A method, device and electronic device for judging power supply reliability data

Abstract

A method, device and electronic device for judging power supply reliability data, which relate to the field of data security. The method is applied to a power grid management system, and the method includes: obtaining power supply data of a target area, where the power supply data includes multiple sub-power data with different sources; performing traceability classification on the power supply data to obtain multiple traceability data sets; based on a preset multiple judgment dimensions, performing quality scoring on the multiple traceability data sets to obtain judgment scores corresponding to each of the multiple traceability data sets; selecting the traceability data set corresponding to the highest judgment score as the data set to be uploaded to the chain; using multiple traceability data sets except the data set to be uploaded to the chain to supplement information for the data set to be uploaded to the chain to obtain a high-quality power supply reliability data set. Implementing the technical solution provided by this application solves the problems that the current power supply reliability data increases the workload and work difficulty of staff and increases the possibility of quality judgment errors.

Description

Technical Field

[0001] The present application relates to the technical field of data security, and specifically to a power supply reliability data analysis method, device and electronic equipment. Background Art

[0002] Power supply reliability data is the basis of power reliability management and runs through the entire reliability management process. According to the power reliability data management requirements, reliability data filling and reporting must be timely, accurate and complete; however, the collection, reporting and inspection of power supply reliability data are prone to data leakage, tampering, lack of integrity and other problems, making it difficult to carry out power management work.

[0003] Therefore, blockchain technology has been introduced into the current power grid management system. The power supply reliability data such as power outage information and power outage events are evaluated for quality and low-quality data is screened out. The high-quality power supply reliability data is then hashed in real time and the hash value is uploaded to the chain for evidence. When uploading the data to the chain, the upload interface is modified to enable it to have monitoring, evidence collection, and configuration functions, thereby promoting the authenticity, reliability, and integrity of the power supply reliability data.

[0004] However, with the large-scale construction of power grids, the amount of power supply reliability data is becoming increasingly large, and the types of business are also increasing. The quality assessment of power supply reliability data is mainly carried out through manual online verification, which not only increases the workload and difficulty of the staff, but also greatly increases the possibility of errors in the quality assessment of power supply reliability data. Summary of the invention

[0005] In response to the problem that current power supply reliability data increases the workload and difficulty of staff and increases the possibility of errors in quality assessment, the present application provides a power supply reliability data assessment method, device and electronic equipment.

[0006] In a first aspect, the present application provides a power supply reliability data analysis method, which is applied to a power grid management system, and the method includes:

[0007] Acquiring power supply data of a target area, wherein the power supply data includes power supply sub-data from multiple sources;

[0008] Performing source tracing classification on the power supply data to obtain multiple source tracing data sets;

[0009] Based on the preset multiple evaluation dimensions, quality scores are performed on the multiple traceability data sets to obtain evaluation scores corresponding to the multiple traceability data sets;

[0010] The traceability data set corresponding to the highest judgment score is selected as the data set to be uploaded to the chain;

[0011] Using multiple of the traceability data sets other than the data set to be chained, supplement information to the data set to be chained to obtain a high-quality power supply reliability data set.

[0012] Optionally, obtain the power supply logic chain in the target area, where the power supply logic chain includes multiple power supply logic points;

[0013] Based on the power supply logic chain, classify multiple sub-power supply data into categories to obtain multiple power supply data clusters, where one power supply data cluster corresponds to one power supply logic point, and one power supply data cluster includes at least one sub-power supply data of a category;

[0014] Perform permutations and combinations on multiple power supply data clusters to obtain multiple traceability data sets.

[0015] Optionally, count the category composition of the data in the first traceability data set, where the first traceability data set is any one of the multiple traceability data sets;

[0016] According to the category composition, set the evaluation factor set of the first traceability data set, where the evaluation factor set includes at least one research and judgment dimension;

[0017] Generate a research and judgment evaluation system table according to the evaluation factor set;

[0018] Use the research and judgment evaluation system table to perform quality evaluation on the first traceability data set to obtain a quality evaluation matrix;

[0019] Calculate the eigenvalues of the quality evaluation matrix to obtain the research and judgment score of the first traceability data set.

