Enterprise business distributed management system based on artificial intelligence

Abstract

The invention discloses an enterprise business distributed management system based on artificial intelligence, and relates to the technical field of artificial intelligence, the system comprises a monitoring center, the monitoring center is in communication connection with a data acquisition module, a data preprocessing module, a data processing module, a data analysis module, a database module, a prediction model module and an enterprise early warning module; the method comprises the following steps: acquiring business data of an enterprise, and preprocessing the business data to obtain preprocessed business data; performing comprehensive processing on the preprocessed business data to obtain a comprehensive index; obtaining a service data comprehensive evaluation coefficient according to the comprehensive index, and further obtaining a historical service data comprehensive evaluation coefficient; according to the historical business data comprehensive evaluation coefficient, a business data evaluation prediction model is constructed, then a predicted business data comprehensive evaluation coefficient is obtained, and early warning management is carried out on an enterprise; and the operation efficiency of an enterprise is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an enterprise business distributed management system based on artificial intelligence. Background Art

[0002] Distributed management systems achieve real-time monitoring and management of each business unit of an enterprise by distributing management functions to different nodes, solving problems such as slow information transmission and slow decision-making response.

[0003] By introducing artificial intelligence technology into distributed management systems, enterprises can achieve data-driven decision-making, automate a large number of repetitive tasks, predict business trends, optimize resource allocation, etc., effectively cope with the complexity and diversity challenges in enterprise management, and realize intelligent and efficient management processes. This not only improves the operational efficiency of enterprises, but also provides strong technical support for the long-term development of enterprises.

[0004] Therefore, an enterprise business distributed management system based on artificial intelligence is now provided. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide an enterprise business distributed management system based on artificial intelligence.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an enterprise business distributed management system based on artificial intelligence, comprising a monitoring center, wherein the monitoring center is communicatively connected with a data acquisition module, a data preprocessing module, a data processing module, a data analysis module, a database module, a prediction model module and an enterprise early warning module;

[0007] The data collection module is used to collect the business data of the enterprise;

[0008] The data preprocessing module is used to preprocess the business data and label the collection moments of the same collection cycle as i=1, 2, 3, ..., n, where n is a natural number;

[0009] Preprocessing the business data at different collection times in the same collection cycle to obtain preprocessed business data;

[0010] The preprocessed business data includes preprocessed sales data, preprocessed financial data, preprocessed customer data, preprocessed production data, preprocessed market data, preprocessed employee information data and preprocessed logistics data;

[0011] The pre-processed sales data includes the mean sales volume, the mean sales quantity, the mean sales channel quantity and the mean sales cycle; the pre-processed financial data includes the mean financial account data, the mean financial account data and the mean account profit data; the pre-processed customer data includes the mean customer quantity, the mean customer order quantity and the mean customer satisfaction; the pre-processed production data includes the mean production output, the mean production efficiency, the mean production cost and the mean production quality data; the pre-processed market data includes the mean market research data and the mean competition quantity; the pre-processed employee information data includes the mean employee salary and the mean employee performance; the pre-processed logistics data includes the mean logistics cost, the mean logistics efficiency and the mean warehousing cost;

[0012] The data processing module is used to perform comprehensive processing on the pre-processed sales data, pre-processed production data and pre-processed logistics data, and the calculation formula for obtaining the enterprise operation index is:

[0013]

[0014] Among them, α is the enterprise operation index; G 销售 is the mean sales value, Y 销售 is the mean sales quantity, Q 销售 is the mean number of sales channels, T 销售 is the average sales cycle, A 生产 is the mean production output, D 生产 is the mean production efficiency, F 生产 is the mean production cost, H 生产 is the mean of production quality data, Z 物流 is the mean logistics cost, X 物流 is the mean value of logistics efficiency, V 物流 is the mean of storage cost, ε1 is other influencing factors of preprocessed sales data; ε2 is other influencing factors of preprocessed production data; ε3 is other influencing factors of preprocessed logistics data;

[0015] The calculation formula for the enterprise operating index obtained by preprocessing financial data and preprocessing market data is:

[0016] Among them, β is the enterprise operation index; S 财务 is the mean value of financial income data, R 财务 is the mean value of financial accounting data, W 财务 is the mean of book profit data, J 市场 is the mean of market research data, H 市场 is the mean of the number of competitors, δ1 is other influencing factors of preprocessed financial data; δ2 is other influencing factors of preprocessed market data;

