A green electricity budget management system and method

Abstract

The present invention discloses a green electricity budget management system and method. The present invention relates to the technical field of green electricity budget data management, and solves the technical problems that data may be incorrect and difficult to be discovered and corrected in a timely manner, the data storage is relatively chaotic, and a large amount of duplicate data occupies the storage space. For duplicate data, the present invention can not only accurately identify it, but also effectively compress and store a large amount of duplicate data through a unique equalization and compression processing method, reducing the occupation of data storage space, lowering the data storage cost, and at the same time improving the management efficiency of data storage. For non-duplicate data, a storage processing method based on the acquisition time and binary features is adopted, and reasonable grouping and conversion storage are carried out according to the data capacity and binary features, further optimizing the data storage structure and facilitating rapid retrieval and analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of green electricity budget data management, and specifically to a green electricity budget management system and method. Background Art

[0002] Green electricity budget management is crucial for the efficient operation and sustainable development of green electricity enterprises. With the increase in green electricity projects and the explosive growth of data volume, traditional green electricity budget management methods face many challenges.

[0003] The patent application with the publication number CN117609167A discloses a data classification processing and storage management system, including a data acquisition module, a temporary storage module, a timing module, a decompression module, and a compression storage module; the data acquisition module is used to acquire a data file and then transmit it to the temporary storage module; the temporary storage module is used to temporarily store the data file; the timing module can, according to the time preset by the staff, then compress the data file in the temporary storage module through the decompression module; the decompression module is used to compress the data file in the temporary storage module and decompress the compressed data file in the compression storage module; the compression storage module is used to store the compressed data file.

[0004] However, existing data management methods often lack systematicness and efficiency, and are difficult to meet the requirements in aspects such as data accuracy, correlation analysis, and storage optimization. Data may be incorrect and difficult to be discovered and corrected in a timely manner. The correlation between different types of data has not been fully explored. Data storage is also relatively chaotic, with a large amount of duplicate data occupying storage space, and the storage structure of the data is not conducive to quick query and analysis. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a green electricity budget management system and method, which solves the problems that data may be incorrect and difficult to be discovered and corrected in a timely manner, and data storage is also relatively chaotic, with a large amount of duplicate data occupying storage space.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A green electricity budget management system, including:

[0007] A data collection and processing module, which is used to collect green electricity budget data, and at the same time preprocess the obtained green electricity budget data to obtain preprocessed data, and transmit the preprocessed data to the preprocessed data analysis module;

[0008] The preprocessing data analysis module is used to analyze the acquired preprocessing data. By sorting and analyzing the time series of the preprocessing data, and performing secondary classification based on the source of the preprocessing data to obtain secondary classification data, and at the same time transmitting the secondary classification data to the data storage and analysis module;

[0009] The data storage and analysis module is used to store and analyze the acquired secondary classification data. It evenly divides the data warehouse according to the data types corresponding to the secondary classification data, and performs division processing according to the time period. At the same time, it extracts duplicate data and judges the necessity of elimination, generates processing information, and then stores the secondary classification data according to the processing information to generate storage management information, and transmits the storage management information to the management information output module;

[0010] The management information output module is used to display the acquired storage management information to the corresponding operators.

[0011] As a further solution of the present invention, the specific manner in which the data acquisition and processing module processes the green power budget data to obtain preprocessing data is as follows:

[0012] Acquire the green power budget data and label it. At the same time, analyze and judge the acquired green power budget data, eliminate the incorrect data in the green power budget data, and classify the remaining green power budget data according to the data type to obtain preprocessing data of the same type.

[0013] As a further solution of the present invention, the specific manner in which the preprocessing data analysis module analyzes the preprocessing data is as follows:

[0014] Acquire the preprocessing data, label the corresponding different green power budget data in the preprocessing data as i, and i = 1, 2,..., j, where j represents the number of green power budget data in the preprocessing data of the same type. At the same time, acquire the time series corresponding to the green power budget data i in the preprocessing data, and sort it from far to near according to the time series. Then acquire the data source corresponding to the green power budget data i, perform secondary classification on the green power budget data i according to the data source to obtain secondary classification data, and at the same time perform correlation analysis on the obtained secondary classification data, and integrate the secondary classification data with existing correlations.