[0020] Optionally, obtain the power supply data sets corresponding to multiple research and judgment dimensions in the evaluation factor set;

[0021] Calculate the discrete values corresponding to multiple power supply data sets;

[0022] Based on the discrete values corresponding to multiple power supply data sets, calculate the weight values of multiple research and judgment dimensions in the evaluation factor set;

[0023] According to the weight values of multiple research and judgment dimensions in the evaluation factor set, adjust the preset research and judgment evaluation system table to obtain the research and judgment evaluation system table corresponding to the evaluation factor set.

[0024] Optionally, the step of using multiple of the traceability data sets other than the data set to be chained to supplement information to the data set to be chained to obtain a high-quality power supply reliability data set specifically includes:

[0025] Extract the data sequence of the dataset to be uploaded to the chain, where the data sequence includes normal data, abnormal data, null values, and error data;

[0026] Preprocess the data sequence to obtain a reference data sequence;

[0027] Extract respective corresponding multiple replacement data sequences from multiple traceability data sets other than the dataset to be uploaded to the chain;

[0028] Calculate the correlation degree between the reference data sequence and multiple replacement data sequences;

[0029] Select the data in the replacement data sequence with the highest correlation degree among multiple replacement data sequences, and replace the abnormal data, null values, and error data in the reference data sequence.

[0030] Optionally, after obtaining the high-quality power supply reliability dataset, it further includes:

[0031] If the judgment score of the second traceability dataset is greater than or equal to the preset judgment score threshold, where the second traceability dataset is any traceability dataset other than the dataset to be uploaded to the chain among multiple traceability data sets;

[0032] Then use multiple traceability data sets other than the second traceability dataset to supplement information for the second traceability dataset to obtain the high-quality power supply reliability dataset corresponding to the second traceability data.

[0033] In a second aspect, the present application provides a power supply reliability data judgment device, where the device is a power grid management system, and the power grid management system includes a receiving module, a processing module, and an output module, where:

[0034] The receiving module is used to obtain power supply data of a target area, where the power supply data includes multiple sub-power supply data from different sources;

[0035] The processing module is used to perform traceability classification on the power supply data to obtain multiple traceability data sets; based on a preset multiple judgment dimensions, perform quality scoring on multiple traceability data sets to obtain judgment scores corresponding to each of the multiple traceability data sets; select the traceability dataset corresponding to the highest judgment score as the dataset to be uploaded to the chain;

[0036] The output module is used to use multiple traceability data sets other than the dataset to be uploaded to the chain to supplement information for the dataset to be uploaded to the chain to obtain a high-quality power supply reliability dataset, and preferentially upload the high-quality power supply reliability dataset to the chain.

[0037] In a third aspect, the present application provides an electronic device, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory, so that the electronic device executes the method described in any one of the first aspect.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which when executed, execute the method described in any one of the first aspect.

[0039] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0040] 1. By performing traceability analysis on the power supply data in the target area, the present application splits the power supply data into multiple different traceability data sets. During the splitting process, due to the logical correlation of the power supply data, and this logical correlation reflects different business types, traceability data sets are constructed according to this logical correlation, which is convenient for subsequently evaluating the quality performance of each item of power supply data in the traceability data set in the overall data, thereby improving its data reliability; then, according to a plurality of preset research and judgment dimensions, quality scores are respectively given to the multiple traceability data sets. Among them, the research and judgment dimensions of each traceability data set are not necessarily exactly the same, so as to reduce the impact of irrelevant research and judgment dimensions on the overall judgment. Finally, the traceability data set corresponding to the highest research and judgment score is selected as the data set to be uploaded to the chain, so as to realize the batch uploading of high-quality power supply data, greatly improving the research and judgment efficiency of the power supply data. In addition, since there may still be some outliers in the data set to be uploaded to the chain, multiple traceability data sets other than the data set to be uploaded to the chain are used to supplement the information of the data set to be uploaded to the chain, so as to further improve the data quality of the power supply data uploaded to the chain.

