Multi-dimensional monitoring data quality evaluation method and system for communication base station backup battery pack

Abstract

The present invention discloses a method and system for evaluating the quality of multi-dimensional monitoring data of a communication base station backup battery pack, which relates to the technical field of battery quality evaluation, including: obtaining multi-dimensional monitoring data of the communication base station backup battery pack; extracting monitoring data of each dimension in the multi-dimensional monitoring data, and constructing a multi-dimensional monitoring data matrix; segmenting the multi-dimensional monitoring data matrix according to time series to obtain multiple segmentation period data matrices; performing cluster analysis on the multiple dimensional monitoring data vectors in each segmentation period data matrix to obtain clustering results; based on the clustering results, calculating the monitoring data score corresponding to the monitoring data of each dimension; calculating the comprehensive quality score based on the monitoring data score corresponding to the monitoring data of each dimension, and performing quality evaluation on the communication base station backup battery pack based on the comprehensive quality score. The present invention alleviates the technical problem of poor accuracy in quality evaluation of battery packs in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of battery quality evaluation, and in particular to a method and system for evaluating the quality of multi-dimensional monitoring data of a backup battery pack of a communication base station. Background Art

[0002] With the large-scale deployment of 5G networks and the prevalence of edge computing scenarios, communication base station backup battery packs have developed a monitoring system that includes multi-dimensional data such as voltage, temperature, charge and discharge curves, and internal resistance change rate. Existing monitoring systems generally use a single-dimensional threshold alarm mechanism, which is unable to effectively integrate temporal and spatial distribution characteristics. According to industry statistics, ineffective operation and maintenance costs caused by data quality issues in current base station backup power systems account for 27% of annual maintenance expenses. Traditional communication base station backup battery pack quality assessment methods are single-dimensional and do not consider consistency indicators between battery pack clusters. This leads to technical problems such as poor accuracy in quality assessment of communication base station backup battery packs. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for evaluating the quality of multi-dimensional monitoring data of a communication base station backup battery pack in order to solve at least one of the above technical problems.

[0004] In the first aspect, an embodiment of the present invention provides a method for evaluating the quality of multi-dimensional monitoring data of a communication base station backup battery pack, which is applied to the communication base station backup battery pack; the method includes: obtaining multi-dimensional monitoring data of the communication base station backup battery pack; extracting monitoring data of each dimension in the multi-dimensional monitoring data, and constructing a multi-dimensional monitoring data matrix; segmenting the multi-dimensional monitoring data matrix according to time series to obtain multiple segmentation period data matrices; performing cluster analysis on the multiple dimensional monitoring data vectors in each segmentation period data matrix to obtain clustering results; based on the clustering results, calculating the monitoring data score corresponding to the monitoring data of each dimension; calculating the comprehensive quality score based on the monitoring data score corresponding to the monitoring data of each dimension, and performing quality evaluation on the communication base station backup battery pack based on the comprehensive quality score.

[0005] Optionally, the multi-dimensional monitoring data includes: total voltage monitoring data, cell voltage monitoring data of each battery cell, current monitoring data, and temperature monitoring data of multiple temperature sampling points.

[0006] Optionally, the monitoring data of each dimension in the multidimensional monitoring data is extracted to construct a multidimensional monitoring data matrix, including: extracting the monitoring data of each dimension in the multidimensional monitoring data to obtain a dimensional data set corresponding to each dimension; the number of sampling points contained in the dimensional data set corresponding to each dimension is the same; based on the dimensional data set corresponding to each dimension, a multidimensional monitoring data matrix is constructed; wherein the number of rows of the multidimensional monitoring data matrix is the total number of dimensions in the multidimensional monitoring data, and the number of columns of the multidimensional monitoring data matrix is the number of sampling points.

[0007] Optionally, cluster analysis is performed on the multiple dimensional monitoring data vectors in each segmentation period data matrix to obtain clustering results, including: expanding each row of each segmentation period data matrix by columns to obtain multiple dimensional monitoring data vectors corresponding to each segmentation period data matrix; based on a preset clustering algorithm, cluster analysis is performed on each dimensional monitoring data vector to obtain a cluster center set, a cluster label set and a cluster result set corresponding to each cluster label corresponding to each dimensional monitoring data vector.

