Enterprise intelligent management system and method based on big data

Abstract

The invention relates to the technical field of enterprise management, and discloses an intelligent enterprise management system and method based on big data, and the system comprises a multi-dimensional collection module and an intelligent management module. According to the enterprise intelligent management system and method based on big data, management data of all regional branch companies, working data of employees and market research data are acquired through a multi-dimensional acquisition module and are classified to form a data set, and an intelligent management module analyzes the cost-benefit ratio, the index achievement rate, the net flow rate and the operation score of each regional branch company; the output ratio and the individual score of each employee are analyzed, the intelligent management module analyzes the fluctuation rate of historical product prices, the fluctuation rate of peer product prices, the fluctuation rate of product failure rates, and the fluctuation rate and the fluctuation coefficient of peer product failure rates of each region, and the multi-dimensional analysis integration capability is high; the intelligent management module judges operation states of regional branch companies, working states of employees and market fluctuation trends and performs scheduling measures, so that efficient operation and high emergency response capability are achieved.

Description

Technical Field

[0001] The present invention relates to the field of enterprise management technology, and in particular to an enterprise intelligent management system and method based on big data. Background Art

[0002] Big data technology can solve the problem of efficient data collection and massive storage, and ensure the integrity and security of data. The core part of big data technology is to process and analyze data, involving a series of processes such as data cleaning, conversion, and mining. By using algorithms such as machine learning and deep learning, hidden patterns and trends can be mined from the data. In order to enable non-technical personnel to understand and use the results of data analysis, big data technology intuitively displays data in the form of charts and graphs to help people better understand the meaning behind the data. Big data technology has a wide range of applications, including but not limited to finance, corporate management, medical care, e-commerce, transportation, etc. In the financial field, big data can help banks conduct risk assessment and fraud detection. In the field of corporate management, big data technology can help companies screen the most suitable candidates during the recruitment process. By analyzing historical recruitment data, companies can identify successful recruitment patterns and candidate characteristics. It can also be used for employee performance evaluation and training needs analysis, thereby improving the efficiency and effectiveness of human resource management, helping manufacturing companies to monitor the supply chain in real time, optimize logistics routes, and reduce transportation time and costs. In addition, big data technology can help companies better understand consumer behavior and market demand, optimize product recommendations and pricing strategies, and thus formulate more effective marketing strategies.

[0003] At present, traditional enterprise intelligent management systems based on big data lack in-depth analysis capabilities and find it difficult to comprehensively analyze the correlation of multi-dimensional data, which leads to delays in management decisions. When dealing with complex and changing market environments, cross-regional data integration capabilities are insufficient, emergency response capabilities are poor, and operational flexibility is low. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides an enterprise intelligent management system and method based on big data, which has the advantages of strong multi-dimensional analysis and integration capabilities, efficient operation and strong emergency response capabilities, etc., and solves the problems that traditional enterprise intelligent management systems based on big data lack in-depth analysis capabilities and poor emergency response capabilities.

[0005] (II) Technical solution To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an enterprise intelligence management system based on big data, comprising a multi-dimensional acquisition module and an intelligent management module; The multi-dimensional acquisition module is composed of a regional management unit, an employee management unit and a market data unit. The regional management unit collects regional data sets through a network connection to a database, and the regional data sets include management data of all regional branches. The employee management unit collects employee data sets through a network connection to a database, and the employee data sets include work data of all employees. The market data unit collects market data sets through a network connection to a big data platform, and the market data sets include market research data of all regions. The intelligent management module consists of an operation analysis unit, an individual analysis unit, a market analysis unit and a scheduling management unit. The operation analysis unit analyzes the cost-benefit ratio of each regional branch based on the regional data set. , target achievement rate and net liquidity , and generate the corresponding operation score The individual analysis unit analyzes the output ratio of each employee based on the regional data set and the employee data set. , and generate the corresponding individual scores The market analysis unit is provided with a monitoring cycle of fixed duration. , combined with the market data set, analyze the volatility of historical product prices in each region , the volatility of peer product prices , Fluctuation of product failure rate The volatility of the failure rate of peer products , and generate the corresponding volatility coefficient The dispatch management unit is set with a fixed range of operating thresholds , individual threshold and fluctuation threshold , combined with the operational score , individual rating and volatility , judge the operating status of regional branches, the work status of employees, and the market fluctuation trends in the region, and take corresponding scheduling measures.

