Power supply data management method and system based on data middle platform

Abstract

The present invention discloses a power supply data management method and system based on a data middle station, which relates to the technical field of data management. The present invention collects historical power supply data of the data middle station and constructs a power supply data standard. At the same time, the collected historical power supply data is verified and processed based on the constructed power supply data standard. After the processing is completed, the power supply data is collected in real time and the missing power supply data is supplemented. After the data is supplemented, the supplemented real-time power supply data is analyzed by data analysis, and the power supply line fault position is located according to the analysis result. At the same time, the power supply line fault position is analyzed by data mining. After the analysis is completed, the power supply data of the power grid is preliminarily distributed and managed based on the analyzed power supply data and the distribution result is verified, thereby improving the actual processing capability of power supply data management.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and in particular to a power supply data management method and system based on a data middle platform. Background Art

[0002] With the large-scale integration of distributed energy resources, which are characterized by high randomness and volatility, into the power grid, the power system faces new requirements in terms of power supply reliability and relay protection. However, the current power grid lacks control and monitoring of the power supply of each circuit, which cannot meet actual needs.

[0003] The prior art, such as the invention patent application with announcement number: CN113852196A, discloses a power supply system data management system, which method includes: a central processing unit and a total data storage library and a tracing unit connected to the central processing unit, characterized in that the total data storage library is connected to a data import unit and a data sorting module, the data sorting module is connected to a data comparison module, the data comparison module is connected to a data anomaly warning module, the data anomaly warning module is connected to an anomaly analysis module, the anomaly analysis module is connected to the central processing unit, and the central processing unit is connected to a data verification end through a communication module.

[0004] From the above solutions, it can be seen that the current management of power supply data often only stays at the level of data analysis and verification, lacks actual monitoring and control management, and has certain limitations. Summary of the Invention

[0005] The purpose of the present invention is to provide a power supply data management method and system based on a data middle platform, which solves the problems existing in the background technology.

[0006] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a power supply data management method based on a data center, which specifically includes the following steps:

[0007] S1. Collect historical power supply data from the data center and establish a power supply data standard. At the same time, perform data verification and processing on the collected historical power supply data based on the established power supply data standard to obtain processed historical power supply data.

[0008] S2. Collect power supply data from the data center in real time, calculate the collection success rate of real-time power supply data based on the processed historical power supply data, and complete the real-time power supply data based on the collection success rate to obtain the completed real-time power supply data;

[0009] S3. Analyze the completed real-time power supply data through data analysis, locate the power supply line fault location based on the analysis results, and analyze the power supply line fault location through data mining to obtain analyzed power supply data;

[0010] S31, analyzing the completed real-time power supply data by means of power load analysis, and locating the power supply line fault location according to the analysis results, including the following steps:

[0011] S32. Perform line loss analysis on the power supply line fault location by data mining;

[0012] S33, summarizing the real-time power supply data after line loss analysis and power load analysis to obtain analyzed power supply data;

[0013] S4. Perform preliminary distribution management of the power supply data of the power grid based on the analyzed power supply data, specifically including:

[0014] A power supply distribution management model is constructed based on the analyzed power supply data, and a genetic algorithm is used to perform preliminary distribution management of the power supply data of the power grid based on the constructed power supply distribution management model;

[0015] Construct a power supply distribution management model based on data linkage;

[0016] ;

[0017] in, represents the power distribution data of the x-th power line, F represents the power distribution management model, Indicates the line loss status of the x-th power line, Represents the data after the load test on the x-th power line;

[0018] S5. Verify and optimize the power supply data of the power grid after preliminary allocation management through power grid reliability simulation, and manage the power supply data of the power grid in real time based on the verification and optimization results.

[0019] Preferably, the collecting of historical power supply data of the data center and constructing a power supply data standard, and performing data verification processing on the collected historical power supply data based on the constructed power supply data standard to obtain the processed historical power supply data include the following steps:

[0020] S11. Collect historical power supply data from the data center and establish power supply data standards;

[0021] A set of standard power supply data structures is set up including: power supply circuit information, power supply voltage, power supply current, meter number and time;

[0022] After the power supply data structure is defined, the four-tuple Save the collected historical power supply data in the form of

[0023] in, Indicates the power supply data standard. Indicates a timestamp, Indicates the power supply circuit information, including the specific location information of the power supply node and the corresponding power line length, Indicates the supply voltage, Indicates the supply current, Indicates the meter number;

[0024] S12. Perform data verification and processing on the collected historical power supply data based on the constructed power supply data standard.

