Operation and maintenance data processing system considering closeness and thinness relationship

Abstract

The invention discloses an operation and maintenance data processing system considering a close-to-open relationship, relates to the technical field of data processing, and solves the problem that in the prior art, relevance between operation and maintenance data is rarely deeply analyzed, so that range delimitation of power grid work such as fault positioning or maintenance is not facilitated. The system is provided with the data acquisition unit, the closeness-thinness relation extraction unit and the visualization unit, the operation and maintenance data of the power grid can be acquired, the association degree between the data is analyzed through the closeness-thinness relation extraction unit, and the association relation between acquisition points is measured by comprehensively considering voltage, current, frequency, power and temperature by the unit; and obtaining a hydrophilic-hydrophobic relationship considering multi-dimensional factors. And finally, displaying the association relationships on a map through a visualization unit. In a word, the method can more comprehensively consider the interaction between the power grid equipment, helps operation and maintenance personnel to more accurately delimit the fault influence range, optimizes the maintenance strategy, and improves the operation and maintenance efficiency and response speed of the power grid.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an operation and maintenance data processing system that takes into account affinity relationships. Background Art

[0002] With the rapid development of information technology, the digitization of power grid operation and maintenance data has become an important trend in power system management. Through the widespread installation of smart meters and the application of big data technology, the distribution network has accumulated a large amount of data in daily operation. These data are not only used in load forecasting, substation optimization site selection and operation management, but also greatly improve the efficiency of the power grid. Digital technology enables the effective collection and storage of power grid operation and maintenance data, providing a solid foundation for further data analysis and application. The digital and intelligent construction of power grids, such as that implemented by State Grid Chongqing Electric Power, relies on technologies such as "big cloud, Internet of Things, mobile, and intelligent chain", integrates multi-dimensional data such as power grid and equipment operation monitoring data, and improves the level of intelligent operation and maintenance of power grids.

[0003] Although significant progress has been made in the collection and storage of power grid operation and maintenance data, current methods for processing these data are mostly focused directly on fault prediction and accurate fault location. Although this method can quickly identify and respond to faults, it rarely conducts in-depth analysis of the correlation between operation and maintenance data within the scope of power grid work such as fault location or maintenance. This deficiency is not conducive to the scope delineation of power grid work such as fault location or maintenance, because the lack of analysis of the correlation between operation and maintenance data may lead to inaccurate maintenance scope or omission of key areas, affecting the efficiency of emergency repairs and the speed of power grid recovery.

[0004] In view of this, an operation and maintenance data processing system that takes closeness into consideration is needed. Summary of the invention

[0005] In view of the problem that the existing technology rarely conducts in-depth analysis on the correlation between operation and maintenance data, which is not conducive to the delimitation of the scope of power grid work such as fault location or maintenance, the present invention provides an operation and maintenance data processing system that takes into account affinity relationships, which can collect power grid operation and maintenance data, and analyze the degree of correlation between data through an affinity relationship extraction unit, and finally display these correlations on a map through a visualization unit. Such a system can more comprehensively consider the interaction between power grid equipment, help operation and maintenance personnel to more accurately define the scope of fault impact, optimize maintenance strategies, and improve the operation and maintenance efficiency and response speed of the power grid. The specific technical solution is as follows:

[0006] An operation and maintenance data processing system considering affinity relationships, comprising:

[0007] A data collection unit is connected to the affinity extraction unit, and is used to collect power grid operation and maintenance data, and transmit the collected raw data to the affinity extraction unit after pre-processing to extract the degree of association between the data;

[0008] The affinity relationship extraction unit is connected to the data collection unit and the visualization unit respectively, and obtains the association relationship of each data based on the Apriori algorithm;

[0009] The visualization unit is connected to the affinity extraction unit, and based on the association relationship between the data and the geographical location of each data collection point, a visualization display is performed on the map according to the association relationship.

[0010] Preferably, the data acquisition unit includes a data acquisition module, which is used to collect power grid operation and maintenance data, and the power grid operation and maintenance data includes the voltage, current, frequency, power and temperature of substations, transmission lines and transformers; the data acquisition module also obtains the geographical location longitude and latitude coordinates of the substations, transmission lines and transformers.

