A management system based on enterprise financial planning

Abstract

The present application relates to the field of management system, especially to a kind of management system based on enterprise financial planning, the system includes data acquisition module, to obtain several information data from enterprise information system;Data storage module is connected with the data acquisition module, to store several information data obtained by the data acquisition module and store several information data into different database;Data analysis module is connected with the data storage module, to determine strategy mode;Data processing module is connected with the data processing module, to determine adjustment mode;Data optimization module is connected with the data processing module, to optimize the database after processing.The present application improves the accuracy and efficiency of management system to enterprise financial planning.

Description

Technical Field

[0001] This invention relates to the field of management system technology, and in particular to a management system based on corporate financial planning. Background Technology

[0002] As businesses grow larger and their operations become increasingly complex, traditional manual financial planning and management methods are no longer sufficient to meet their needs. This demand has driven the application of information technology in the financial sector, prompting businesses to seek more efficient and accurate management tools.

[0003] Chinese Patent Application Publication No. CN116523646A discloses a financial fund management system and method for enterprises, including: an enterprise financial fund management platform, which includes a server, and the server is communicatively connected to a data storage module, a fund expenditure classification and summary module, a fund transaction risk analysis module, and an enterprise profit monitoring and analysis module. This invention analyzes the fund expenditures of the corresponding enterprise through the fund expenditure classification and summary module, and obtains the abnormal expenditure category through classification and screening when the total expenditure amount is abnormal. The fund transaction risk analysis module classifies and marks the corresponding enterprise's transaction objects according to their levels and performs transaction risk analysis on the corresponding transactions, further ensuring the security of enterprise fund expenditures. Furthermore, the enterprise profit monitoring and analysis module analyzes enterprise profits, helping relevant managers to understand the enterprise's operating status in a timely and accurate manner and make reasonable subsequent enterprise plans. However, the existing technology has the following problems:

[0004] The management system lacks management of corporate financial planning, resulting in low accuracy and efficiency in the system's handling of corporate financial planning. Summary of the Invention

[0005] Therefore, the present invention provides a management system based on corporate financial planning to overcome the problems of low accuracy and low efficiency of existing management systems in corporate financial planning.

[0006] To achieve the above objectives, the present invention provides a management system based on corporate financial planning, comprising:

[0007] The data acquisition module is used to acquire various information data from the enterprise information system.

[0008] A data storage module, which is connected to the data acquisition module, is used to store several pieces of information data acquired by the data acquisition module and store the several pieces of information data into different databases;

[0009] A data analysis module, which is connected to the data storage module, is used to determine the strategy approach;

[0010] A data processing module, which is connected to the data processing module, is used to determine the adjustment method;

[0011] The data optimization module, which is connected to the data processing module, is used to optimize the processed database.

[0012] Furthermore, the data pool of the data storage module clusters several pieces of information data and determines the comparison result of poor clustering based on the feature evaluation value of several pieces of information data being greater than or equal to the preset feature value.

[0013] Furthermore, under the condition that the data storage module stores several data with poor clustering into the fuzzy label database, the data analysis module determines a strategy for comparing the relevance with similar small and micro enterprises based on the comparison results where the order of magnitude of the fuzzy label database is less than a preset order of magnitude. This strategy is based on the enterprise management evaluation value being less than or equal to the preset enterprise management evaluation value, and determines to reduce the data in the fuzzy label database using a management adjustment coefficient.

[0014] Furthermore, under the condition that the data storage module stores several data with poor clustering results into the fuzzy label database, the data analysis module determines a strategy for comparing the relevance with similar large and medium-sized enterprises based on the comparison results of the fuzzy label database being greater than or equal to a preset order of magnitude. This strategy involves determining how to update the data in the fuzzy label database using an update coefficient based on the enterprise's comprehensive evaluation value being less than or equal to the preset enterprise comprehensive evaluation value.

[0015] Furthermore, under the condition that the data storage module stores several data with poor clustering into the fuzzy label database, the data analysis module determines the strategy for comparing its own database based on the comparison results where the fluctuation range of the fuzzy label database data is less than or equal to a preset fluctuation range.