[0025] Preferably, the data verification process of the collected historical power supply data based on the constructed power supply data standard includes the following steps:

[0026] By comparing the power supply data sent by the corresponding numbered meter with the power supply data collected by the data center one by one according to the saved timestamps, when the timestamps of the power supply data sent by the corresponding numbered meter and the power supply data collected by the data center are consistent, it is determined that the source data verification has passed;

[0027] When the timestamps of the power supply data sent by the corresponding numbered meter and the power supply data collected by the data center are inconsistent, the data verification is determined to have failed, indicating that there is an error in the power supply data collected by the data center, and the erroneous data is deleted;

[0028] The historical power supply data after deletion is set to the processed historical power supply data.

[0029] Preferably, the real-time collection of power supply data from the data center and calculation of the collection success rate of the real-time power supply data based on the processed historical power supply data, and the completion of the real-time power supply data based on the collection success rate, to obtain the completed real-time power supply data include the following steps:

[0030] S21. Calculating a collection success rate of real-time power supply data based on the processed historical power supply data;

[0031] The success rate of collecting real-time power supply data = (number of meters successfully collected / number of meters in historical power supply data) × 100%;

[0032] S22. Complete the real-time power supply data based on the collection success rate.

[0033] Preferably, the method of completing the real-time power supply data based on the collection success rate includes the following steps:

[0034] In the process of real-time collection of power supply data from the data center, if collection fails, the meter numbers that have not been successfully collected are traversed and located based on the power supply data from the real-time data center.

[0035] After positioning is completed, the latest 10 sets of historical power supply data for the corresponding meter number are extracted, and data fitting and prediction are performed. The real-time power supply data is supplemented based on the data fitting and prediction results;

[0036] The data fitting prediction results are as follows:

[0037] ;

[0038] Where e represents the natural logarithm, Indicates the The height of the group's historical power supply data, For the The coordinates of the center position of the coordinate axis of the group historical power supply data, Indicates the The width of the historical power supply data of the group, represents the historical power supply data after fitting, The coordinates of the historical power supply data in the coordinate system.

[0039] Preferably, the analyzing the completed real-time power supply data by means of power load analysis and locating the power supply line fault position according to the analysis result comprises the following steps:

[0040] Calculate the user's power load threshold based on the completed real-time power supply data;

[0041] The calculation formula for the user's electricity load threshold is as follows:

[0042] ;

[0043] in, Indicates the user's power load threshold. Indicates the supply voltage, Indicates the supply current;

[0044] Analyze the user's electricity meter through electricity load analysis;

[0045] The set meter electricity load analysis formula is as follows:

[0046] ;

[0047] in, Indicates the number of types of electrical equipment on the Xth power line, Indicates the number of type s electrical equipment on the x-th power line, It is represented by the power load data of the i-th type of electrical equipment, It is represented as the power load data of the jth power-consuming device in the i-type power-consuming device.

[0048] Preferably, the performing line loss analysis on the power supply line fault location by data mining comprises the following steps:

[0049] Summarize the power supply data of all power supply lines with potential faults and perform line loss analysis;

[0050] Traverse the power outage status in the power supply data of the location where the potential fault exists;

[0051] ;

[0052] set up When , it means that the xth power line is supplying power normally at time t. When , it means that the xth power line is out of power at time t;

[0053] Traverse the failure rate in the power supply data of locations with potential fault hazards;

[0054] ;

[0055] in, Indicates the average failure rate of power supply data, represents the failure rate of the x-th power line, Represents a collection of power lines;

[0056] Calculate the line loss status of the power line based on the power outage status, failure rate and length of the power line;

[0057] The line loss status calculation formula is as follows:

[0058] ;

[0059] in, Indicates the length of the x-th power line.