[0015] As a further solution of the present invention, the specific manner in which the data storage and analysis module stores and analyzes the secondary classification data is as follows:

[0016] A data warehouse is established, and secondary classification data is obtained at the same time. The data capacity of the data warehouse is evenly divided into n storage spaces according to the data types corresponding to the secondary classification data, and n = 1, 2, …, m, where m represents the number of storage spaces. Then, the secondary classification data is sequentially matched with the storage spaces with corresponding labels, and at the same time, a group of storage spaces is randomly selected as a template object for analysis;

[0017] The secondary classification data in the target object is obtained, and the secondary classification data is identified and classified into duplicate data and non-duplicate data. Then, the duplicate data is labeled as o, and o = 1, 2, …, p, where p represents the data types of the duplicate data. And the duplicate data o is judged for elimination. All the duplicate data corresponding to the same type of duplicate data o is obtained, and the corresponding acquisition time is obtained. The obtained acquisition time is judged. If there is duplicate data with the same acquisition time, the duplicate data with the latest acquisition time is retained, and the remaining duplicate data is eliminated. If there is no duplicate data with the same time, the duplicate data is not processed and retained. And so on, all the duplicate data o is processed, and at the same time, the storage space is divided according to the data capacity of the duplicate data and the non-duplicate data.

[0018] As a further solution of the present invention, the specific method for the data storage and analysis module to divide the storage space according to the data capacity of the duplicate data and the non-duplicate data is as follows:

[0019] Then, the processed duplicate data and non-duplicate data are obtained, and the data capacity corresponding to the duplicate data and the data capacity corresponding to the non-duplicate data are obtained. At the same time, the ratio of the two is calculated, and the obtained ratio is used as the division standard of the storage space, and the storage space is divided into a first storage space and a second storage space. Then, the duplicate data and the non-duplicate data are respectively subjected to storage analysis.

[0020] As a further solution of the present invention, the specific method for the data storage and analysis module to perform storage analysis on the duplicate data is as follows:

[0021] All the duplicate data is obtained and the corresponding data capacity is obtained. Then, the duplicate data is evenly divided into nine equal parts according to the data capacity, and an evenly divided duplicate data group is obtained, which is labeled as 1, 2, …, 9. And the obtained evenly divided duplicate data group is compressed to obtain a compressed duplicate data packet, and then it is stored to generate storage management information.

[0022] As a further solution of the present invention, the specific method for the data storage and analysis module to perform storage analysis on the non-duplicate data is as follows:

[0023] Obtain all non-duplicate data and combine the non-duplicate data according to the corresponding acquisition time to obtain a non-duplicate data group. Then, evenly divide the non-duplicate data group according to the data capacity of the non-duplicate data group to obtain i non-duplicate data groups, where i = 1, 2, …, j, and j represents the number of non-duplicate data groups. At the same time, convert the non-duplicate data group i into binary, and obtain the number of non-zero binary numbers in the converted non-duplicate data group i, and process according to the number of non-zero binary numbers;

[0024] If the number of non-zero binary numbers is even, then replace the even-position non-zero binary numbers in the non-duplicate data group i, and at the same time perform a reverse order process on the whole after replacement, and store the non-duplicate data group after the reverse order process to generate storage management information.

[0025] If the number of non-zero binary numbers is odd, then replace the odd-position non-zero binary numbers in the non-duplicate data group i, and perform a reverse order on the whole after replacement to generate storage management information;

[0026] Finally, transmit the generated storage management information to the management information output module.

[0027] A green electricity budget management method, which specifically includes the following steps:

[0028] Step 1: Collect green electricity budget data, and at the same time preprocess the obtained green electricity budget data to obtain preprocessed data;

[0029] Step 2: Analyze the obtained preprocessed data, sort and analyze the time series of the preprocessed data, and perform secondary classification based on the source of the preprocessed data to obtain secondary classification data;

[0030] Step 3: Perform storage analysis on the obtained secondary classification data, evenly divide the data warehouse according to the corresponding data types of the secondary classification data, and divide according to the time period. At the same time, extract duplicate data and judge the necessity of elimination to generate processing information, and then store the secondary classification data according to the processing information to generate storage management information;

[0031] Step 4: Display the generated storage management information to the corresponding operators.

[0032] Beneficial effects

[0033] The present invention provides a green electricity budget management system and method. Compared with the prior art, it has the following beneficial effects:

[0034] Through strict data verification rules and logical checks, the present invention can accurately detect and eliminate incorrect data in green electricity budget data. For example, in the inspection examples of cost data and power generation data, it ensures the authenticity and reliability of the data, making the data relied on by enterprises during the budget preparation and decision-making processes more accurate, avoiding budget deviations and decision-making mistakes caused by incorrect data, and improving the stability and efficiency of enterprise operations.