[0011] Preferably, the data acquisition unit includes a data preprocessing module, which first performs data preprocessing on the voltage, current, frequency, power and temperature data, including data cleaning, and the data cleaning includes:

[0012] Handling missing values: Identify and handle missing values ​​in the data. The method of handling missing values ​​in the data is to fill the missing values ​​with the mean of the sample values ​​before and after the missing values.

[0013] Outlier processing: Identify and process outliers. The means of identifying outliers is to use statistical analysis methods, including IQR, or rules based on business logic to identify outliers. The means of processing outliers is to replace the outliers with the mean of the nearest non-outlier values ​​before and after the outliers.

[0014] Duplicate value processing: Identify and remove duplicate records in the data.

[0015] Preferably, the data acquisition unit includes a data storage module, which is used to store data processed by the data preprocessing module. The data storage module regards the substation, transmission line or transformer as a collection point, that is, a storage unit, and each storage unit stores a geographical location latitude and longitude coordinates. A storage unit stores voltage, current, frequency, power and temperature at different sampling times according to different collection times.

[0016] Preferably, the affinity extraction unit includes a transaction database construction module, an Apriori algorithm application module and a result quantification module; the transaction database construction module forms transaction records after level processing based on the data collected by the data collection unit, and summarizes the transaction records into a data set, the Apriori algorithm processes the data set to obtain association rules, the support and confidence of each rule, and the result quantification module outputs a quantitative association degree based on the confidence.

[0017] Preferably, the transaction database construction module performs the following operations:

[0018] S1: Obtain the voltage, current, frequency, power and temperature at each sampling time;

[0019] S2: Classify voltage, current, frequency, power and temperature respectively. The classification methods of voltage, current, frequency, power and temperature are the same. Taking voltage as an example, the ranking is as follows:

[0020] The voltage is divided into M levels, that is, L = {L1, L2, ..., L i ,…,L M}, where L is the level sequence and Li is the i-th level in the level sequence. The grading operation based on sorting is as follows:

[0021]

[0022] Where L is the final output level, L M is the Mth classification type set, λ P is the set threshold, V is the voltage at the current sampling moment;

[0023] S3: Based on the voltage, current, frequency, power or temperature level of each collection point, the state change of each collection point at each sampling moment is recorded as a transaction;

[0024] S4: Aggregate the transaction records of all nodes into a data set, where each record is an itemset.

[0025] Preferably, the Apriori algorithm application module uses the Apriori algorithm to obtain the degree of association between the collection points, and specifically performs the following operations:

[0026] S01: Determine the minimum support threshold. The frequency of frequent itemsets in all transactions must not be lower than this value.

[0027] S02: Starting from a single item, generate all possible item sets;

[0028] S03: Calculate support, which is used to measure the frequency of itemsets appearing in all transactions, as follows:

[0029]

[0030] Where num(xy) is the number of transactions in which item sets X and Y appear simultaneously, and num(allsamples) is the total number of transactions;

[0031] S03: retain the item sets whose support is higher than the minimum support threshold, these item sets are called frequent item sets;

[0032] S04: Generate association rules from frequent item sets and calculate the confidence of each rule. The calculation formula is as follows:

[0033]

[0034] Where Support(X∪Y) is the frequency of the union of item sets X and Y in all transactions, and Support(X) is the frequency of item set X in all transactions.

[0035] Preferably, the visualization unit includes a data integration module and a fusion labeling module, wherein:

[0036] The data integration module is used to collect geographic information and association rule results. The geographic information is the longitude and latitude coordinates of the geographic location of each collection point. The association rule results are association rules obtained from the Apriori algorithm, including the support and confidence of each rule. The data integration module integrates the geographic location information of the node with the association rule results and marks the strength of its association rule for each node.

[0037] The fusion marking module performs visual marking on the map based on the information of the map and the data integration module. The fusion marking module performs the following operations:

[0038] Use libraries dedicated to map visualization, such as Echarts, Geopandas, Plotly, etc., and then use map visualization tools to draw the basic map framework.