[0008] Optionally, the preset clustering algorithm includes a K-Means algorithm.

[0009] Optionally, based on the clustering result, calculating the monitoring data score corresponding to the monitoring data of each dimension includes: calculating the monitoring data score of the monitoring data vector of each dimension based on the cluster center set and the clustering result set; wherein the calculation formula of the monitoring data score includes:

[0010]

[0011]

[0012] Wherein, Score(C, S) is the monitoring data score based on the cluster center, Score(S) is the monitoring data score based on the clustering result, C is the cluster center set, min() is the minimum function, max() is the maximum function, sum() is the summation function, α and β are scoring factors, a, b, and c are different preset monitoring data scores, and S is the clustering result set.

[0013] Optionally, calculating the comprehensive quality score based on the monitoring data score corresponding to the monitoring data of each dimension includes: performing weighted summation on the monitoring data score corresponding to the monitoring data of each dimension to obtain the comprehensive quality score.

[0014] In the second aspect, an embodiment of the present invention also provides a multi-dimensional monitoring data quality evaluation system for a communication base station backup battery pack, which is applied to the communication base station backup battery pack; it includes: an acquisition module, a construction module, a segmentation module, a clustering module, a calculation module and an evaluation module, wherein the acquisition module is used to acquire the multi-dimensional monitoring data of the communication base station backup battery pack; the construction module is used to extract the monitoring data of each dimension in the multi-dimensional monitoring data and construct a multi-dimensional monitoring data matrix; the segmentation module is used to segment the multi-dimensional monitoring data matrix according to time series to obtain multiple segmentation period data matrices; the clustering module is used to perform cluster analysis on the multiple dimensional monitoring data vectors in each segmentation period data matrix respectively to obtain clustering results; the calculation module is used to calculate the monitoring data score corresponding to the monitoring data of each dimension based on the clustering results; the evaluation module is used to calculate the comprehensive quality score based on the monitoring data score corresponding to the monitoring data of each dimension, and perform quality evaluation on the communication base station backup battery pack based on the comprehensive quality score.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device comprising: a memory, a processor, and a computer program stored on the memory and runnable on the processor. When the processor executes the computer program, it implements a multi-dimensional monitoring data quality evaluation method for a communication base station backup battery pack as provided in an embodiment of the present invention.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, a multi-dimensional monitoring data quality evaluation method for a communication base station backup battery group as provided in an embodiment of the present invention is implemented.

[0017] The present invention provides a method and system for evaluating the quality of multi-dimensional monitoring data of a communication base station backup battery pack. The method collects multi-dimensional monitoring data of the base station backup battery pack, performs matrix formation, matrix segmentation, and cluster analysis respectively, obtains clustering results, and finally calculates the monitoring data score for quality evaluation based on the clustering results. The method can comprehensively consider the multi-dimensional status of the communication base station backup battery pack, make the quality evaluation more accurate, and alleviate the technical problem of poor accuracy in the quality evaluation of the communication base station backup battery pack in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 A flowchart of a method for evaluating the quality of multi-dimensional monitoring data of a backup battery pack of a communication base station provided by an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of a multi-dimensional monitoring data quality evaluation system for a communication base station backup battery pack provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] Example 1

[0023] Figure 1 This is a flow chart of a method for evaluating the quality of multi-dimensional monitoring data of a communication base station backup battery pack according to an embodiment of the present invention. The method is applied to a communication base station backup battery pack. Figure 1 As shown, the method specifically includes the following steps:

[0024] Step S102: Acquire multi-dimensional monitoring data of the backup battery pack of the communication base station. Specifically, the multi-dimensional monitoring data includes: total voltage monitoring data, cell voltage monitoring data of each battery cell, current monitoring data, and temperature monitoring data of multiple temperature sampling points.

[0025] Step S104: extracting monitoring data of each dimension in the multi-dimensional monitoring data and constructing a multi-dimensional monitoring data matrix.