[0041] 2. When researching and judging multiple traceability data sets, since the data with the same attributes in each traceability data set come from different sources, this leads to different degrees of dispersion of the data in the traceability data set. At this time, if a unified research and judgment standard is adopted, it will be difficult to distinguish the quality of the data in the region. Therefore, for the research and judgment dimensions of the traceability data set, the present application calculates the dispersion of the data under each research and judgment dimension, converts the dispersion value into the weight value of each research and judgment dimension, and finally adjusts the evaluation indicators of the preset research and judgment evaluation system table according to the weight value of each research and judgment dimension, so that it is more suitable for the data distribution of the current traceability data set, thereby improving the research and judgment accuracy of the traceability data set. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flowchart of a method for researching and judging power supply reliability data provided by an embodiment of the present application.

[0043] Figure 2 It is a schematic structural diagram of a power supply reliability data judgment device provided by an embodiment of the present application.

[0044] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0045] Explanation of reference numerals: 1, receiving module; 2, processing module; 3, output module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed implementation manners

[0046] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0047] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "for example" or "for illustration" is intended to present relevant concepts in a specific manner.

[0048] In the description of the embodiments of the present application, the meaning of the term "plurality" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0049] Power supply reliability data is the basis of power reliability management work and runs through the entire process of reliability management. At present, for power supply reliability data, not only is it required to improve its data quality, but also to achieve automated data collection, intelligent analysis, traceable management, and transparent supervision to ensure the accuracy, timeliness, and integrity of the data. However, in the processes of collecting, filling in, and checking power supply reliability data, problems such as data leakage, tampering, and lack of integrity are likely to occur, making it difficult to carry out power management work. The blockchain platform has technical features such as asymmetric encryption, consensus mechanism, timestamp, and credible traceability, which can support the business requirements of accurate, timely, complete, traceable management, and transparent supervision of data required for current power supply reliability management. These include links such as data collection, research and judgment, and confirmation, and relevant business data such as power outage information, power outage events, and reliability data are respectively stored in the blockchain in real time through hash encryption, thereby improving the accuracy of power supply reliability data management.

[0050] However, with the large-scale construction of the power grid, the amount of power supply reliability data is increasing day by day, and the types of business are also constantly increasing. The quality research and judgment of power supply reliability data mainly rely on manual online verification, which not only increases the workload and difficulty of staff, but also greatly increases the possibility of errors in the quality research and judgment of power supply reliability data.

[0051] To solve the above problems, this application provides a method for judging power supply reliability data. This method is applied to a power grid management system, such as Figure 1 shown, this method includes steps S101 to S105, and the above steps are as follows:

[0052] S101. Obtain the power supply data of the target area. The power supply data includes multiple sub-power data from different sources.

[0053] In the above step, the power supply data can be understood as the event log of power outage events and power supply events, which includes basic information such as the operating status of power supply equipment (fault, power supply interruption, and normal power supply), the power supply data upload method (automatic upload or manual inspection), power supply time, power supply quantity, power supply area, and regional power consumption. By summarizing these basic information, multiple sub-power data are formed.

[0054] S102. Conduct traceability classification on the power supply data to obtain multiple traceability data sets.

[0055] In the above steps, since the sources of the aggregated power supply data are not unique, this means that even for the same type of data, there may be differences in data quality. For example, there may be accuracy deviations and delay deviations between the residential electricity consumption data automatically uploaded by the regional distribution box and the residential electricity consumption data statistically obtained through each residential electricity meter in the same region. And this kind of difference will lead to unreasonable power supply planning for this region in the follow-up, and then cause power supply shortages or even power outages. Therefore, in order to reduce the impact of this difference on the overall power supply planning, this application classifies the power supply data by tracing its source to form multiple traceable data sets, and then conducts an overall study and judgment on the multiple traceable data sets, so as to reduce the impact of this difference on data quality and further on the overall power supply planning. Specifically:

[0056] In the classification process, first obtain the power supply logic chain in the target area. The power supply logic chain includes multiple power supply logic points such as power generation, power transmission, power transformation, power distribution, and user-side power consumption. Each power supply logic point has its corresponding function and generated sub-power supply data. Therefore, this application can classify the multiple sub-power supply data in the current power supply data based on the power supply logic chain to obtain multiple power supply data clusters. Among them, one power supply data cluster corresponds to one power supply logic point. Further, since the data sources of the same power supply logic point may not be unique, the categories in the same power supply data cluster may not be unique either, but one power supply data cluster contains at least one category of sub-power supply data; then, since it is difficult to directly judge the quality of each data separately within the same power supply data cluster, this application arranges and combines the multiple power supply data clusters according to the power supply logic chain to generate multiple different traceable data sets. The traceable data sets constructed in this way can not only better reflect the internal structure of the power supply system, make the data classification more in line with the actual operation of the power supply system, but also make the quality study and judgment of the data more systematic.