[0039] Then, each node is represented on the map using a scatter plot display, and the size or color of the scatter points can be adjusted according to the strength of the association rule, where the strength of the association rule includes support, confidence, and the sum of support and confidence.

[0040] During this period, according to the strength of the association rules, the color and size of the scatter plot are selected so that the visual effect matches the association strength of the data;

[0041] Moreover, according to the preset confidence and support thresholds, unimportant rules are filtered and important association rules are highlighted on the map;

[0042] Finally, the final map visualization results are presented to users, allowing them to intuitively understand the correlation and impact of each node in the power system.

[0043] A computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the operation and maintenance data processing system that considers affinity as described above.

[0044] A processor is used to run a program, wherein the program, when running, executes the operation and maintenance data processing system that considers affinity as described above.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The present invention is provided with a data acquisition unit, a affinity extraction unit and a visualization unit, which can collect power grid operation and maintenance data, analyze the degree of correlation between data through the affinity extraction unit, and finally display these correlations on a map through the visualization unit. Such a system can more comprehensively consider the interaction between power grid equipment, help operation and maintenance personnel to more accurately define the scope of fault impact, optimize maintenance strategies, and improve the operation and maintenance efficiency and response speed of the power grid.

[0047] 2. The affinity extraction unit of the present invention comprehensively considers voltage, current, frequency, power and temperature to measure the association between the collection points, and through multi-data multi-dimensional data mining and association analysis, the affinity acquisition result is more accurate. In addition, in the transaction database construction module, the sampling time classification storage is used to make the voltage, current, frequency, power and temperature naturally form a discrete data distribution form, and the discrete data distribution is further used for classification and storage, which is more conducive to the efficient execution of the subsequent Apriori algorithm.

[0048] 3. The visualization unit of the present invention visualizes the map based on the degree of association, providing an intuitive and efficient tool for power grid operation and maintenance. It can present the complex power grid data and the correlation between devices in a graphical way, helping operation and maintenance personnel to quickly identify key nodes and potential risk areas, thereby accelerating the fault location and emergency repair process. This visualization method not only improves the response speed and processing efficiency of power grid operation and maintenance, but also helps to accurately define the scope of fault impact, optimize resource allocation, achieve preventive maintenance, and enhance the reliability and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0050] Figure 1 It is a schematic diagram of the overall framework of the system of the present invention;

[0051] Figure 2 Constructing a flow chart for the transaction database of the present invention;

[0052] Figure 3 A flow chart for obtaining the degree of association between various collection points in the present invention;

[0053] Figure 4 This is a flowchart of the operation of the fusion labeling module of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0056] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0057] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0058] In one embodiment of the present invention, an operation and maintenance data processing system considering affinity relationship is provided, such as Figure 1As shown, it includes a data acquisition unit, a affinity extraction unit and a visualization unit. The data acquisition unit is connected to the affinity extraction unit, and the affinity extraction unit is connected to the visualization unit. The data acquisition unit pre-processes the collected original data and then transmits it to the affinity extraction unit to extract the degree of association between the data. The affinity extraction unit obtains the association between each data and then transmits it to the visualization unit. The visualization unit performs visualization on a map based on the association between the data and the geographical location of each data collection point. Each unit is introduced in detail below.

[0059] The data acquisition unit includes a data acquisition module, a data preprocessing module and a data storage module.

[0060] The data acquisition module is used to collect power grid operation and maintenance data, including the voltage, current, frequency, power and temperature of substations, transmission lines and transformers. In addition, the geographical location and longitude coordinates of substations, transmission lines and transformers are also obtained.

[0061] The data preprocessing module first performs data preprocessing on the voltage, current, frequency, power and temperature data, including data cleaning, which includes:

[0062] 1. Dealing with missing values: Identify and deal with missing values ​​in the data, either by deleting them or filling them (such as using the mean, median, or mode).

[0063] 2. Outlier processing: Identify and process outliers. Outliers can be identified through statistical analysis methods (such as IQR) or rules based on business logic, and appropriate processing measures can be taken.

[0064] 3. Duplicate value processing: Identify and delete duplicate records in the data to ensure the uniqueness of the data.