[0035] By adopting advanced correlation analysis methods, such as through Pearson correlation coefficients and regression models, etc., to conduct correlation analysis on different types of green electricity budget data, the internal relationships between the data can be clearly revealed. This helps enterprises deeply understand the operation mechanism of the green electricity business.

[0036] For duplicate data, not only can it be accurately identified, but also through unique equalization and compression processing methods, a large amount of duplicate data is effectively compressed and stored, reducing the occupancy of data storage space, lowering the data storage cost, and at the same time improving the management efficiency of data storage. For non-duplicate data, a storage processing method based on acquisition time and binary characteristics is adopted, and reasonable grouping and conversion storage are carried out according to the data capacity and binary characteristics, further optimizing the data storage structure for quick retrieval and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a system block diagram of the present invention;

[0038] Figure 2 It is a step method diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0040] Embodiment 1. Please refer to Figure 1 , this application provides a green electricity budget management system, including: a data acquisition and processing module, a preprocessing data analysis module, a data storage and analysis module, and a management information output module. At the same time, in combination with Figure 1 it can be known that the above functional modules are unidirectionally electrically connected.

[0041] The data acquisition and processing module is used to collect green electricity budget data, and at the same time preprocess the obtained green electricity budget data to obtain preprocessed data, and transmit the preprocessed data to the preprocessing data analysis module.

[0042] Obtain the green power budget data and label it. At the same time, analyze and judge the obtained green power budget data, and eliminate the incorrect data in the green power budget data. The specific incorrect data is discovered and corrected through data verification rules and logical checks. For example, for the equipment purchase cost in the cost data, check whether it is within a reasonable market price range. If the purchase price of a certain equipment far exceeds the market average price of the same type of equipment and there is no reasonable explanation, it may be incorrect data; for the power generation data, if the power generation volume in a certain period exceeds several times the rated power generation volume of the equipment and there is no special situation (such as temporary over-generation caused by extreme weather), then this data is suspicious. Then classify the remaining green power budget data according to the data type to obtain preprocessed data of the same type. The specific green power budget data includes cost data, revenue data, power generation data, and market data. Integrate all cost data into a cost data set, revenue data into a revenue data set, power generation data into a power generation data set, and market data into a market data set, so as to obtain the corresponding preprocessed data of the same type.

[0043] Preprocessed data analysis module, which is used to analyze the obtained preprocessed data. By sorting and analyzing the time series of the preprocessed data, and performing secondary classification based on the source of the preprocessed data to obtain secondary classification data, and at the same time transmit the secondary classification data to the data storage and analysis module.

[0044] Get the preprocessed data, and the preprocessed data here are represented as the same type of preprocessed data, and label the corresponding different green electricity budget data in the preprocessed data as i, and i=1, 2, ..., j, where j represents the number of green electricity budget data in the same type of preprocessed data, and at the same time get the time series corresponding to the green electricity budget data i in the preprocessed data, and sort them from far to near according to the time series, then get the data source corresponding to the green electricity budget data i, and the data source here is represented as the collection source corresponding to the data, for example, power generation data is usually directly obtained from the monitoring system of the power generation equipment, such as the power output data of the wind turbine and the power generation efficiency data of the solar panel, which are all obtained by the equipment itself. The sensors are used to collect and record data in real time; cost data mostly comes from project planning documents, financial statements, and procurement contracts. For example, the equipment procurement cost can be extracted from the equipment procurement contract at the beginning of the project, and the operation and maintenance cost is determined based on the maintenance expense details recorded by the financial department. The green electricity budget data i is secondary classified according to the data source to obtain secondary classified data, and the secondary classified data obtained are analyzed for correlation, and the secondary classified data with correlation are integrated. The specific method of correlation analysis here is to identify by calculating the correlation, and specifically to determine by the Pearson correlation coefficient. Taking the power generation and the power of the power generation equipment as an example, a regression model can be established. Assuming that there is a linear relationship between the power generation (Y) and the power of the power generation equipment (X), by collecting the power generation data of multiple groups of different power equipment, the regression equation Y=aX+b is fitted, where a and b are regression coefficients. If the goodness of fit of the regression equation is high, it means that the correlation between the power generation and the equipment power can be well described by the equation, that is, the equipment power has a significant explanatory power for the power generation.