[0006] Preferably, the expression of the regional data set is , to The first to the Management data of regional branches, including project indicators, total expenditure costs, total revenue profits, number of resignations and number of new employees, Indicates the number of employees in each regional branch.

[0007] Preferably, the expression of the employee data set is , to The first to the The work data of each employee includes total sales, visitor frequency, customer satisfaction rating and number of awards. Indicates the length of service of each employee.

[0008] Preferably, the expression of the market data set is , to The first to the Market research data for each region, including historical product prices, prices of peer products, product failure rates, and failure rates of peer products. Indicates the specific time for obtaining market research data for each region.

[0009] Preferably, the operation score The calculation process is as follows: S11. Extract the first management data of regional branches and The project indicators of regional branches are marked as , will The total expenditure cost of the regional branches is marked as , will The total profit of the regional branches is marked as , will The number of people leaving the regional branch is marked as , will The number of new employees in the regional branch is marked as ; S12, calculate the Cost-effectiveness ratio of regional branches , and its calculation formula is as follows: In the formula, represents the growth coefficient, It means that the total expenditure cost will be magnified by the multiple of the growth coefficient to limit the growth of the total expenditure cost; S13, calculate the The target achievement rate of regional branches , and its calculation formula is as follows: S14, calculate the Net turnover rate of regional branches , and its calculation formula is as follows: In the formula, Indicates Number of employees in each regional branch; S15. Calculate the Operational scores of regional branches , and its calculation formula is as follows: In the formula, represents the evaluation weight for the cost-effectiveness ratio, represents the evaluation weight for the indicator achievement rate, represents the evaluation weight for net turnover rate, , Indicates according to , and Weight, comprehensive cost-effectiveness ratio, target achievement rate and net turnover rate, get the Operational scores of regional branches.

[0010] Preferably, the individual score The calculation process is as follows: S21. Extract the employee data set Regional branch The work data of each employee and The total sales of employees is marked as , will The visitor frequency of employees is marked as , will Customer satisfaction ratings of employees are marked as , will The number of times an employee wins an award is marked as ; S22. According to Management data of regional branches, calculate the Per capita indicators of regional branches , and its calculation formula is as follows: S23, calculate the Output per employee , and its calculation formula is as follows: S24, calculate the Individual ratings of employees , and its calculation formula is as follows: In the formula, represents the evaluation weight for output ratio, Represents the evaluation weight for visitor frequency, represents the evaluation weight for customer satisfaction rating, represents the evaluation weight for the number of awards, Indicates The length of service of each employee, represents the evaluation weight for length of service, , Indicates according to , , , and The weight is based on the comprehensive output ratio, visitor frequency, customer satisfaction score, number of awards and length of service to obtain the first Individual ratings for each employee.

[0011] Preferably, the fluctuation coefficient The calculation process is as follows: S31. Extract monitoring cycles based on market data sets within, no. Market research data for each region and monitor the cycle within, no. The historical product prices for the regions are marked as , to From the first time point to the The historical product price at a point in time will be monitored over the period within, no. The prices of peer products in the region are marked as , to From the first time point to the The price of peer products at a certain time point will be monitored over the period within, no. The product failure rate in each region is marked as , to From the first time point to the The product failure rate at a time point will be monitored over the period within, no. The failure rate of peer products in the region is marked as , to From the first time point to the Failure rate of peer products at a point in time; S32, calculate the The volatility of historical product prices in each region , and its calculation formula is as follows: In the formula, Indicates The area in The historical product price at a point in time, Indicates monitoring cycle within, no. The average price of historical products in a region; S33, calculate the The volatility of the prices of peer products in the region , and its calculation formula is as follows: In the formula, Indicates The area in The price of peer products at a certain point in time, Indicates monitoring cycle within, no. The average price of peer products in the region; S34, calculate the Fluctuation of product failure rate in each region , and its calculation formula is as follows: In the formula, Indicates The area in The product failure rate at a point in time, Indicates monitoring cycle within, no. The average value of product failure rate in each region; S35, calculate the The volatility of the failure rate of peer products in each region , and its calculation formula is as follows: In the formula, Indicates The area in The failure rate of peer products at a time point, Indicates monitoring cycle within, no. The average failure rate of peer products in the region; S36, calculate the The volatility coefficient of the region , and its calculation formula is as follows: In the formula, Indicates Historical product price volatility in each region Price volatility compared with peer products The absolute difference of Represents the evaluation weight for product price volatility, Indicates Product failure rate fluctuation rate in each region Failure rate volatility compared with peer products The absolute difference of represents the evaluation weight for the product failure rate volatility, , Indicates according to and Weight, comprehensive product price volatility and product failure rate volatility, get the The volatility coefficient of a region.

