An evaluation and analysis system and method for power grid fault characteristic data

Abstract

This application relates to the field of power grid data evaluation technology, and in particular to an evaluation and analysis system and method for power grid fault characteristic data. The method includes: obtaining a first weight corresponding to each preset power grid fault characteristic data sequence based on its coefficient of variation; obtaining a second weight corresponding to each preset power grid fault characteristic data sequence based on its correlation with a power grid fault characteristic data quality evaluation sequence; if the difference between the first weight and the second weight corresponding to each preset power grid fault characteristic data is less than or equal to a preset difference threshold, then determining a corresponding target weight based on the first weight, the second weight, and an initial weight adjustment coefficient; and obtaining a quality evaluation value for the target power grid fault characteristic data based on the target weight corresponding to each preset power grid fault characteristic data and the target power grid fault characteristic data. This invention can improve the accuracy of the power grid fault characteristic data quality evaluation and analysis results.

Description

Technical Field

[0001] This invention relates to the field of power grid data evaluation technology, and in particular to an evaluation and analysis system and method for power grid fault characteristic data. Background Technology

[0002] In modern power systems, the accurate evaluation and analysis of power grid fault characteristic data is crucial for ensuring the safe and stable operation of the power grid. Currently, commonly used methods for evaluating power grid fault characteristic data mainly include single-weight determination methods or simple combination methods. Single-weight determination methods refer to using only one method to determine the weights of power grid fault characteristic data, such as subjective weighting methods (e.g., expert scoring) or objective weighting methods (e.g., the coefficient of variation method). Simple combination methods refer to simply combining multiple weight determination methods, i.e., directly performing weighted averaging or similar operations. Both single-weight determination methods and simple combination methods have certain limitations, namely, a single evaluation dimension or the inability to dynamically adjust weight allocation according to data characteristics, leading to inaccurate final evaluation results. How to improve the accuracy of power grid fault characteristic data quality evaluation and analysis results is an urgent problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide an evaluation and analysis system and method for power grid fault characteristic data, so as to improve the accuracy of the evaluation and analysis results of power grid fault characteristic data.

[0004] According to a first aspect of the present invention, an evaluation and analysis method for power grid fault characteristic data is provided, the method comprising the following steps:

[0005] S100, obtain the first weight corresponding to each preset power grid fault feature data according to the coefficient of variation of each preset power grid fault feature data sequence in n preset power grid fault feature data sequences; n is the number of power grid fault feature data used to evaluate the quality of power grid fault feature data.

[0006] S200, based on the correlation between each preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence in the n preset power grid fault feature data sequences, obtain the second weight corresponding to each preset power grid fault feature data.

[0007] S300, if the difference between the first weight and the second weight corresponding to each preset power grid fault feature data is less than or equal to a preset difference threshold, then the target weight corresponding to each preset power grid fault feature data is determined according to the first weight, the second weight, and the initial weight adjustment coefficient; otherwise, the target weight corresponding to each preset power grid fault feature data is determined according to the first weight, the second weight, and the first weight adjustment coefficient; the first weight adjustment coefficient is not equal to the initial weight adjustment coefficient, and the first weight adjustment coefficient is obtained based on the initial weight adjustment coefficient.

[0008] S400: Obtain the quality evaluation value of the target power grid fault feature data based on the target weight corresponding to each preset power grid fault feature data and the target power grid fault feature data.

[0009] According to a second aspect of the present invention, an evaluation and analysis system for power grid fault characteristic data is also provided. The system includes a non-transient computer-readable storage medium and a processor. The storage medium stores at least one instruction or at least one program, which is loaded and executed by the processor to implement the above-described method for evaluating and analyzing power grid fault characteristic data.