[0045] In a specific example, suppose we have a set of pre-processed data of the same type for revenue data, including monthly electricity sales revenue data for the past three years. First, we number these data from to (assuming one piece of data per month), obtain the time series corresponding to each piece of data (such as January 2021, February 2021, etc.) and sort them. The source of these data is the financial records of the electricity sales department. Based on this source, they are secondary classified into a set of electricity sales revenue source data.

[0046] The data storage and analysis module is used to store and analyze the acquired secondary classification data, evenly divide the data warehouse according to the data type corresponding to the secondary classification data, and divide the data according to the time period. At the same time, it extracts duplicate data and determines the necessity of eliminating it, generates processing information, and then stores the secondary classification data according to the processing information to generate storage management information, and transmits the storage management information to the management information output module.

[0047] Build a data warehouse, and at the same time obtain secondary classification data. Divide the data capacity of the data warehouse evenly according to the data types corresponding to the secondary classification data to obtain n storage spaces, where n = 1, 2, …, m, and m represents the number of storage spaces. Specifically, the value of m is the same as the number of data types of the secondary classification data. Then, match the secondary classification data with the storage spaces with corresponding labels in sequence, and this matching is in the order of labels. For example, if the labels of the secondary classification data are 1, 2, 3, then match the secondary classification data with label 1 with storage space 1, and so on. At the same time, select any group of storage spaces as the template object for analysis;

[0048] Obtain the secondary classification data in the target object, and at the same time identify and classify the secondary classification data to obtain duplicate data and non-duplicate data. Here, the duplicate data only simply represents data with the same value. Then, label the duplicate data as o, where o = 1, 2, …, p, and p represents the data types of the duplicate data. Then, perform a deletion judgment on the duplicate data o, obtain all the duplicate data corresponding to the same type of duplicate data o, and obtain the corresponding acquisition time. Judge the obtained acquisition time. If there are duplicate data with the same acquisition time, retain the duplicate data with the latest acquisition time, and delete the remaining duplicate data. Here, retaining the latest acquisition time specifically refers to the duplicate data corresponding to the latest acquisition time. For example, if there are three groups of duplicate data of the same type, and they are all generated on the same day, and the corresponding times are 09:23, 14:08, and 16:15 respectively, then retain the duplicate data corresponding to the time 16:15, and delete the remaining duplicate data. If there are no duplicate data with the same time, do not process the duplicate data and retain it. And so on, process all the duplicate data o;

[0049] Then, obtain the processed duplicate data and non-duplicate data. Here, the duplicate data and non-duplicate data are the data in the same group of secondary classification data. Obtain the data capacity corresponding to the duplicate data and the data capacity corresponding to the non-duplicate data. At the same time, calculate the ratio of the two, and use the obtained ratio as the division standard for the storage space, and divide the storage space into the first storage space and the second storage space. When storing data, store the data with a larger data capacity in the larger storage space. For example, if the data capacity of the non-duplicate data is greater than that of the duplicate data, then among the first storage space and the second storage space obtained by division, use the larger storage space to store the non-duplicate data. Then, perform storage analysis on the duplicate data and non-duplicate data respectively;

[0050] The specific method for storing and analyzing duplicate data is as follows: Obtain all duplicate data and the corresponding data capacity, then evenly divide the duplicate data into nine equal parts according to the data capacity to obtain evenly divided duplicate data groups, labeled as 1, 2, …, 9, and perform compression processing on the obtained evenly divided duplicate data groups to obtain compressed duplicate data packets, and then store them to generate storage management information.

[0051] For example, in the cost data of a green power enterprise, there are a large number of duplicate records regarding the procurement of a certain type of solar panel. These duplicate records are scattered in different purchase orders and financial statements. By data sorting, all of them are found, and it is calculated that the total storage space they occupy is 100 GB (this is the data capacity).

[0052] Then, these duplicate data are evenly divided into nine parts according to the data capacity to form evenly divided duplicate data groups, labeled from 1 to 9. Taking the above-mentioned duplicate data of solar panel procurement as an example, if its total capacity is 100 GB, then each evenly divided duplicate data group is approximately 11.1 GB (100 GB ÷ 9, and in actual division, it can be flexibly processed according to the data characteristics to ensure relatively balanced grouping).

[0053] Next, implement an efficient compression algorithm (such as lossless compression algorithms LZ77, LZ78, etc.) for each evenly divided duplicate data group for compression processing to convert it into a compressed duplicate data packet.