[0012] Preferably, the operation score Below the operational threshold When the branch is in a state of instability, it means that the branch's operation status is unstable and the company's project indicators need to be reduced by 5-10%. Below individual threshold , it means that the employee's work status is unstable and training courses are needed.

[0013] Preferably, the fluctuation coefficient Exceeding the volatility threshold When , it indicates that the market fluctuation trend in this area is unstable and it is necessary to adjust the product price and marketing strategy in this area.

[0014] A method for enterprise intelligent management based on big data, comprising the following steps: Step 1: Connect the database and big data platform through the network to obtain the management data of all regional branches, the work data of all employees and the market research data of all regions, and classify them into regional data sets, employee data sets and market data sets; Step 2: Analyze the cost-effectiveness ratio of each regional branch based on the regional data set , target achievement rate and net liquidity , and generate the corresponding operation score ; Step 3: Analyze the output ratio of each employee based on the regional data set and employee data set , and generate the corresponding individual scores ; Step 4: Set a fixed monitoring period , combined with the market data set, analyze the volatility of historical product prices in each region , the volatility of peer product prices , Fluctuation of product failure rate The volatility of the failure rate of peer products , and generate the corresponding volatility coefficient ; Step 5: Set a fixed range of operational thresholds , individual threshold and fluctuation threshold , combined with the operational score , individual rating and volatility , judge the operating status of regional branches, the work status of employees, and the market fluctuation trends in the region, and take corresponding scheduling measures.

[0015] Compared with the prior art, the present invention provides an enterprise intelligence management system and method based on big data, which has the following beneficial effects: 1. The present invention connects the database and the big data platform through a multi-dimensional acquisition module network to obtain the management data of all regional branches, the work data of all employees and the market research data of all regions, and classifies them into data sets. The intelligent management module analyzes the cost-effectiveness ratio of each regional branch. , target achievement rate and net liquidity , and generate the corresponding operation score , which is used to directly reflect operational efficiency, help enterprise managers identify inefficient and efficient areas, and optimize resource allocation in a targeted manner. The intelligent management module analyzes the output ratio of each employee. , and generate the corresponding individual scores , used to accurately evaluate employees' work performance and contribution, to motivate employees and improve work efficiency, the intelligent management module sets a fixed-duration monitoring cycle , and then analyze the volatility of historical product prices in each region , the volatility of peer product prices , Fluctuation of product failure rate The volatility of the failure rate of peer products , and generate the corresponding volatility coefficient , monitor product and competitor fluctuations, help managers understand the stability of the market environment, help to adjust pricing and product improvement strategies in a timely manner, provide early warning of product quality issues, and improve the response speed of product maintenance and after-sales service. It has strong multi-dimensional analysis and integration capabilities.