[0057] S103. Based on a preset multiple study and judgment dimensions, perform quality scoring on the multiple traceable data sets to obtain the corresponding study and judgment scores for each of the multiple traceable data sets.

[0058] In the above steps, when performing quality scoring on multiple traceability data sets, the genus compositions of the data in the multiple traceability data sets may be different, and data of different genera require different judgment dimensions for judgment to reduce the impact of irrelevant judgment dimensions on the judgment results. Among them, the multiple judgment dimensions include accuracy, timeliness, stability, integrity, consistency, and relevance, etc. Therefore, when performing quality scoring, first count the genus composition corresponding to each of the multiple traceability data sets to determine the evaluation factor set corresponding to each of the multiple traceability data sets. The rating factor set includes the judgment dimensions that need to be focused on for the traceability data set, and each evaluation factor set includes at least one judgment dimension. Among them, the evaluation factor set can be constructed by using the analytic hierarchy process or the principal component analysis method, or can be constructed by referring to a pre-established data judgment dimension table. This application does not make a limitation; then, generate a judgment evaluation system table according to the evaluation factor set; it should be further explained that the judgment evaluation system table is an evaluation table of fuzzy data, which includes evaluation indicators of multiple judgment dimensions. Each judgment dimension is divided into 3 evaluations: high, medium, and low. Each evaluation corresponds to a set score value and a numerical range for judgment. Of course, the evaluation division of each judgment dimension is determined according to the specific situation; after obtaining the judgment evaluation system tables of multiple traceability data sets, judge the corresponding data scores according to the data in the multiple traceability data sets to generate a quality evaluation matrix; the quality evaluation matrix includes the quality scores of each data in the traceability data set. Finally, calculate the eigenvalues of the quality evaluation matrix to obtain the judgment scores corresponding to each of the multiple traceability data sets. The above method relies on the power supply logic inherent in the pre-constructed traceability data set itself. In the subsequent judgment process, it is not only to perform quality analysis on a certain data. By generating a quality evaluation matrix by aggregating all the data in the traceability data set, and then using the characteristic that the eigenvalue of the quality evaluation matrix can represent the degree of data association within the matrix, taking the eigenvalue as the comprehensive judgment score for comprehensively evaluating the data quality of all the data in the traceability data set, that is, considering the quality of a single data itself and also the quality of a single data in the entire traceability data set, so that the judgment score is more accurate. In addition, this application also normalizes various types of data with different attributes by constructing a judgment evaluation system table, enabling them to have the same judgment attributes. In addition, since there are many data sources in the traceability data set, and the data is very complex and in various forms, it is difficult to construct a corresponding judgment evaluation system table. However, the above method does not need to consider this data difference, only needs to understand the general change trend of the data, and focuses on judging the performance of a single data in the whole, so as to find the traceability data set with the optimal combination form among multiple traceability data sets, and use this as the high-quality data set to be uploaded to provide a reasonable data basis for subsequent power supply planning.

[0059] In a possible implementation manner, when generating the research and judgment evaluation system table, since the data in the traceability data set is obtained by permutation and combination, the degree of dispersion of the data in each traceability data set is different. If the unified research and judgment method is still adopted in the research and judgment evaluation system table, it will lead to the omission of the evaluation of some key data or incorrect evaluation. Therefore, in order to more accurately evaluate the quality of a single piece of data in this application, first, the power supply data sets corresponding to each of the multiple research and judgment dimensions in the traceability data set are summarized. Among them, a piece of data may require multiple research and judgment dimensions for research and judgment. Therefore, the same piece of data will be divided into one or more power supply data sets; then, the discrete values corresponding to each of the multiple power supply data sets are calculated, and then, according to the discrete values corresponding to each of the multiple power supply data sets, the weight values corresponding to each of the multiple research and judgment dimensions are calculated. Specifically: First, the data in the multiple power supply data sets are standardized to obtain the standard data corresponding to each power supply data set to eliminate the influence of the data dimension; then, the probability distribution of each standard data in each power supply data set is calculated, and further, the entropy value of each power supply data set is calculated. The following calculation formula can be used:

[0060]

[0061] Among them, is the probability distribution of the i-th standard data in the j-th power supply data set, is the value of the i-th standard data in the j-th power supply data set, and m is the total number of data in the j-th power supply data set.