[0065] The data storage module is used to store the data processed by the data preprocessing module. The data storage module regards the substation, transmission line or transformer as a collection point, that is, a storage unit. Each storage unit stores a geographical location longitude and latitude coordinates, that is, the geographical location longitude and latitude coordinates of the current collection point. A storage unit stores voltage, current, frequency, power and temperature at different sampling times according to different collection times. It is convenient for further data mining and processing.

[0066] The affinity relationship extraction unit includes a transaction database construction module, an Apriori algorithm application module and a result quantification module.

[0067] like Figure 2 As shown, the following operations are performed in the transaction database construction module:

[0068] S1: Get the voltage, current, frequency, power and temperature at each sampling time.

[0069] S2: Classify voltage, current, frequency, power or temperature respectively. The following takes voltage as an example to illustrate the sorting:

[0070] The voltage is divided into M levels, that is, L = {L1, L2, ..., L i ,…,L M}, where L is the level sequence and Li is the i-th level in the level sequence. The grading operation based on sorting is as follows:

[0071]

[0072] Where L is the final output level, L M is the Mth classification type set, λ P is the set threshold, and V is the voltage at the current sampling moment.

[0073] S3: Based on the voltage, current, frequency, power or temperature level of each collection point, the state change of each collection point at each sampling moment is recorded as a transaction. For example, if the voltage of node A is "L1", the current is "L2", the frequency is "L2", the power is "L2", and the temperature is "L4", it can be expressed as {voltage = L1, current = L2, frequency = L2, power = L2, temperature = L4}.

[0074] S4: Aggregate the transaction records of all nodes into a data set, where each record is an itemset.

[0075] The Apriori algorithm application module uses the Apriori algorithm to obtain the degree of association between each collection point, such as Figure 3 As shown, perform the following operations:

[0076] S01: Determine the minimum support threshold, for example 5%, which means that the frequency of frequent itemsets in all transactions must not be lower than this value.

[0077] S02: Starting from a single item, generate all possible item sets;

[0078] S03: Calculate support, which is used to measure the frequency of item sets appearing in all transactions. The formula is:

[0079]

[0080] Where num(xy) is the number of transactions in which item sets X and Y appear at the same time, and num(allsamples) is the total number of transactions. Support refers to the frequency of an item set appearing in all transactions. In the Apriori algorithm, support is determined by calculating the number of transactions in which item sets X and Y appear at the same time. For example, if {voltage = L2} and {current = L4} appear at the same time in multiple transaction records, then the support of this item set is the number of these transactions divided by the total number of transaction records.

[0081] S03: retain the item sets whose support is higher than the minimum support threshold, these item sets are called frequent item sets;

[0082] S04: Generate association rules from frequent item sets and calculate the confidence of each rule. Confidence is used to measure the conditional probability of item set Y appearing under the condition that item set X appears. The formula is:

[0083]

[0084] Where Support(X∪Y) is the frequency of the union of item sets X and Y in all transactions, and Support(X) is the frequency of item set X in all transactions.

[0085] The result quantification module is used to output the quantitative association degree, and the quantitative association degree is realized by the confidence. In this embodiment, the quantitative association degree is equal to the confidence. The higher the confidence value, the greater the possibility of item set Y appearing when item set X appears, and the stronger the association degree. Usually, we will set a confidence threshold, such as 0.7 or 0.8. Only when the confidence is higher than this threshold, we think that there is a strong association relationship between item sets X and Y.

[0086] The visualization unit includes a data integration module and a fusion labeling module.

[0087] The data integration module is used to collect geographic information and association rule results. The geographic information is the longitude and latitude coordinates of the geographic location of each collection point. The association rule results are association rules obtained from the Apriori algorithm, including the support and confidence of each rule. The data integration module integrates the geographic location information of the node with the association rule results, and marks the strength of its association rule for each node.

[0088] The fusion marking module performs visual marking on the map based on the information of the map and the data integration module. Figure 4 As shown, the fusion labeling module performs the following operations:

[0089] Use libraries dedicated to map visualization, such as Echarts, Geopandas, Plotly, etc., and then use map visualization tools to draw the basic map framework.