[0016] 2. The present invention sets a fixed range of operating thresholds through an intelligent management module , individual threshold and fluctuation threshold , combined with the operational score , individual rating and volatility , judge the operating status of regional branches, the working status of employees, and the market fluctuation trend in the region, and the operation score Below the operational threshold When the branch is in a state of instability, it means that the branch's operation status is unstable and the company's project indicators need to be reduced by 5-10%. Below individual threshold When , it means that the employee's work status is unstable and training courses are needed. Exceeding the volatility threshold When , it means that the market fluctuation trend in this area is unstable, and it is necessary to adjust the product prices and marketing strategies in this area, and the emergency response capability is strong with efficient operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the system flow of the present invention; Figure 2 It is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Because the traditional enterprise intelligence management system based on big data lacks in-depth analysis capabilities, it is difficult to fully analyze the correlation of multi-dimensional data, which leads to delays in management decisions. When dealing with complex and changing market environments, the cross-regional data integration capabilities are insufficient, the emergency response capabilities are poor, and the operational flexibility is low. Therefore, an enterprise intelligence management system and method based on big data is provided. Please refer to Figure 1 , an enterprise intelligent management system based on big data, including a multi-dimensional acquisition module and an intelligent management module; The multi-dimensional acquisition module consists of a regional management unit, an employee management unit, and a market data unit. The regional management unit collects regional data sets through a network connection to the database. The regional data sets include the management data of all regional branches. The expression of the regional data set is , to The first to the Management data of regional branches, including project indicators, total expenditure costs, total revenue profits, number of resignations and number of new employees, Indicates the number of employees in each regional branch and comprehensively collects the operating data of all regional branches, which is conducive to the subsequent rapid evaluation of the operating status of each branch; The employee management unit collects employee data sets through a network connection to the database. The employee data set includes the work data of all employees. The expression of the employee data set is , to The first to the The work data of each employee includes total sales, visitor frequency, customer satisfaction rating and number of awards. Specifically, visitor frequency includes but is not limited to the frequency of telephone communication with customers and face-to-face consultation with customers. The sources of awards include but are not limited to internal competitions and industry competitions. Indicates the length of service of each employee; The market data unit collects market data sets through the network connection to the big data platform. The market data sets include market research data of all regions. The expression of the market data set is , to The first to the The market research data of each region includes historical product prices, prices of peer products, product failure rates and failure rates of peer products. Specifically, historical product prices and product failure rates are only for the company's own products, while prices of peer products and failure rates of peer products are only for competing companies' products. The company's own products and competing companies' products belong to the same category and correspond to the same consumer group. Indicates the specific time of obtaining market research data for each region, comprehensively collects internal management data and external market data of the enterprise, provides a full range of real-time data support for subsequent management decisions, and avoids the problem of data islands; The intelligent management module consists of an operation analysis unit, an individual analysis unit, a market analysis unit, and a scheduling management unit. The operation analysis unit analyzes the cost-benefit ratio of each regional branch based on the regional data set. , target achievement rate and net liquidity , and generate the corresponding operation score , the calculation process is as follows: S11. Extract the first management data of regional branches and The project indicators of regional branches are marked as , will The total expenditure cost of the regional branches is marked as , will The total profit of the regional branches is marked as , will The number of people leaving the regional branch is marked as , will The number of new employees in the regional branch is marked as ; S12, calculate the Cost-effectiveness ratio of regional branches , and its calculation formula is as follows: In the formula, represents the growth coefficient, It means that the total expenditure cost will be magnified by the multiple of the growth coefficient to limit the growth of the total expenditure cost; S13, calculate the The target achievement rate of regional branches , and its calculation formula is as follows: S14, calculate the Net turnover rate of regional branches , and its calculation formula is as follows: In the formula, Indicates Number of employees in each regional branch; S15. Calculate the Operational scores of regional branches , and its calculation formula is as follows: In the formula, represents the evaluation weight for the cost-effectiveness ratio, represents the evaluation weight for the indicator achievement rate, represents the evaluation weight for net turnover rate, , Indicates according to , and Weight, comprehensive cost-effectiveness ratio, target achievement rate and net turnover rate, get the Each regional branch has an operation score. , used to intuitively reflect