Power grid financial data management and control method and system based on cloud data platform, and medium

Abstract

The invention provides a power grid financial data management and control method and system based on a cloud data platform, and a medium. The method comprises the following steps: receiving first power grid financial declaration information; performing repeatability verification on the first power grid financial declaration information; if the power grid financial information is not repeated with the power grid financial information in the cloud data platform, inputting the power grid financial information into the cloud data platform; identifying a power grid financial category to which the first power grid financial declaration information belongs according to the first power grid financial declaration information; obtaining a plurality of shared task pools corresponding to the power grid financial category to which the first power grid financial declaration information belongs; based on the processing efficiency, the task backlog amount and the load capacity of the plurality of shared task pools, selecting a target shared task pool from the plurality of shared task pools through a preset selection algorithm; and distributing the first power grid financial declaration information to the target shared task pool for processing the power grid financial declaration information. According to the invention, the cloud data platform is used to realize intelligent management and control of power grid financial data, and the power grid financial business processing efficiency is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid financial management and control, and in particular to a power grid financial data management and control method, system and medium based on a cloud data platform. Background Art

[0002] In the traditional operation process of power grid financial management, the financial department must first prepare business reports and transmit them to the designated department head through fax, PC, printing, etc., and then notify the relevant department head to convey them to the employees in the department under the head's jurisdiction. The steps involved in the above process system are quite cumbersome and inefficient, which can easily lead to problems such as complicated information transmission process, loss of report information, and scattered management information, which reduces the efficiency of power grid financial business processing and increases communication costs.

[0003] Power grid enterprises are large-scale group enterprises that build and operate power grids. They have many subordinate member units, many users, and many suppliers, and they have large amounts of assets, funds, and information. With the improvement of internal management requirements of enterprises, it is necessary to accelerate the research and promotion of financial system big data integration and analysis, fully collect data, explore data value, give full play to financial management functions, and provide strong financial support for power grid enterprises to achieve their strategic goals. Moreover, power grid financial big data has extensive and close connections with the economy and society. The value of power big data is not only limited to the power industry, but also reflected in the operation of the national economy, social progress, and innovation and development of various industries.

[0004] Cloud computing is an emerging business computing model developed from distributed computing, parallel processing, and grid computing. Cloud data platform is a general term for data integration, data analysis, data integration, data distribution, and data early warning platforms based on cloud computing models; and how to apply cloud data platform to power grid financial data management and control to achieve intelligent and efficient power grid financial data management and control is the focus of current power grid financial field. Summary of the invention

[0005] In order to solve at least one of the above technical problems, the present invention proposes a power grid financial data management and control method, system and medium based on a cloud data platform, which can use the data integration, data analysis, data consolidation and data distribution capabilities of the cloud data platform to realize intelligent management and control of power grid financial data, thereby effectively improving the efficiency of power grid financial business processing.

[0006] The first aspect of the present invention proposes a power grid financial data management and control method based on a cloud data platform, the method comprising:

[0007] Receiving first power grid financial declaration information submitted from a power grid financial declaration terminal;

[0008] Conduct duplication check on the First Grid's financial declaration information and all grid financial information stored in the cloud data platform;

[0009] If the first power grid financial declaration information is not repeated with all power grid financial information in the cloud data platform, the first power grid financial declaration information is allowed to be entered into the cloud data platform for power grid financial data management and control;

[0010] Identify the power grid financial category to which the first power grid belongs based on its financial declaration information;

[0011] Acquire multiple shared task pools corresponding to the power grid financial category to which the first power grid financial declaration information belongs, and respectively acquire the processing efficiency, task backlog, and load capacity of each shared task pool;

[0012] Based on the processing efficiency, task backlog and load capacity of the multiple shared task pools, a target shared task pool is selected from the multiple shared task pools through a preset selection algorithm;

[0013] The first power grid financial declaration information is distributed to the target shared task pool for power grid financial declaration information processing.

[0014] Furthermore, the financial declaration information of the first power grid is verified for duplication with all power grid financial information stored in the cloud data platform, including:

[0015] All power grid financial information in the preset cloud data platform contains corresponding hash values;

[0016] Performing hash calculation on the financial declaration information of the first power grid through a hash algorithm to obtain a corresponding hash value;

[0017] Compare the hash values ​​of the financial declaration information of the first power grid with the hash values ​​of all power grid financial information in the cloud data platform;

[0018] If the comparison is consistent, the financial declaration information of the first power grid is deemed to be repeated; otherwise, the financial declaration information of the first power grid is deemed not to be repeated.

[0019] Furthermore, based on the processing efficiency, task backlog and load capacity of the multiple shared task pools, a target shared task pool is selected from the multiple shared task pools through a preset selection algorithm, specifically including:

[0020] Compare the processing efficiency of each shared task pool with the processing efficiency of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the processing efficiency item of the former shared task pool;

[0021] Compare the task backlog of each shared task pool with the task backlog of the remaining other shared task pools one by one. If the former is lower than the latter, add 1 point to the task backlog of the former shared task pool;

[0022] Compare the load capacity of each shared task pool with the load capacity of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the load capacity item of the former shared task pool;

[0023] After all shared task pools have completed the pairwise comparison of processing efficiency, task backlog, and load capacity, the scores of each shared task pool in the processing efficiency item, the score in the task backlog item, and the score in the load capacity item are counted;

[0024] The preset processing efficiency, task backlog and load capacity have different influence weights on the selected target shared task pool. The score of each shared task pool in the processing efficiency item is multiplied by the corresponding influence weight to obtain the weight score of the processing efficiency item; the score of each shared task pool in the task backlog item is multiplied by the corresponding influence weight to obtain the weight score of the task backlog item; the score of each shared task pool in the load capacity item is multiplied by the corresponding influence weight to obtain the weight score of the load capacity item;

[0025] Adding the weight score of the processing efficiency item, the weight score of the task backlog item, and the weight score of the load capacity item of each shared task pool to obtain a first value based on each shared task pool;

[0026] Adding the impact weight of the processing efficiency item, the impact weight of the task backlog item, and the impact weight of the load capacity item to obtain a second value;

[0027] Divide the first value of each shared task pool by the second value to obtain a processing capacity score of each shared task pool that can currently receive new power grid financial declaration information;

[0028] The shared task pool with the highest processing capability score is selected as the target shared task pool.

