Management system based on enterprise financial planning

Abstract

The invention relates to the field of management systems, in particular to a management system based on enterprise financial planning, and the system comprises a data obtaining module which is used for obtaining a plurality of pieces of information data from an enterprise information system; the data storage module is connected with the data acquisition module and is used for storing the information data acquired by the data acquisition module and storing the information data into different databases; the data analysis module is connected with the data storage module and is used for determining a strategy mode; the data processing module is connected with the data processing module and is used for determining an adjusting mode; and the data optimization module is connected with the data processing module and is used for optimizing the processed database. According to the invention, the accuracy and efficiency of enterprise financial planning by the management system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of management systems, and in particular to a management system based on enterprise financial planning. Background Art

[0002] As enterprises continue to expand and their businesses become increasingly complex, traditional manual financial planning and management methods can no longer meet the needs of enterprises. This demand has promoted the application of information technology in the financial field, prompting enterprises to seek more efficient and accurate management tools.

[0003] Chinese patent application publication number: CN116523646A. A financial fund management system and method for use in an enterprise is disclosed, including: an enterprise financial fund management platform, the enterprise financial fund management platform 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 supervision and analysis module; the present invention uses the fund expenditure classification and summary module to analyze the fund expenditure of the corresponding enterprise and obtains the abnormal expenditure category through classification and investigation when the total expenditure amount is judged to be abnormal, and the fund transaction risk analysis module performs grade analysis and marking on the transaction objects of the corresponding enterprise and performs transaction risk analysis on the corresponding transactions, further ensuring the safety of the enterprise's fund expenditure, and performs enterprise profit analysis through the enterprise profit supervision and analysis module, which helps the corresponding management personnel to timely and accurately understand the enterprise's operating conditions and make reasonable subsequent enterprise plans. However, the prior art has the following problems:

[0004] The management system lacks management of corporate financial planning, which leads to low accuracy and low efficiency of the management system in corporate financial planning. Summary of the invention

[0005] To this end, the present invention provides a management system based on enterprise financial planning to overcome the problems of low accuracy and low efficiency of enterprise financial planning in the management system in the prior art.

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

[0007] A data acquisition module is used to obtain certain information data from the enterprise information system;

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

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

[0010] A data processing module, connected to the data processing module, for determining an adjustment method;

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

[0012] Furthermore, the data pool of the data storage module clusters the plurality of information data and determines a comparison result with poor clustering condition according to the characteristic evaluation value of the plurality of information data being greater than or equal to the preset characteristic value.

[0013] Furthermore, under the condition that the data analysis module stores a number of data with poor clustering conditions into the fuzzy label database, the data analysis module determines the strategy of comparing the data with the same type of small and micro enterprises according to the comparison result that the order of magnitude of the fuzzy label database is less than the preset order of magnitude, and determines to reduce the data of the fuzzy label database by the management adjustment coefficient based on the enterprise management evaluation value being less than or equal to the preset enterprise management evaluation value.

[0014] Furthermore, under the condition that the data storage module stores a number of data with poor clustering conditions into the fuzzy label database, the data analysis module determines the strategy of comparing the data with the large and medium-sized enterprises of the same type according to the comparison result of the fuzzy label database whose order of magnitude is greater than or equal to the preset order of magnitude, and determines to update the data of the fuzzy label database with the update coefficient based on the enterprise comprehensive evaluation value being less than or equal to the preset enterprise comprehensive evaluation value.

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

[0016] Furthermore, under the condition that the data analysis module stores several data with poor clustering conditions into the fuzzy label database, the data analysis module determines the strategy for comparing with the external enterprise database according to the comparison result that the fluctuation range of the fuzzy label database data is greater than the preset fluctuation range, and determines to update the data of the fuzzy label database to the corresponding value with the first preset update coefficient 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 the own database.

[0017] Furthermore, under the condition that the data analysis module stores several data with poor clustering conditions into the fuzzy label database, the data analysis module determines the strategy for comparing with the external enterprise database according to the comparison result that the fluctuation range of the fuzzy label database data is greater than the preset fluctuation range, and determines to update the data of the fuzzy label database to the corresponding value with the second preset update coefficient 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 the own database.