[0090] Then, each node is represented on the map and displayed using a scatter plot. The size or color of the scatter points can be adjusted according to the strength of the association rule (such as support and confidence) to intuitively show the degree of association between the nodes.

[0091] During this period, according to the strength of the association rules, the appropriate color and size are selected for the scatter plot, so that the visual effect matches the association strength of the data. At the same time, users are allowed to view detailed association rule information by clicking on the nodes, which enhances the user experience.

[0092] In addition, based on the preset confidence and support thresholds, unimportant rules are filtered out and important association rules are highlighted on the map. The color, label and scale of the map can be adjusted as needed to improve the readability and aesthetics of the map.

[0093] Finally, the final map visualization results are presented to users, allowing them to intuitively understand the correlation and impact of each node in the power system.

[0094] Through these steps, complex data analysis results can be transformed into intuitive map visualization, helping users quickly identify and understand the mutual influence and correlation between nodes in the power system.

[0095] In summary, the present invention is provided with a data acquisition unit, an affinity extraction unit and a visualization unit, which can collect power grid operation and maintenance data, analyze the degree of correlation between data through the affinity extraction unit, and finally display these correlations on the map through the visualization unit. Such a system can more comprehensively consider the interaction between power grid equipment, help operation and maintenance personnel to more accurately define the scope of fault impact, optimize maintenance strategies, and improve the operation and maintenance efficiency and response speed of the power grid. Moreover, the affinity extraction unit of the present invention comprehensively considers voltage, current, frequency, power and temperature to measure the correlation between collection points, and through multi-data multi-dimensional data mining and association analysis, the affinity acquisition result is more accurate. In addition, in the transaction database construction module, the sampling time classification storage is used to make the voltage, current, frequency, power and temperature naturally form a discrete data distribution form, and further use the discrete data distribution for classification and storage, which is more conducive to the efficient execution of the subsequent Apriori algorithm. In addition, the visualization unit of the present invention performs a visualization display of the map based on the degree of correlation, providing an intuitive and efficient tool for power grid operation and maintenance, which can present the correlation between complex power grid data and equipment in a graphical manner, helping operation and maintenance personnel to quickly identify key nodes and potential risk areas, thereby accelerating the fault location and emergency repair process. This visualization method not only improves the response speed and processing efficiency of power grid operation and maintenance, but also helps to accurately define the scope of fault impact, optimize resource allocation, achieve preventive maintenance, and enhance the reliability and stability of the power grid.

[0096] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0097] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0098] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0099] If 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 storage medium. Based on this understanding, the technical solution of the present invention, 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, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An operation and maintenance data processing system considering intimacy, characterized in that: include: A data collection unit is connected to the affinity extraction unit, and is used to collect power grid operation and maintenance data, and transmit the collected raw data to the affinity extraction unit after pre-processing to extract the degree of association between the data; The affinity relationship extraction unit is connected to the data collection unit and the visualization unit respectively, and obtains the association relationship of each data based on the Apriori algorithm; The visualization unit is connected to the affinity extraction unit, and based on the association relationship between the data and the geographical location of each data collection point, a visualization display is performed on the map according to the association relationship.

2. The operation and maintenance data processing system considering intimacy according to claim 1, characterized in that: The data acquisition unit includes a data acquisition module, which is used to collect power grid operation and maintenance data, including the voltage, current, frequency, power and temperature of substations, transmission lines and transformers; the data acquisition module also obtains the geographical location longitude and latitude coordinates of substations, transmission lines and transformers.

3. The operation and maintenance data processing system considering intimacy according to claim 2, characterized in that: The data acquisition unit includes a data preprocessing module, which first performs data preprocessing on the voltage, current, frequency, power and temperature data, including data cleaning, and the data cleaning includes: Handling missing values: Identify and handle missing values ​​in the data by filling them with the mean of the sampled values ​​before and after the missing values. Outlier processing: Identify and process outliers. The means of identifying outliers is to use statistical analysis methods, including IQR, or rules based on business logic to identify outliers. The means of processing outliers is to replace outliers with the mean of the nearest non-outlier values ​​before and after the outlier. Duplicate value processing: Identify and remove duplicate records in the data.