[0010] Compared with the prior art, the present invention has at least the following beneficial effects:

[0011] This invention determines weights by comprehensively applying the coefficient of variation method and gray-scale correlation analysis method, taking into account both the dispersion of power grid fault feature data and its correlation with the data quality sequence. This avoids the limitations of a single method and allows the weights corresponding to each preset power grid fault feature data to more accurately reflect their impact on the quality of the power grid fault feature data. Furthermore, this invention dynamically selects the initial weight adjustment coefficient or the first weight adjustment coefficient based on the difference between the first weight and the second weight to determine the target weight for obtaining the quality evaluation value, ensuring the rationality of the weight allocation, further improving the accuracy of the evaluation results, and increasing the adaptability and flexibility of the evaluation method of this invention. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart of the evaluation and analysis method for power grid fault characteristic data provided in Embodiment 1 of the present invention;

[0014] Figure 2 A flowchart illustrating the process of obtaining n preset power grid fault feature data sequences and power grid fault feature data quality evaluation sequences as provided in Embodiment 1 of the present invention;

[0015] Figure 3 This is the first flowchart of the process for obtaining the first weight adjustment coefficient provided in Embodiment 1 of the present invention;

[0016] Figure 4 The second flowchart illustrates the process of obtaining the first weight adjustment coefficient as provided in Embodiment 1 of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1:

[0019] According to this embodiment, as Figure 1 As shown, an evaluation and analysis method for power grid fault characteristic data is provided, the method comprising the following steps:

[0020] S100, obtain the first weight corresponding to each preset power grid fault feature data according to the coefficient of variation of each preset power grid fault feature data sequence in n preset power grid fault feature data sequences; n is the number of power grid fault feature data used to evaluate the quality of power grid fault feature data.

[0021] In this embodiment, the value range of any preset power grid fault characteristic data is 0-1. As a specific implementation, the n preset power grid fault characteristic data include data real-time rate, data accuracy rate, data completeness rate, and data reliability rate, and the n preset power grid fault characteristic data sequences include a data real-time rate sequence, a data accuracy rate sequence, a data completeness rate sequence, and a data reliability rate sequence. As a specific implementation, n=4, the n preset power grid fault characteristic data are data real-time rate, data accuracy rate, data completeness rate, and data reliability rate, and the n preset power grid fault characteristic data sequences are a data real-time rate sequence, a data accuracy rate sequence, a data completeness rate sequence, and a data reliability rate sequence.

[0022] As a specific implementation method, such as Figure 2 As shown, the process of obtaining n preset power grid fault characteristic data sequences and power grid fault characteristic data quality evaluation sequences includes:

[0023] S010, Obtain the power grid fault feature dataset A, A = {A1, A2, ..., A...} p ,…,A q}, A p For the p-th group of power grid fault characteristic data, A p =[A p,0 A p,1 A p,2 ,…,A p,i ,…,A p,n A p,0 Let A be the quality evaluation value of the power grid fault characteristic data included in the p-th group of power grid fault characteristic data. p,i Let be the i-th preset power grid fault feature data value included in the p-th group of power grid fault feature data, where i ranges from 1 to n, p ranges from 1 to q, and q is the number of groups of power grid fault feature data included in A.

[0024] In this embodiment, A is a pre-constructed dataset. Any set of power grid fault feature data in A includes a power grid fault feature data quality evaluation value that corresponds to n preset power grid fault feature data values. That is, when the 1st, 2nd, ..., ith, ..., nth preset power grid fault feature data value is A... p,1 A p,2 ,…,A p,i ,…,A p,n At that time, the accurate value of the power grid fault characteristic data quality evaluation value is A. p,0 .

[0025] S020, iterate through A, sequentially... p A in p,j Append to the j-th specified sequence; the j-th specified sequence is initialized as an empty sequence, and the value of j ranges from 0 to n.

[0026] It should be understood that after traversing A, the 0th specified sequence includes all power grid fault feature data quality evaluation values ​​in A, the i-th specified sequence includes all the i-th preset power grid fault feature data values ​​in A, and the p-th power grid fault feature data quality evaluation value in the 0th specified sequence corresponds to the p-th i-th preset power grid fault feature data value in the i-th specified sequence.