[0017] The calculation formula for the enterprise management index obtained by preprocessing customer data and preprocessing employee information data is as follows:

[0018] Among them, γ is the enterprise management index; U 客户 is the mean number of customers, O 客户 is the mean number of customer orders, P 客户 is the mean customer satisfaction, K 员工信息 is the average employee salary, L 员工信息 is the employee performance mean, Other factors affecting the pre-processing of customer data; Other influencing factors for pre-processing employee information data;

[0019] The comprehensive index includes the enterprise operation index, enterprise management index and enterprise management index;

[0020] The data analysis module is used to obtain a comprehensive evaluation coefficient of business data according to the comprehensive index;

[0021] The database module is used to store the comprehensive evaluation coefficient of business data and obtain the comprehensive evaluation coefficient of historical business data;

[0022] The prediction model module is used to construct a business data evaluation prediction model based on the comprehensive evaluation coefficient of historical business data, and then obtain the comprehensive evaluation coefficient of predicted business data;

[0023] The enterprise early warning module is used to perform early warning management on the enterprise based on the comprehensive evaluation coefficient of the predicted business data.

[0024] Furthermore, the process of the data collection module collecting business data includes:

[0025] The business data includes sales data, financial data, customer data, production data, market data, employee information data and logistics data;

[0026] The sales data includes product sales, sales volume, sales channel number and sales cycle; the production data includes production output, production efficiency, production cost and production quality data; the logistics data includes logistics cost, logistics efficiency and warehousing cost; the financial data includes financial account data, financial account data and account profit data; the market data includes market research data and competition volume; the customer data includes customer volume, customer order volume and customer satisfaction; the employee information data includes employee salary and employee performance;

[0027] According to the attributes of the business data of the enterprise, the business data is divided into data source types, including operation data source, business data source and management data source;

[0028] The business data collection module includes an operation data source collection node, a business data source collection node and a management data source collection node, and sets the same collection cycle. The collection cycle includes several collection moments, which are used to collect sales data, production data and logistics data in the operation data source, financial data and market data in the business data source, and customer data and employee information data in the management data source.

[0029] Furthermore, the process of the data analysis module obtaining the comprehensive evaluation coefficient of the business data includes:

[0030] According to the enterprise operation index, enterprise management index and enterprise management index, the calculation formula for the comprehensive evaluation coefficient of business data is:

[0031] Among them, θ is the comprehensive evaluation coefficient of business data; α is the enterprise operation index; β is the enterprise management index; γ is the enterprise management index.

[0032] Furthermore, the database module stores the comprehensive evaluation coefficient of the business data, and the process of obtaining the comprehensive evaluation coefficient of the historical business data includes:

[0033] The database module is provided with a real-time database and a historical database;

[0034] The comprehensive evaluation coefficient of the business data obtained in the current collection cycle is uploaded to the real-time database for storage. When the real-time database receives the comprehensive evaluation coefficient of the business data for the next collection cycle, the comprehensive evaluation coefficient of the business data of the previous collection cycle is sent to the historical database for storage, and the comprehensive evaluation coefficient of the business data stored in the historical database is marked as the comprehensive evaluation coefficient of historical business data.

[0035] Furthermore, the process of constructing the business data evaluation prediction model by the prediction model module includes:

[0036] Divide several groups of historical business data comprehensive evaluation coefficients into training sets, validation sets and test sets, and construct a standard prediction model based on long short-term memory network technology; input the training sets, validation sets and test sets into the standard prediction model, train the standard prediction model, obtain the trained standard prediction model, and record the trained standard prediction model as the business data evaluation prediction model;

[0037] According to the business data evaluation prediction model, the calculation formula for obtaining the comprehensive evaluation coefficient of the predicted business data is:

[0038] in, is the comprehensive evaluation coefficient of the predicted business data; b i 、m i+n is the weight coefficient; c i is a power; is the comprehensive evaluation coefficient of historical business data in the corresponding collection period; τ i is the exponential transformation parameter.