[0029] Furthermore, after the first power grid financial declaration information is approved for entry into the cloud data platform, the method further includes:

[0030] Construct a prediction model for power grid financial processing time;

[0031] The power grid financial processing time prediction model is trained and optimized through sample data to obtain an optimized power grid financial processing time prediction model;

[0032] Extracting financial attribute information and time attribute information from the financial declaration information of the first power grid, wherein the financial attribute information at least includes the amount of declaration documents and the completeness of the information; and the time attribute information at least includes monthly information and daily information;

[0033] The financial attribute information and time attribute information of the first power grid financial declaration information are combined with the current operation status of the cloud data platform, and predicted through the power grid financial processing time prediction model to obtain the estimated processing time of the declaration process;

[0034] The estimated processing time of the declaration process will be fed back to the power grid financial declaration terminal.

[0035] Furthermore, after obtaining the estimated processing time of the declaration process by predicting through the power grid financial processing time prediction model, the method further includes:

[0036] Acquire multiple historical power grid financial declaration data, each historical power grid financial declaration data at least includes historical power grid financial declaration information, the operation status of the cloud data platform in historical time, and the actual processing time of the historical power grid financial declaration information;

[0037] Based on the historical power grid financial declaration information in each historical power grid financial declaration data, corresponding financial attribute information and time attribute information are extracted;

[0038] Performing feature operations on the financial attribute information and time attribute information of each historical power grid financial declaration data to obtain attribute feature values ​​of each historical power grid financial declaration data;

[0039] Performing feature operations on the financial attribute information and the time attribute information of the first power grid financial declaration information to obtain attribute feature values ​​of the first power grid financial declaration information;

[0040] Performing a difference operation on the attribute characteristic value of each historical power grid financial declaration data and the attribute characteristic value of the first power grid financial declaration information to obtain a difference based on each historical power grid financial declaration data;

[0041] Determine whether the difference is less than a second preset threshold value, and if so, mark the corresponding historical power grid financial declaration data as valuable data and enter it into the first database;

[0042] Based on each historical power grid financial declaration data in the first database, the financial attribute information and time attribute information of the historical power grid financial declaration information are combined with the operation status of the historical time cloud data platform, and predicted through the power grid financial processing time prediction model to obtain the estimated processing time of the historical power grid financial declaration information;

[0043] Based on each historical power grid financial declaration data in the first database, performing a difference operation between the actual processing time of the historical power grid financial declaration information and the estimated processing time of the historical power grid financial declaration information to obtain a processing time difference;

[0044] Performing a vector sum operation on the processing time differences corresponding to all historical power grid financial declaration data in the first database to obtain a processing time difference vector sum;

[0045] Dividing the sum of the processing time difference vectors by the amount of historical power grid financial declaration data in the first database, to obtain an average processing time difference vector;

[0046] The predicted estimated processing time of the declaration process of the financial declaration information of the first power grid is added to the average vector of the processing time difference to obtain a revised estimated processing time of the declaration process.

[0047] Further, feature operations are performed on the financial attribute information and time attribute information of each historical power grid financial declaration data to obtain the attribute feature value of each historical power grid financial declaration data; feature operations are performed on the financial attribute information and time attribute information of the first power grid financial declaration information to obtain the attribute feature value of the first power grid financial declaration information; a difference operation is performed on the attribute feature value of each historical power grid financial declaration data and the attribute feature value of the first power grid financial declaration information to obtain the difference based on each historical power grid financial declaration data, specifically including:

[0048] It is assumed that the cloud data platform has different load levels every month of the year and different load levels every day of the month, and a monthly load level correspondence table and a daily load level correspondence table are established respectively;

[0049] The amount of declaration documents of the financial attribute information of each historical power grid financial declaration data is divided by the information completeness to obtain the financial attribute characteristic value of each historical power grid financial declaration data;

[0050] Based on the monthly information in the time attribute information of each historical power grid financial declaration data, the corresponding monthly load degree is found according to the monthly load degree corresponding table; based on the daily information in the time attribute information of each historical power grid financial declaration data, the corresponding daily load degree is found according to the daily load degree corresponding table; the monthly load degree corresponding to each historical power grid financial declaration data is multiplied by the corresponding daily load degree to obtain the time attribute characteristic value of each historical power grid financial declaration data;

[0051] Dividing the amount of declaration documents of the financial attribute information of the first power grid financial declaration information by the information completeness, to obtain the financial attribute characteristic value of the first power grid financial declaration information;

[0052] Based on the monthly information in the time attribute information of the financial declaration information of the first power grid, the corresponding monthly load degree is found out according to the monthly load degree correspondence table; based on the daily information in the time attribute information of the financial declaration information of the first power grid, the corresponding daily load degree is found out according to the daily load degree correspondence table; the monthly load degree corresponding to the financial declaration information of the first power grid is multiplied by the corresponding daily load degree to obtain the time attribute characteristic value of the financial declaration information of the first power grid;

[0053] Perform a difference operation on the financial attribute characteristic value of each historical power grid financial declaration data and the financial attribute characteristic value of the first power grid financial declaration information to obtain a difference degree A1 of the financial attribute characteristic value;

[0054] Perform a difference operation on the time attribute characteristic value of each historical power grid financial declaration data and the time attribute characteristic value of the first power grid financial declaration information to obtain a time attribute characteristic value difference A2;

[0055] The influence weight of the preset financial attribute information on the difference calculation between the historical power grid financial declaration data and the first power grid financial declaration information is K1, and the influence weight of the time attribute information on the difference calculation between the historical power grid financial declaration data and the first power grid financial declaration information is K2;

[0056] Multiply the financial attribute characteristic value difference A1 by the corresponding influence weight K1 to obtain the financial attribute characteristic value difference weight value A1*K1, and multiply the time attribute characteristic value difference A2 by the corresponding influence weight K2 to obtain the time attribute characteristic value difference weight value A2*K2;

[0057] Add the financial attribute characteristic value difference weight value A1*K1 and the time attribute characteristic value difference weight value A2*K2 to obtain the difference weight sum (A1*K1+A2*K2), and divide the difference weight sum (A1*K1+A2*K2) by (K1+K2) to obtain the difference (A1*K1+A2*K2) / (K1+K2) based on each historical power grid financial declaration data.

[0058] The present invention also proposes a power grid financial data management and control system based on a cloud data platform, comprising a memory and a processor, wherein the memory comprises a power grid financial data management and control method program based on a cloud data platform, and when the power grid financial data management and control method program based on a cloud data platform is executed by the processor, the following steps are implemented:

[0059] Receiving first power grid financial declaration information submitted from a power grid financial declaration terminal;

[0060] Conduct duplication check on the First Grid's financial declaration information and all grid financial information stored in the cloud data platform;

[0061] If the first power grid financial declaration information is not repeated with all power grid financial information in the cloud data platform, the first power grid financial declaration information is allowed to be entered into the cloud data platform for power grid financial data management and control;

[0062] Identify the power grid financial category to which the first power grid belongs based on its financial declaration information;

[0063] Acquire multiple shared task pools corresponding to the power grid financial category to which the first power grid financial declaration information belongs, and respectively acquire the processing efficiency, task backlog, and load capacity of each shared task pool;

[0064] Based on the processing efficiency, task backlog and load capacity of the multiple shared task pools, a target shared task pool is selected from the multiple shared task pools through a preset selection algorithm;

[0065] The first power grid financial declaration information is distributed to the target shared task pool for power grid financial declaration information processing.

