the connector refers to the temperature range in which it can work normally. Good temperature resistance can ensure that the connector maintains normal operation under various temperature environments; the intrusion of moisture has a great impact on the insulation performance of the connector, which can easily rust metal parts and increase the risk of leakage and short circuit. The better the moisture resistance of the connector, the higher the corresponding quality; seismic resistance is the key to maintaining a stable connection of the connector in a complex application environment. Good seismic resistance can increase the service life of the connector and reduce the frequency of maintenance and replacement; the service life of the connector is the primary indicator to measure its performance reliability. The longer the service life, the better the quality of the connector.

[0036] Based on this, the embodiment of the present invention obtains the clustering results of each data point parameter item through the mean shift clustering algorithm to evaluate the quality of the connector, thereby accurately identifying abnormal data points that do not belong to any clustering cluster, and the quality abnormal connectors corresponding to the abnormal data points.

[0037] For example, in an embodiment of the present invention, the parameter items of each data point in the connector data set are processed by the mean shift clustering algorithm, which also includes: the values ​​of the parameter items corresponding to each connector are grouped into a data point, and the connector data set is obtained after preprocessing.

[0038] Among them, the preprocessing method can be missing data interpolation, data format conversion, etc., which can be specifically set according to actual needs, and the embodiment of the present invention does not impose too many restrictions here.

[0039] It can be understood that a data point is each parameter item corresponding to a connector, and a connector data set is a collection of multiple connectors and their corresponding parameter items.

[0040] After obtaining the parameter items corresponding to each data point based on the above steps, in order to facilitate data processing, each parameter item can also be normalized, which can be set according to actual needs.

[0041] S2: The parameter items of each data point in the connector data set are processed by the mean shift clustering algorithm to obtain the cluster center of the cluster where each data point is located, and the data points in the same cluster are recorded as similar data points; the neighborhood of the data point is obtained with the data point as the center.

[0042] By way of example, in an embodiment of the present invention, parameter items of each data point in a connector data set are processed by a mean shift clustering algorithm to obtain the cluster center of the cluster cluster in which each data point is located, including: calculating the weighted average value within a preset bandwidth of the data point by a Gaussian kernel function, and obtaining the mean shift vector of the data point according to the difference between the weighted average value and the data point; moving the data point in the direction of the corresponding mean shift vector; repeating the position movement step until a preset iteration termination condition is reached to obtain the cluster center corresponding to each data point.

[0043] The preset bandwidth can be set according to actual needs.

[0044] For example, the preset iteration termination condition may be that the change in the cluster center position is less than a preset threshold and / or reaches a preset number of iterations.

[0045] The preset threshold and the preset number of iterations can be set according to actual needs. The specific steps of obtaining the cluster center of each data point cluster by the mean shift clustering algorithm can be implemented according to the prior art, and the embodiments of the present invention will not be described in detail here.

[0046] For example, in an embodiment of the present invention, obtaining a neighborhood of a data point with the data point as the center includes: presetting a neighborhood radius of the data point; and obtaining other data points within the neighborhood radius of the data point as the neighborhood of the data point.

[0047] Among them, the size of the neighborhood radius of the data point can be set to half of the mean of the Euclidean distances between all data points in the cluster where the data point is located and the cluster center; the size of the neighborhood radius can be set specifically according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0048] It can be understood that the above-mentioned acquisition of other data points within the neighborhood radius of the data point as the neighborhood of the data point, that is, the data points whose Euclidean distance to the data point is less than or equal to the neighborhood radius, and the obtained neighborhood range of the data point at least includes the data point itself.

[0049] Based on the above steps, the mean shift clustering algorithm is used to process the parameter items of each data point in the connector data set, and multiple clusters and data points that do not belong to any cluster can be obtained. The data points that do not belong to any cluster are connectors with abnormal quality.

[0050] By way of example, in an embodiment of the present invention, the parameter items of each data point in the connector data set are processed by a mean shift clustering algorithm to obtain the cluster center of the cluster where each data point is located, and also includes: eliminating data points without corresponding clusters as abnormal data points.

[0051] In this way, the embodiment of the present invention processes each data point in the connector data set through the mean shift clustering algorithm, and can accurately filter out abnormal data points that do not belong to any clustering cluster. The identification result of the connector corresponding to the abnormal data point is quality abnormality.