[0027] S030, the 0th specified sequence obtained after traversing A is determined as the power grid fault feature data quality evaluation sequence, and the i-th specified sequence obtained after traversing A is determined as the i-th preset power grid fault feature data sequence among the n preset power grid fault feature data sequences.

[0028] Based on S010-S030, this embodiment can accurately obtain n preset power grid fault feature data sequences and power grid fault feature data quality evaluation sequences.

[0029] As a specific implementation method, the first weight corresponding to the i-th preset power grid fault feature data among the n preset power grid fault feature data is w. i,1 w i,1 =d i / (∑ n i＝1 d i ), d i Let d be the coefficient of variation of the i-th preset power grid fault characteristic data sequence. i =v i / u i v i Let u be the standard deviation of the i-th preset power grid fault characteristic data sequence. i Let be the mean of the i-th preset power grid fault characteristic data sequence, where i ranges from 1 to n.

[0030] S200, based on the correlation between each preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence in the n preset power grid fault feature data sequences, obtain the second weight corresponding to each preset power grid fault feature data.

[0031] As a specific implementation method, the second weight corresponding to the i-th preset power grid fault feature data among the n preset power grid fault feature data is w. i,2 w i,2 =r i / (∑ n i＝1 r i ), r i Let be the correlation degree between the i-th preset power grid fault characteristic data sequence and the power grid fault characteristic data quality evaluation sequence, where i ranges from 1 to n. Those skilled in the art will understand that the process of obtaining the correlation degree between the two sequences is prior art and will not be described further here. Optionally, the resolution coefficient ρ in the process of obtaining the correlation degree between the two sequences is set to 0.5.

[0032] S300, if the difference between the first weight and the second weight corresponding to each preset power grid fault feature data is less than or equal to a preset difference threshold, then the target weight corresponding to each preset power grid fault feature data is determined according to the first weight, the second weight, and the initial weight adjustment coefficient; otherwise, the target weight corresponding to each preset power grid fault feature data is determined according to the first weight, the second weight, and the first weight adjustment coefficient; the first weight adjustment coefficient is not equal to the initial weight adjustment coefficient, and the first weight adjustment coefficient is obtained based on the initial weight adjustment coefficient.

[0033] In this embodiment, if the difference between the first weight and the second weight corresponding to each preset power grid fault characteristic data is less than or equal to a preset difference threshold, then w i =k0×w i,1 +(1-k0)×w i,2 w i w i,1 and w i,2 Let k0 be the target weight, first weight, and second weight corresponding to the i-th preset power grid fault characteristic data among n preset power grid fault characteristic data, and k0 be the initial weight adjustment coefficient. Optionally, k0 can be an empirical value, with a range of 0.4 ≤ k0 ≤ 0.6, for example, k0 = 0.5. Optionally, the preset difference threshold can be an empirical value, for example, the preset difference threshold can be e / n, where e is a preset percentage, with a range of 1% ≤ e ≤ 20%.

[0034] In this embodiment, when the difference between the first weight and the second weight corresponding to each preset power grid fault feature data is less than or equal to a preset difference threshold, the weights of the first weight and the second weight are allocated according to the initial weight adjustment coefficient when determining the target weight. The initial weight adjustment coefficient is a preset value. Even if the accuracy of the preset initial weight adjustment coefficient does not meet the requirements, the final target weight is relatively accurate because the difference between the first weight and the second weight corresponding to each preset power grid fault feature data is less than or equal to the preset difference threshold. Therefore, the final quality evaluation value of the target power grid fault feature data is also relatively accurate and can meet the accuracy requirements.

[0035] In this embodiment, if the difference between the first weight and the second weight corresponding to at least one preset power grid fault characteristic data is greater than a preset difference threshold, then w i =k1×w i,1 +(1-k1)×w i,2 w i w i,1 and w i,2 Let k1 be the target weight, the first weight, and the second weight corresponding to the i-th preset power grid fault feature data among n preset power grid fault feature data, and k1 be the adjustment coefficient of the first weight. As a preferred specific implementation, such as... Figure 3 As shown, the process of obtaining the first weighting adjustment coefficient includes:

[0036] S310, obtain the coefficient of variation of the power grid fault characteristic data quality evaluation sequence.