[0062] From the above probability distribution formula, the importance degree (weight value) of each data in each power supply data set can be clearly understood, so as to facilitate the subsequent calculation of the entropy value.

[0063] The entropy value calculation formula is:

[0064]

[0065] Among them, is the entropy value of the j-th power supply data set, is the probability distribution of the i-th standard data in the j-th power supply data set, and m is the total number of data in the j-th power supply data set.

[0066] In the above formula, can also be understood as the weight value of the i-th standard data in the j-th power supply data set. Multiply the weight value of each standard data by its corresponding entropy value contribution coefficient , so as to obtain the entropy value contribution value of each standard data to the power supply data set, and then sum the entropy value contribution values of all data to obtain the overall entropy value of the power supply data set. Finally, in order to prevent the distortion of the final entropy value, it is also necessary to multiply by the limiting coefficient , so as to ensure that the finally obtained entropy value is within a reasonable range (0 to 1). The above formula fully considers the impact of each data in the power supply data set on the overall power supply data set, thus providing a very effective data basis for the adjustment of each research and judgment dimension.

[0067] Finally, convert the entropy values of each research and judgment dimension into weight values, and the following formula can be used:

[0068]

[0069] Among them, is the weight of the jth research and judgment dimension, is the entropy value of the jth research and judgment dimension, and n is the number of research and judgment dimensions of the traceability data set.

[0070] In the above formula, can be understood as a measure of the stability degree of the research and judgment dimension. When is smaller, the stability degree is higher. When is larger, the stability degree is lower; is the sum of the stability degrees of all research and judgment dimensions. The above formula as a whole can be regarded as setting the weight ratio of each research and judgment dimension according to the stability degree of each research and judgment dimension. The higher the stability degree, the greater the weight, and the lower the stability degree, the smaller the weight.

[0071] After determining the weight values of multiple research and judgment dimensions in the evaluation factor set, adjust the preset research and judgment evaluation system table, so as to obtain a research and judgment evaluation system table that better fits different traceability data sets, and then improve the accuracy of the research and judgment score.

[0072] S104. Select the traceability data set corresponding to the highest research and judgment score as the data set to be uploaded to the chain.

[0073] S105. Use multiple traceability data sets other than the data set to be uploaded to the chain to supplement the information of the data set to be uploaded to the chain, and obtain a high-quality power supply reliability data set.

[0074] In the above steps S104 to S105, the traceability dataset with the highest judgment score can be regarded as the traceability dataset with the best data quality. However, the traceability dataset corresponding to the highest judgment score is only of relatively better data quality compared to other traceability datasets, and there are still some gray data in it, such as abnormal data, null values, error data, etc. Therefore, before uploading to the chain, this part of the data needs to be further corrected. Specifically: extract the data sequence of the dataset to be uploaded to the chain, and then preprocess the data sequence to obtain a reference data sequence. Preprocessing can be understood as replacing the gray data in the data sequence with preset standardized data, but the standardized data can only be regarded as data under ideal conditions and cannot be directly used as real data. Therefore, in this application, multiple replacement data sequences corresponding to each are extracted from multiple traceability datasets except the dataset to be uploaded to the chain. The data in the replacement data sequence is the same as the data in the reference data sequence in terms of data volume, sorting, and data type, only the numerical value of each data is different; then, calculate the correlation degree between the reference data sequence and the multiple replacement data sequences. Specifically, the calculation formula for the correlation degree between a single data in the reference data sequence and the replacement data sequence can adopt the following formula:

[0075]

[0076] Among them, is the correlation degree, is the k-th data value in the replacement data sequence, is the k-th data value in the reference data sequence, is the adjustment coefficient.