[0066] Furthermore, the financial declaration information of the first power grid is verified for duplication with all power grid financial information stored in the cloud data platform, including:

[0067] All power grid financial information in the preset cloud data platform contains corresponding hash values;

[0068] Performing hash calculation on the financial declaration information of the first power grid through a hash algorithm to obtain a corresponding hash value;

[0069] Compare the hash values ​​of the financial declaration information of the first power grid with the hash values ​​of all power grid financial information in the cloud data platform;

[0070] If the comparison is consistent, the financial declaration information of the first power grid is deemed to be repeated; otherwise, the financial declaration information of the first power grid is deemed not to be repeated.

[0071] Furthermore, based on the processing efficiency, task backlog and load capacity of the multiple shared task pools, a target shared task pool is selected from the multiple shared task pools through a preset selection algorithm, specifically including:

[0072] Compare the processing efficiency of each shared task pool with the processing efficiency of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the processing efficiency item of the former shared task pool;

[0073] Compare the task backlog of each shared task pool with the task backlog of the remaining other shared task pools one by one. If the former is lower than the latter, add 1 point to the task backlog of the former shared task pool;

[0074] Compare the load capacity of each shared task pool with the load capacity of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the load capacity item of the former shared task pool;

[0075] After all shared task pools have completed the pairwise comparison of processing efficiency, task backlog, and load capacity, the scores of each shared task pool in the processing efficiency item, the score in the task backlog item, and the score in the load capacity item are counted;

[0076] The preset processing efficiency, task backlog and load capacity have different influence weights on the selected target shared task pool. The score of each shared task pool in the processing efficiency item is multiplied by the corresponding influence weight to obtain the weight score of the processing efficiency item; the score of each shared task pool in the task backlog item is multiplied by the corresponding influence weight to obtain the weight score of the task backlog item; the score of each shared task pool in the load capacity item is multiplied by the corresponding influence weight to obtain the weight score of the load capacity item;

[0077] Adding the weight score of the processing efficiency item, the weight score of the task backlog item, and the weight score of the load capacity item of each shared task pool to obtain a first value based on each shared task pool;

[0078] Adding the impact weight of the processing efficiency item, the impact weight of the task backlog item, and the impact weight of the load capacity item to obtain a second value;

[0079] Divide the first value of each shared task pool by the second value to obtain a processing capacity score of each shared task pool that can currently receive new power grid financial declaration information;

[0080] The shared task pool with the highest processing capability score is selected as the target shared task pool.

[0081] The present invention also proposes a computer-readable storage medium, which includes a power grid financial data control method program based on a cloud data platform. When the power grid financial data control method program based on a cloud data platform is executed by a processor, the steps of the power grid financial data control method based on a cloud data platform as described above are implemented.

[0082] The present invention proposes a power grid financial data management and control method, system and medium based on a cloud data platform, which utilizes the data integration, data analysis, data consolidation and data distribution capabilities of the cloud data platform to realize intelligent management and control of power grid financial data, thereby effectively improving the efficiency of power grid financial business processing.

[0083] Additional aspects and advantages of the present invention will be given in part in the following description, and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 A flow chart of a power grid financial data management and control method based on a cloud data platform of the present invention is shown;

[0085] Figure 2 A flow chart showing the repeatability check of power grid financial information by the cloud data platform of the present invention;

[0086] Figure 3 A forecasting flow chart showing the estimated processing time of the declaration process of the present invention is shown;

[0087] Figure 4 A block diagram of a power grid financial data management and control system based on a cloud data platform of the present invention is shown. DETAILED DESCRIPTION

[0088] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0089] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0090] Figure 1 A flow chart of a power grid financial data management and control method based on a cloud data platform of the present invention is shown.

[0091] like Figure 1 As shown, the first aspect of the present invention proposes a power grid financial data management and control method based on a cloud data platform, the method comprising:

[0092] S102, receiving first power grid financial declaration information submitted by a power grid financial declaration terminal;

[0093] S104, performing duplication check on the first power grid financial declaration information and all power grid financial information stored in the cloud data platform;

[0094] S106, if the first power grid financial declaration information is not repeated with all power grid financial information in the cloud data platform, the first power grid financial declaration information is allowed to be entered into the cloud data platform for power grid financial data management and control;

[0095] S108, identifying the power grid financial category to which the first power grid financial declaration information belongs according to the first power grid financial declaration information;

[0096] S110, obtaining multiple shared task pools corresponding to the power grid financial category to which the first power grid financial declaration information belongs, and respectively obtaining the processing efficiency, task backlog and load capacity of each shared task pool;

[0097] S112, based on the processing efficiency, task backlog and load capacity of the multiple shared task pools, and through a preset selection algorithm, a target shared task pool is selected from the multiple shared task pools;

[0098] S114, allocating the first power grid financial declaration information to the target shared task pool for power grid financial declaration information processing.

[0099] It should be noted that the various data and information required for the power grid financial data control and processing in this application are extracted from a third-party platform. To be precise, the technical solution of this application is a solution for obtaining information, data, and parameters for technical processing through external data interaction with a third-party power grid management platform. The technical solution of this application is a means of information acquisition, data interaction and extraction, and data processing technology, which is non-artificially set rules or economic / market / policy / project laws. The technical solution of this application has technical laws.

[0100] The power grid financial data management and control method based on the cloud data platform of the present invention utilizes the data integration, data analysis, data consolidation, and data distribution capabilities of the cloud data platform to realize intelligent management and control of power grid financial data, thereby effectively improving the processing efficiency of power grid financial business.

[0101] According to a specific embodiment of the invention, receiving first power grid financial declaration information submitted by a power grid financial declaration terminal specifically includes:

[0102] The power grid financial reporting terminal sends identity information to the cloud data platform;

[0103] The cloud data platform authenticates the identity information of the power grid financial reporting terminal;

[0104] After identity authentication is passed, authority will be allocated according to the identity of the power grid financial reporting terminal;

[0105] The power grid financial reporting terminal uses the public key of the cloud data platform to encrypt the first power grid financial reporting information, obtains the ciphertext information and transmits it to the cloud data platform;

[0106] The cloud data platform uses a private key to decrypt the ciphertext information and obtain the financial declaration information of the First Grid.

[0107] It will be appreciated that each grid finance category has multiple shared task pools.