[0052] It should be noted that the abnormal data points identified by the mean shift clustering algorithm are data points that are obviously discrete and free from the clusters. Normal data points will gradually tend to their cluster centers during the iteration process. However, although some data points can be classified into clusters, they do not always tend to their cluster centers during the process of iteratively obtaining cluster centers, but go back and forth during the iteration process. Although such data points are classified into the corresponding clusters, they may also be abnormal data.

[0053] Based on this, the embodiment of the present invention further analyzes the degree of tendency between each data point and the cluster center during the process of iteratively obtaining its cluster center, thereby accurately identifying abnormal data points in the cluster cluster, that is, continuing to perform the following steps.

[0054] S3: The central directivity of the data point is obtained by the numerical difference between each parameter item of the data point and each parameter item of the corresponding cluster center before and after each iteration.

[0055] It should be noted that by analyzing the displacement of the data point with respect to the cluster center during the iterative clustering process, the degree to which the data point points to the cluster center can be obtained. However, the importance of the parameter items corresponding to the data points for the connector quality assessment is different, and the degree of influence on the calculation of the abnormality degree during the iteration process is also different.

[0056] Based on this, the embodiment of the present invention obtains the weight of each parameter item when calculating the degree of abnormality, and weights the degree of deviation between each parameter item of the data point and the cluster center parameter item based on the weight, so as to accurately obtain the center directivity of each data point. The higher the center directivity, the higher the possibility that the data point is a normal data point.

[0057] For example, in an embodiment of the present invention, a method for obtaining weights of parameter items includes: obtaining the weights of each parameter item by using a hierarchical analysis method.

[0058] Specifically, when the weight of each parameter item is obtained through the hierarchical analysis method, the connector quality assessment can be used as the target layer, each parameter item as the criterion layer, and the specific value of each parameter item as the parameter layer to construct a hierarchical model; the 1-9 scaling method is used to represent the relative importance of each parameter item, and each pair of parameter items is compared. The corresponding scores are set according to the degree of their influence on the connector quality to construct a judgment matrix; each column of the judgment matrix is ​​normalized and summed by row, and the sum of each row is divided by the sum of all rows to finally obtain the weight of each parameter item.

[0059] It is understandable that the specific steps of obtaining the weight of each parameter item through the hierarchical analysis method can be implemented through the existing technology, and the embodiment of the present invention will not be described in detail here.

[0060] For example, in the embodiment of the present invention, the central directivity of the data point is determined, and the specific relationship can be as follows: ; is the central directivity of the ith data point, is the number of iterations of the mean shift clustering algorithm, is the number of parameter items in the data point, is the weight of the jth parameter item, is the i-th data point The value of the jth parameter item before the iteration, is the i-th data point The value of the jth parameter item after iterations, is the value of the jth parameter item of the cluster center corresponding to the ith data point.

[0061] In the above formula, represents the i-th data point The difference between the jth parameter item before the iteration and the jth parameter item of its cluster center, represents the i-th data point The larger the ratio of the difference between the jth parameter item and the jth parameter item of its cluster center after the iteration, the greater the difference between the two. After the iteration, the closer the j-th parameter item is to the j-th parameter item of the cluster center corresponding to the ith data point, the greater the displacement before and after the iteration. The higher the probability of tending to the cluster center after iterations, the higher the corresponding center directness.

[0062] The weight of the parameter item is used to weight the degree of tendency of each parameter item to the cluster center when determining the central directivity of the data point, so as to accurately obtain the degree to which the current data point tends to the cluster center.

[0063] After obtaining the central directivity of each data point based on the above steps, continue to perform the following steps.

[0064] S4: Correct the center directivity of the data point by the Euclidean distance difference between the initial position of the data point and the corresponding cluster center to obtain the abnormality degree of the data point.

[0065] It should be noted that the above steps can obtain the central directivity of the data point by analyzing the displacement difference between the parameter item of the data point before and after iteration and the parameter item of the cluster center. The data point near the cluster center is more likely to be a normal data point, but the displacement difference between such data points before and after iteration and the cluster center will be smaller, resulting in a lower central directivity of the obtained data point.

[0066] Based on this, the embodiment of the present invention obtains the Euclidean distance between the initial position of each data point and the cluster center, and corrects the center directivity of the data point by the Euclidean distance, so as to accurately obtain the true abnormality degree of each data point in the cluster.