[0039] Furthermore, the process of the enterprise early warning module performing early warning management on the enterprise according to the comprehensive evaluation coefficient of the predicted business data includes:

[0040] Preset comprehensive evaluation coefficient of standard business data;

[0041] If the comprehensive evaluation coefficient of the predicted business data is greater than the comprehensive evaluation coefficient of the standard business data, an enterprise warning information is generated and sent to the corresponding enterprise management personnel;

[0042] If the comprehensive evaluation coefficient of the predicted business data is less than or equal to the comprehensive evaluation coefficient of the standard business data, it means that the company's business is normal.

[0043] Compared with the prior art, the beneficial effects of the present invention are: by collecting the business data of the enterprise and pre-processing the business data, pre-processed business data is obtained; the pre-processed business data is comprehensively processed to obtain a comprehensive index; based on the comprehensive index, a comprehensive evaluation coefficient of the business data is obtained, and then a comprehensive evaluation coefficient of the historical business data is obtained; based on the comprehensive evaluation coefficient of the historical business data, a business data evaluation prediction model is constructed, and then the comprehensive evaluation coefficient of the predicted business data is obtained, and early warning management is performed on the enterprise; the operational efficiency of the enterprise is greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0045] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0046] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0047] like Figure 1 As shown, an enterprise business distributed management system based on artificial intelligence includes a monitoring center, and the monitoring center is communicatively connected with a data acquisition module, a data preprocessing module, a data processing module, a data analysis module, a database module, a prediction model module and an enterprise early warning module;

[0048] The data collection module is used to collect the business data of the enterprise;

[0049] The data preprocessing module is used to preprocess the business data to obtain preprocessed business data;

[0050] The data processing module is used to perform comprehensive processing on the pre-processed business data to obtain a comprehensive index;

[0051] The data analysis module is used to obtain a comprehensive evaluation coefficient of business data according to the comprehensive index;

[0052] The database module is used to store the comprehensive evaluation coefficient of business data and obtain the comprehensive evaluation coefficient of historical business data;

[0053] The prediction model module is used to construct a business data evaluation prediction model based on the comprehensive evaluation coefficient of historical business data, and then obtain the comprehensive evaluation coefficient of predicted business data;

[0054] The enterprise early warning module is used to perform early warning management on the enterprise based on the comprehensive evaluation coefficient of the predicted business data.

[0055] It should be further explained that, in the specific implementation process, the specific process of the data collection module collecting business data includes:

[0056] It should be further explained that the business data includes sales data, financial data, customer data, production data, market data, employee information data and logistics data;

[0057] According to the attributes of the business data of the enterprise, the business data is divided into data source types, including operation data source, business data source and management data source;

[0058] It should be further explained that the operational data source includes the sales data, production data and logistics data of the enterprise within two years; the business data source includes the financial data and market data of the enterprise within two years; the management data source includes the customer data and employee information data of the enterprise within two years;

[0059] The business data collection module includes an operation data source collection node, a business data source collection node and a management data source collection node, and sets the same collection cycle and the collection cycle includes several collection moments;

[0060] The operation data source collection node is used to collect sales data, production data and logistics data in the operation data source;

[0061] The business data source collection node is used to collect financial data and market data in the business data source;

[0062] The management data source collection node is used to collect customer data and employee information data in the management data source;

[0063] It should be further explained that the sales data includes product sales volume, sales quantity, number of sales channels and sales cycle; the production data includes production output, production efficiency, production cost and production quality data; the logistics data includes logistics cost, logistics efficiency and warehousing cost; the financial data includes financial income data, financial expenditure data and account profit data; the market data includes market research data and the number of competitors; the customer data includes the number of customers, the number of customer orders and customer satisfaction; the employee information data includes employee salary and employee performance.

[0064] The data acquisition module uploads the collected business data to the data preprocessing module.