[0108] According to a specific embodiment of the present invention, identifying the power grid financial category to which the first power grid financial declaration information belongs includes:

[0109] Extract multiple key information from the financial declaration information of the First Grid based on the attention mechanism;

[0110] If there are multiple grid finance categories, multiple key information is matched with each grid finance category one by one, and the matching degree is calculated;

[0111] The power grid financial category with the highest matching degree is used as the power grid financial category of the first power grid financial declaration information.

[0112] According to a specific embodiment of the present invention, multiple key information are matched with each power grid financial category one by one, and the matching degree is calculated, which specifically includes:

[0113] It is preset that each key information has different influence weights on determining the financial category of the power grid;

[0114] Each key information is matched with each power grid financial category. If the match is successful, the corresponding key information item of the corresponding power grid financial category is added 1 point;

[0115] After multiple key information are matched with all power grid financial categories respectively, the scores of each power grid financial category in each key information item are counted;

[0116] Based on each power grid financial category, the score of each key information item is multiplied by the corresponding impact weight to obtain the weight score of each key information item;

[0117] Based on each power grid financial category, the weighted scores of each key information item are added together to obtain the matching degree.

[0118] It can be understood that the power grid financial declaration information can be power grid financial reimbursement documents, such as salary payment application forms and travel expense reimbursement documents, but is not limited thereto.

[0119] like Figure 2 As shown, the first power grid financial declaration information is checked for duplication with all power grid financial information stored in the cloud data platform, specifically including:

[0120] S202, all power grid financial information in the preset cloud data platform includes corresponding hash values;

[0121] S204, performing hash calculation on the financial declaration information of the first power grid through a hash algorithm to obtain a corresponding hash value;

[0122] S206, comparing the hash value of the first power grid financial declaration information with the hash values ​​of all power grid financial information in the cloud data platform;

[0123] S208, if the comparison is consistent, it is determined that the financial declaration information of the first power grid is repeated, otherwise, it is determined that the financial declaration information of the first power grid is not repeated.

[0124] It can be understood that since the amount of information contained in the power grid financial information is large, if the entire content of the two power grid financial information is compared, the amount of information to be compared will be large, affecting the efficiency of the repeatability verification. The present invention greatly reduces the amount of information to be compared through hash value comparison and improves the efficiency of the repeatability verification.

[0125] According to an embodiment of the present invention, based on the processing efficiency, task backlog and load capacity of multiple shared task pools, a target shared task pool is selected from the multiple shared task pools through a preset selection algorithm, specifically including:

[0126] Compare the processing efficiency of each shared task pool with the processing efficiency of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the processing efficiency item of the former shared task pool;

[0127] Compare the task backlog of each shared task pool with the task backlog of the remaining other shared task pools one by one. If the former is lower than the latter, add 1 point to the task backlog of the former shared task pool;

[0128] Compare the load capacity of each shared task pool with the load capacity of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the load capacity item of the former shared task pool;

[0129] After all shared task pools have completed the pairwise comparison of processing efficiency, task backlog, and load capacity, the scores of each shared task pool in the processing efficiency item, the score in the task backlog item, and the score in the load capacity item are counted;

[0130] The preset processing efficiency, task backlog and load capacity have different influence weights on the selected target shared task pool. The score of each shared task pool in the processing efficiency item is multiplied by the corresponding influence weight to obtain the weight score of the processing efficiency item; the score of each shared task pool in the task backlog item is multiplied by the corresponding influence weight to obtain the weight score of the task backlog item; the score of each shared task pool in the load capacity item is multiplied by the corresponding influence weight to obtain the weight score of the load capacity item;

[0131] Adding the weight score of the processing efficiency item, the weight score of the task backlog item, and the weight score of the load capacity item of each shared task pool to obtain a first value based on each shared task pool;

[0132] Adding the impact weight of the processing efficiency item, the impact weight of the task backlog item, and the impact weight of the load capacity item to obtain a second value;

[0133] Divide the first value of each shared task pool by the second value to obtain a processing capacity score of each shared task pool that can currently receive new power grid financial declaration information;

[0134] The shared task pool with the highest processing capability score is selected as the target shared task pool.

[0135] It can be understood that when the cloud data platform receives new power grid financial declaration information, it needs to be assigned to a shared task pool for processing. Since the processing efficiency, task backlog, and load capacity of multiple shared task pools are different, if the new power grid financial declaration information is randomly assigned, it may cause some shared task pools to have too much backlog, while some shared task pools are idle. Based on this, the present invention compares the processing efficiency, task backlog, and load capacity of multiple shared task pools in pairs, thereby selecting the shared task pool with the highest processing capacity that can currently receive new power grid financial declaration information from multiple shared task pools as the target shared task pool, so as to more reasonably and evenly distribute the power grid financial declaration information to each shared task pool.

[0136] According to a specific embodiment of the present invention, after allocating the first power grid financial declaration information to the target shared task pool for power grid financial declaration information processing, the method further includes:

[0137] Acquire all power grid financial declaration information including the first power grid financial declaration information in the target shared task pool;

[0138] Respectively obtain the processing deadline information, backlog time information, and importance information of each power grid financial declaration information in the target shared task pool;

[0139] The processing period of each power grid financial declaration information in the target shared task pool is compared and challenged with the processing period of the remaining other power grid financial declaration information one by one. If the processing period of the former is before the processing period of the latter, 1 point is added to the processing period item of the former power grid financial declaration information; the backlog time of each power grid financial declaration information in the target shared task pool is compared and challenged with the backlog time of the remaining other power grid financial declaration information one by one. If the backlog time of the former is greater than the backlog time of the latter, 1 point is added to the backlog time item of the former power grid financial declaration information; the importance of each power grid financial declaration information in the target shared task pool is compared and challenged with the importance of the remaining other power grid financial declaration information one by one. If the importance of the former is greater than the importance of the latter, 1 point is added to the importance item of the former power grid financial declaration information;

[0140] After all power grid financial declaration information in the target shared task pool has completed the pairwise comparison challenge of processing deadline, backlog time and importance, the score of the processing deadline item, the score of the backlog time item and the score of the importance item of each power grid financial declaration information are counted;

[0141] The preset processing deadline, backlog time and importance have different impact weights for the priority processing of power grid financial declaration information. The score of the processing deadline item of each power grid financial declaration information is multiplied by the corresponding impact weight to obtain the weight score of the processing deadline item; the score of the backlog time item of each power grid financial declaration information is multiplied by the corresponding impact weight to obtain the weight score of the backlog time item; the score of the importance item of each power grid financial declaration information is multiplied by the corresponding impact weight to obtain the weight score of the importance item;

[0142] The weight score of the processing deadline item, the weight score of the backlog time item, and the weight score of the importance item of each power grid financial declaration information are added together to obtain a third value based on each power grid financial declaration information;

[0143] Add the impact weight of the processing deadline item, the impact weight of the backlog time item, and the impact weight of the importance item to obtain a fourth value;

[0144] Dividing the third value of each power grid financial declaration information by the fourth value to obtain a priority processing score for each power grid financial declaration information;

[0145] All power grid financial declaration information in the target shared task pool is sorted according to the priority processing score.