[0016] Furthermore, under the condition that the data storage module stores several data with poor clustering into the fuzzy label database, the data analysis module determines the strategy of comparing with the external enterprise database based on the comparison result that the fluctuation range of the fuzzy label database data is greater than the preset fluctuation range. Based on the ratio of the fluctuation range of the fuzzy label database data to the preset fluctuation range being less than or equal to the preset ratio of its own database, the data in the fuzzy label database is updated to the corresponding value with the first preset update coefficient.

[0017] Furthermore, under the condition that the data storage module stores several data with poor clustering into the fuzzy label database, the data analysis module determines the strategy of comparing with the external enterprise database based on the comparison result that the fluctuation range of the fuzzy label database data is greater than the preset fluctuation range. Based on the fact that the ratio of the fluctuation range of the fuzzy label database data to the preset fluctuation range is greater than the preset ratio of its own database, the data in the fuzzy label database is updated to the corresponding value with the second preset update coefficient.

[0018] Furthermore, under the condition of determining to perform an external enterprise database comparison, the data processing module determines to reduce the data in the fuzzy label database to the corresponding value by using a first preset management adjustment coefficient, based on the comparison result that the ratio of the fluctuation amplitude of the fuzzy label database data to the preset fluctuation amplitude is less than or equal to the preset external enterprise database ratio.

[0019] Furthermore, under the condition of determining to perform an external enterprise database comparison, the data processing module determines, based on the comparison result that the ratio of the fluctuation amplitude of the fuzzy label database data to the preset fluctuation amplitude is greater than the preset ratio of the external enterprise database, to reduce the data of the fuzzy label database to the corresponding value using a second preset management adjustment coefficient.

[0020] Furthermore, under the condition that the data processing module performs a comparison, if the fuzzy label database is compared with its own database, the data optimization module determines to supplement data from the label database to the fuzzy label database; if the fuzzy label database is compared with an external enterprise database, the data optimization module determines to supplement data from the external enterprise database to the fuzzy label database.

[0021] Compared with the prior art, the beneficial effect of the present invention is that it performs cluster analysis on several information data through the data pool of the data storage module, and determines the clustering situation by comparing the feature evaluation value with the preset feature evaluation value, thereby reducing the influence of manual intervention and subjective judgment, and thus improving the accuracy and efficiency of the management system evaluation.

[0022] Furthermore, the present invention uses a data storage module to classify and store information data into different databases according to clustering. After the data storage module stores data with poor clustering into a fuzzy label database, the data analysis module determines the subsequent analysis strategy based on the comparison between the magnitude of the fuzzy label database and a preset magnitude, thereby making data analysis more efficient.

[0023] Furthermore, under the condition that the data analysis module determines the correlation with similar small and micro enterprises, the data processing module will perform further adjustment operations. Specifically, the adjustment mode is determined based on the comparison result between the enterprise management evaluation value and the preset enterprise management evaluation value, thereby improving the accuracy of the management system.

[0024] Furthermore, under the condition that the data analysis module determines the correlation with similar large and medium-sized enterprises, the data processing module will further determine the adjustment mode based on the comparison result between the enterprise's comprehensive evaluation value and the preset enterprise comprehensive evaluation value. This helps to reduce data redundancy and outdated information, thereby improving the accuracy and efficiency of the management system.

[0025] Furthermore, this invention stores several data points with poor clustering performance into a fuzzy label database via a data storage module. The data analysis module then determines subsequent strategies based on a comparison between the fluctuation range of the fuzzy label database data and a preset fluctuation range. This helps to promptly identify the causes of data anomalies, thereby improving the efficiency of the management system in processing data.

[0026] Furthermore, after the data analysis module determines the strategy for comparing its own database, the data processing module further analyzes the ratio of the fluctuation range of the fuzzy label database to the preset fluctuation range, as well as the comparison result of the ratio with the preset ratio of its own database, in order to determine the adjustment method for data updates, thereby improving the accuracy of the management system.