[0065] It should be further explained that, in the specific implementation process, the specific process of the data preprocessing module preprocessing the business data includes:

[0066] The collection moments of the same collection cycle are numbered as i=1, 2, 3, ..., n, where n is a natural number;

[0067] Preprocessing the business data at different collection times in the same collection cycle to obtain preprocessed business data;

[0068] It should be further explained that the pre-processed business data includes pre-processed sales data, pre-processed financial data, pre-processed customer data, pre-processed production data, pre-processed market data, pre-processed employee information data and pre-processed logistics data;

[0069] The specific process of preprocessing the sales data in the business data includes:

[0070] According to the sales data, obtaining pre-processed sales data;

[0071] It should be further explained that the pre-processed sales data includes the average sales value, the average sales quantity, the average sales channel quantity and the average sales cycle;

[0072] The calculation formula for the mean sales volume is:

[0073] Among them, G 销售 is the mean sales value; g i is the sales volume at the corresponding collection time;

[0074] The calculation formula for the mean sales quantity is:

[0075] Among them, Y 销售 is the mean sales quantity; y i is the sales quantity at the corresponding collection time;

[0076] The calculation formula for the mean number of sales channels is:

[0077] Among them, Q 销售 is the mean number of sales channels; q i is the number of sales channels at the corresponding collection time;

[0078] The calculation formula for the sales cycle mean is:

[0079] Among them, T 销售 is the mean of sales cycle; t i is the sales cycle corresponding to the collection time.

[0080] The specific process of preprocessing the financial data in the business data includes:

[0081] Obtaining preprocessed financial data based on the financial data;

[0082] It should be further explained that the pre-processed financial data includes the mean value of financial incoming data, the mean value of financial outgoing data and the mean value of account book profit data;

[0083] The calculation formula for the mean value of the financial income data is:

[0084] Among them, S 财务 is the mean value of financial income data; i The financial account data corresponding to the collection time;

[0085] The calculation formula for the mean value of the financial account data is:

[0086] Among them, R 财务 is the mean value of financial accounting data; r i The financial accounting data corresponding to the collection time;

[0087] The calculation formula for the mean value of the account book profit data is:

[0088] Among them, W 财务 is the mean value of book profit data; w i It is the account book profit data at the corresponding collection time.

[0089] The specific process of preprocessing the customer data in the business data includes:

[0090] According to the customer data, obtaining pre-processed customer data;

[0091] It should be further explained that the pre-processed customer data includes the mean number of customers, the mean number of customer orders, and the mean customer satisfaction;

[0092] The calculation formula for the mean number of customers is:

[0093] Among them, U 客户 is the mean number of customers; u i is the number of customers at the corresponding collection time;

[0094] The calculation formula for the mean number of customer orders is:

[0095] Among them, O 客户 is the average number of customer orders; i is the number of customer orders at the corresponding collection time;

[0096] The calculation formula for the customer satisfaction mean is:

[0097] Among them, P 客户 is the mean customer satisfaction; p i is the customer satisfaction at the corresponding collection time.

[0098] The specific process of preprocessing the production data in the business data includes:

[0099] According to the production data, obtaining pre-processed production data;

[0100] It should be further explained that the pre-processed production data includes the mean value of production output, the mean value of production efficiency, the mean value of production cost and the mean value of production quality data;

[0101] The calculation formula for the mean production output is:

[0102] Among them, A 生产 is the mean value of production output; a i is the production output at the corresponding collection time;

[0103] The calculation formula of the mean production efficiency is:

[0104] Among them, D 生产 is the mean production efficiency; d i is the production efficiency corresponding to the collection time;

[0105] The calculation formula of the mean production cost is:

[0106] Among them, F 生产 is the mean production cost; f i is the production cost corresponding to the collection time;

[0107] The calculation formula for the mean value of the production quality data is:

[0108] Among them, H 生产 is the mean value of production quality data; h i is the production quality data corresponding to the collection time.

[0109] The specific process of preprocessing the market data in the business data includes:

[0110] According to the market data, obtaining pre-processed market data;

[0111] It should be further explained that the pre-processed market data includes the mean of market research data and the mean of competition quantity;

[0112] The calculation formula for the mean of the market research data is:

[0113] Among them, J 市场 is the mean of the market research data; j is the market research data at the corresponding collection time;

[0114] The calculation formula of the competition quantity mean is:

[0115] Among them, H 市场 is the mean value of competition quantity; h i is the number of competitions at the corresponding collection time.

[0116] The specific process of preprocessing the employee information data in the business data includes:

[0117] According to the employee information data, obtaining pre-processed employee information data;

[0118] It should be further explained that the pre-processed employee information data includes the employee salary mean and the employee performance mean;

[0119] The calculation formula for the average employee salary is:

[0120] Among them, K 员工信息 is the average employee salary; k i The employee salary corresponding to the collection time;

[0121] The calculation formula for the employee performance mean is:

[0122] Among them, L 员工信息 is the employee performance mean; i is the employee performance at the corresponding collection time.