[0060] Preferably, the preliminary allocation and management of the power supply data of the power grid based on the analyzed power supply data includes the following steps:

[0061] S41. Based on the constructed power distribution management model, set the genetic population size, number of iterations, and chromosome encoding;

[0062] Each population is set as a set of analyzed power supply data;

[0063] S42, randomly select b design drawing data in each category to generate the initial population ;

[0064] S43, using the constructed power distribution management model as a fitness function, and calculating the fitness of each individual in the population;

[0065] S44, selecting the best individuals from all individuals based on the roulette wheel method;

[0066] S45. Based on the chromosome coding of the selected excellent individuals, the selected excellent individuals are crossed in a sequential crossover manner, and the new population generated after the crossover is set to ;

[0067] S46. Randomly select an individual in the population to mutate with a set probability, and set the population after the mutation to be ;

[0068] S47, comparing the fitness difference between the initial population and the population after crossover mutation using the genetic algorithm;

[0069] S48, iteratively execute steps S44-S46, and output the optimal solution when the maximum number of iterations is reached;

[0070] The optimal solution output is set as the preliminary distribution management data of the power grid power supply data.

[0071] Preferably, the verifying and optimizing the power supply data of the power grid after the preliminary allocation management by means of power grid reliability simulation, and managing the power supply data of the power grid in real time based on the verification and optimization results includes the following steps:

[0072] The power supply data of the power grid after preliminary allocation management is verified through power load analysis. When the verification result is less than the calculated user power load threshold, it means that there are no safety hazards in the allocation management result. Otherwise, the allocation management result is recalculated and staff are arranged for maintenance.

[0073] The present invention also discloses a power supply data management system based on a data middle platform, which is used to implement a power supply data management method based on a data middle platform. The system includes: a data acquisition module, a data processing module, a data analysis module, a power supply distribution module, and a power supply distribution verification module;

[0074] The data acquisition module is used to collect power supply data and historical power supply data of the data center in real time;

[0075] The data processing module is used to process the power supply data and historical power supply data collected in real time;

[0076] The data analysis module is used to analyze the power supply data collected in real time after processing;

[0077] The power distribution module is used to distribute power according to the real-time analysis results;

[0078] The power supply allocation verification module is used to verify the power supply allocation result.

[0079] The beneficial effects of the present invention are:

[0080] (1) The present invention collects historical power supply data from the data center and constructs a power supply data standard. At the same time, the collected historical power supply data is verified based on the constructed power supply data standard. After the processing is completed, the power supply data is collected in real time and the missing power supply data is supplemented. After the data is supplemented, the supplemented real-time power supply data is analyzed by data analysis, and the power supply line fault position is located according to the analysis results. At the same time, the power supply line fault position is analyzed by data mining. After the analysis is completed, the power supply data of the power grid is preliminarily distributed and managed based on the analyzed power supply data and the distribution results are verified, thereby improving the actual processing capability of power supply data management.

[0081] (2) The present invention improves the reliability of power supply data processing by collecting historical power supply data from the data center and constructing a power supply data standard, and verifying the historical power supply data based on the constructed standard.

[0082] (3) The present invention improves the effectiveness of power supply data processing by collecting power supply data from the data center in real time and calculating the collection success rate, while supplementing the power supply data that has not been successfully collected.

[0083] (4) The present invention determines the power consumption status of each circuit and the existing safety hazards by performing load analysis on the completed power supply data. After locating the safety hazards, the present invention performs line loss analysis on the fault location of the power supply line through data mining, further determines the line loss status of the fault location, and improves the safety of the power supply of the power grid.

[0084] (5) The present invention improves the intelligence of power supply of the power grid by constructing a power supply distribution management model and determining the power supply required for each circuit through a genetic algorithm based on the constructed power supply distribution management model. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0086] Figure 1 It is a flow chart of the power supply data management method of the data middle station of the present invention. DETAILED DESCRIPTION

[0087] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0088] In a specific embodiment of the present invention,

[0089] Reference Figure 1 As shown, the present invention provides a power supply data management method and system based on a data middle platform, including:

[0090] S1. Collect historical power supply data from the data center and establish a power supply data standard. At the same time, perform data verification and processing on the collected historical power supply data based on the established power supply data standard to obtain processed historical power supply data.