[0027] Furthermore, after the data analysis module determines the strategy for comparing external enterprise databases, the data processing module further analyzes the ratio of the fluctuation range of the fuzzy label database data to the preset fluctuation range, as well as the comparison result of this ratio with the preset external enterprise database ratio, in order to determine the data adjustment method, thereby improving the comparability and reliability of the data and thus improving the accuracy of the management system.

[0028] Furthermore, after receiving the comparison results from the data processing module, the data optimization module of this invention determines the corresponding optimization method based on the specific object being compared, thereby improving the accuracy and efficiency of the management system. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the module connection structure of the management system based on enterprise financial planning according to an embodiment of the present invention;

[0030] Figure 2 This is a flowchart for determining clustering qualification in an embodiment of the present invention;

[0031] Figure 3 This is a flowchart illustrating the strategy for determining the correlation between the amount of data in the enterprise fuzzy label database and the amount of data in enterprises of the same type but different sizes, as described in this embodiment of the invention.

[0032] Figure 4 This is a flowchart illustrating the database comparison strategy for determining the fuzzy label database in an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0034] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0035] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0036] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0037] Please see Figures 1-4 As shown, Figure 1 This is a schematic diagram of the module connection structure of the management system based on enterprise financial planning according to an embodiment of the present invention; Figure 2 This is a flowchart for determining clustering qualification in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the strategy for determining the correlation between the amount of data in the enterprise fuzzy label database and the amount of data in enterprises of the same type but different sizes, as described in this embodiment of the invention. Figure 4 This is a flowchart illustrating the database comparison strategy for determining the fuzzy label database in an embodiment of the present invention.

[0038] This invention provides an embodiment of a management system based on corporate financial planning, comprising:

[0039] The data acquisition module is used to acquire various information data from the enterprise information system.

[0040] A data storage module, which is connected to the data acquisition module, is used to store several pieces of information data acquired by the data acquisition module and store the several pieces of information data into different databases;

[0041] A data analysis module, connected to the data storage module, is used to determine the strategy for comparing the data volume of the fuzzy label database.

[0042] A data processing module, connected to the data processing module, is used to determine the adjustment method for the amount of data in the fuzzy label database;

[0043] The data optimization module, which is connected to the data processing module, is used to optimize the processed database.

[0044] Specifically, the data pool of the data storage module clusters several pieces of information data and determines the clustering qualification based on the comparison results of the feature evaluation values ​​of several pieces of information data with the preset feature evaluation values;

[0045] If the feature evaluation value is less than the preset feature value, then the clustering is deemed qualified;

[0046] If the feature evaluation value is greater than or equal to the preset feature value, then the clustering is determined to be unqualified;

[0047] In this embodiment of the invention, the preset feature evaluation value is 0.8. The preset feature evaluation value is obtained based on several historical feature evaluation values, but the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0048] Specifically, this invention performs cluster analysis on several pieces of information data through the data pool of the data storage module, and determines the qualification of clustering by comparing the feature evaluation value with the preset feature evaluation value, thereby reducing the influence of manual intervention and subjective judgment, and thus improving the accuracy and efficiency of the management system evaluation.

[0049] Specifically, the data storage module calculates the feature evaluation value according to the following formula and sets it as follows:

[0050]

[0051] Where S represents the feature evaluation value, ai represents the average distance from the i-th data point to other points in the same cluster to which it belongs, bi represents the average distance from the i-th data point to all its nearest neighboring points that do not belong to the same cluster, and max{ai,bi} represents taking the larger value of ai and bi.

[0052] Specifically, the data storage module stores several information data into different databases based on the clustering qualification. If the clustering is qualified, it determines that the data will be stored in the label database; if the clustering is unqualified, it determines that the data will be stored in the fuzzy label database.