operational efficiency, help enterprise managers identify inefficient and efficient areas, and optimize resource allocation in a targeted manner; The individual analysis unit analyzes the output ratio of each employee based on the regional data set and employee data set. , and generate the corresponding individual scores , the calculation process is as follows: S21. Extract the employee data set Regional branch The work data of each employee and The total sales of employees is marked as , will The visitor frequency of employees is marked as , will Customer satisfaction ratings of employees are marked as , will The number of times an employee wins an award is marked as ; S22. According to Management data of regional branches, calculate the Per capita indicators of regional branches , and its calculation formula is as follows: S23, calculate the Output per employee , and its calculation formula is as follows: S24, calculate the Individual ratings of employees , and its calculation formula is as follows: In the formula, represents the evaluation weight for output ratio, Represents the evaluation weight for visitor frequency, represents the evaluation weight for customer satisfaction rating, represents the evaluation weight for the number of awards, Indicates The length of service of each employee, represents the evaluation weight for length of service, , Indicates according to , , , and The weight is based on the comprehensive output ratio, visitor frequency, customer satisfaction score, number of awards and length of service to obtain the first Each employee in a branch office in a different region has a corresponding individual score. , used to accurately evaluate employees' work performance and contribution, so as to motivate employees and improve work efficiency; The market analysis unit is set with a fixed-length monitoring cycle , combined with the market data set, analyze the volatility of historical product prices in each region , the volatility of peer product prices , Fluctuation of product failure rate The volatility of the failure rate of peer products , and generate the corresponding volatility coefficient , the calculation process is as follows: S31. Extract monitoring cycles based on market data sets within, no. Market research data for each region and monitor the cycle within, no. The historical product prices for the regions are marked as , to From the first time point to the The historical product price at a point in time will be monitored over the period within, no. The prices of peer products in the region are marked as , to From the first time point to the The price of peer products at a certain time point will be monitored over the period within, no. The product failure rate in each region is marked as , to From the first time point to the The product failure rate at a time point will be monitored over the period within, no. The failure rate of peer products in the region is marked as , to From the first time point to the Failure rate of peer products at a point in time; S32, calculate the The volatility of historical product prices in each region , and its calculation formula is as follows: In the formula, Indicates The area in The historical product price at a point in time, Indicates monitoring cycle within, no. The average price of historical products in a region; S33, calculate the The volatility of the prices of peer products in the region , and its calculation formula is as follows: In the formula, Indicates The area in The price of peer products at a certain point in time, Indicates monitoring cycle within, no. The average price of peer products in the region; S34, calculate the Fluctuation of product failure rate in each region , and its calculation formula is as follows: In the formula, Indicates The area in The product failure rate at a point in time, Indicates monitoring cycle within, no. The average value of product failure rate in each region; S35, calculate the The volatility of the failure rate of peer products in each region , and its calculation formula is as follows: In the formula, Indicates The area in The failure rate of peer products at a time point, Indicates monitoring cycle within, no. The average failure rate of peer products in the region; S36, calculate the The volatility coefficient of the region , and its calculation formula is as follows: In the formula, Indicates Historical product price volatility in each region Price volatility compared with peer products The absolute difference of Represents the evaluation weight for product price volatility, Indicates Product failure rate fluctuation rate in each region Failure rate volatility compared with peer products The absolute difference of represents the evaluation weight for the product failure rate volatility, , Indicates according to and Weight, comprehensive product price volatility and product failure rate volatility, get the The volatility coefficient of each region can be quantitatively analyzed to monitor the fluctuation of products and competitors, helping managers understand the stability of the market environment, timely adjust pricing and product improvement strategies, warn of product quality problems in advance, and improve the response speed of product maintenance and after-sales service; The dispatch management unit sets a fixed range of operating thresholds , individual threshold and fluctuation threshold , combined with the operational score , individual rating and volatility , judge the operating status of regional branches, the work status of employees, and the market fluctuation trend in the region, and take corresponding scheduling measures, operation scoring Below the operational threshold When the branch is in a state of instability, it means that the branch's operation status is unstable and the company's project indicators need to be reduced by 5-10%. Below individual threshold When , it means that the employee's work status is unstable and training courses are needed. Exceeding the volatility threshold When the market fluctuation trend in this area is unstable, it means that the product price and marketing strategy in this area need to be adjusted, which will help improve the company's emergency response capabilities and operational flexibility.