[0067] For example, in the embodiment of the present invention, the abnormality degree of a data point is determined, and specifically, the following relationship can be referred to: ; is the abnormality degree of the ith data point, is the Euclidean distance between the ith data point and the corresponding cluster center, is the mean Euclidean distance between all data points in the cluster where the i-th data point is located and the cluster center, is the central directivity of the ith data point, is the number of points in the neighborhood of the ith data point The central directivity of similar data points, is the number of similar data points in the neighborhood of the i-th data point, is the relationship between the ith data point and its neighborhood The Euclidean distance between similar data points is is an exponential function with base e.

[0068] In the above formula, It represents the ratio of the Euclidean distance between the ith data point and the corresponding cluster center to the mean of the Euclidean distances between all data points in the cluster where the ith data point is located and the cluster center. The larger the value is, the farther the Euclidean distance between the ith data point and the corresponding cluster center is compared with the data points in the cluster where the ith data point is located, that is, the higher the possibility that the ith data point is an abnormal data point.

[0069] Indicates the number of points in the neighborhood of the i-th data point The ratio of the central directivity of the first similar data point to the i-th data point. The larger the value, the smaller the central directivity of the i-th data point is relative to the similar data points in the neighborhood of the i-th data point. The higher the possibility that the i-th data point is an abnormal data point, the greater the corresponding degree of abnormality.

[0070] It means that the Euclidean distance between the data points is used to weight the average of the central directivity ratio between the ith data point and each similar data point in the neighborhood. The larger the value, the greater the difference between the ith data point and each similar data point in the neighborhood, the greater the possibility that the data point is an abnormal data point, and the corresponding degree of abnormality is also greater.

[0071] After obtaining the abnormality degree of each data point in the cluster based on the above steps, the abnormal data in the cluster can be accurately identified based on the abnormality degree of the data point.

[0072] S5: The analysis result of the connector data is obtained by comparing the abnormality degree of the data point with the preset threshold.

[0073] The preset threshold may be set to 0.9; the preset threshold may be set specifically according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0074] By way of example, in an embodiment of the present invention, an analysis result of the connector data is obtained by comparing the degree of abnormality of a data point with a preset threshold, including: if the degree of abnormality of a data point is greater than the preset threshold, the connector analysis result corresponding to the data point is abnormal; otherwise, the connector analysis result corresponding to the data point is normal.

[0075] It can be understood that if the degree of abnormality of a data point is greater than a preset threshold, it means that the quality of the connector corresponding to the data point is poor. In order to facilitate the analysis of parameter items of connectors with poor quality, the abnormal connectors with poor quality and the corresponding data can be associated and stored to facilitate subsequent data analysis.

[0076] For example, in an embodiment of the present invention, the analysis result of the connector data is obtained, and then the method further includes: in response to the connector analysis result corresponding to the data point being abnormal, marking the abnormal connector and the value of the corresponding parameter item and storing them in an abnormal database.

[0077] The specific step of marking the abnormal connector and the value of the corresponding parameter item and then associating and storing them can be achieved through the existing technology, and the embodiment of the present invention will not be described in detail here.

[0078] It can be seen that in the embodiment of the present invention, when analyzing the data of the connector, the parameter items of each data point in the connector data set can be processed by the mean shift clustering algorithm to obtain the cluster center of the cluster where each data point is located, and the data points in the same cluster are recorded as similar data points; the neighborhood of the data point is obtained with the data point as the center; the central directivity of the data point is obtained by the numerical difference between each parameter item of the data point and each parameter item of the corresponding cluster center before and after each iteration; ; is the abnormality degree of the ith data point, is the Euclidean distance between the ith data point and the corresponding cluster center, is the mean Euclidean distance between all data points in the cluster where the i-th data point is located and the cluster center, , are the i-th data point, the i-th data point in the neighborhood of the i-th data point, and The central directivity of similar data points, is the number of similar data points in the neighborhood of the i-th data point, is the relationship between the ith data point and its neighborhood The Euclidean distance between similar data points is is an exponential function with e as the base; the analysis result of the connector data is obtained by comparing the abnormality degree of the data point with the preset threshold.

[0079] In this way, the embodiment of the present invention clusters the data points in the connector data set through the mean shift clustering algorithm, and can accurately identify abnormal data points that deviate from the cluster cluster, thereby accurately obtaining connectors with abnormal quality. In this process, the embodiment of the present invention takes into account that although some data points can be divided into cluster clusters, they do not tend to the cluster center during the clustering process; based on this, the embodiment of the present invention obtains the degree of tendency of each data point to the cluster center during the iteration process, and the difference in the Euclidean distance between the initial position of the data point and the cluster center, and accurately obtains the degree of abnormality of each data point, so that connector data with abnormal quality can be accurately identified in the cluster cluster, reducing the impact of such abnormal data points on the detection results, and effectively improving the accuracy of the analysis results of the obtained connector.