[0146] It can be understood that the present invention updates the priority processing scores of all power grid financial declaration information in the target shared task pool in real time, and sorts them based on the current priority processing scores, so as to sequentially process multiple power grid financial declaration information according to the priority processing scores, further realizing intelligent and humanized management and control of power grid financial data.

[0147] like Figure 3 As shown, after the first power grid financial declaration information is approved to be entered into the cloud data platform, the method further includes:

[0148] S302, constructing a power grid financial processing time prediction model;

[0149] S304, training and optimizing the power grid financial processing time prediction model through sample data to obtain an optimized power grid financial processing time prediction model;

[0150] S306, extracting financial attribute information and time attribute information from the financial declaration information of the first power grid, wherein the financial attribute information at least includes the amount of declaration documents and the completeness of the information; the time attribute information at least includes monthly information and daily information;

[0151] S308, combining the financial attribute information and time attribute information of the first power grid financial declaration information with the current operating status of the cloud data platform, and predicting through a power grid financial processing time prediction model to obtain an estimated processing time for the declaration process;

[0152] S310, feeding back the estimated processing time of the declaration process to the power grid financial declaration terminal.

[0153] It can be understood that the present invention constructs a power grid financial processing time prediction model, and combines the financial attribute information and time attribute information of the first power grid financial declaration information with the current operating status of the cloud data platform to predict the estimated processing time of the declaration process, and feeds back the estimated processing time of the declaration process to the power grid financial declaration terminal, thereby facilitating the declarants of the power grid financial declaration terminal to understand the processing time of their own declaration information in advance, so as to take planning measures.

[0154] According to an embodiment of the present invention, after obtaining the estimated processing time of the declaration process by predicting through the power grid financial processing time prediction model, the method further includes:

[0155] Acquire multiple historical power grid financial declaration data, each historical power grid financial declaration data at least includes historical power grid financial declaration information, the operation status of the cloud data platform in historical time, and the actual processing time of the historical power grid financial declaration information;

[0156] Based on the historical power grid financial declaration information in each historical power grid financial declaration data, corresponding financial attribute information and time attribute information are extracted;

[0157] Performing feature operations on the financial attribute information and time attribute information of each historical power grid financial declaration data to obtain attribute feature values ​​of each historical power grid financial declaration data;

[0158] Performing feature operations on the financial attribute information and the time attribute information of the first power grid financial declaration information to obtain attribute feature values ​​of the first power grid financial declaration information;

[0159] Performing a difference operation on the attribute characteristic value of each historical power grid financial declaration data and the attribute characteristic value of the first power grid financial declaration information to obtain a difference based on each historical power grid financial declaration data;

[0160] Determine whether the difference is less than a second preset threshold value, and if so, mark the corresponding historical power grid financial declaration data as valuable data and enter it into the first database;

[0161] Based on each historical power grid financial declaration data in the first database, the financial attribute information and time attribute information of the historical power grid financial declaration information are combined with the operation status of the historical time cloud data platform, and predicted through the power grid financial processing time prediction model to obtain the estimated processing time of the historical power grid financial declaration information;

[0162] Based on each historical power grid financial declaration data in the first database, performing a difference operation between the actual processing time of the historical power grid financial declaration information and the estimated processing time of the historical power grid financial declaration information to obtain a processing time difference;

[0163] Performing a vector sum operation on the processing time differences corresponding to all historical power grid financial declaration data in the first database to obtain a processing time difference vector sum;

[0164] Dividing the sum of the processing time difference vectors by the amount of historical power grid financial declaration data in the first database, to obtain an average processing time difference vector;

[0165] The predicted estimated processing time of the declaration process of the financial declaration information of the first power grid is added to the average vector of the processing time difference to obtain a revised estimated processing time of the declaration process.

[0166] It can be understood that the present invention obtains multiple historical power grid financial declaration data, and uses multiple historical power grid financial declaration data to correct the estimated processing time of the declaration process of the first power grid financial declaration information evaluated, so that the financial declarant can obtain a more accurate estimated processing time of the declaration process.

[0167] According to an embodiment of the present invention, a feature operation is performed on the financial attribute information and time attribute information of each historical power grid financial declaration data to obtain an attribute feature value of each historical power grid financial declaration data; a feature operation is performed on the financial attribute information and time attribute information of the first power grid financial declaration information to obtain an attribute feature value of the first power grid financial declaration information; a difference operation is performed on the attribute feature value of each historical power grid financial declaration data and the attribute feature value of the first power grid financial declaration information to obtain a difference degree based on each historical power grid financial declaration data, specifically including:

[0168] It is assumed that the cloud data platform has different load levels every month of the year and different load levels every day of the month, and a monthly load level correspondence table and a daily load level correspondence table are established respectively;

[0169] The amount of declaration documents of the financial attribute information of each historical power grid financial declaration data is divided by the information completeness to obtain the financial attribute characteristic value of each historical power grid financial declaration data;

[0170] Based on the monthly information in the time attribute information of each historical power grid financial declaration data, the corresponding monthly load degree is found according to the monthly load degree corresponding table; based on the daily information in the time attribute information of each historical power grid financial declaration data, the corresponding daily load degree is found according to the daily load degree corresponding table; the monthly load degree corresponding to each historical power grid financial declaration data is multiplied by the corresponding daily load degree to obtain the time attribute characteristic value of each historical power grid financial declaration data;

[0171] Dividing the amount of declaration documents of the financial attribute information of the first power grid financial declaration information by the information completeness, to obtain the financial attribute characteristic value of the first power grid financial declaration information;

[0172] Based on the monthly information in the time attribute information of the financial declaration information of the first power grid, the corresponding monthly load degree is found out according to the monthly load degree correspondence table; based on the daily information in the time attribute information of the financial declaration information of the first power grid, the corresponding daily load degree is found out according to the daily load degree correspondence table; the monthly load degree corresponding to the financial declaration information of the first power grid is multiplied by the corresponding daily load degree to obtain the time attribute characteristic value of the financial declaration information of the first power grid;

[0173] Perform a difference operation on the financial attribute characteristic value of each historical power grid financial declaration data and the financial attribute characteristic value of the first power grid financial declaration information to obtain a difference degree A1 of the financial attribute characteristic value;