[0091] S2. Collect power supply data from the data center in real time, calculate the collection success rate of real-time power supply data based on the processed historical power supply data, and complete the real-time power supply data based on the collection success rate to obtain the completed real-time power supply data;

[0092] S3. Analyze the completed real-time power supply data through data analysis, locate the power supply line fault location based on the analysis results, and analyze the power supply line fault location through data mining to obtain the analyzed power supply data, including the following steps:

[0093] S31. Analyze the completed real-time power supply data through power load analysis, and locate the power supply line fault location based on the analysis results;

[0094] S32. Perform line loss analysis on the power supply line fault location by data mining;

[0095] S33, summarizing the real-time power supply data after line loss analysis and power load analysis to obtain analyzed power supply data;

[0096] S4. Perform preliminary distribution management of the power supply data of the power grid based on the analyzed power supply data, specifically including:

[0097] A power supply distribution management model is constructed based on the analyzed power supply data, and a genetic algorithm is used to perform preliminary distribution management of the power supply data of the power grid based on the constructed power supply distribution management model;

[0098] Construct a power supply distribution management model based on data linkage;

[0099] ;

[0100] in, represents the power distribution data of the x-th power line, F represents the power distribution management model, Indicates the line loss status of the x-th power line, Represents the data after the load test on the x-th power line;

[0101] S5. Verify and optimize the power supply data of the power grid after preliminary allocation and management through power grid reliability simulation, and manage the power supply data of the power grid in real time based on the verification and optimization results;

[0102] Further, refer to Figure 1 As shown, collecting historical power supply data from the data center and constructing a power supply data standard, and performing data verification processing on the collected historical power supply data based on the constructed power supply data standard, to obtain the processed historical power supply data includes the following steps:

[0103] S11. Collect historical power supply data from the data center and establish power supply data standards;

[0104] A set of standard power supply data structures is set up including: power supply circuit information, power supply voltage, power supply current, meter number and time;

[0105] Furthermore, after the power supply data structure is defined, the four-tuple Save the collected historical power supply data in the form of

[0106] in, Indicates the power supply data standard. Indicates a timestamp, Indicates the power supply circuit information, including the specific location information of the power supply node and the corresponding power line length, Indicates the supply voltage, Indicates the supply current, Indicates the meter number;

[0107] S12. Performing data verification and processing on the collected historical power supply data based on the established power supply data standard;

[0108] By comparing the power supply data sent by the corresponding numbered meter with the power supply data collected by the data center one by one according to the saved timestamps, when the timestamps of the power supply data sent by the corresponding numbered meter and the power supply data collected by the data center are consistent, it is determined that the source data verification has passed;

[0109] When the timestamps of the power supply data sent by the corresponding numbered meter and the power supply data collected by the data center are inconsistent, the data verification is determined to have failed, indicating that there is an error in the power supply data collected by the data center, and the erroneous data is deleted;

[0110] Set the deleted historical power supply data to the processed historical power supply data;

[0111] Further, refer to Figure 1 As shown, the power supply data of the data center is collected in real time, and the collection success rate of the real-time power supply data is calculated based on the processed historical power supply data. At the same time, the real-time power supply data is supplemented based on the collection success rate. The obtained supplemented real-time power supply data includes the following steps:

[0112] S21. Calculating a collection success rate of real-time power supply data based on the processed historical power supply data;

[0113] The success rate of collecting real-time power supply data = (number of meters successfully collected / number of meters in historical power supply data) × 100%;

[0114] S22. Supplement the real-time power supply data based on the collection success rate;

[0115] In the process of real-time collection of power supply data from the data center, if collection fails, the meter numbers that have not been successfully collected are traversed and located based on the power supply data from the real-time data center.

[0116] After positioning is completed, the latest 10 sets of historical power supply data for the corresponding meter number are extracted, and data fitting and prediction are performed. The real-time power supply data is supplemented based on the data fitting and prediction results;

[0117] The data fitting prediction results are as follows:

[0118] The Gaussian fitting expression is as follows:

[0119] ;

[0120] Where e represents the natural logarithm, Indicates the The height of the group's historical power supply data, For the The coordinates of the center position of the coordinate axis of the group historical power supply data, Indicates the The width of the historical power supply data of the group, Represents the historical power supply data after fitting;

[0121] Further, refer to Figure 1 As shown, analyzing the completed real-time power supply data by means of power load analysis and locating the power supply line fault location according to the analysis results includes the following steps:

[0122] Calculate the user's power load threshold based on the completed real-time power supply data;

[0123] The calculation formula for the user's electricity load threshold is as follows:

[0124] ;

[0125] in, Indicates the user's power load threshold. Indicates the supply voltage, Indicates the supply current;

[0126] Furthermore, the user's electricity meter is analyzed by means of electricity load analysis;

[0127] The set meter electricity load analysis formula is as follows:

[0128] ;

[0129] in, Indicates the number of types of electrical equipment on the xth power line, Indicates the number of type s electrical equipment on the x-th power line, It is represented by the power load data of the i-th type of electrical equipment, It is represented as the power load data of the jth power-consuming device in the i-type power-consuming device;

[0130] Furthermore, the data collected after the load test is compared with the calculated user power load threshold;

[0131] When the data after the load test is greater than or equal to the calculated user power load threshold, it means that there is a potential fault in the power supply line; otherwise, there is no potential fault.

[0132] Further, refer to Figure 1 As shown in FIG, line loss analysis of the power supply line fault location by data mining includes the following steps:

[0133] Summarize the power supply data of all power supply lines with potential faults and perform line loss analysis;

[0134] Traverse the power outage status in the power supply data of the location where the potential fault exists;

[0135] ;

[0136] set up When , it means that the xth power line is supplying power normally at time t. When , it means that the xth power line is out of power at time t;

[0137] Traverse the failure rate in the power supply data of locations with potential fault hazards;

[0138] ;

[0139] in, Indicates the average failure rate of power supply data, represents the failure rate of the x-th power line, Represents a collection of power lines;

[0140] Furthermore, the line loss status of the power line is calculated based on the power outage status, failure rate and length of the power line;

[0141] The line loss status calculation formula is as follows:

[0142] ;

[0143] in, represents the length of the xth power line;

[0144] Further, refer to Figure 1 As shown, performing preliminary distribution management of the power supply data of the power grid based on the analyzed power supply data includes the following steps:

[0145] S41. Based on the constructed power distribution management model, set the genetic population size, number of iterations, and chromosome encoding;

[0146] Each population is set as a set of analyzed power supply data;

[0147] S42. Randomly select from each category Design drawing data to generate the initial population ;

[0148] S43, using the constructed power distribution management model as a fitness function, and calculating the fitness of each individual in the population;

[0149] Assume that each individual represents a set of environmental carrying capacity evaluation index system data;

[0150] The formula for calculating the fitness of an individual is as follows:

[0151] ;

[0152] in, Indicates the The fitness of each individual;

[0153] S44, selecting the best individuals from all individuals based on the roulette wheel method;

[0154] The calculation formula for selecting the best individual among all individuals in the roulette wheel method is as follows:

[0155] ;

[0156] in, Represents an individual The probability of being selected, represents the population size;

[0157] S45. Based on the chromosome coding of the selected excellent individuals, the selected excellent individuals are crossed in a sequential crossover manner, and the new population generated after the crossover is set to ;

[0158] S46. Randomly select an individual in the population to mutate with a set probability, and set the population after the mutation to be ;

[0159] S47, comparing the fitness difference between the initial population and the population after crossover mutation using the genetic algorithm;

[0160] S48, iteratively execute steps S44-S46, and output the optimal solution when the maximum number of iterations is reached;

[0161] The optimal solution output is set as the preliminary distribution management data of the power grid power supply data;

[0162] Further, refer to Figure 1 As shown, verifying and optimizing the power supply data of the power grid after preliminary allocation management by means of power grid reliability simulation, and managing the power supply data of the power grid in real time based on the verification and optimization results includes the following steps:

[0163] The power supply data of the power grid after the initial allocation management is verified through power load analysis. If the verification result is less than the calculated user power load threshold, it means that there are no safety hazards in the allocation management result. Otherwise, the allocation management result is recalculated and staff are arranged for maintenance;

[0164] In a specific embodiment, the power supply data management system based on the data middle platform is used to implement a power supply data management method based on the data middle platform, and the system includes: a data acquisition module, a data processing module, a data analysis module, a power supply distribution module, and a power supply distribution verification module;

[0165] The data acquisition module is used to collect power supply data and historical power supply data of the data center in real time;

[0166] The data processing module is used to process the power supply data and historical power supply data collected in real time;

[0167] The data analysis module is used to analyze the power supply data collected in real time after processing;

[0168] The power distribution module is used to distribute power according to the real-time analysis results;

[0169] The power supply allocation verification module is used to verify the power supply allocation result.