[0123] The specific process of preprocessing the logistics data in the business data includes:

[0124] According to the logistics data, obtaining pre-processing logistics data;

[0125] It should be further explained that the pre-processed logistics data includes the mean value of logistics cost, the mean value of logistics efficiency and the mean value of storage cost;

[0126] The calculation formula of the mean logistics cost is:

[0127] Among them, Z 物流 is the mean value of logistics cost; i is the logistics cost corresponding to the collection time;

[0128] The calculation formula of the mean logistics efficiency is:

[0129] Among them, X 物流 is the mean value of logistics efficiency; x i is the logistics efficiency corresponding to the collection time;

[0130] The calculation formula of the mean storage cost is:

[0131] Among them, V 物流 is the mean storage cost; v i is the storage cost corresponding to the collection time.

[0132] The data preprocessing module uploads the preprocessed business data to the data processing module.

[0133] It should be further explained that, in the specific implementation process, the specific process of the data processing module performing comprehensive processing on the pre-processed business data includes:

[0134] Obtain a comprehensive index based on pre-processed business data;

[0135] It should be further explained that the comprehensive index includes the enterprise operation index, enterprise management index and enterprise management index;

[0136] The calculation formula for the enterprise operation index is obtained by comprehensively processing the pre-processed sales data, pre-processed production data, and pre-processed logistics data:

[0137]

[0138] Wherein α is the enterprise operation index; ε1 is other influencing factors of pre-processed sales data; ε2 is other influencing factors of pre-processed production data; ε3 is other influencing factors of pre-processed logistics data; the other influencing factors ε1 of pre-processed sales data, the other influencing factors ε2 of pre-processed production data and the other influencing factors ε3 of pre-processed logistics data are set by professionals in this field according to actual needs;

[0139] The calculation formula for the enterprise operating index obtained by preprocessing financial data and preprocessing market data is:

[0140] Wherein, β is the business operation index; δ1 is other influencing factors of pre-processed financial data; δ2 is other influencing factors of pre-processed market data; the other influencing factors δ1 of pre-processed financial data and the other influencing factors δ2 of pre-processed market data are set by professionals in this field according to actual needs;

[0141] The calculation formula for the enterprise management index obtained by preprocessing customer data and preprocessing employee information data is as follows:

[0142] Among them, γ is the enterprise management index; Other factors affecting the pre-processing of customer data; Other influencing factors for pre-processing employee information data; Other influencing factors for pre-processing customer data And other influencing factors of preprocessing employee information data It is set by professionals in this field according to actual needs.

[0143] The data processing module uploads the obtained comprehensive index to the data analysis module.

[0144] It should be further explained that, in the specific implementation process, the specific process of the data analysis module obtaining the comprehensive evaluation coefficient of the business data includes:

[0145] According to the enterprise operation index α, enterprise management index β and enterprise management index γ, the calculation formula for the comprehensive evaluation coefficient of business data is:

[0146] Among them, θ is the comprehensive evaluation coefficient of business data.

[0147] The data analysis module uploads the obtained comprehensive evaluation coefficient of the business data to the database module.

[0148] It should be further explained that, in the specific implementation process, the database module stores the comprehensive evaluation coefficient of the business data, and the specific process of obtaining the comprehensive evaluation coefficient of the historical business data includes:

[0149] The database module is provided with a real-time database and a historical database;

[0150] The comprehensive evaluation coefficient of the business data obtained in the current collection cycle is uploaded to the real-time database for storage. When the real-time database receives the comprehensive evaluation coefficient of the business data for the next collection cycle, the comprehensive evaluation coefficient of the business data of the previous collection cycle is sent to the historical database for storage, and the comprehensive evaluation coefficient of the business data stored in the historical database is marked as the comprehensive evaluation coefficient of historical business data.

[0151] The database module uploads the comprehensive evaluation coefficients of historical business data to the prediction model module.