[0020] See also Figure 2 , an enterprise intelligence management method based on big data, comprising the following steps: Step 1: Connect the database and big data platform through the network to obtain the management data of all regional branches, the work data of all employees and the market research data of all regions, and classify them into regional data sets, employee data sets and market data sets; Step 2: Analyze the cost-effectiveness ratio of each regional branch based on the regional data set , target achievement rate and net liquidity , and generate the corresponding operation score ; Step 3: Analyze the output ratio of each employee based on the regional data set and employee data set , and generate the corresponding individual scores ; Step 4: Set a fixed monitoring period , combined with the market data set, analyze the volatility of historical product prices in each region , the volatility of peer product prices , Fluctuation of product failure rate The volatility of the failure rate of peer products , and generate the corresponding volatility coefficient , strong multi-dimensional analysis and integration capabilities; Step 5: Set a fixed range of operational thresholds , individual threshold and fluctuation threshold , combined with the operational score , individual rating and volatility , judge the operating status of regional branches, the work status of employees, and the market fluctuation trends in the region, and take corresponding scheduling measures, with efficient operation and strong emergency response capabilities.

[0021] Example 1: In this experiment, the Anhui regional branch was selected as the research object. According to statistics, within one month, the project target of the regional branch was 5 million yuan, the total cost of expenditure was 1 million yuan, the total profit of income was 1.5 million yuan, the number of people who resigned was 10, and the number of people who joined was 15; The cost-effectiveness ratio of the regional branch for: In the formula, represents the growth coefficient; The target achievement rate of the branch in this region , and its calculation formula is as follows: Net turnover rate of branches in this region , and its calculation formula is as follows: In the formula, Indicates the number of employees in the branch in this region; Operational rating of the branch in this region , and its calculation formula is as follows: In the formula, represents the evaluation weight for the cost-effectiveness ratio, represents the evaluation weight for the indicator achievement rate, represents the evaluation weight for net turnover rate, According to the weight, comprehensive cost-effectiveness ratio, target achievement rate and net turnover rate, the operating score of the regional branch is .

[0022] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An enterprise intelligence management system based on big data, characterized by: Including multi-dimensional acquisition module and intelligent management module; The multi-dimensional acquisition module is composed of a regional management unit, an employee management unit and a market data unit. The regional management unit collects regional data sets through a network connection to a database, and the regional data sets include management data of all regional branches. The employee management unit collects employee data sets through a network connection to a database, and the employee data sets include work data of all employees. The market data unit collects market data sets through a network connection to a big data platform, and the market data sets include market research data of all regions. The intelligent management module consists of an operation analysis unit, an individual analysis unit, a market analysis unit and a scheduling management unit. The operation analysis unit analyzes the cost-benefit ratio of each regional branch based on the regional data set. , target achievement rate and net liquidity , and generate the corresponding operation score The individual analysis unit analyzes the output ratio of each employee based on the regional data set and the employee data set. , and generate the corresponding individual scores The market analysis unit is provided with a monitoring cycle of fixed duration. , combined with the market data set, analyze the volatility of historical product prices in each region , the volatility of peer product prices , Fluctuation of product failure rate The volatility of the failure rate of peer products , and generate the corresponding volatility coefficient The dispatch management unit is set with a fixed range of operating thresholds , individual threshold and fluctuation threshold , combined with the operational score , individual rating and volatility coefficient , judge the operating status of regional branches, the work status of employees, and the market fluctuation trends in the region, and take corresponding scheduling measures.

2. According to claim 1, the enterprise intelligence management system based on big data is characterized by: The expression of the regional dataset is , to The first to the Management data of regional branches, including project indicators, total expenditure costs, total revenue profits, number of resignations and number of new employees, Indicates the number of employees in each regional branch.

3. The enterprise intelligence management system based on big data according to claim 2 is characterized by: The expression of the employee data set is , to The first to the The work data of each employee includes total sales, visitor frequency, customer satisfaction rating and number of awards. Indicates the length of service of each employee.

4. The enterprise intelligence management system based on big data according to claim 3 is characterized by: The expression of the market data set is , to The first to the Market research data for each region, including historical product prices, prices of peer products, product failure rates, and failure rates of peer products. Indicates the specific time for obtaining market research data for each region.

5. The enterprise intelligence management system based on big data according to claim 4 is characterized by: The operational score The calculation process is as follows: S11. Extract the first management data of regional branches and The project indicators of regional branches are marked as , will The total expenditure cost of the regional branches is marked as , will The total profit of the regional branches is marked as , will The number of people leaving the regional branch is marked as , will The number of new employees in the regional branch is marked as ; S12, calculate the Cost-effectiveness ratio of regional branches , and its calculation formula is as follows: In the formula, represents the growth coefficient, It means that the total expenditure cost will be magnified by the multiple of the growth coefficient to limit the growth of the total expenditure cost; S13, calculate the The target achievement rate of regional branches , and its calculation formula is as follows: S14, calculate the Net turnover rate of regional branches , and its calculation formula is as follows: In the formula, Indicates Number of employees in each regional branch; S15. Calculate the Operational scores of regional branches , and its calculation formula is as follows: In the formula, represents the evaluation weight for the cost-effectiveness ratio, represents the evaluation weight for the indicator achievement rate, represents the evaluation weight for net turnover rate, , Indicates according to , and Weight, comprehensive cost-effectiveness ratio, target achievement rate and net turnover rate, get the Operational scores of regional branches.