[0077] In the above formula, represents the minimum value of the difference between the replacement data sequence and the reference data sequence at all data points. It can be understood as a measure of the most similar situation. In the entire range of data comparison, it is the difference of the point that is closest to the reference sequence, and is used to judge the relative size of other differences in the future, represents the maximum value of the difference between the replacement data sequence and the reference data sequence at all data points. It can be understood as a measure of the most dissimilar situation. It determines the maximum range of data deviation and provides an upper limit for measuring the relative deviation degree of other differences; in addition, in order to weaken the excessive influence of the maximum value of the difference between the replacement data sequence and the reference data sequence at all data points on the correlation degree, an adjustment coefficient is also set to limit its influence degree. For example, in the power supply reliability data, if there are individual extremely abnormal data points, making the maximum value of the data difference between the two sequences very large, then when calculating the correlation degree, the correlation degree of most data points may be underestimated. At this time, the set adjustment coefficient can limit it to a reasonable range, thereby reducing its influence on the correlation degree. From the form of the above formula, it can be understood that when and When the difference value is smaller, the value of the denominator is closer to the value of the numerator, and the correlation degree is higher at this time. This formula judges the correlation degree of data from two dimensions: the minimum value of the data difference and the maximum value of the data difference between two sequences, which greatly improves the reliability of the calculation result of the correlation degree. Finally, the data with the highest correlation degree is selected to replace the abnormal data, null values and error data in the reference data sequence, so as to improve the data set to be chained finally.

[0078] In a possible implementation manner, for the remaining traceability data sets, there may still be data sets with high data quality. Therefore, after obtaining the high-quality power supply reliability data set, it further includes: judging the size relationship between the research and judgment score in the remaining traceability data sets and the preset research and judgment score threshold. If there is a research and judgment score of a certain traceability data set greater than or equal to the preset research and judgment score threshold, then use multiple traceability data sets except this traceability data set to supplement the information of this traceability data set, and obtain the high-quality power supply reliability data set corresponding to this traceability data, and finally also use this traceability data set as the traceability data set to be chained preferentially. For the traceability data sets with lower research and judgment scores, they are chained or not chained in turn according to the order of the research and judgment scores from high to low, so as to ensure the chaining efficiency of high-quality power supply data.

[0079] Referring to Figure 2 , this application also provides a power supply reliability data research and judgment device. The device is a power grid management system, and the power grid management system includes a receiving module 1, a processing module 2, and an output module 3, where:

[0080] The receiving module 1 is used to obtain the power supply data of the target area, and the power supply data includes multiple sub-power supply data from different sources;

[0081] The processing module 2 is used to conduct traceability classification on the power supply data to obtain multiple traceability data sets; based on a preset multiple research and judgment dimensions, conduct quality scoring on the multiple traceability data sets to obtain the research and judgment scores corresponding to each of the multiple traceability data sets; select the traceability data set corresponding to the highest research and judgment score as the data set to be chained;

[0082] The output module 3 is used to use multiple traceability data sets except the data set to be chained to supplement the information of the data set to be chained, obtain a high-quality power supply reliability data set, and chain the high-quality power supply reliability data set preferentially.

[0083] In a possible implementation manner, obtain the power supply logic chain in the target area, and the power supply logic chain includes multiple power supply logic points;

[0084] Based on the power supply logic chain, conduct genus classification on multiple sub-power supply data to obtain multiple power supply data clusters, where one power supply data cluster corresponds to one power supply logic point, and one power supply data cluster includes at least one genus sub-power supply data;

[0085] Arrange and combine multiple power supply data clusters to obtain multiple traceability data sets.

[0086] In a possible implementation, count the genus composition of the data in the first traceability data set, where the first traceability data set is any one of the multiple traceability data sets;

[0087] According to the genus composition, set the evaluation factor set of the first traceability data set, and the evaluation factor set includes at least one research and judgment dimension;

[0088] Generate a research and judgment evaluation system table according to the evaluation factor set;

[0089] Use the research and judgment evaluation system table to conduct a quality evaluation on the first traceability data set to obtain a quality evaluation matrix;

[0090] Calculate the eigenvalues of the quality evaluation matrix to obtain the research and judgment score of the first traceability data set.