[0152] It should be further explained that, in the specific implementation process, the specific process of the prediction model module constructing the business data evaluation prediction model includes:

[0153] Dividing several groups of historical business data comprehensive evaluation coefficients into training sets, validation sets and test sets; constructing a standard prediction model based on long short-term memory network technology; inputting the training sets, validation sets and test sets into the standard prediction model, training the standard prediction model, obtaining a trained standard prediction model, and recording the trained standard prediction model as a business data evaluation prediction model;

[0154] According to the business data evaluation prediction model, the calculation formula for obtaining the comprehensive evaluation coefficient of the predicted business data is:

[0155] in, is the comprehensive evaluation coefficient of the predicted business data; b i 、m i+n is the weight coefficient; c i is a power; is the comprehensive evaluation coefficient of historical business data in the corresponding collection period; τ i is the exponential transformation parameter.

[0156] The prediction model module uploads the obtained comprehensive evaluation coefficient of the predicted business data to the enterprise early warning module.

[0157] It should be further explained that, in the specific implementation process, the enterprise early warning module performs early warning management on the enterprise according to the comprehensive evaluation coefficient of the predicted business data, and the specific process includes:

[0158] Preset comprehensive evaluation coefficient of standard business data;

[0159] If the comprehensive evaluation coefficient of the predicted business data is greater than the comprehensive evaluation coefficient of the standard business data, an enterprise warning information is generated and sent to the corresponding enterprise management personnel;

[0160] If the comprehensive evaluation coefficient of the predicted business data is less than or equal to the comprehensive evaluation coefficient of the standard business data, it means that the company's business is normal.

[0161] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An enterprise business distributed management system based on artificial intelligence, including a monitoring center, characterized in that: The monitoring center is communicatively connected with a data acquisition module, a data preprocessing module, a data processing module, a data analysis module, a database module, a prediction model module and an enterprise early warning module; The data collection module is used to collect the business data of the enterprise; The data preprocessing module is used to preprocess the business data and label the collection moments of the same collection cycle as i=1, 2, 3, ..., n, where n is a natural number; Preprocessing the business data at different collection times in the same collection cycle to obtain preprocessed business data; The preprocessed business data includes preprocessed sales data, preprocessed financial data, preprocessed customer data, preprocessed production data, preprocessed market data, preprocessed employee information data and preprocessed logistics data; The pre-processed sales data includes the mean sales volume, the mean sales quantity, the mean sales channel quantity and the mean sales cycle; the pre-processed financial data includes the mean financial account data, the mean financial account data and the mean book profit data; the pre-processed customer data includes the mean customer quantity, the mean customer order quantity and the mean customer satisfaction; the pre-processed production data includes the mean production output, the mean production efficiency, the mean production cost and the mean production quality data; the pre-processed market data includes the mean market research data and the mean competition quantity; the pre-processed employee information data includes the mean employee salary and the mean employee performance; The pre-processed logistics data includes the mean value of logistics cost, the mean value of logistics efficiency and the mean value of storage cost; The data processing module is used to perform comprehensive processing on the pre-processed sales data, pre-processed production data and pre-processed logistics data, and the calculation formula for obtaining the enterprise operation index is: Among them, α is the enterprise operation index; G 销售 is the mean sales value, Y 销售 is the mean sales quantity, Q 销售 is the mean number of sales channels, T 销售 is the average sales cycle, A 生产 is the mean production output, D 生产 is the mean production efficiency, F 生产 is the mean production cost, H 生产 is the mean of production quality data, Z 物流 is the mean logistics cost, X 物流 is the mean value of logistics efficiency, V 物流 is the mean of storage cost, ε1 is other influencing factors of preprocessed sales data; ε2 is other influencing factors of preprocessed production data; ε3 is other influencing factors of preprocessing logistics data; The calculation formula for the enterprise operating index obtained by preprocessing financial data and preprocessing market data is: Among them, β is the enterprise operation index; S 财务 is the mean value of financial income data, R 财务 is the mean value of financial accounting data, W 财务 is the mean of book profit data, J 市场 is the mean of market research data, H 市场 is the mean of the number of competitors, δ1 is other influencing factors of preprocessed financial data; δ2 is other influencing factors of preprocessed market data; The calculation formula for the enterprise management index obtained by preprocessing customer data and preprocessing employee information data is as follows: Among them, γ is the enterprise management index; U 客户 is the mean number of customers, O 客户 is the mean number of customer orders, P 客户 is the mean customer satisfaction, K 员工信息 is the average employee salary, L 员工信息 is the average employee performance, Other factors affecting the pre-processing of customer data; Other influencing factors for pre-processing employee information data; The comprehensive index includes the enterprise operation index, enterprise management index and enterprise management index; The data analysis module is used to obtain a comprehensive evaluation coefficient of business data according to the comprehensive index; The database module is used to store the comprehensive evaluation coefficient of business data and obtain the comprehensive evaluation coefficient of historical business data; The prediction model module is used to construct a business data evaluation prediction model based on the comprehensive evaluation coefficient of historical business data, and then obtain the comprehensive evaluation coefficient of predicted business data; The enterprise early warning module is used to perform early warning management on the enterprise based on the comprehensive evaluation coefficient of the predicted business data.