[0054] For example, for the evenly divided duplicate data group labeled 1, by using the LZ77 algorithm for compression, the original 11.1 GB of data may be compressed to about 2 GB, significantly reducing the storage space requirement.

[0055] Finally, store these compressed duplicate data packets in a specified storage medium (such as an enterprise-level data storage server, a distributed storage system, etc.), and generate a detailed record of storage management information, including key information such as the packet number, storage path, data capacity before and after compression, and generation time.

[0056] For example, record that the storage path of compressed duplicate data packet 1 is “ / data / greenpower / cost / compressed / 1”, the capacity before compression is 11.1 GB, the capacity after compression is 2 GB, and the generation time is “2024-01-01 10:00:00”, etc.

[0057] The specific method for storing and analyzing non-repetitive data is as follows: Obtain all non-repetitive data and combine the non-repetitive data according to the corresponding acquisition time to obtain a non-repetitive data group. Then, evenly divide the non-repetitive data group according to the data capacity of the non-repetitive data group to obtain i non-repetitive data groups, where i = 1, 2, …, j, and j represents the number of non-repetitive data groups. At the same time, convert the non-repetitive data group i into binary, and obtain the number of non-zero binary numbers in the converted non-repetitive data group i, and process according to the number of non-zero binary numbers;

[0058] If the number of non-zero binary numbers is even, then replace the non-zero binary numbers in the even positions in the non-repetitive data group i. Here, the replacement means replacing the binary number 1 with 0, and at the same time, perform a reverse order process on the whole after replacement, and store the non-repetitive data group after the reverse order process to generate storage management information.

[0059] If the number of non-zero binary numbers is odd, then replace the non-zero binary numbers in the odd positions in the non-repetitive data group i, and perform a reverse order on the whole after replacement to generate storage management information;

[0060] Finally, transmit the generated storage management information to the management information output module.

[0061] Suppose the total is 60MB. We divide it into 3 groups on average (j = 3), and each group is about 20MB (i = 1, 2, 3). Take the first non-repetitive data group (i = 1) as an example, and convert this group of power generation data into binary form. Suppose after conversion, it is found through statistics that the number of non-zero binary numbers in the obtained binary data string is even, for example, there are 10 non-zero binary numbers. At this time, the system will find the non-zero binary numbers in the even positions (such as the 1s in the 2nd, 4th, 6th, 8th, 10th, etc. positions), replace them all with 0, and then perform a reverse order arrangement on the entire binary data string. For example, the original binary data string is "101100101011" (for the convenience of example, the actual data conversion will generate a longer and more complex binary string according to the data value). After replacing the non-zero binary numbers in the even positions, it becomes "100100100011", and after reversing, it gets "110010010001". Finally, store this group of processed binary data in the specified storage area, such as " / greenpower / generation_data / processed_group1", and record the storage management information, such as "Data source: [specific power generation equipment number and time period], data capacity before processing: about 20MB, processing method: replacement of non-zero binary numbers in even positions and reverse order, storage time: [specific date and time]", etc.

[0062] The management information output module is used to display the obtained storage management information to the corresponding operator.

[0063] Example 2. Please refer to Figure 2 , this application provides a green electricity budget management method, which specifically includes the following steps:

[0064] Step 1: Collect green electricity budget data, and at the same time preprocess the obtained green electricity budget data to obtain preprocessed data. The processing method here is the same as that of the data collection processing module in Example 1;

[0065] Step 2: Analyze the obtained preprocessed data. Sort and analyze the time series of the preprocessed data, and perform secondary classification based on the source of the preprocessed data to obtain secondary classification data. The processing method here is the same as that of the preprocessed data analysis module in Example 1;

[0066] Step 3: Store and analyze the obtained secondary classification data. Divide the data warehouse equally according to the data types corresponding to the secondary classification data, and perform division processing according to the time period. At the same time, extract duplicate data and judge the necessity of deletion, generate processing information, and then store the secondary classification data according to the processing information to generate storage management information. The processing method here is the same as that of the data storage analysis module in Example 1;

[0067] Step 4: Display the generated storage management information to the corresponding operators.