[0080] An embodiment of the present invention further discloses a connector data intelligent analysis system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a connector data intelligent analysis method provided by the present invention is implemented.

[0081] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0082] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM, a dynamic random access memory DRAM, a static random access memory SRAM, an enhanced dynamic random access memory EDRAM, a high bandwidth memory HBM, a hybrid memory cube HMC, etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0083] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0084] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A connector data intelligent analysis method, characterized in that: include: The parameter items of each data point in the connector data set are processed by the mean shift clustering algorithm to obtain the cluster center of each data point, and the data points in the same cluster are recorded as the same type of data points; the neighborhood of the data point is obtained with the data point as the center; The central directivity of the data point is obtained by the numerical difference between each parameter item of the data point and each parameter item of the corresponding cluster center before and after each iteration; ； is the abnormality degree of the ith data point, is the Euclidean distance between the ith data point and the corresponding cluster center, is the mean Euclidean distance between all data points in the cluster where the i-th data point is located and the cluster center, , are the i-th data point, the i-th data point in the neighborhood of the i-th data point, and The central directivity of similar data points, is the number of similar data points in the neighborhood of the i-th data point, is the relationship between the ith data point and its neighborhood The Euclidean distance between similar data points is is an exponential function with base e; The analysis result of the connector data is obtained by comparing the abnormality degree of the data point with the preset threshold.

2. A connector data intelligent analysis method according to claim 1, characterized in that: The parameter items of each data point in the connector data set are processed by the mean shift clustering algorithm, and the above also include: The values ​​of the parameter items corresponding to each connector are combined into a data point, and the connector data set is obtained after preprocessing.

3. A connector data intelligent analysis method according to claim 2, characterized in that: The method of processing the parameter items of each data point in the connector data set by the mean shift clustering algorithm to obtain the cluster center of the cluster where each data point is located includes: The weighted average value of the data point within the preset bandwidth is calculated by the Gaussian kernel function, and the mean shift vector of the data point is obtained according to the difference between the weighted average value and the data point; the data point is moved along the direction of the corresponding mean shift vector; the position movement step is repeated until the preset iteration termination condition is reached, and the cluster center corresponding to each data point is obtained.

4. A connector data intelligent analysis method according to claim 3, characterized in that: The method further includes: processing the parameter items of each data point in the connector data set by the mean shift clustering algorithm to obtain the cluster center of the cluster where each data point is located. Data points without corresponding clusters are removed as abnormal data points.

5. The connector data intelligent analysis method according to claim 1, characterized in that: The step of obtaining a neighborhood of the data point with the data point as the center includes: Preset the neighborhood radius of a data point; obtain other data points within the neighborhood radius of the data point as the neighborhood of the data point.

6. A connector data intelligent analysis method according to claim 1, characterized in that: The central directivity of the data point satisfies the relationship: ； is the central directivity of the ith data point, is the number of iterations of the mean shift clustering algorithm, is the number of parameter items in the data point, is the weight of the jth parameter item, , are the i-th data point The value of the jth parameter item before and after the iteration, is the value of the jth parameter item of the cluster center corresponding to the ith data point.

7. A connector data intelligent analysis method according to claim 6, characterized in that: The method for obtaining the weight of a parameter item includes: The weight of each parameter is obtained through the hierarchical analysis method.

8. A connector data intelligent analysis method according to claim 1, characterized in that: The analysis result of the connector data is obtained by comparing the abnormality degree of the data point with the preset threshold, including: If the degree of abnormality of a data point is greater than a preset threshold, the connector analysis result corresponding to the data point is abnormal; otherwise, the connector analysis result corresponding to the data point is normal.

9. A connector data intelligent analysis method according to claim 8, characterized in that: The analysis result of the connector data is obtained, and then further includes: In response to the analysis result of the connector corresponding to the data point being abnormal, the abnormal connector and the value of the corresponding parameter item are marked and stored in the abnormal database.

10. A connector data intelligent analysis system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a connector data intelligent analysis method according to any one of claims 1-9 is implemented.

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