[0037] In this embodiment, the coefficient of variation of the power grid fault characteristic data quality evaluation sequence is the ratio of the standard deviation to the mean of the power grid fault characteristic data quality evaluation sequence.

[0038] S320, obtain the coefficient of variation similarity between each preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence in n preset power grid fault feature data sequences; the coefficient of variation similarity between any preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence is negatively correlated with the difference in coefficient of variation between the preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence.

[0039] As a preferred embodiment, the coefficient of variation similarity y between the i-th preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence among n preset power grid fault feature data sequences is y. i When d i When ≠d0, y i =1 / |d i -d0|,d i Let be the coefficient of variation of the i-th preset power grid fault characteristic data sequence, and d0 be the coefficient of variation of the power grid fault characteristic data quality evaluation sequence. The value of i ranges from 1 to n. When d i When y = d0, i It is positive infinity.

[0040] In this embodiment, the greater the similarity of the coefficient of variation between a predetermined power grid fault feature data sequence and a power grid fault feature data quality evaluation sequence, the more similar the dispersion of the predetermined power grid fault feature data sequence and the dispersion of the power grid fault feature data quality evaluation sequence are. Therefore, a stronger correlation is more likely to exist between the predetermined power grid fault feature data and the power grid fault feature data quality evaluation value. Conversely, the smaller the similarity of the coefficient of variation between a predetermined power grid fault feature data sequence and a power grid fault feature data quality evaluation sequence, the less similar the dispersion of the predetermined power grid fault feature data sequence and the dispersion of the power grid fault feature data quality evaluation sequence are. For example, if the dispersion of the predetermined power grid fault feature data sequence is very small while the dispersion of the power grid fault feature data quality evaluation sequence is very large, or if the dispersion of the predetermined power grid fault feature data sequence is very large while the dispersion of the power grid fault feature data quality evaluation sequence is very small, a strong correlation is less likely to exist between the predetermined power grid fault feature data and the power grid fault feature data quality evaluation value. If the coefficient of variation similarity between a certain preset power grid fault feature data sequence (e.g., data real-time rate sequence) and the power grid fault feature data quality evaluation sequence is greater than the coefficient of variation similarity between another preset power grid fault feature data sequence (e.g., data accuracy rate sequence) and the power grid fault feature data quality evaluation sequence, then the confidence level is higher if the weight corresponding to the data real-time rate is greater than the weight corresponding to the data accuracy rate.

[0041] S330, the initial weight adjustment coefficient is adjusted based on the similarity of the coefficient of variation between each preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence in the n preset power grid fault feature data sequences, to obtain the first weight adjustment coefficient.

[0042] As a preferred embodiment, such as Figure 4 As shown, S330 includes:

[0043] S331. Sort the n preset power grid fault feature data in descending order of their corresponding coefficient of variation similarity to obtain the first sorting result.

[0044] In this embodiment, the coefficient of variation similarity of the preset power grid fault feature data that is earlier in the first sorting result is greater than or equal to the coefficient of variation similarity of the preset power grid fault feature data that is later in the sorting result.

[0045] S332, sort the n preset power grid fault characteristic data in descending order according to the corresponding first weight to obtain the second sorting result.

[0046] In this embodiment, the first weight corresponding to the preset power grid fault feature data that is earlier in the second sorting result is greater than or equal to the first weight corresponding to the preset power grid fault feature data that is later in the sorting result.

[0047] S333, obtain the number of preset power grid fault feature data groups z1 with the same relative order and the number of preset power grid fault feature data groups z2 with inconsistent relative order in the first sorting result and the second sorting result.