[0091] In a possible implementation, obtain the power supply data sets corresponding to each of the multiple research and judgment dimensions in the evaluation factor set;

[0092] Calculate the discrete values corresponding to each of the multiple power supply data sets;

[0093] Based on the discrete values corresponding to each of the multiple power supply data sets, calculate the weight values of the multiple research and judgment dimensions in the evaluation factor set;

[0094] Adjust the preset research and judgment evaluation system table according to the weight values of the multiple research and judgment dimensions in the evaluation factor set to obtain the research and judgment evaluation system table corresponding to the evaluation factor set.

[0095] In a possible implementation, use multiple traceability data sets other than the data set to be uploaded to the chain to supplement information for the data set to be uploaded to the chain to obtain a high-quality power supply reliability data set, specifically including:

[0096] Extract the data sequence of the data set to be uploaded to the chain, and the data sequence includes normal data, abnormal data, null values, and error data;

[0097] Preprocess the data sequence to obtain a reference data sequence;

[0098] Extract the corresponding multiple replacement data sequences from multiple traceability data sets other than the data set to be uploaded to the chain;

[0099] Calculate the correlation degree between the reference data sequence and the multiple replacement data sequences;

[0100] Select the data in the replacement data sequence with the highest correlation degree among the multiple replacement data sequences to replace the abnormal data, null values, and error data in the reference data sequence.

[0101] In a possible implementation, after obtaining the high-quality power supply reliability data set, it further includes:

[0102] If the judgment score of the second traceability data set is greater than or equal to the preset judgment score threshold, the second traceability data set is any traceability data set except the data set to be uploaded in the multiple traceability data sets;

[0103] Then, use the multiple traceability data sets except the second traceability data set to supplement the information of the second traceability data set to obtain the high-quality power supply reliability data set corresponding to the second traceability data.

[0104] It should be noted that: when the device provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual application, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be repeated here.

[0105] This application also discloses an electronic device. Refer to Figure 3 , Figure 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0106] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0107] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0108] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0109] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by invoking the data stored in the memory 305, it performs various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0110] Among them, the memory 305 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. Refer to Figure 3 , the memory 305, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a method of judging power supply reliability data.

[0111] In Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input and obtain the data input by the user; while the processor 301 can be used to call the application program stored in the memory 305 for a method of judging power supply reliability data. When executed by one or more processors 301, the electronic device 300 is caused to execute one or more of the methods as described in the foregoing embodiments. It should be noted that, for the foregoing method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described order of actions, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0112] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0113] In several implementation manners provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0114] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, in each embodiment of this application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0116] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical discs.

[0117] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and the practice of the present disclosure, those skilled in the art will readily think of other implementation manners of the present disclosure.

[0118] The present application aims to cover any variations, uses, or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for judging power supply reliability data, characterized in that, Applied to the power grid management system, the method includes: Obtain the power supply data of the target area, where the power supply data includes multiple sub-power data with different sources; Conduct traceability classification on the power supply data to obtain multiple traceability data sets, specifically including: Obtain the power supply logic chain within the target area, where the power supply logic chain includes multiple power supply logic points; Based on the power supply logic chain, classify multiple sub-power data into categories to obtain multiple power supply data clusters, where one power supply data cluster corresponds to one power supply logic point, and one power supply data cluster includes at least one category of sub-power data; Arrange and combine multiple power supply data clusters to obtain multiple traceability data sets; Based on a preset multiple judgment dimensions, perform quality scoring on multiple traceability data sets to obtain judgment scores corresponding to each of the multiple traceability data sets, specifically including: Count the category composition of the data in the first traceability data set, where the first traceability data set is any one of the multiple traceability data sets; According to the category composition, set the evaluation factor set of the first traceability data set, where the evaluation factor set includes at least one judgment dimension; According to the evaluation factor set, generate a judgment evaluation system table, specifically including: Obtain the power supply data sets corresponding to each of the multiple judgment dimensions in the evaluation factor set; Calculate the discrete values corresponding to each of the multiple power supply data sets; Based on the discrete values corresponding to each of the multiple power supply data sets, calculate the weight values of the multiple judgment dimensions in the evaluation factor set; According to the weight values of the multiple judgment dimensions in the evaluation factor set, adjust the preset judgment evaluation system table to obtain the judgment evaluation system table corresponding to the evaluation factor set; Use the judgment evaluation system table to perform quality evaluation on the first traceability data set to obtain a quality evaluation matrix; Calculate the eigenvalues of the quality evaluation matrix to obtain the judgment score of the first traceability data set; Select the traceability data set corresponding to the highest judgment score as the data set to be uploaded to the chain; Use multiple traceability data sets other than the data set to be uploaded to the chain to supplement information for the data set to be uploaded to the chain to obtain a high-quality power supply reliability data set, specifically including: Extract the data sequence of the data set to be uploaded to the chain, where the data sequence includes normal data, abnormal data, null values, and error data; Preprocess the data sequence to obtain a reference data sequence; Extract their respective corresponding multiple replacement data sequences from multiple traceability data sets other than the data set to be uploaded to the chain; Calculate the correlation degree between the reference data sequence and multiple replacement data sequences; Select the data in the replacement data sequence with the highest correlation degree among multiple replacement data sequences to replace the abnormal data, null values, and error data in the reference data sequence.