[0026] Step S106 , segmenting the multi-dimensional monitoring data matrix according to the time series to obtain a plurality of segmentation period data matrices.

[0027] Step S108 , performing cluster analysis on the multiple dimensional monitoring data vectors in each segmentation period data matrix to obtain clustering results.

[0028] Step S110: Calculate the monitoring data score corresponding to the monitoring data of each dimension based on the clustering result.

[0029] Step S112: Calculate a comprehensive quality score based on the monitoring data score corresponding to the monitoring data of each dimension, and perform a quality evaluation on the backup battery pack of the communication base station based on the comprehensive quality score.

[0030] In an optional implementation provided by an embodiment of the present invention, step S102 specifically includes the following steps:

[0031] Step S1021: Connect to the host computer of the base station backup power system.

[0032] Step S1022: Import the communication protocol corresponding to the model of the base station backup battery pack into the host computer software.

[0033] Step S1023 selects multi-dimensional monitoring data for base station backup battery packs that have been operating stably for the past year, including total voltage monitoring data, cell voltage monitoring data for each battery cell, current monitoring data, and temperature monitoring data at multiple temperature sampling points. The data is then exported in CVS (Comma-Separated Values) format. For example, the cell voltage monitoring data may include measured data for a total of 16 cells, and the temperature monitoring data may include measured data from four temperature sampling points.

[0034] Specifically, step S104 further includes the following steps:

[0035] Step S1041: extracting monitoring data of each dimension from the multi-dimensional monitoring data to obtain a dimensional data set corresponding to each dimension; the dimensional data set corresponding to each dimension contains the same number of sampling points;

[0036] Step S1042: construct a multidimensional monitoring data matrix based on the dimensional data set corresponding to each dimension; wherein the number of rows of the multidimensional monitoring data matrix is the total number of dimensions in the multidimensional monitoring data, and the number of columns of the multidimensional monitoring data matrix is the number of sampling points.

[0037] Specifically, the monitoring data of each dimension is extracted separately, and each value of the monitoring data of each dimension represents a feature point.

[0038] For example, the information collected by the base station backup power system includes 1 total voltage monitoring data, which is expressed as , v i The voltage monitoring data of 16 single cells is expressed as , , V i Represents the voltage monitoring data of the i-th single cell, N is an integer set, v k is the voltage value collected for the kth time. The temperature measured data of the four temperature collection points are expressed as , , T i represents the temperature monitoring data of the i-th temperature collection point, t k Indicates the voltage value collected for the kth time. 1 current measured data, expressed as ,ik Indicates the current value collected for the kth time.

[0039] Then, the total number of dimensions is recorded as M, which is the sum of the dimensions of the total voltage monitoring data, the single cell voltage monitoring data of each battery cell, the current monitoring data, and the temperature monitoring data of multiple temperature sampling points. For example, M=22. total 、V S 、T S , I are expanded according to the original sequence to form an M×n multi-dimensional monitoring data matrix D. Each row of the multi-dimensional monitoring data matrix D represents the eigenvalue of a specific dimension.

[0040] Specifically, in step S106, the multidimensional monitoring data matrix D is segmented according to the time series. Because the collected multidimensional data is time series data, corresponding data exists in each dimension at each time point. The data segmentation cursor is denoted as p, the segmentation period is denoted as q, and the initial value of p is 1. For example, if the data collection frequency is 1s and data segmentation is performed daily, the initial value of q is 86400. The change pattern of p and q is: for each segmentation, p = q + 1, q = i·q, where i∈[1,n].

[0041] Specifically, step S108 includes the following steps:

[0042] Step S1081, expanding each row of each segmentation period data matrix by columns to obtain multiple dimensional monitoring data vectors corresponding to each segmentation period data matrix;

[0043] Step S1082: Based on a preset clustering algorithm, cluster analysis is performed on each dimensional monitoring data vector to obtain a cluster center set, a cluster label set, and a cluster result set corresponding to each dimensional monitoring data vector.

[0044] Preferably, the preset clustering algorithm includes a K-Means algorithm.