[0048] In this embodiment, if a set of preset power grid fault feature data has the same relative order in both the first and second sorting results, then this set is considered a set of preset power grid fault feature data with the same relative order in both the first and second sorting results. For example, if the i-th preset power grid fault feature data and the (i+1)-th preset power grid fault feature data both precede or follow the i-th preset power grid fault feature data in both the first and second sorting results, then the i-th and (i+1)-th preset power grid fault feature data are considered a set of preset power grid fault feature data with the same relative order in both the first and second sorting results. Conversely, if the i-th preset power grid fault feature data in the first sorting result... If the i-th preset power grid fault feature data is before the (i+1)-th preset power grid fault feature data, and the i-th preset power grid fault feature data in the second sorting result is after the (i+1)-th preset power grid fault feature data (or the i-th preset power grid fault feature data in the first sorting result is after the (i+1)-th preset power grid fault feature data, and the i-th preset power grid fault feature data in the second sorting result is before the (i+1)-th preset power grid fault feature data), then the i-th preset power grid fault feature data and the (i+1)-th preset power grid fault feature data are a set of preset power grid fault feature data whose relative order is inconsistent between the first sorting result and the second sorting result.

[0049] It should be understood that any two preset power grid fault feature data in the n preset power grid fault feature data form a group, and the total number of groups corresponding to the n preset power grid fault feature data is: n×(n-1) / 2.

[0050] S334, obtain the matching degree of the first sorting result and the second sorting result based on z1 and z2; the matching degree of the first sorting result and the second sorting result is positively correlated with z1, and the matching degree of the first sorting result and the second sorting result is negatively correlated with z2.

[0051] In one specific implementation, the matching degree between the first sorting result and the second sorting result is 2×(z1-z2) / (n×(n-1)).

[0052] S335, adjust the initial weight adjustment coefficient according to the matching degree of the first sorting result and the second sorting result to obtain the first weight adjustment coefficient.

[0053] As a preferred specific embodiment, k1 = k0×(1 - b1×(1 - c) / 2), where c is the matching degree between the first sorting result and the second sorting result, k0 is the initial weight adjustment coefficient, k1 is the first weight adjustment coefficient, b1 is the preset downward adjustment range, -1 ≤ c ≤ 1, 0 < b1 < 1; if the difference value between the first weight and the second weight corresponding to each preset power grid fault feature data is less than or equal to the preset difference threshold, w i = k0×w i,1 +(1 - k0)×w i,2 , w i , w i,1 and w i,2 are the target weight, the first weight, and the second weight corresponding to the i-th preset power grid fault feature data among n preset power grid fault feature data respectively.

[0054] Based on this preferred specific embodiment, the matching degree c reflects the consistency degree between the first sorting result and the second sorting result. When c is larger, it indicates that the two sorting results are more consistent, and the calculation result of the first weight can be better supported by the similarity of the coefficient of variation; conversely, when c is smaller, it indicates that the two sorting results have larger differences, and the calculation result of the first weight is less supported by the similarity of the coefficient of variation; by dynamically adjusting k1 through the matching degree c, the weight adjustment coefficient can change according to the data characteristics, thereby improving the accuracy of the evaluation result; b1 is used to control the downward adjustment range of k1 when the matching degree c is small. The larger b1 is, the larger the downward adjustment range of k1 is, indicating that the trust degree in the first weight is lower. Thus, by introducing the matching degree c and the preset downward adjustment range b1, the dynamic adjustment of the weight adjustment coefficient is achieved, which can improve the accuracy and reliability of the evaluation result and enhance the controllability and adaptability of the method in this embodiment.

[0055] S400. Obtain the quality evaluation value of the target power grid fault feature data according to the target weight corresponding to each preset power grid fault feature data and the target power grid fault feature data.

[0056] In this embodiment, the target power grid fault feature data includes n preset power grid fault feature data values, and the quality evaluation value of the target power grid fault feature data is g, g = ∑ n i＝1 (w i ×f i ), f i is the i-th preset power grid fault feature data value included in the target power grid fault feature data, 0 ≤ f i ≤ 1, w i is the target weight corresponding to the i-th preset power grid fault feature data among n preset power grid fault feature data.