2. According to the enterprise business distributed management system based on artificial intelligence according to claim 1, it is characterized in that: The process of collecting business data by the data collection module includes: The business data includes sales data, financial data, customer data, production data, market data, employee information data and logistics data; The sales data includes product sales, sales volume, sales channel number and sales cycle; the production data includes production output, production efficiency, production cost and production quality data; the logistics data includes logistics cost, logistics efficiency and warehousing cost; the financial data includes financial account data, financial account data and account profit data; the market data includes market research data and competition volume; the customer data includes customer volume, customer order volume and customer satisfaction; the employee information data includes employee salary and employee performance; According to the attributes of the business data of the enterprise, the business data is divided into data source types, including operation data source, business data source and management data source; The business data collection module includes an operation data source collection node, a business data source collection node and a management data source collection node, and sets the same collection cycle. The collection cycle includes several collection moments, which are used to collect sales data, production data and logistics data in the operation data source, financial data and market data in the business data source, and customer data and employee information data in the management data source.

3. The enterprise business distributed management system based on artificial intelligence according to claim 2 is characterized in that: The process of obtaining the comprehensive evaluation coefficient of business data by the data analysis module includes: According to the enterprise operation index, enterprise management index and enterprise management index, the calculation formula for the comprehensive evaluation coefficient of business data is: Among them, θ is the comprehensive evaluation coefficient of business data; α is the enterprise operation index; β is the enterprise management index; γ is the enterprise management index.

4. The enterprise business distributed management system based on artificial intelligence according to claim 3 is characterized in that: The database module stores the comprehensive evaluation coefficient of business data, and the process of obtaining the comprehensive evaluation coefficient of historical business data includes: The database module is provided with a real-time database and a historical database; The comprehensive evaluation coefficient of the business data obtained in the current collection cycle is uploaded to the real-time database for storage. When the real-time database receives the comprehensive evaluation coefficient of the business data for the next collection cycle, the comprehensive evaluation coefficient of the business data of the previous collection cycle is sent to the historical database for storage, and the comprehensive evaluation coefficient of the business data stored in the historical database is marked as the comprehensive evaluation coefficient of historical business data.

5. The enterprise business distributed management system based on artificial intelligence according to claim 4 is characterized in that: The process of constructing a business data evaluation prediction model by the prediction model module includes: Divide several groups of historical business data comprehensive evaluation coefficients into training sets, validation sets and test sets, and construct a standard prediction model based on long short-term memory network technology; input the training sets, validation sets and test sets into the standard prediction model, train the standard prediction model, obtain the trained standard prediction model, and record the trained standard prediction model as the business data evaluation prediction model; According to the business data evaluation prediction model, the calculation formula for obtaining the comprehensive evaluation coefficient of the predicted business data is: in, is the comprehensive evaluation coefficient of the predicted business data; b i 、m i+n is the weight coefficient; c i is a power; is the comprehensive evaluation coefficient of historical business data in the corresponding collection period; τ i is the exponential transformation parameter.

6. The enterprise business distributed management system based on artificial intelligence according to claim 5 is characterized in that: The process of the enterprise early warning module performing early warning management on the enterprise according to the comprehensive evaluation coefficient of the predicted business data includes: Preset comprehensive evaluation coefficient of standard business data; If the comprehensive evaluation coefficient of the predicted business data is greater than the comprehensive evaluation coefficient of the standard business data, an enterprise warning information is generated and sent to the corresponding enterprise management personnel; If the comprehensive evaluation coefficient of the predicted business data is less than or equal to the comprehensive evaluation coefficient of the standard business data, it means that the company's business is normal.

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