[0053] Specifically, under the condition that the data storage module stores several data that are not clustered properly into the fuzzy label database, the data analysis module determines the strategy for comparing the data volume of the enterprise's fuzzy label database with the data volume of enterprises of the same type but different sizes based on the comparison result between the order of magnitude of the fuzzy label database and the preset order of magnitude.

[0054] If the order of magnitude of the fuzzy label database is smaller than the preset order of magnitude, then a strategy for comparing the correlation with the data volume of similar small and micro enterprises is determined.

[0055] If the order of magnitude of the fuzzy label database is greater than or equal to the preset order of magnitude, then a strategy for comparing the correlation with the data volume of similar large and medium-sized enterprises is determined.

[0056] Among them, the order of magnitude of the fuzzy label database is the amount of data in the fuzzy label database;

[0057] In this embodiment of the invention, the preset order of magnitude is 10TB. The preset order of magnitude is obtained by averaging several historical orders of magnitude. However, the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0058] Specifically, this invention uses a data storage module to classify and store information data into different databases based on clustering qualification. After the data storage module stores the data of non-qualified clustering data into the fuzzy label database, the data analysis module determines the subsequent analysis strategy based on the comparison between the magnitude of the fuzzy label database and the preset magnitude, thereby making data analysis more efficient.

[0059] Specifically, under the condition that the data analysis module determines the correlation between the data volume and the data volume of similar small and micro enterprises, the data processing module determines the mode for adjusting the data volume of the fuzzy label database based on the comparison result between the enterprise management evaluation value and the preset enterprise management evaluation value.

[0060] If the enterprise management evaluation value is less than or equal to the preset enterprise management evaluation value, then the amount of data in the fuzzy label database is reduced by the management adjustment coefficient L.

[0061] If the enterprise management evaluation value is greater than the preset enterprise management evaluation value, then it is determined that there is no need to adjust the data volume of the fuzzy label database;

[0062] In this embodiment of the invention, the data volume of the reduced fuzzy label database is set to Ih, and Ih = I × L, where I represents the data volume of the fuzzy label database and L represents the management adjustment coefficient.

[0063] In this embodiment of the invention, the preset enterprise management evaluation value is 0.85. The preset enterprise management evaluation value is obtained by averaging the historical enterprise management evaluation values. However, the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0064] Specifically, the data processing module calculates the enterprise management evaluation value according to the following formula, and sets it as follows:

[0065]

[0066] Where P represents the enterprise management evaluation value, F represents the enterprise financial performance, Fi represents the financial performance of the i-th type of micro and small enterprises, C represents the enterprise customer satisfaction, and Ci represents the customer satisfaction of the i-th type of micro and small enterprises.

[0067] In this embodiment of the invention, corporate financial performance refers to the company's profitability, and corporate customer satisfaction refers to the degree of matching between customer expectations and customer experience.

[0068] Specifically, the data processing module determines the management adjustment coefficient L1 based on the difference between the enterprise management evaluation value and the preset enterprise management evaluation value and the preset difference.

[0069] Specifically, the data processing module calculates and sets the management adjustment coefficient according to the following formula:

[0070]

[0071] Where L represents the management adjustment coefficient, ΔP represents the difference between the enterprise management evaluation value and the preset enterprise management evaluation value, and ΔP0 represents the preset difference.

[0072] Specifically, the present invention determines the correlation with similar small and micro enterprises through the data analysis module, and then the data processing module will perform further adjustment operations. Specifically, the adjustment mode is determined based on the comparison result between the enterprise management evaluation value and the preset enterprise management evaluation value, thereby improving the accuracy of the management system.

[0073] Specifically, the data processing module determines a mode for adjusting the amount of data in the fuzzy label database based on the comparison results between the enterprise's comprehensive evaluation value and the preset enterprise comprehensive evaluation value, under the condition that the data analysis module has determined the relevance with large and medium-sized enterprises of the same type.

[0074] If the comprehensive evaluation value of the enterprise is less than or equal to the preset comprehensive evaluation value of the enterprise, then the data volume of the fuzzy label database is updated by the update coefficient Q.