[0057] This embodiment determines weights by comprehensively applying the coefficient of variation method and gray-scale correlation analysis method. It takes into account the dispersion of power grid fault feature data and its correlation with the data quality sequence, avoiding the limitations of a single method. This allows the weights corresponding to each preset power grid fault feature data to more accurately reflect their impact on the quality of the power grid fault feature data. Moreover, this embodiment dynamically selects the initial weight adjustment coefficient or the first weight adjustment coefficient based on the difference between the first weight and the second weight to determine the target weight for obtaining the quality evaluation value. This ensures the rationality of the weight allocation, further improves the accuracy of the evaluation results, and increases the adaptability and flexibility of the evaluation method in this embodiment.

[0058] Example 2:

[0059] This embodiment provides an evaluation and analysis system for power grid fault characteristic data. The system includes a non-transient computer-readable storage medium and a processor. The storage medium stores at least one instruction or at least one program segment, which is loaded and executed by the processor to implement the above-described evaluation and analysis method for power grid fault characteristic data.

[0060] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. It should also be understood that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A method for evaluating and analyzing power grid fault characteristic data, characterized in that, The method includes the following steps: S100, obtain the first weight corresponding to each preset power grid fault feature data according to the coefficient of variation of each preset power grid fault feature data sequence in n preset power grid fault feature data sequences; n is the number of power grid fault feature data used to evaluate the quality of power grid fault feature data; S200, based on the correlation between each preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence in the n preset power grid fault feature data sequences, obtain the second weight corresponding to each preset power grid fault feature data. S300, if the difference between the first weight and the second weight corresponding to each preset power grid fault feature data is less than or equal to a preset difference threshold, then the target weight corresponding to each preset power grid fault feature data is determined according to the first weight, the second weight, and the initial weight adjustment coefficient; otherwise, the target weight corresponding to each preset power grid fault feature data is determined according to the first weight, the second weight, and the first weight adjustment coefficient; the first weight adjustment coefficient is not equal to the initial weight adjustment coefficient, and the first weight adjustment coefficient is obtained based on the initial weight adjustment coefficient; S400: Obtain the quality evaluation value of the target power grid fault feature data based on the target weight corresponding to each preset power grid fault feature data and the target power grid fault feature data.

2. The evaluation and analysis method for power grid fault characteristic data according to claim 1, characterized in that, The process of obtaining the first weighting adjustment coefficient includes: S310, Obtain the coefficient of variation of the power grid fault characteristic data quality evaluation sequence; S320, obtain the coefficient of variation similarity between each preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence in n preset power grid fault feature data sequences; the coefficient of variation similarity between any preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence is negatively correlated with the difference in coefficient of variation between the preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence; S330, the initial weight adjustment coefficient is adjusted based on the similarity of the coefficient of variation between each preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence in the n preset power grid fault feature data sequences, to obtain the first weight adjustment coefficient.

3. The evaluation and analysis method for power grid fault characteristic data according to claim 2, characterized in that, The S330 includes: S331, Sort the n preset power grid fault feature data according to the order of their corresponding coefficient of variation similarity from large to small, and obtain the first sorting result; S332, sort the n preset power grid fault characteristic data in descending order according to the corresponding first weight to obtain the second sorting result; S333, obtain the number of preset power grid fault feature data groups z1 with the same relative order and the number of preset power grid fault feature data groups z2 with different relative order in the first sorting result and the second sorting result; S334, obtain the matching degree of the first sorting result and the second sorting result based on z1 and z2; the matching degree of the first sorting result and the second sorting result is positively correlated with z1, and the matching degree of the first sorting result and the second sorting result is negatively correlated with z2; S335, adjust the initial weight adjustment coefficient according to the matching degree of the first sorting result and the second sorting result to obtain the first weight adjustment coefficient.