[0045] Specifically, let each segmentation period data matrix be E, then the segmentation period data matrix E is an M×q matrix, and each row of data represents an eigenvalue of a specific dimension.

[0046] Each row of the segmentation period data matrix E is expanded column by column to form a q×1 column vector F, where each column vector F corresponds to a dimensional monitoring data vector.

[0047] The K-Means algorithm is used to calculate the cluster centers of each column vector F. Considering the special scenario of base station backup power, where the battery is in a floating charge state for a long time, the voltage, current, and temperature remain essentially unchanged. Data fluctuations only occur during charging and discharging. Therefore, the K value of the K-Means algorithm is preferably 2. Because the K value of the K-Means algorithm is 2, the column vector F has only two cluster centers, denoted as the cluster center set C = {c1, c2}, where c1 and c2 are the two cluster centers, and two cluster labels, denoted as the cluster label set L = {l1, l2}, where l1 and l2 are the cluster labels corresponding to the two cluster centers c1 and c2, respectively.

[0048] Count the total number of cluster label sets L={l1,l2} in the column vector F and record it as the clustering result set S={s1,s2}, where s1 and s2 are the total number of clusters corresponding to the two cluster labels l1 and l2 respectively.

[0049] Specifically, step S110 further includes the following steps:

[0050] Step S1101: Calculate the monitoring data score of each dimension monitoring data vector based on the cluster center set and the cluster result set. The calculation formula for the monitoring data score includes:

[0051]

[0052]

[0053] Where Score(C, S) is the monitoring data score based on the cluster center, Score(S) is the monitoring data score based on the clustering result, C is the cluster center set, min() is the minimum function, max() is the maximum function, sum() is the summation function, α and β are scoring factors, a, b, and c are different preset monitoring data scores, and S is the clustering result set.

[0054] Specifically, first calculate the ratio of the two cluster centers in C. If the ratio is much less than 8%, the score is 0. The calculation formula is as follows:

[0055]

[0056] Where max(C) means finding the maximum value of the cluster center set C, and min(C) means finding the minimum value of the cluster center set C. If the score of this step is 0, the calculation ends. If the score of this step is not 0, the Score(S) function is used to continue calculating the score. The Score calculation formula is as follows:

[0057]

[0058] Where sum(S) represents the sum of the clustering result set S. α and β are scoring factors, preferably α = 0.35 and β = 0.2. a, b, and c are different preset monitoring data scores, for example, a = 100, b = 60, and c = 30. After this calculation, the monitoring data score of the column vector F is obtained.

[0059] Repeat the above calculation until the monitoring data scores of the corresponding column vector F are calculated for each dimension of the monitoring data in the current data segmentation period q, and the total voltage score is recorded as g V , the single cell voltage fraction is G VS ={g s1 ,g s2 ,……,g s16}, where g si is the voltage fraction of the i-th cell, i=1,2,…,16, and the current fraction is g I , temperature fraction G TS ={g t1 ,g t2 ,g t3 ,g t4}, g tj is the temperature score corresponding to the jth temperature collection point, j=1,2,3,4.

[0060] Specifically, step S112 further includes the following steps: performing weighted summation on the monitoring data scores corresponding to the monitoring data of each dimension to obtain a comprehensive quality score.

[0061] Specifically, the total score of the data in the current data segmentation period q is calculated according to the weight ratio. The specific implementation is: according to g V 、g VS , G I , G TS , according to the total voltage weight r v , single cell voltage weight r sv-avg , current weight r i , temperature weight r st-avg Perform weighted calculation on the comprehensive quality score of the data within the current data segmentation period q, and the average monitoring data score of the single voltage is recorded as g SV-avg , the average monitoring data score of temperature is recorded as g ST-avg , the calculation formula is as follows:

[0062] S total (X)=r v ·g V +r sv-avg ·g SV-avg +r i ·g I +r st-avg ·g ST-avg

[0063] In an optional implementation provided by the embodiment of the present invention, r v =0.3,r sv-avg =0.2, r i =0.4, r st-avg =0.1.