[0075] If the comprehensive evaluation value of the enterprise is greater than the preset comprehensive evaluation value of the enterprise, then there is no need to adjust the amount of data in the fuzzy label database.

[0076] In this embodiment of the invention, the data volume of the updated fuzzy label database is set to Ig, and Ig = I × Q, where I represents the data volume of the fuzzy label database and Q represents the management adjustment coefficient.

[0077] In this embodiment of the invention, the preset comprehensive enterprise evaluation value is 0.8. The preset comprehensive enterprise evaluation value is obtained by averaging the comprehensive evaluation values ​​of several enterprises in history. However, the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0078] Specifically, the data processing module calculates the enterprise's comprehensive evaluation value according to the following formula, and sets it as follows:

[0079]

[0080] Where G represents the enterprise's comprehensive evaluation value, M represents the enterprise's market size, Mi represents the market size of the i-th similar large and medium-sized enterprise, R represents the enterprise's resource input, Ri represents the resource input of the i-th similar large and medium-sized enterprise, S represents the enterprise's resource output, and Si represents the resource output of the i-th similar large and medium-sized enterprise.

[0081] In this embodiment of the invention, the enterprise market size is the share of the enterprise in the industry, the enterprise resource input is the resources invested by the enterprise to achieve its business objectives, and the enterprise resource output is the material products and services produced by the enterprise through the investment of various resources.

[0082] Specifically, the data processing module determines the update coefficient Q based on the difference between the enterprise's comprehensive evaluation value and the preset enterprise comprehensive evaluation value and the preset difference.

[0083] Specifically, the data processing module calculates and sets the update coefficients according to the following formula:

[0084]

[0085] Where Q represents the update coefficient, ΔG represents the difference between the enterprise's comprehensive evaluation value and the preset enterprise comprehensive evaluation value, and ΔP0 represents the preset difference.

[0086] Specifically, this invention, by determining the relevance of the data analysis module to similar large and medium-sized enterprises, further determines the adjustment mode based on the comparison results between the enterprise's comprehensive evaluation value and the preset enterprise comprehensive evaluation value. This helps reduce data redundancy and outdated information, thereby improving the accuracy and efficiency of the management system.

[0087] Specifically, under the condition that the data storage module stores several data with poor clustering into the fuzzy label database, the data analysis module determines the strategy for database comparison of the fuzzy label database based on the comparison result of the fluctuation range of the data volume of the fuzzy label database with the preset fluctuation range.

[0088] If the fluctuation range of the data volume in the fuzzy label database is less than or equal to the preset fluctuation range, then the strategy for performing self-database comparison is determined.

[0089] If the fluctuation range of the data volume in the fuzzy label database is greater than the preset fluctuation range, then the strategy of comparing with external enterprise databases is determined.

[0090] In this embodiment of the invention, the preset fluctuation range is 2%. The preset fluctuation range is obtained by taking the average value of the fluctuation range of several companies in history. However, the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0091] Specifically, the data processing module calculates the fluctuation range of the fuzzy label database data according to the following formula, and sets it as follows:

[0092]

[0093] Where A represents the fluctuation range of the fuzzy label database data, and xi represents the i-th data point. This represents the average amount of data in the database, where n represents the total amount of data in the database.

[0094] Specifically, this invention stores several data points that fail clustering into a fuzzy label database using a data storage module. The data analysis module then determines the subsequent strategy based on a comparison between the fluctuation range of the data volume in the fuzzy label database and a preset fluctuation range. This helps to promptly identify the causes of data anomalies, thereby improving the efficiency of the management system in processing data.

[0095] Specifically, the data processing module, under the condition of performing its own comparison, determines the method of adjusting the amount of data in the fuzzy label database based on the comparison result of the ratio of the fluctuation range of the fuzzy label database to the preset fluctuation range and the preset ratio of its own database.