[0174] Perform a difference operation on the time attribute characteristic value of each historical power grid financial declaration data and the time attribute characteristic value of the first power grid financial declaration information to obtain a time attribute characteristic value difference A2;

[0175] The influence weight of the preset financial attribute information on the difference calculation between the historical power grid financial declaration data and the first power grid financial declaration information is K1, and the influence weight of the time attribute information on the difference calculation between the historical power grid financial declaration data and the first power grid financial declaration information is K2;

[0176] Multiply the financial attribute characteristic value difference A1 by the corresponding influence weight K1 to obtain the financial attribute characteristic value difference weight value A1*K1, and multiply the time attribute characteristic value difference A2 by the corresponding influence weight K2 to obtain the time attribute characteristic value difference weight value A2*K2;

[0177] Add the financial attribute characteristic value difference weight value A1*K1 and the time attribute characteristic value difference weight value A2*K2 to obtain the difference weight sum (A1*K1+A2*K2), and divide the difference weight sum (A1*K1+A2*K2) by (K1+K2) to obtain the difference (A1*K1+A2*K2) / (K1+K2) based on each historical power grid financial declaration data.

[0178] According to a specific embodiment of the present invention, a difference operation is performed on the financial attribute characteristic value of each historical power grid financial declaration data and the financial attribute characteristic value of the first power grid financial declaration information to obtain the financial attribute characteristic value difference A1, which specifically includes: subtracting the financial attribute characteristic value of the first power grid financial declaration information from the financial attribute characteristic value of each historical power grid financial declaration data to obtain a first difference, and dividing the absolute value of the first difference by the financial attribute characteristic value of the first power grid financial declaration information to obtain the financial attribute characteristic value difference A1.

[0179] According to a specific embodiment of the present invention, a difference operation is performed on the time attribute characteristic value of each historical power grid financial declaration data and the time attribute characteristic value of the first power grid financial declaration information to obtain the time attribute characteristic value difference A2, which specifically includes: subtracting the time attribute characteristic value of the first power grid financial declaration information from the time attribute characteristic value of each historical power grid financial declaration data to obtain a second difference, and dividing the absolute value of the second difference by the time attribute characteristic value of the first power grid financial declaration information to obtain the time attribute characteristic value difference A2.

[0180] Figure 4 A block diagram of a power grid financial data management and control system based on a cloud data platform of the present invention is shown.

[0181] like Figure 4 As shown, the second aspect of the present invention further proposes a power grid financial data control system 4 based on a cloud data platform, comprising a memory 41 and a processor 42, wherein the memory comprises a power grid financial data control method program based on a cloud data platform, and when the power grid financial data control method program based on a cloud data platform is executed by the processor, the following steps are implemented:

[0182] Receiving first power grid financial declaration information submitted from a power grid financial declaration terminal;

[0183] Conduct duplication check on the First Grid's financial declaration information and all grid financial information stored in the cloud data platform;

[0184] If the first power grid financial declaration information is not repeated with all power grid financial information in the cloud data platform, the first power grid financial declaration information is allowed to be entered into the cloud data platform for power grid financial data management and control;

[0185] Identify the power grid financial category to which the first power grid belongs based on its financial declaration information;

[0186] Acquire multiple shared task pools corresponding to the power grid financial category to which the first power grid financial declaration information belongs, and respectively acquire the processing efficiency, task backlog, and load capacity of each shared task pool;

[0187] Based on the processing efficiency, task backlog and load capacity of the multiple shared task pools, a target shared task pool is selected from the multiple shared task pools through a preset selection algorithm;

[0188] The first power grid financial declaration information is distributed to the target shared task pool for power grid financial declaration information processing.

[0189] According to an embodiment of the present invention, the first power grid financial declaration information is verified for repeatability with all power grid financial information stored in the cloud data platform, specifically including:

[0190] All power grid financial information in the preset cloud data platform contains corresponding hash values;

[0191] Performing hash calculation on the financial declaration information of the first power grid through a hash algorithm to obtain a corresponding hash value;

[0192] Compare the hash values ​​of the financial declaration information of the first power grid with the hash values ​​of all power grid financial information in the cloud data platform;

[0193] If the comparison is consistent, the financial declaration information of the first power grid is deemed to be repeated; otherwise, the financial declaration information of the first power grid is deemed not to be repeated.

[0194] According to an embodiment of the present invention, based on the processing efficiency, task backlog and load capacity of multiple shared task pools, a target shared task pool is selected from the multiple shared task pools through a preset selection algorithm, specifically including:

[0195] Compare the processing efficiency of each shared task pool with the processing efficiency of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the processing efficiency item of the former shared task pool;

[0196] Compare the task backlog of each shared task pool with the task backlog of the remaining other shared task pools one by one. If the former is lower than the latter, add 1 point to the task backlog of the former shared task pool;

[0197] Compare the load capacity of each shared task pool with the load capacity of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the load capacity item of the former shared task pool;

[0198] After all shared task pools have completed the pairwise comparison of processing efficiency, task backlog, and load capacity, the scores of each shared task pool in the processing efficiency item, the score in the task backlog item, and the score in the load capacity item are counted;

[0199] The preset processing efficiency, task backlog and load capacity have different influence weights on the selected target shared task pool. The score of each shared task pool in the processing efficiency item is multiplied by the corresponding influence weight to obtain the weight score of the processing efficiency item; the score of each shared task pool in the task backlog item is multiplied by the corresponding influence weight to obtain the weight score of the task backlog item; the score of each shared task pool in the load capacity item is multiplied by the corresponding influence weight to obtain the weight score of the load capacity item;

[0200] Adding the weight score of the processing efficiency item, the weight score of the task backlog item, and the weight score of the load capacity item of each shared task pool to obtain a first value based on each shared task pool;

[0201] Adding the impact weight of the processing efficiency item, the impact weight of the task backlog item, and the impact weight of the load capacity item to obtain a second value;

[0202] Divide the first value of each shared task pool by the second value to obtain a processing capacity score of each shared task pool that can currently receive new power grid financial declaration information;

[0203] The shared task pool with the highest processing capability score is selected as the target shared task pool.

[0204] The third aspect of the present invention also proposes a computer-readable storage medium, which includes a power grid financial data management and control method program based on a cloud data platform. When the power grid financial data management and control method program based on a cloud data platform is executed by a processor, the steps of the power grid financial data management and control method based on a cloud data platform as described above are implemented.

[0205] The power grid financial data management and control method, system and medium based on the cloud data platform proposed in the present invention utilize the data integration, data analysis, data consolidation and data distribution capabilities of the cloud data platform to realize intelligent management and control of power grid financial data, thereby effectively improving the efficiency of power grid financial business processing.