[0018] Furthermore, under the condition of determining to perform a comparison with an external enterprise database, the data processing module determines to reduce the data in the fuzzy label database to a corresponding value 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 to the preset fluctuation range 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 to reduce the data of the fuzzy label database to a corresponding value 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 to the preset fluctuation range is greater than the preset external enterprise database ratio.

[0020] Furthermore, under the condition that the data processing module performs 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, it 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 lies in that the present invention performs cluster analysis on a number of 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 human intervention and subjective judgment, thereby improving the accuracy and efficiency of the management system evaluation.

[0022] Furthermore, the present invention classifies and stores information data into different databases according to clustering conditions through the data storage module. After the data storage module stores data with poor clustering conditions into the fuzzy label database through the data analysis module, the subsequent analysis strategy is determined according to the comparison result between the order of magnitude of the fuzzy label database and the preset order of magnitude, thereby making data analysis more efficient.

[0023] Furthermore, under the condition that the data analysis module of the present invention determines the correlation with small and micro enterprises of the same type, the data processing module will perform further adjustment operations, and specifically determine the adjustment mode according to 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 according to the comparison result between the enterprise comprehensive evaluation value and the preset enterprise comprehensive evaluation value, which helps to reduce data redundancy and outdated information, thereby improving the accuracy and efficiency of the management system.

[0025] Furthermore, after the present invention stores several data with poor clustering conditions into the fuzzy label database through the data storage module, the data analysis module determines the subsequent strategy according to the comparison result of the data fluctuation range of the fuzzy label database and the preset fluctuation range, which helps to timely discover the cause of the data anomaly, thereby improving the efficiency of the management system in data processing.

[0026] Furthermore, after the present invention determines the strategy for comparing its own database through the data analysis module, the data processing module will further analyze the ratio of the data fluctuation amplitude of the fuzzy label database to the preset fluctuation amplitude, as well as the comparison result of the ratio with the preset ratio of its own database, to determine the adjustment method for data updating, thereby improving the accuracy of the management system.

[0027] Furthermore, after the present invention determines the strategy for comparing with the external enterprise database through the data analysis module, the data processing module further analyzes the ratio of the data 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, to determine the data adjustment method, thereby improving the comparability and reliability of the data, thereby improving the accuracy of the management system.

[0028] Furthermore, after receiving the comparison result from the data processing module, the data optimization module of the present invention determines a corresponding optimization method according to the specific object of comparison, thereby improving the accuracy and efficiency of the management system. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0030] Figure 2 A flow chart for determining cluster eligibility for an embodiment of the present invention;

[0031] Figure 3 A flowchart of a strategy for determining the correlation between the data volume of an enterprise fuzzy label database and the data volume of enterprises of the same type but different sizes according to an embodiment of the present invention;

[0032] Figure 4 The present invention is a flowchart of a method for determining a fuzzy label database for database comparison strategy according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0034] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0035] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0036] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0037] See also Figure 1-Figure 4 As shown, Figure 1 This is a schematic diagram of the module connection structure of a management system based on enterprise financial planning according to an embodiment of the present invention; Figure 2 A flow chart for determining cluster eligibility for an embodiment of the present invention; Figure 3 A flowchart of a strategy for determining the correlation between the data volume of an enterprise fuzzy label database and the data volume of enterprises of the same type but different sizes according to an embodiment of the present invention; Figure 4 The present invention is a flowchart of a method for determining a fuzzy label database for database comparison strategy according to an embodiment of the present invention.

[0038] The embodiment of the present invention is used for a management system based on enterprise financial planning, including:

[0039] A data acquisition module is used to obtain certain information data from the enterprise information system;

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

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

[0042] A data processing module, which is connected to the data processing module and is used to determine a data volume adjustment method for the fuzzy label database;

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

[0044] Specifically, the data pool of the data storage module clusters the plurality of information data and determines clustering eligibility according to a comparison result between the characteristic evaluation values ​​of the plurality of information data and the preset characteristic evaluation values;

[0045] If the characteristic evaluation value is less than the preset characteristic value, the clustering is determined to be qualified;

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

[0047] In the embodiment of the present invention, the preset feature evaluation value is 0.8, and the preset feature evaluation value is obtained based on several historical feature evaluation values, but the above value is not limited thereto, and those skilled in the art may also adjust the value according to actual needs.