[0096] If the ratio is less than or equal to the preset ratio of its own database, then the data volume of the fuzzy label database is updated to the corresponding value by the first preset coefficient;

[0097] If the ratio is greater than the preset ratio of its own database, then it is determined that the data volume of the fuzzy label database will be updated to the corresponding value by the second preset coefficient;

[0098] The ratio is the ratio of the fluctuation amplitude of the fuzzy label database data to the preset fluctuation amplitude;

[0099] In this embodiment of the invention, the preset self-ratio is 0.5. The preset self-ratio is obtained by taking the average of the self-ratios of several companies in history. However, the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0100] In this embodiment of the invention, the data volume of the updated fuzzy label database is set to Iz, and Iz = I × Ti, where I represents the data of the fuzzy label database, Ti represents the i-th preset adjustment coefficient, i takes the value of 1 or 2, T1 is the first preset adjustment coefficient, and T2 is the second preset adjustment coefficient.

[0101] Specifically, after the data analysis module determines the strategy for comparing its own database, the data processing module further analyzes the ratio of the fluctuation range of the fuzzy label database to the preset fluctuation range, as well as the comparison result of the ratio with the preset ratio of its own database, in order to determine the adjustment method for data updates, thereby improving the accuracy of the management system.

[0102] Specifically, when the data processing module determines to perform an external enterprise database comparison, it determines the method of adjusting the amount of data in the fuzzy label database based on the comparison result of the ratio of the fluctuation range of the fuzzy label database data volume to the preset fluctuation range and the preset ratio of the external enterprise database.

[0103] If the ratio is less than or equal to the preset external enterprise database ratio, then it is determined that the amount of data in the fuzzy label database will be reduced to the corresponding value by the first preset reduction adjustment coefficient;

[0104] If the ratio is greater than the preset external enterprise database ratio, then it is determined that the amount of data in the fuzzy label database will be reduced to the corresponding value by the second preset reduction adjustment coefficient;

[0105] The ratio is the ratio of the fluctuation range of the fuzzy label database data volume to the preset fluctuation range;

[0106] In this embodiment of the invention, the preset external enterprise ratio is 1.2. The preset external enterprise ratio is obtained by averaging several historical external enterprise ratios. However, the above value is not limited to this. Those skilled in the art can also adjust the value according to actual needs.

[0107] In this embodiment of the invention, the reduced data volume of the fuzzy label database is set to Ic, and Ic = I × Ki, where I represents the data volume of the fuzzy label database, Ki represents the i-th preset reduction adjustment coefficient, i takes the value of 1 or 2, K1 is the first preset reduction adjustment coefficient, and K2 is the second preset reduction adjustment coefficient.

[0108] Specifically, after the data analysis module determines the strategy for comparing external enterprise databases, the data processing module further analyzes the ratio of the fluctuation range of the fuzzy label database to the preset fluctuation range, as well as the comparison result of the ratio with the preset external enterprise database ratio, in order to determine the data adjustment method, thereby improving the comparability and reliability of the data and thus improving the accuracy of the management system.

[0109] Specifically, the data optimization module determines the optimization method of the fuzzy label database based on the comparison object, under the condition that the data processing module performs comparison.

[0110] If the fuzzy label database is compared with its own database, the amount of data to be added from the label database to the fuzzy label database is determined.

[0111] If the fuzzy label database is compared with an external enterprise database, the amount of data to be added to the fuzzy label database from the external enterprise database is determined.

[0112] Specifically, after receiving the comparison results from the data processing module, the data optimization module of this invention determines the corresponding optimization method based on the specific object being compared, thereby improving the accuracy and efficiency of the management system.