4. The operation and maintenance data processing system considering intimacy according to claim 3 is characterized in that: The data acquisition unit includes a data storage module, which is used to store data processed by the data preprocessing module. The data storage module regards the substation, transmission line or transformer as a collection point, that is, a storage unit. Each storage unit stores a geographical location latitude and longitude coordinates. A storage unit stores voltage, current, frequency, power and temperature at different sampling times according to different collection times.

5. The operation and maintenance data processing system considering intimacy according to claim 1, characterized in that: The affinity extraction unit includes a transaction database construction module, an Apriori algorithm application module and a result quantification module; the transaction database construction module forms transaction records after level processing based on the data collected by the data collection unit, and summarizes the transaction records into a data set, the Apriori algorithm processes the data set to obtain association rules, the support and confidence of each rule, and the result quantification module outputs a quantitative association degree based on the confidence.

6. The operation and maintenance data processing system considering intimacy according to claim 5, characterized in that: The transaction database construction module performs the following operations: S1: Obtain the voltage, current, frequency, power and temperature at each sampling time; S2: Classify voltage, current, frequency, power and temperature respectively. The classification methods of voltage, current, frequency, power and temperature are the same. Taking voltage as an example, the ranking is as follows: The voltage is divided into M levels, that is, L = {L1, L2, ..., L i ,…,L M }, where L is the level sequence and Li is the i-th level in the level sequence. The specific grading operation based on sorting is as follows: Where L is the final output level, L M is the Mth classification type set, λ P is the set threshold, V is the voltage at the current sampling moment; S3: Based on the voltage, current, frequency, power or temperature level of each collection point, the state change of each collection point at each sampling moment is recorded as a transaction; S4: Aggregate the transaction records of all nodes into a data set, where each record is an itemset.

7. The operation and maintenance data processing system considering intimacy and distance according to claim 5, characterized in that: The Apriori algorithm application module uses the Apriori algorithm to obtain the degree of association between each collection point, and specifically performs the following operations: S01: Determine the minimum support threshold. The frequency of frequent itemsets in all transactions must not be lower than this value. S02: Starting from a single item, generate all possible item sets; S03: Calculate support, which is used to measure the frequency of itemsets appearing in all transactions, as follows: Where num(xy) is the number of transactions in which item sets X and Y appear simultaneously, and num(allsamples) is the total number of transactions; S04: retain the item sets whose support is higher than the minimum support threshold, these item sets are called frequent item sets; S05: Generate association rules from frequent item sets and calculate the confidence of each rule. The calculation formula is as follows: Where Support(X∪Y) is the frequency of the union of item sets X and Y in all transactions, and Support(X) is the frequency of item set X in all transactions.

8. The operation and maintenance data processing system considering affinity relationship according to claim 1, characterized in that: The visualization unit includes a data integration module and a fusion labeling module, where: The data integration module is used to collect geographic information and association rule results. The geographic information is the longitude and latitude coordinates of the geographic location of each collection point. The association rule results are association rules obtained from the Apriori algorithm, including the support and confidence of each rule. The data integration module integrates the geographic location information of the node with the association rule results and marks the strength of its association rule for each node. The fusion marking module performs visual marking on the map based on the information of the map and the data integration module. The fusion marking module performs the following operations: Use a library dedicated to map visualization, and then use map visualization tools to draw a basic map framework; Then, each node is represented on the map using a scatter plot, and the size or color of the scatter points is adjusted according to the strength of the association rule, wherein the strength of the association rule includes support, confidence, and the sum of support and confidence; During this period, according to the strength of the association rules, the color and size of the scatter plot are selected so that the visual effect matches the association strength of the data; Moreover, according to the preset confidence and support thresholds, unimportant rules are filtered and important association rules are highlighted on the map; Finally, the final map visualization results are presented to users, allowing them to intuitively understand the correlation and impact of each node in the power system.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the operation and maintenance data processing system considering affinity as described in any one of claims 1 to 8.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the operation and maintenance data processing system considering affinity and distance relationship as described in any one of claims 1 to 8.

Images