2. The method according to claim 1, wherein After obtaining the high-quality power supply reliability data set, it further includes: If the judgment score of the second traceability data set is greater than or equal to the preset judgment score threshold, where the second traceability data set is any traceability data set other than the data set to be uploaded to the chain among the multiple traceability data sets; Then, multiple of the traceability data sets except the second traceability data set are used to supplement information to the second traceability data set, and a high-quality power supply reliability data set corresponding to the second traceability data is obtained.

3. A power supply reliability data analysis and judgment device, characterized in that, The device is a power grid management system, and the power grid management system includes a receiving module, a processing module, and an output module, where: The receiving module is configured to obtain power supply data of a target area, and the power supply data includes multiple sub-power supply data with different sources; The processing module is configured to perform traceability classification on the power supply data to obtain multiple traceability data sets, specifically including: Obtain a power supply logic chain in the target area, and the power supply logic chain includes multiple power supply logic points; Based on the power supply logic chain, perform category division on multiple sub-power supply data to obtain multiple power supply data clusters, where one power supply data cluster corresponds to one power supply logic point, and one power supply data cluster includes at least one category of sub-power supply data; Perform permutation and combination on multiple power supply data clusters to obtain multiple traceability data sets; Based on a preset multiple judgment dimensions, perform quality scoring on multiple traceability data sets to obtain judgment scores corresponding to each of the multiple traceability data sets, specifically including: Count the category composition of data in the first traceability data set, and the first traceability data set is any one of the multiple traceability data sets; According to the category composition, set an evaluation factor set for the first traceability data set, and the evaluation factor set includes at least one judgment dimension; According to the evaluation factor set, generate a judgment evaluation system table, which specifically further includes: Obtain power supply data sets corresponding to multiple judgment dimensions in the evaluation factor set; Calculate the discrete values corresponding to multiple power supply data sets; Based on the discrete values corresponding to multiple power supply data sets, calculate the weight values of multiple judgment dimensions in the evaluation factor set; According to the weight values of multiple judgment dimensions in the evaluation factor set, adjust a preset judgment evaluation system table to obtain a judgment evaluation system table corresponding to the evaluation factor set; Adopt the judgment evaluation system table to perform quality evaluation on the first traceability data set to obtain a quality evaluation matrix; Calculate the eigenvalue of the quality evaluation matrix to obtain the judgment score of the first traceability data set; Select the traceability data set corresponding to the highest judgment score as the data set to be uploaded to the chain; The output module is configured to use multiple traceability data sets except the data set to be uploaded to the chain to supplement information to the data set to be uploaded to the chain to obtain a high-quality power supply reliability data set, specifically including: Extract the data sequence of the data set to be uploaded to the chain, and the data sequence includes normal data, abnormal data, null values, and error data; Preprocess the data sequence to obtain a reference data sequence; Extract respective corresponding multiple replacement data sequences from multiple traceability data sets except the data set to be uploaded to the chain; Calculate the correlation degree between the reference data sequence and multiple replacement data sequences; Select the data in the replacement data sequence with the highest correlation degree among multiple replacement data sequences to replace the abnormal data, null values, and error data in the reference data sequence.

4. An electronic device, characterized in that, It includes a processor (301), a memory (305), a user interface (303) and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions which, when executed, execute the method according to any one of claims 1 to 2.

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