[0113] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0114] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A management system based on corporate financial planning, characterized in that, include: The data acquisition module is used to acquire information data from the enterprise information system; A data storage module, which is connected to the data acquisition module, is used to store several pieces of information data acquired by the data acquisition module and store the several pieces of information data into different databases; The data analysis module, which is connected to the data storage module, is used to determine the strategy for comparing the data volume of the fuzzy label database based on the order of magnitude of the fuzzy label database or the fluctuation range of the fuzzy label database data volume under the condition that the clustering is unqualified. The data processing module, which is connected to the data analysis module, is used to determine the method of adjusting the amount of data in the fuzzy label database based on the enterprise management evaluation value or the enterprise comprehensive evaluation value, under the strategy of comparing the amount of data in the fuzzy label database. A data optimization module, which is connected to the data processing module, is used to determine the optimization method for the fuzzy label database based on the comparison object. The data analysis module, under the condition that the data storage module stores several unqualified clustered data into the fuzzy label database, determines the strategy for comparing its own database based on the comparison results where the fluctuation range of the fuzzy label database data volume is less than or equal to a preset fluctuation range. Under the condition that the data storage module stores several unqualified clustered data into the fuzzy label database, the data analysis module determines the strategy of comparing the data volume of the fuzzy label database with the external enterprise database based on the comparison result that the fluctuation range of the data volume of the fuzzy label database is greater than the preset fluctuation range. Based on the fact that the ratio of the fluctuation range of the data volume of the fuzzy label database to the preset fluctuation range is less than or equal to the preset ratio of its own database, the data in the fuzzy label database is updated to the corresponding value with the first preset update coefficient. The data analysis module, under the condition that the data storage module stores several data that are not clustered correctly into the fuzzy label database, determines the strategy of comparing the data volume fluctuation of the fuzzy label database with the external enterprise database based on the comparison result that the fluctuation range of the data volume of the fuzzy label database is greater than the preset fluctuation range. Based on the fact that the ratio of the fluctuation range of the data volume of the fuzzy label database to the preset fluctuation range is greater than the preset ratio of its own database, it determines to update the data of the fuzzy label database to the corresponding value with the second preset update coefficient.

2. The management system based on enterprise financial planning according to claim 1, characterized in that, The data pool of the data storage module clusters several pieces of information data and determines the comparison results of unqualified clustering based on the feature evaluation value of several pieces of information data being greater than or equal to a preset feature value.

3. The management system based on enterprise financial planning according to claim 2, characterized in that, The data analysis module, under the condition that the data storage module stores several unqualified clustered data into the fuzzy label database, determines a strategy for comparing the relevance with similar small and micro enterprises based on the comparison results where the order of magnitude of the fuzzy label database is less than a preset order of magnitude. This strategy is based on the enterprise management evaluation value being less than or equal to the preset enterprise management evaluation value, and determines to reduce the amount of data in the fuzzy label database.

4. The management system based on enterprise financial planning according to claim 2, characterized in that, The data analysis module, under the condition that the data storage module stores several unqualified clustered data into the fuzzy label database, determines the strategy of comparing the relevance with large and medium-sized enterprises of the same type based on the comparison results of the fuzzy label database being greater than or equal to a preset order of magnitude. Based on the enterprise's comprehensive evaluation value being less than or equal to the preset enterprise comprehensive evaluation value, the data volume for updating the fuzzy label database is determined.

5. The management system based on enterprise financial planning according to claim 1, characterized in that, Under the condition of determining to perform an external enterprise database comparison, the data processing module determines to reduce the data volume of the fuzzy label database to the corresponding value by using a first preset management adjustment coefficient, based on the comparison result that the ratio of the fluctuation range of the fuzzy label database data volume to the preset fluctuation range is less than or equal to the preset external enterprise database ratio.

6. The management system based on enterprise financial planning according to claim 1, characterized in that, Under the condition of performing an external enterprise database comparison, the data processing module determines to reduce the data volume of the fuzzy label database to the corresponding value by using a second preset management adjustment coefficient, based on the comparison result that the ratio of the fluctuation range of the fuzzy label database data volume to the preset fluctuation range is greater than the preset ratio of the external enterprise database.

7. The management system based on enterprise financial planning according to claim 1, characterized in that, If the data optimization module compares the fuzzy label database with its own database under the condition that the data processing module is performing a comparison, it determines to supplement the fuzzy label database with data from the label database. If the fuzzy label database is compared with an external enterprise database, then it is determined that data will be added to the fuzzy label database from the external enterprise database.

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