[0206] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0207] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0208] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0209] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0210] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product 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 execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0211] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A power grid financial data management and control method based on a cloud data platform, characterized in that: The method comprises: Receiving first power grid financial declaration information submitted from a power grid financial declaration terminal; Conduct duplication check on the First Grid's financial declaration information and all grid financial information stored in the cloud data platform; If the first power grid financial declaration information is not repeated with all power grid financial information in the cloud data platform, the first power grid financial declaration information is allowed to be entered into the cloud data platform for power grid financial data management and control; Identify the power grid financial category to which the first power grid belongs based on its financial declaration information; Acquire multiple shared task pools corresponding to the power grid financial category to which the first power grid financial declaration information belongs, and respectively acquire the processing efficiency, task backlog, and load capacity of each shared task pool; Based on the processing efficiency, task backlog and load capacity of the multiple shared task pools, a target shared task pool is selected from the multiple shared task pools through a preset selection algorithm; The first power grid financial declaration information is distributed to the target shared task pool for power grid financial declaration information processing.

2. A method for controlling power grid financial data based on a cloud data platform according to claim 1, characterized in that: The First Grid’s financial declaration information is verified against all grid financial information stored in the cloud data platform, including: All power grid financial information in the preset cloud data platform contains corresponding hash values; Performing hash calculation on the financial declaration information of the first power grid through a hash algorithm to obtain a corresponding hash value; Compare the hash values ​​of the financial declaration information of the first power grid with the hash values ​​of all power grid financial information in the cloud data platform; If the comparison is consistent, the financial declaration information of the first power grid is deemed to be repeated; otherwise, the financial declaration information of the first power grid is deemed not to be repeated.

3. A method for controlling power grid financial data based on a cloud data platform according to claim 1, characterized in that: Based on the processing efficiency, task backlog and load capacity of multiple shared task pools, a target shared task pool is selected from the multiple shared task pools through a preset selection algorithm, specifically including: Compare the processing efficiency of each shared task pool with the processing efficiency of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the processing efficiency item of the former shared task pool; Compare the task backlog of each shared task pool with the task backlog of the remaining other shared task pools one by one. If the former is lower than the latter, add 1 point to the task backlog of the former shared task pool; Compare the load capacity of each shared task pool with the load capacity of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the load capacity item of the former shared task pool; After all shared task pools have completed the pairwise comparison of processing efficiency, task backlog, and load capacity, the scores of each shared task pool in the processing efficiency item, the score in the task backlog item, and the score in the load capacity item are counted; The preset processing efficiency, task backlog and load capacity have different influence weights on the selected target shared task pool. The score of each shared task pool in the processing efficiency item is multiplied by the corresponding influence weight to obtain the weight score of the processing efficiency item; the score of each shared task pool in the task backlog item is multiplied by the corresponding influence weight to obtain the weight score of the task backlog item; the score of each shared task pool in the load capacity item is multiplied by the corresponding influence weight to obtain the weight score of the load capacity item; Adding the weight score of the processing efficiency item, the weight score of the task backlog item, and the weight score of the load capacity item of each shared task pool to obtain a first value based on each shared task pool; Adding the impact weight of the processing efficiency item, the impact weight of the task backlog item, and the impact weight of the load capacity item to obtain a second value; Divide the first value of each shared task pool by the second value to obtain a processing capacity score of each shared task pool that can currently receive new power grid financial declaration information; The shared task pool with the highest processing capability score is selected as the target shared task pool.

4. A method for controlling power grid financial data based on a cloud data platform according to claim 1, characterized in that: After the first power grid financial declaration information is approved for entry into the cloud data platform, the method further includes: Construct a prediction model for power grid financial processing time; The power grid financial processing time prediction model is trained and optimized through sample data to obtain an optimized power grid financial processing time prediction model; Extracting financial attribute information and time attribute information from the financial declaration information of the first power grid, wherein the financial attribute information at least includes the amount of declaration documents and the completeness of the information; and the time attribute information at least includes monthly information and daily information; The financial attribute information and time attribute information of the first power grid financial declaration information are combined with the current operation status of the cloud data platform, and predicted through the power grid financial processing time prediction model to obtain the estimated processing time of the declaration process; The estimated processing time of the declaration process will be fed back to the power grid financial declaration terminal.

5. A method for controlling power grid financial data based on a cloud data platform according to claim 4, characterized in that: After obtaining the estimated processing time of the declaration process by predicting through the power grid financial processing time prediction model, the method further includes: Acquire multiple historical power grid financial declaration data, each historical power grid financial declaration data at least includes historical power grid financial declaration information, the operation status of the cloud data platform in historical time, and the actual processing time of the historical power grid financial declaration information; Based on the historical power grid financial declaration information in each historical power grid financial declaration data, corresponding financial attribute information and time attribute information are extracted; Performing feature operations on the financial attribute information and time attribute information of each historical power grid financial declaration data to obtain attribute feature values ​​of each historical power grid financial declaration data; Performing feature operations on the financial attribute information and the time attribute information of the first power grid financial declaration information to obtain attribute feature values ​​of the first power grid financial declaration information; Performing a difference operation on the attribute characteristic value of each historical power grid financial declaration data and the attribute characteristic value of the first power grid financial declaration information to obtain a difference based on each historical power grid financial declaration data; Determine whether the difference is less than a second preset threshold value, and if so, mark the corresponding historical power grid financial declaration data as valuable data and enter it into the first database; Based on each historical power grid financial declaration data in the first database, the financial attribute information and time attribute information of the historical power grid financial declaration information are combined with the operation status of the historical time cloud data platform, and predicted through the power grid financial processing time prediction model to obtain the estimated processing time of the historical power grid financial declaration information; Based on each historical power grid financial declaration data in the first database, performing a difference operation between the actual processing time of the historical power grid financial declaration information and the estimated processing time of the historical power grid financial declaration information to obtain a processing time difference; Performing a vector sum operation on the processing time differences corresponding to all historical power grid financial declaration data in the first database to obtain a processing time difference vector sum; Dividing the sum of the processing time difference vectors by the amount of historical power grid financial declaration data in the first database, to obtain an average processing time difference vector; The predicted estimated processing time of the declaration process of the financial declaration information of the first power grid is added to the average vector of the processing time difference to obtain a revised estimated processing time of the declaration process.