[0048] Specifically, the present invention performs cluster analysis on a number of information data through the data pool of the data storage module, and determines the clustering eligibility by comparing the feature evaluation value with the preset feature evaluation value, thereby reducing the influence of human intervention and subjective judgment, thereby improving the accuracy and efficiency of management system evaluation.

[0049] Specifically, the data storage module calculates the feature evaluation value according to the following formula, setting:

[0050]

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

[0052] Specifically, the data storage module stores a number of information data into different databases according to clustering eligibility. If the clustering is qualified, it is determined to be stored in the label database; if the clustering is unqualified, it is determined to be stored in the fuzzy label database.

[0053] Specifically, the data analysis module determines the strategic method of correlation between the data volume of the enterprise fuzzy label database and the data volume of enterprises of the same type but different sizes according to the comparison result of the order of magnitude of the fuzzy label database and the preset order of magnitude, under the condition that the data storage module stores a number of data that fail to meet the clustering requirements into the fuzzy label database;

[0054] If the fuzzy label database is smaller than the preset level, a strategy for comparing the correlation with the data of small and micro enterprises of the same type is determined;

[0055] If the fuzzy label database has an order of magnitude greater than or equal to the preset order of magnitude, then a strategy for comparing the correlation with the data volume of large and medium-sized enterprises of the same type 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 the embodiment of the present invention, the preset order of magnitude is 10TB, and the preset order of magnitude is obtained based on the average value of several historical orders of magnitude, but the above value is not limited thereto, and technicians in this field may also adjust the value according to actual needs.

[0058] Specifically, the present invention classifies information data into different databases according to clustering eligibility through a data storage module, and after the data storage module stores data that do not meet clustering requirements into a fuzzy label database through a data analysis module, determines a subsequent analysis strategy based on a comparison result between the order of magnitude of the fuzzy label database and a preset order of magnitude, thereby making data analysis more efficient.

[0059] Specifically, the data processing module determines the mode for adjusting the data volume of the fuzzy label database according to the comparison result of the enterprise management evaluation value with the preset enterprise management evaluation value, under the condition that the data analysis module determines the correlation with the data volume comparison of the small and micro enterprises of the same type;

[0060] If the enterprise management evaluation value is less than or equal to the preset enterprise management evaluation value, determine to reduce the data volume of the fuzzy label database by the management adjustment coefficient L;

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

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

[0063] In the embodiment of the present invention, the preset enterprise management evaluation value is 0.85, and the preset enterprise management evaluation value is obtained by averaging several historical enterprise management evaluation values, but the above value is not limited to this, and technical personnel in this field 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:

[0065]

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

[0067] In the embodiment of the present invention, the enterprise financial performance is the profit level of the enterprise, and the enterprise customer satisfaction is the matching degree between customer expectations and customer experience.

[0068] Specifically, the data processing module determines the management adjustment coefficient L1 according to 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 the management adjustment coefficient according to the following formula and sets:

[0070]

[0071] Among them, 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, under the condition that the data analysis module of the present invention determines the correlation with small and micro enterprises of the same type, the data processing module will perform further adjustment operations, and determine the adjustment mode specifically according to 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 the mode for adjusting the data volume of the fuzzy label database according to the comparison result between the enterprise comprehensive evaluation value and the preset enterprise comprehensive evaluation value under the condition that the data analysis module determines the correlation with the large and medium-sized enterprises of the same type;

[0074] If the enterprise comprehensive evaluation value is less than or equal to the preset enterprise comprehensive evaluation value, then determine to update the data amount of the fuzzy label database with the update coefficient Q;

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

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

[0077] In the embodiment of the present invention, the preset enterprise comprehensive evaluation value is 0.8, and the preset enterprise comprehensive evaluation value is obtained by averaging the comprehensive evaluation values ​​of several historical enterprises, but the above value is not limited to this, and technical personnel in this field can also adjust the value according to actual needs.

[0078] Specifically, the data processing module calculates the comprehensive evaluation value of the enterprise according to the following formula:

[0079]

[0080] Among them, G represents the comprehensive evaluation value of the enterprise, M represents the market size of the enterprise, Mi represents the market size of the ith large and medium-sized enterprise of the same type, R represents the resource input of the enterprise, Ri represents the resource input of the ith large and medium-sized enterprise of the same type, S represents the resource output of the enterprise, and Si represents the resource output of the ith large and medium-sized enterprise of the same type.