[0064] This step calculates the comprehensive quality score of a single data segment, and finally decides whether to eliminate the data segment based on the comprehensive quality score: for example, when the comprehensive quality score is greater than or equal to 60 points, retain and store the data; otherwise, eliminate and delete the data within the data segmentation period q.

[0065] Example 2

[0066] Figure 2 This is a schematic diagram of a multi-dimensional monitoring data quality evaluation system for a communication base station backup battery pack according to an embodiment of the present invention, which is applied to a communication base station backup battery pack. Figure 2 As shown, it includes: an acquisition module 10, a construction module 20, a segmentation module 30, a clustering module 40, a calculation module 50 and an evaluation module 60.

[0067] Specifically, the acquisition module 10 is used to obtain multi-dimensional monitoring data of the backup battery pack of the communication base station;

[0068] A construction module 20 is used to extract monitoring data of each dimension in the multi-dimensional monitoring data and construct a multi-dimensional monitoring data matrix;

[0069] A segmentation module 30 is used to segment the multi-dimensional monitoring data matrix according to the time series to obtain multiple segmentation period data matrices;

[0070] The clustering module 40 is used to perform cluster analysis on the multiple-dimensional monitoring data vectors in each segmentation period data matrix to obtain clustering results;

[0071] A calculation module 50 is used to calculate the monitoring data score corresponding to the monitoring data of each dimension based on the clustering results;

[0072] The evaluation module 60 is configured to calculate a comprehensive quality score based on the monitoring data score corresponding to the monitoring data of each dimension, and perform a quality evaluation on the backup battery pack of the communication base station based on the comprehensive quality score.

[0073] Specifically, the construction module 20 is also used to: extract the monitoring data of each dimension in the multidimensional monitoring data to obtain the dimensional data set corresponding to each dimension; the number of sampling points contained in the dimensional data set corresponding to each dimension is the same; based on the dimensional data set corresponding to each dimension, construct a multidimensional monitoring data matrix; wherein the number of rows of the multidimensional monitoring data matrix is the total number of dimensions in the multidimensional monitoring data, and the number of columns of the multidimensional monitoring data matrix is the number of sampling points.

[0074] Specifically, the clustering module 40 is also used to: expand each row of each segmentation period data matrix by column to obtain multiple dimensional monitoring data vectors corresponding to each segmentation period data matrix; based on a preset clustering algorithm, perform cluster analysis on each dimensional monitoring data vector respectively to obtain a cluster center set corresponding to each dimensional monitoring data vector, a cluster label set and a cluster result set corresponding to each cluster label.

[0075] The present invention also provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for evaluating the quality of multi-dimensional monitoring data of a backup battery pack of a communication base station as provided in an embodiment of the present invention is implemented.

[0076] The present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, a multi-dimensional monitoring data quality evaluation method for a communication base station backup battery pack as provided in an embodiment of the present invention is implemented.

[0077] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0078] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for evaluating the quality of multi-dimensional monitoring data of a backup battery pack of a communication base station, characterized in that: Applicable to a backup battery pack for a communication base station; the method includes: Obtaining multi-dimensional monitoring data of the communication base station backup battery pack; Extracting monitoring data of each dimension from the multi-dimensional monitoring data to construct a multi-dimensional monitoring data matrix; Segmenting the multi-dimensional monitoring data matrix according to time series to obtain a plurality of segmentation period data matrices; Cluster analysis is performed on the multiple dimensional monitoring data vectors in each segmentation cycle data matrix to obtain clustering results, including: Expand each row of each segmentation cycle data matrix by columns to obtain multiple dimensional monitoring data vectors corresponding to each segmentation cycle data matrix; Based on the preset clustering algorithm, cluster analysis is performed on each dimension monitoring data vector respectively to obtain the cluster center set, cluster label set and cluster result set corresponding to each cluster label corresponding to each dimension monitoring data vector; Based on the clustering results, the monitoring data score corresponding to the monitoring data of each dimension is calculated, including: Based on the cluster center set and the cluster result set, a monitoring data score of each dimensional monitoring data vector is calculated; wherein the calculation formula of the monitoring data score includes: Where Score(C, S) is the monitoring data score based on the cluster center, Score(S) is the monitoring data score based on the clustering result, C is the cluster center set, min() is the minimum function, max() is the maximum function, sum() is the summation function, α and β are scoring factors, a, b, and c are different preset monitoring data scores, and S is the clustering result set; Based on the monitoring data scores corresponding to the monitoring data of each dimension, a comprehensive quality score is calculated, and the quality of the communication base station backup battery pack is evaluated based on the comprehensive quality score.