6. A method for controlling power grid financial data based on a cloud data platform according to claim 5, characterized in that: Performing feature operations on the financial attribute information and time attribute information of each historical power grid financial declaration data to obtain attribute feature values ​​of each historical power grid financial declaration data; Performing feature operations on the financial attribute information and the time attribute information of the first power grid financial declaration information to obtain attribute feature values ​​of the first power grid financial declaration information; Performing a difference operation on the attribute characteristic value of each historical power grid financial declaration data and the attribute characteristic value of the first power grid financial declaration information to obtain a difference based on each historical power grid financial declaration data, specifically including: It is assumed that the cloud data platform has different load levels every month of the year and different load levels every day of the month, and a monthly load level correspondence table and a daily load level correspondence table are established respectively; The amount of declaration documents of the financial attribute information of each historical power grid financial declaration data is divided by the information completeness to obtain the financial attribute characteristic value of each historical power grid financial declaration data; Based on the monthly information in the time attribute information of each historical power grid financial declaration data, the corresponding monthly load degree is found according to the monthly load degree corresponding table; based on the daily information in the time attribute information of each historical power grid financial declaration data, the corresponding daily load degree is found according to the daily load degree corresponding table; the monthly load degree corresponding to each historical power grid financial declaration data is multiplied by the corresponding daily load degree to obtain the time attribute characteristic value of each historical power grid financial declaration data; Dividing the amount of declaration documents of the financial attribute information of the first power grid financial declaration information by the information completeness, to obtain the financial attribute characteristic value of the first power grid financial declaration information; Based on the monthly information in the time attribute information of the financial declaration information of the first power grid, the corresponding monthly load degree is found out according to the monthly load degree correspondence table; based on the daily information in the time attribute information of the financial declaration information of the first power grid, the corresponding daily load degree is found out according to the daily load degree correspondence table; the monthly load degree corresponding to the financial declaration information of the first power grid is multiplied by the corresponding daily load degree to obtain the time attribute characteristic value of the financial declaration information of the first power grid; Perform a difference operation on the financial attribute characteristic value of each historical power grid financial declaration data and the financial attribute characteristic value of the first power grid financial declaration information to obtain a difference degree A1 of the financial attribute characteristic value; Perform a difference operation on the time attribute characteristic value of each historical power grid financial declaration data and the time attribute characteristic value of the first power grid financial declaration information to obtain a time attribute characteristic value difference A2; The influence weight of the preset financial attribute information on the difference calculation between the historical power grid financial declaration data and the first power grid financial declaration information is K1, and the influence weight of the time attribute information on the difference calculation between the historical power grid financial declaration data and the first power grid financial declaration information is K2; Multiply the financial attribute characteristic value difference A1 by the corresponding influence weight K1 to obtain the financial attribute characteristic value difference weight value A1*K1, and multiply the time attribute characteristic value difference A2 by the corresponding influence weight K2 to obtain the time attribute characteristic value difference weight value A2*K2; Add the financial attribute characteristic value difference weight value A1*K1 and the time attribute characteristic value difference weight value A2*K2 to obtain the difference weight sum (A1*K1+A2*K2), and divide the difference weight sum (A1*K1+A2*K2) by (K1+K2) to obtain the difference (A1*K1+A2*K2) / (K1+K2) based on each historical power grid financial declaration data.

7. A power grid financial data management and control system based on a cloud data platform, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a power grid financial data control method program based on a cloud data platform, and the power grid financial data control method program based on a cloud data platform implements the following steps when executed by the processor: Receiving first power grid financial declaration information submitted from a power grid financial declaration terminal; Conduct duplication check on the First Grid's financial declaration information and all grid financial information stored in the cloud data platform; If the first power grid financial declaration information is not repeated with all power grid financial information in the cloud data platform, the first power grid financial declaration information is allowed to be entered into the cloud data platform for power grid financial data management and control; Identify the power grid financial category to which the first power grid belongs based on its financial declaration information; Acquire multiple shared task pools corresponding to the power grid financial category to which the first power grid financial declaration information belongs, and respectively acquire the processing efficiency, task backlog, and load capacity of each shared task pool; Based on the processing efficiency, task backlog and load capacity of the multiple shared task pools, a target shared task pool is selected from the multiple shared task pools through a preset selection algorithm; The first power grid financial declaration information is distributed to the target shared task pool for power grid financial declaration information processing.

8. A power grid financial data management and control system based on a cloud data platform according to claim 7, characterized in that: The First Grid’s financial declaration information is verified against all grid financial information stored in the cloud data platform, including: All power grid financial information in the preset cloud data platform contains corresponding hash values; Performing hash calculation on the financial declaration information of the first power grid through a hash algorithm to obtain a corresponding hash value; Compare the hash values ​​of the financial declaration information of the first power grid with the hash values ​​of all power grid financial information in the cloud data platform; If the comparison is consistent, the financial declaration information of the first power grid is deemed to be repeated; otherwise, the financial declaration information of the first power grid is deemed not to be repeated.

9. The power grid financial data management and control system based on the cloud data platform according to claim 7, characterized in that: Based on the processing efficiency, task backlog and load capacity of multiple shared task pools, a target shared task pool is selected from the multiple shared task pools through a preset selection algorithm, specifically including: Compare the processing efficiency of each shared task pool with the processing efficiency of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the processing efficiency item of the former shared task pool; Compare the task backlog of each shared task pool with the task backlog of the remaining other shared task pools one by one. If the former is lower than the latter, add 1 point to the task backlog of the former shared task pool; Compare the load capacity of each shared task pool with the load capacity of the remaining shared task pools one by one. If the former is higher than the latter, add 1 point to the load capacity item of the former shared task pool; After all shared task pools have completed the pairwise comparison of processing efficiency, task backlog, and load capacity, the scores of each shared task pool in the processing efficiency item, the score in the task backlog item, and the score in the load capacity item are counted; The preset processing efficiency, task backlog and load capacity have different influence weights on the selected target shared task pool. The score of each shared task pool in the processing efficiency item is multiplied by the corresponding influence weight to obtain the weight score of the processing efficiency item; the score of each shared task pool in the task backlog item is multiplied by the corresponding influence weight to obtain the weight score of the task backlog item; the score of each shared task pool in the load capacity item is multiplied by the corresponding influence weight to obtain the weight score of the load capacity item; Adding the weight score of the processing efficiency item, the weight score of the task backlog item, and the weight score of the load capacity item of each shared task pool to obtain a first value based on each shared task pool; Adding the impact weight of the processing efficiency item, the impact weight of the task backlog item, and the impact weight of the load capacity item to obtain a second value; Divide the first value of each shared task pool by the second value to obtain a processing capacity score of each shared task pool that can currently receive new power grid financial declaration information; The shared task pool with the highest processing capability score is selected as the target shared task pool.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a power grid financial data control method program based on a cloud data platform. When the power grid financial data control method program based on a cloud data platform is executed by a processor, the steps of a power grid financial data control method based on a cloud data platform as described in any one of claims 1 to 6 are implemented.

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