[0170] It should be noted that:

[0171] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. The power supply data management method based on the data center is characterized by: The following steps are involved: S1. Collect historical power supply data from the data center and establish a power supply data standard. At the same time, perform data verification and processing on the collected historical power supply data based on the established power supply data standard to obtain processed historical power supply data. S2. Collect power supply data from the data center in real time, calculate the collection success rate of real-time power supply data based on the processed historical power supply data, and complete the real-time power supply data based on the collection success rate to obtain the completed real-time power supply data; S3. Analyze the completed real-time power supply data through data analysis, locate the power supply line fault location based on the analysis results, and analyze the power supply line fault location through data mining to obtain the analyzed power supply data, including the following steps: S31. Analyze the completed real-time power supply data through power load analysis, and locate the power supply line fault location based on the analysis results; S32. Perform line loss analysis on the power supply line fault location by data mining; S33, summarizing the real-time power supply data after line loss analysis and power load analysis to obtain analyzed power supply data; S4. Perform preliminary distribution management of the power supply data of the power grid based on the analyzed power supply data, specifically including: A power distribution management model is constructed based on the analyzed power supply data, and a genetic algorithm is used to perform preliminary distribution management of the power supply data of the power grid based on the constructed power distribution management model; Construct a power supply distribution management model based on data linkage; ； in, represents the power distribution data of the x-th power line, F represents the power distribution management model, Indicates the line loss status of the x-th power line, Represents the data after the load test on the x-th power line; S5. Verify and optimize the power supply data of the power grid after preliminary allocation management through power grid reliability simulation, and manage the power supply data of the power grid in real time based on the verification and optimization results.

2. The power supply data management method based on the data middle platform according to claim 1 is characterized in that: The process of collecting historical power supply data from the data center and constructing a power supply data standard, and performing data verification processing on the collected historical power supply data based on the constructed power supply data standard to obtain the processed historical power supply data includes the following steps: S11. Collect historical power supply data from the data center and establish power supply data standards; A set of standard power supply data structures is set up including: power supply circuit information, power supply voltage, power supply current, meter number and time; After the power supply data structure is defined, the four-tuple Save the collected historical power supply data in the form of Among them, A represents the power supply data standard, T represents the timestamp, D represents the power supply circuit information, including the specific location information of the power supply node and the corresponding power line length, U represents the power supply voltage, I represents the power supply current, and L represents the meter number; S12. Perform data verification and processing on the collected historical power supply data based on the constructed power supply data standard.

3. The power supply data management method based on the data middle platform according to claim 2 is characterized in that: The data verification process of the collected historical power supply data based on the constructed power supply data standard includes the following steps: By comparing the power supply data sent by the corresponding numbered meter with the power supply data collected by the data center one by one according to the saved timestamps, when the timestamps of the power supply data sent by the corresponding numbered meter and the power supply data collected by the data center are consistent, it is determined that the source data verification has passed; When the timestamps of the power supply data sent by the corresponding numbered meter and the power supply data collected by the data center are inconsistent, the data verification is determined to have failed, indicating that there is an error in the power supply data collected by the data center, and the erroneous data is deleted; The historical power supply data after deletion is set to the processed historical power supply data.

4. The power supply data management method based on the data middle platform according to claim 1 is characterized in that: The real-time collection of power supply data from the data center, and calculation of the collection success rate of the real-time power supply data based on the processed historical power supply data, while completing the real-time power supply data based on the collection success rate, to obtain the completed real-time power supply data includes the following steps: S21. Calculating a collection success rate of real-time power supply data based on the processed historical power supply data; The success rate of collecting real-time power supply data = (number of meters successfully collected / number of meters in historical power supply data) × 100%; S22. Complete the real-time power supply data based on the collection success rate.