[0081] In the embodiment of the present 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 input of various resources.

[0082] Specifically, the data processing module determines the update coefficient Q according to the difference between the enterprise comprehensive evaluation value and the preset enterprise comprehensive evaluation value and the preset difference.

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

[0084]

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

[0086] Specifically, under the condition that the data analysis module determines the correlation with the large and medium-sized enterprises of the same type, the data processing module will further determine the adjustment mode according to the comparison result between the comprehensive evaluation value of the enterprise and the preset comprehensive evaluation value of the enterprise, which helps to reduce data redundancy and outdated information, thereby improving the accuracy and efficiency of the management system.

[0087] Specifically, the data analysis module determines the strategy for database comparison of the fuzzy label database according to the comparison result between the fluctuation range of the data volume of the fuzzy label database and the preset fluctuation range, under the condition that the data storage module stores a number of data with poor clustering conditions into the fuzzy label database;

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

[0089] If the fluctuation range of the data volume in the fuzzy label database is greater than the preset fluctuation range, determining a strategy for performing comparison with an external enterprise database;

[0090] In the embodiment of the present invention, the preset fluctuation range is 2%, and the preset fluctuation range is obtained based on the average value of the fluctuation ranges of several historical enterprises, but the above value is not limited to this, and technical personnel in this field 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:

[0092]

[0093] Among them, A represents the fluctuation range of the fuzzy label database data, xi represents the i-th data point, represents the average value of the database data volume, and n represents the database data volume.

[0094] Specifically, after the present invention stores several data that fail to meet the clustering requirements into the fuzzy label database through the data storage module, the data analysis module determines the subsequent strategy based on the comparison result between the fluctuation range of the data volume in the fuzzy label database and the preset fluctuation range, which helps to timely discover the cause of the data anomaly, thereby improving the efficiency of the management system in data processing.

[0095] Specifically, the data processing module determines the way to adjust the data volume of the fuzzy label database according to the comparison result of the ratio of the data volume fluctuation range of the fuzzy label database to the preset fluctuation range and the preset self-database ratio under the condition of determining the self-comparison;

[0096] If the ratio is less than or equal to the preset own database ratio, it is determined to update the data amount of the fuzzy label database to a corresponding value using a first preset coefficient;

[0097] If the ratio is greater than the preset own database ratio, it is determined to update the data amount of the fuzzy label database to a corresponding value using a second preset coefficient;

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

[0099] In the embodiment of the present invention, the preset self-ratio value is 0.5, and the preset self-ratio value is obtained by averaging the self-ratios of several historical enterprises, but the above value is not limited to this, and technical personnel in this field can also adjust the value according to actual needs.

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

[0101] Specifically, after the present invention determines the strategy for comparing its own database through the data analysis module, the data processing module will further analyze the ratio of the fluctuation range of the data volume in 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, to determine the adjustment method for data updating, thereby improving the accuracy of the management system.

[0102] Specifically, the data processing module determines the method of adjusting the data volume of the fuzzy label database according to the comparison result of the ratio of the data volume fluctuation range of the fuzzy label database to the preset fluctuation range and the preset external enterprise database ratio under the condition of determining to compare with the external enterprise database;

[0103] If the ratio is less than or equal to the preset external enterprise database ratio, it is determined to reduce the amount of data in the fuzzy label database to a corresponding value using a first preset reduction adjustment coefficient;

[0104] If the ratio is greater than the preset external enterprise database ratio, it is determined to reduce the amount of data in the fuzzy label database to a corresponding value using a second preset reduction adjustment coefficient;

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

[0106] In the embodiment of the present invention, the preset external enterprise ratio is 1.2, and the preset external enterprise ratio is obtained by averaging several historical external enterprise ratios, but the above value is not limited thereto, and those skilled in the art may also adjust the value according to actual needs.

[0107] In an embodiment of the present invention, the data volume of the reduced fuzzy label database is set to Ic, and Ic=I×Ki is set, wherein I represents the data volume of the fuzzy label database, Ki represents the i-th preset reduction adjustment coefficient, i takes a 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 present invention determines the strategy for comparing with the external enterprise database through the data analysis module, the data processing module further analyzes the ratio of the fluctuation range of the data volume in 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, to determine the data adjustment method, thereby improving the comparability and reliability of the data, thereby improving the accuracy of the management system.