[0068] At the same time, the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0069] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A green electricity budget management system, characterized in that: include: The data acquisition and processing module is used to acquire green electricity budget data, pre-process the acquired green electricity budget data to obtain pre-processed data, and transmit the pre-processed data to the pre-processed data analysis module; The preprocessing data analysis module is used to analyze the acquired preprocessing data by sorting and analyzing the time series of the preprocessing data, performing secondary classification based on the source of the preprocessing data to obtain secondary classification data, and transmitting the secondary classification data to the data storage and analysis module; The data storage and analysis module is used to store and analyze the acquired secondary classification data, evenly divide the data warehouse according to the data type corresponding to the secondary classification data, and divide and process it according to the time period, extract duplicate data and determine the necessity of eliminating it, generate processing information, and then store the secondary classification data according to the processing information to generate storage management information, and transmit the storage management information to the management information output module. The specific method of storing and analyzing duplicate data is as follows: Obtain all duplicate data and the corresponding data capacity, then divide the duplicate data into nine equal parts according to the data capacity to obtain evenly divided duplicate data groups, which are numbered 1, 2, ..., 9, and compress the obtained evenly divided duplicate data groups to obtain compressed duplicate data packets, which are then stored to generate storage management information; The specific method of storing and analyzing non-duplicate data is as follows: Acquire all non-repetitive data and combine the non-repetitive data according to the corresponding acquisition time to obtain non-repetitive data groups, then divide the non-repetitive data groups equally according to the data capacity to obtain i non-repetitive data groups, and i=1, 2, ..., j, where j represents the number of non-repetitive data groups, and perform binary conversion on the non-repetitive data group i, and obtain the number of non-zero binary numbers in the converted non-repetitive data group i, and process according to the number of non-zero binary numbers; If the number of non-zero binary numbers is an even number, the even-numbered non-zero binary numbers in the non-repeating data group i are replaced, and the replaced whole is reversed, and the reversed non-repeating data group is stored to generate storage management information; If the number of non-zero binary numbers is an odd number, the odd-numbered non-zero binary numbers in the non-repeating data group i are replaced, and the replaced whole is reversed to generate storage management information; Finally, the generated storage management information is transmitted to the management information output module; Establish a data warehouse, obtain secondary classified data at the same time, and divide the data capacity of the data warehouse equally according to the data types corresponding to the secondary classified data to obtain n storage spaces, where n=1, 2, ..., m, where m represents the number of storage spaces, then match the secondary classified data with the corresponding numbered storage spaces in turn, and select any one group of storage spaces as a template object for analysis; Acquire the secondary classified data in the target object, and identify and classify the secondary classified data to obtain duplicate data and non-duplicate data. Then, label the duplicate data as o, where o=1, 2, ..., p, where p represents the data type of the duplicate data. Perform elimination judgment on the duplicate data o, acquire all duplicate data corresponding to the same type of duplicate data o, and acquire the corresponding acquisition time. Judge the acquired acquisition time. If there are duplicate data with the same acquisition time, retain the duplicate data with the latest acquisition time, and eliminate the rest of the duplicate data. If there are no duplicate data with the same time, do not process the duplicate data and retain it. Similarly, process all duplicate data o, and divide the storage space according to the data capacity of duplicate data and non-duplicate data. Then, the duplicate data and non-duplicate data after the analysis are obtained, and the data capacity corresponding to the duplicate data and the data capacity corresponding to the non-duplicate data are obtained, and the ratio of the two is calculated at the same time, and the obtained ratio is used as the division standard of the storage space, and the storage space is divided into a first storage space and a second storage space, and then the storage analysis of the duplicate data and the non-duplicate data is performed respectively; The management information output module is used to display the acquired storage management information to the corresponding operator.

2. A green electricity budget management system according to claim 1, characterized in that: The specific method in which the data acquisition and processing module pre-processes the green electricity budget data to obtain pre-processed data is: The green electricity budget data is obtained and labeled, and the obtained green electricity budget data is analyzed and judged, the erroneous data in the green electricity budget data is eliminated, and the remaining green electricity budget data is classified according to the data type to obtain pre-processed data of the same type.

3. A green electricity budget management system according to claim 1, characterized in that: The specific method in which the preprocessing data analysis module analyzes the preprocessing data is as follows: The preprocessed data is obtained, and different green electricity budget data corresponding to the preprocessed data are labeled as i, and i=1, 2, ..., j, where j represents the number of green electricity budget data in the same type of preprocessed data. At the same time, the time series corresponding to the green electricity budget data i in the preprocessed data is obtained, and the time series is sorted from far to near. Then, the data source corresponding to the green electricity budget data i is obtained, and the green electricity budget data i is secondary classified according to the data source to obtain secondary classified data. At the same time, the obtained secondary classified data are subjected to correlation analysis, and the secondary classified data with correlation are integrated.

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