2. The method for evaluating the quality of multi-dimensional monitoring data of a communication base station backup battery pack according to claim 1, characterized in that: The multi-dimensional monitoring data includes: total voltage monitoring data, cell voltage monitoring data of each battery cell, current monitoring data, and temperature monitoring data of multiple temperature sampling points.

3. The method for evaluating the quality of multi-dimensional monitoring data of a communication base station backup battery pack according to claim 1, characterized in that: Extracting monitoring data of each dimension in the multi-dimensional monitoring data and constructing a multi-dimensional monitoring data matrix includes: Extracting monitoring data of each dimension from the multi-dimensional monitoring data to obtain a dimensional data set corresponding to each dimension; the dimensional data set corresponding to each dimension contains the same number of sampling points; Constructing a multi-dimensional monitoring data matrix based on the dimensional data sets corresponding to each dimension; The number of rows of the multi-dimensional monitoring data matrix is the total number of dimensions in the multi-dimensional monitoring data, and the number of columns of the multi-dimensional monitoring data matrix is the number of sampling points.

4. The method for evaluating the quality of multi-dimensional monitoring data of a communication base station backup battery pack according to claim 1, characterized in that: The preset clustering algorithm includes the K-Means algorithm.

5. The method for evaluating the quality of multi-dimensional monitoring data of a backup battery pack of a communication base station according to claim 1, characterized in that: Based on the monitoring data scores corresponding to the monitoring data of each dimension, a comprehensive quality score is calculated, including: The monitoring data scores corresponding to the monitoring data of each dimension are weighted and summed to obtain a comprehensive quality score.

6. A multi-dimensional monitoring data quality evaluation system for a communication base station backup battery pack, characterized in that: Applied to the backup battery pack of a communication base station; including: acquisition module, construction module, segmentation module, clustering module, calculation module and evaluation module, among which, The acquisition module is used to acquire multi-dimensional monitoring data of the communication base station backup battery pack; The construction module is used to extract the monitoring data of each dimension in the multi-dimensional monitoring data and construct a multi-dimensional monitoring data matrix; The segmentation module is used to segment the multi-dimensional monitoring data matrix according to time series to obtain multiple segmentation period data matrices; The clustering module is used to perform cluster analysis on the multiple dimensional monitoring data vectors in each segmentation period data matrix to obtain clustering results, including: Expand each row of each segmentation cycle data matrix by columns to obtain multiple dimensional monitoring data vectors corresponding to each segmentation cycle data matrix; Based on the preset clustering algorithm, cluster analysis is performed on each dimension monitoring data vector respectively to obtain the cluster center set, cluster label set and cluster result set corresponding to each cluster label corresponding to each dimension monitoring data vector; The calculation module is used to calculate the monitoring data score corresponding to the monitoring data of each dimension based on the clustering result, including: Based on the cluster center set and the cluster result set, a monitoring data score of each dimensional monitoring data vector is calculated; wherein the calculation formula of the monitoring data score includes: Where Score(C, S) is the monitoring data score based on the cluster center, Score(S) is the monitoring data score based on the clustering result, C is the cluster center set, min() is the minimum function, max() is the maximum function, sum() is the summation function, α and β are scoring factors, a, b, and c are different preset monitoring data scores, and S is the clustering result set; The evaluation module is used to calculate a comprehensive quality score based on the monitoring data score corresponding to the monitoring data of each dimension, and to perform quality evaluation on the communication base station backup battery pack based on the comprehensive quality score.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for evaluating the quality of multi-dimensional monitoring data of a backup battery pack of a communication base station is implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by the processor, the multi-dimensional monitoring data quality evaluation method of the communication base station backup battery pack according to any one of claims 1 to 5 is implemented.

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