[0109] Specifically, the data optimization module determines the optimization method of the fuzzy label database according to the comparison object under the condition that the data processing module performs the comparison;

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

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

[0112] Specifically, after receiving the comparison result from the data processing module, the data optimization module of the present invention determines the corresponding optimization method according to the specific object of comparison, thereby improving the accuracy and efficiency of the management system.

[0113] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle 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 fall within the protection scope of the present invention.

[0114] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A management system based on enterprise financial planning, characterized in that: include: A data acquisition module is used to obtain certain information data from the enterprise information system; A data storage module, which is connected to the data acquisition module and is used to store the information data acquired by the data acquisition module and store the information data into different databases; A data analysis module connected to the data storage module is used to determine a strategy for comparing the data volume of the fuzzy label database according to the order of magnitude of the fuzzy label database or the fluctuation range of the data in the fuzzy label database under the condition of unqualified clustering; A data processing module, which is connected to the data processing module, and is used to determine a method for adjusting the data volume of the fuzzy label database according to the enterprise management evaluation value or the enterprise comprehensive evaluation value under the condition of a data volume comparison strategy based on the data volume of the fuzzy label database; The data optimization module is connected to the data processing module and is used to determine a method for optimizing the fuzzy label database according to the comparison object.

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 the plurality of information data and determines a comparison result that the clustering is unqualified according to the characteristic evaluation value of the plurality of information data being greater than or equal to the preset characteristic value.

3. The management system based on enterprise financial planning according to claim 2 is characterized in that: The data analysis module, under the condition that the data storage module stores a number of data that fail clustering into the fuzzy label database, determines a strategy for comparing correlation with small and micro enterprises of the same type based on the comparison result that the order of magnitude of the fuzzy label database is less than a preset order of magnitude, and determines to reduce the amount of data in the fuzzy label database based on the enterprise management evaluation value being less than or equal to the preset enterprise management evaluation value.

4. The management system based on enterprise financial planning according to claim 2 is characterized in that: The data analysis module, under the condition that the data storage module stores a number of data that fail clustering into the fuzzy label database, determines the strategy of comparing the correlation with large and medium-sized enterprises of the same type according to the comparison result that the order of magnitude of the fuzzy label database is greater than or equal to the preset order of magnitude, and determines the amount of data to update the fuzzy label database based on the comprehensive evaluation value of the enterprise being less than or equal to the preset comprehensive evaluation value of the enterprise.

5. The management system based on enterprise financial planning according to claim 1, characterized in that: The data analysis module determines a strategy for comparing its own database based on a comparison result that the fluctuation range of the data volume in the fuzzy label database is less than or equal to a preset fluctuation range, under the condition that the data storage module stores a number of data that fail to meet the clustering requirements in the fuzzy label database.

6. The management system based on enterprise financial planning according to claim 1, characterized in that: The data analysis module determines the strategy for comparing with the external enterprise database based on the comparison result that the fluctuation range of the data volume in the fuzzy label database is greater than the preset fluctuation range, under the condition that the data storage module stores several data that fail the clustering into the fuzzy label database, and determines to update the data in the fuzzy label database to the corresponding value with the first preset update coefficient based on the ratio of the fluctuation range of the data in the fuzzy label database to the preset fluctuation range being less than or equal to the preset ratio of the own database.

7. The management system based on enterprise financial planning according to claim 1, characterized in that: The data analysis module determines the strategy for comparing with the external enterprise database based on the comparison result that the fluctuation range of the data volume in the fuzzy label database is greater than the preset fluctuation range, under the condition that the data storage module stores several data that fail the clustering into the fuzzy label database. The data analysis module determines the strategy for updating the data in the fuzzy label database to the corresponding value with the second preset update coefficient based on the fact that the ratio of the fluctuation range of the data volume in the fuzzy label database to the preset fluctuation range is greater than the preset ratio of the own database.

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

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

10. The management system based on enterprise financial planning according to claim 1, characterized in that: 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 the own database under the condition that the data processing module performs the comparison; If the fuzzy label database is compared with the external enterprise database, it is determined to supplement the data from the external enterprise database to the fuzzy label database.

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