4. The evaluation and analysis method for power grid fault characteristic data according to claim 3, characterized in that, k1 = k0×(1 - b1×(1 - c) / 2), where c is the matching degree between the first sorting result and the second sorting result, k0 is the initial weight adjustment coefficient, k1 is the first weight adjustment coefficient, b1 is the preset downward adjustment range, -1 ≤ c ≤ 1, 0 < b1 < 1; if the difference value between the first weight and the second weight corresponding to each preset power grid fault characteristic data is less than or equal to the preset difference threshold, w i = k0×w i,1 +(1 - k0)×w i,2 ,w i 、w i,1 and w i,2 are the target weight, the first weight, and the second weight corresponding to the i-th preset power grid fault characteristic data among n preset power grid fault characteristic data respectively.

5. The evaluation and analysis method for power grid fault characteristic data according to claim 2, characterized in that, The similarity of the coefficient of variation between the i-th preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence among n preset power grid fault feature data sequences is y. i When d i When ≠d0, y i =1 / |d i -d0|,d i Let d0 be the coefficient of variation of the i-th preset power grid fault feature data sequence, and let d0 be the coefficient of variation of the power grid fault feature data quality evaluation sequence. The value of i ranges from 1 to n.

6. The evaluation and analysis method for power grid fault characteristic data according to claim 1, characterized in that, The first weight corresponding to the i-th preset power grid fault feature data among n preset power grid fault feature data is w. i,1 w i,1 =d i / (∑ n i＝ 1d i ), d i Let d be the coefficient of variation of the i-th preset power grid fault characteristic data sequence. i =v i / u i v i Let u be the standard deviation of the i-th preset power grid fault characteristic data sequence. i Let be the mean of the i-th preset power grid fault characteristic data sequence, where i ranges from 1 to n.

7. The evaluation and analysis method for power grid fault characteristic data according to claim 1, characterized in that, The second weight corresponding to the i-th preset power grid fault feature data among n preset power grid fault feature data is w. i,2 w i,2 =r i / (∑ n i＝ 1r i ), r i Let be the correlation between the i-th preset power grid fault feature data sequence and the power grid fault feature data quality evaluation sequence, where i ranges from 1 to n.

8. The evaluation and analysis method for power grid fault characteristic data according to claim 1, characterized in that, The process of obtaining n preset power grid fault characteristic data sequences and power grid fault characteristic data quality evaluation sequences includes: S010, Obtain the power grid fault feature dataset A, A = {A1, A2, ..., A...} p ,…,A q }, A p For the p-th group of power grid fault characteristic data, A p =[A p,0 A p,1 A p,2 ,…,A p,i ,…,A p,n A p,0 Let A be the quality evaluation value of the power grid fault characteristic data included in the p-th group of power grid fault characteristic data. p,i Let i be the i-th preset power grid fault feature data value included in the p-th group of power grid fault feature data, where i ranges from 1 to n, p ranges from 1 to q, and q is the number of groups of power grid fault feature data included in A. S020, iterate through A, sequentially... p A in p,j Append to the j-th specified sequence; the j-th specified sequence is initialized as an empty sequence, and the value of j ranges from 0 to n; S030, the 0th specified sequence obtained after traversing A is determined as the power grid fault feature data quality evaluation sequence, and the i-th specified sequence obtained after traversing A is determined as the i-th preset power grid fault feature data sequence among the n preset power grid fault feature data sequences.

9. The evaluation and analysis method for power grid fault characteristic data according to claim 1, characterized in that, The n preset power grid fault characteristic data sequences include data real-time rate sequence, data accuracy rate sequence, data completeness rate sequence, and data reliability rate sequence.

10. An evaluation and analysis system for power grid fault characteristic data, characterized in that, The system includes a non-transitory computer-readable storage medium and a processor; wherein the storage medium stores at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the evaluation and analysis method for power grid fault characteristic data as described in any one of claims 1-9.

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