6. The enterprise intelligence management system based on big data according to claim 5 is characterized by: The individual score The calculation process is as follows: S21. Extract the employee data set Regional branch The work data of each employee and The total sales of employees is marked as , will The visitor frequency of employees is marked as , will Customer satisfaction ratings of employees are marked as , will The number of times an employee wins an award is marked as ; S22. According to Management data of regional branches, calculate the Per capita indicators of regional branches , and its calculation formula is as follows: S23, calculate the Output per employee , and its calculation formula is as follows: S24, calculate the Individual ratings of employees , and its calculation formula is as follows: In the formula, represents the evaluation weight for output ratio, Represents the evaluation weight for visitor frequency, represents the evaluation weight for customer satisfaction rating, represents the evaluation weight for the number of awards, Indicates The length of service of each employee, represents the evaluation weight for length of service, , Indicates according to , , , and The weight is based on the comprehensive output ratio, visitor frequency, customer satisfaction score, number of awards and length of service to obtain the first Individual ratings for each employee.

7. The enterprise intelligence management system based on big data according to claim 6 is characterized by: The coefficient of fluctuation The calculation process is as follows: S31. Extract monitoring cycles based on market data sets within, no. Market research data for each region and monitor the cycle within, no. The historical product prices for the regions are marked as , to From the first time point to the The historical product price at a point in time will be monitored over the period within, no. The prices of peer products in the region are marked as , to From the first time point to the The price of peer products at a certain time point will be monitored over the period within, no. The product failure rate in each region is marked as , to From the first time point to the The product failure rate at a time point will be monitored over the period within, no. The failure rate of peer products in the region is marked as , to From the first time point to the Failure rate of peer products at a point in time; S32, calculate the The volatility of historical product prices in each region , and its calculation formula is as follows: In the formula, Indicates The area in The historical product price at a point in time, Indicates monitoring cycle within, no. The average price of historical products in a region; S33, calculate the The volatility of the prices of peer products in the region , and its calculation formula is as follows: In the formula, Indicates The area in The price of peer products at a certain point in time, Indicates monitoring cycle within, no. The average price of peer products in the region; S34, calculate the Fluctuation of product failure rate in each region , and its calculation formula is as follows: In the formula, Indicates The area in The product failure rate at a point in time, Indicates monitoring cycle within, no. The average value of product failure rate in each region; S35, calculate the Fluctuation of failure rate of peer products in each region , and its calculation formula is as follows: In the formula, Indicates The area in The failure rate of peer products at a time point, Indicates monitoring cycle within, no. The average failure rate of peer products in the region; S36, calculate the The volatility coefficient of the region , and its calculation formula is as follows: In the formula, Indicates Historical product price volatility in each region Price volatility compared with peer products The absolute difference of Represents the evaluation weight for product price volatility, Indicates Product failure rate fluctuation rate in each region Failure rate volatility compared with peer products The absolute difference of represents the evaluation weight for the product failure rate volatility, , Indicates according to and Weight, comprehensive product price volatility and product failure rate volatility, get the The volatility coefficient of a region.

8. The enterprise intelligence management system based on big data according to claim 7 is characterized by: The operational score Below the operational threshold When the branch is in a state of instability, it means that the branch's operation status is unstable and the company's project indicators need to be reduced by 5-10%. Below individual threshold , it means that the employee's work status is unstable and training courses are needed.

9. The enterprise intelligence management system based on big data according to claim 8 is characterized by: The coefficient of fluctuation Exceeding the volatility threshold When , it indicates that the market fluctuation trend in this area is unstable and it is necessary to adjust the product price and marketing strategy in this area.

10. A method for enterprise intelligence management based on big data, applied to an enterprise intelligence management system based on big data as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Connect the database and big data platform through the network to obtain the management data of all regional branches, the work data of all employees and the market research data of all regions, and classify them into regional data sets, employee data sets and market data sets; Step 2: Analyze the cost-effectiveness ratio of each regional branch based on the regional data set , target achievement rate and net liquidity , and generate the corresponding operation score ; Step 3: Analyze the output ratio of each employee based on the regional data set and employee data set , and generate the corresponding individual scores ; Step 4: Set a fixed monitoring period , combined with the market data set, analyze the volatility of historical product prices in each region , the volatility of peer product prices , Fluctuation of product failure rate The volatility of the failure rate of peer products , and generate the corresponding volatility coefficient ; Step 5: Set a fixed range of operational thresholds , individual threshold and fluctuation threshold , combined with the operational score , individual rating and volatility coefficient , judge the operating status of regional branches, the work status of employees, and the market fluctuation trends in the region, and take corresponding scheduling measures.

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