5. The power supply data management method based on the data middle platform according to claim 1 is characterized in that: The method of supplementing the real-time power supply data based on the collection success rate includes the following steps: In the process of real-time collection of power supply data from the data center, if collection fails, the meter numbers that have not been successfully collected are traversed and located based on the power supply data from the real-time data center. After positioning is completed, the latest 10 sets of historical power supply data for the corresponding meter number are extracted, and data fitting and prediction are performed. The real-time power supply data is supplemented based on the data fitting and prediction results; The data fitting prediction results are as follows: ； Where, e represents the natural logarithm, Indicates the The height of the group's historical power supply data, For the The coordinates of the center position of the coordinate axis of the group historical power supply data, Indicates the The width of the historical power supply data of the group, represents the historical power supply data after fitting, The coordinates of the historical power supply data in the coordinate system.

6. The power supply data management method based on the data middle platform according to claim 1 is characterized in that: The method of analyzing the completed real-time power supply data by means of power load analysis and locating the power supply line fault position according to the analysis results includes the following steps: Calculate the user's power load threshold based on the completed real-time power supply data; The calculation formula for the user's electricity load threshold is as follows: ； Where P represents the user's power load threshold, U represents the supply voltage, and I represents the supply current; Analyze the user's electricity meter through electricity load analysis; The set meter electricity load analysis formula is as follows: ； in, Indicates the number of types of electrical equipment on the Xth power line, Indicates the number of type s electrical equipment on the x-th power line, It is represented by the power load data of the i-th type of electrical equipment, It is represented as the power load data of the jth power-consuming device in the i-type power-consuming device.

7. The power supply data management method based on the data middle platform according to claim 1 is characterized in that: The line loss analysis of the power supply line fault location by data mining includes the following steps: Summarize the power supply data of all power supply lines with potential faults and perform line loss analysis; Traverse the power outage status in the power supply data of the location where the potential fault exists; ； set up When , it means that the xth power line is supplying power normally at time t. When , it means that the xth power line is out of power at time t; Traverse the failure rate in the power supply data of locations with potential fault hazards; ； in, Indicates the average failure rate of power supply data, represents the failure rate of the x-th power line, where X represents the set of power lines; Calculate the line loss status of the power line based on the power outage status, failure rate and length of the power line; The line loss status calculation formula is as follows: ； in, Indicates the length of the x-th power line.

8. The power supply data management method based on the data middle platform according to claim 1 is characterized in that: The preliminary distribution management of the power supply data of the power grid based on the analyzed power supply data includes the following steps: S41. Based on the constructed power distribution management model, set the genetic population size, number of iterations, and chromosome encoding; Each population is set as a set of analyzed power supply data; S42, randomly select b design drawing data in each category to generate the initial population ; S43, using the constructed power distribution management model as a fitness function, and calculating the fitness of each individual in the population; S44, selecting the best individuals from all individuals based on the roulette wheel method; S45, based on the chromosome coding of the selected excellent individuals, the selected excellent individuals are crossed in a sequential crossover manner, and the new population generated after the crossover is set to ; S46. Randomly select an individual in the population to mutate with a set probability, and set the population after the mutation to be ; S47, comparing the fitness difference between the initial population and the population after crossover mutation using the genetic algorithm; S48, iteratively execute steps S44-S46, and output the optimal solution when the maximum number of iterations is reached; The optimal solution output is set as the preliminary distribution management data of the power grid power supply data.

9. The power supply data management method based on the data middle platform according to claim 1 is characterized in that: The method of verifying and optimizing the power supply data of the power grid after the preliminary allocation management by means of power grid reliability simulation, and managing the power supply data of the power grid in real time based on the verification and optimization results includes the following steps: The power supply data of the power grid after preliminary allocation management is verified through power load analysis. When the verification result is less than the calculated user power load threshold, it means that there are no safety hazards in the allocation management result. Otherwise, the allocation management result is recalculated and staff are arranged for maintenance.

10. A system for implementing the power supply data management method based on a data middle platform according to any one of claims 1 to 9, characterized in that: include: Data acquisition module, data processing module, data analysis module, power supply distribution module and power supply distribution verification module; The data acquisition module is used to collect power supply data and historical power supply data of the data center in real time; The data processing module is used to process the power supply data and historical power supply data collected in real time; The data analysis module is used to analyze the power supply data collected in real time after processing; The power distribution module is used to distribute power according to the real-time analysis results; The power supply allocation verification module is used to verify the power supply allocation result.

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