Digital decision-making system based on project production data analysis

Abstract

The invention discloses a digital decision-making system based on project production data analysis, and relates to the technical field of intelligent decision-making, the system comprises an optimization module, a decision-making module and a compensation module, historical production schemes are optimized to obtain an optimization function set, a target production scheme is obtained, productivity of each production line is calculated, a first decision is made, and a second decision is made; and setting a boundary condition, and compensating the first decision to obtain a second decision. By optimizing the production scheme and dynamically adjusting the production plan, the production efficiency is remarkably improved, production parameters and resource allocation are optimized, energy consumption and raw material waste are reduced, stability and consistency of the production process are improved by utilizing big data analysis and an optimization algorithm, interference of human factors is reduced based on a data-driven decision mode, and the production efficiency is improved. According to the method, the accuracy and reliability of decision making are improved, market demand changes can be quickly responded, large-scale personalized customization is supported, and an enterprise can be helped to achieve intelligentization, high efficiency and greenization of the production process.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent decision-making, and in particular to a digital decision-making system based on project production data analysis. Background Art

[0002] In recent years, AI and ML technologies have become the core force driving big data analysis. They can extract deep insights from massive amounts of data, identify potential patterns, trends, and relationships, and help decision makers make more accurate and efficient decisions. Through big data analysis, they can gain an in-depth understanding of customer needs and market dynamics, thereby formulating targeted strategies.

[0003] At present, a Chinese invention patent with publication number CN117829453A discloses an integrated system for intelligent equipment production and management. The method controls the operation of the production line through preset production parameters and programs, generates a production report by analyzing the collected production data, and makes decisions on adjusting the production parameters and programs based on the production report. However, the related technology does not compare and optimize the actual and ideal production effects produced by historical production plans, lacks the utilization rate of historical data and the scientific nature of production, does not link employee satisfaction with production decisions, lacks humane care for employees, does not control production decisions based on delivery limits and the quality of finished products, lacks product reliability, and is not conducive to customer stickiness, and has certain limitations. Summary of the Invention

[0004] The technical problems solved by the present invention are: the relevant technologies do not compare and optimize the actual and ideal production effects produced based on historical production plans, lack the utilization of historical data and the scientific nature of production, do not link employee satisfaction with production decisions, lack humane care for employees, do not control production decisions based on delivery limits and the quality of finished products, lack product reliability, are not conducive to customer stickiness, and have certain limitations.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a digital decision-making system based on project production data analysis, comprising an optimization module, a decision module and a compensation module; The optimization module optimizes the historical production plan to obtain an optimization function set, substitutes the historical production data corresponding to the historical production plan into the optimization function set, and obtains the target production plan; The decision module calculates the productivity of each production line based on the production department information, and makes a first decision on the target production plan based on the productivity; The compensation module sets boundary conditions, compensates the first decision according to the boundary conditions, and obtains a second decision.

[0006] As a preferred solution of the digital decision-making system based on project production data analysis described in the present invention, the historical production plan is represented by the production mode, personnel allocation plan and material scheduling plan used in the past to produce products, and the production mode includes an intelligent production mode, a networked production mode, a cloud manufacturing production mode, an additive production mode and a service-oriented production mode; The optimization elements include power consumption efficiency, pollutant gas emission, material cost and labor cost. The optimization purpose of the optimization elements is to reduce the optimization elements to the minimum value. The power consumption efficiency is expressed as the ratio of the power used to produce qualified products to the total power.

[0007] As a preferred solution of the digital decision-making system based on project production data analysis described in the present invention, the logic of optimizing historical production plans to obtain the target production plan includes: Each historical production plan is constructed into a plan set z i , the optimization objectives are expressed as A j , where i and j are both natural numbers; Construct a function set covering the optimization objectives, bring the numerical values ​​of the optimization objectives corresponding to each historical production plan into the function set to calculate each function value, perform weighted calculation on each function value, and obtain the first sum value corresponding to each historical production plan; The first value and the second value are set as the first threshold, each first sum value is compared with the first threshold, and the historical production plan corresponding to the first sum value distributed between the first value and the second value is set as the target production plan, and the first value and the second value are respectively expressed as the arithmetic mean and geometric mean of each first sum value.

[0008] As a preferred solution of the digital decision-making system based on project production data analysis described in the present invention, the calculation expression of the function set is: ; ; ; ; in, 、 、 and Together they form a set of functions, is the value of power consumption efficiency corresponding to each historical production plan, is the total amount of pollutant gas emitted, It is the sum of material cost and labor cost.

[0009] As a preferred solution of the digital decision-making system based on project production data analysis described in the present invention, wherein: the production department information includes the production line number, the number of mobile people in the first time period, the current number of people and the average production quantity; The number of employees who have moved during the first time period is represented by the sum of the number of employees who have resigned and the number of employees who have joined in the first time period in the future, wherein the sum of the number of employees who have resigned and the number of employees who have joined in the first time period in the future is an integer, the number of employees who have resigned is represented by a negative number, and the number of employees who have joined is represented by a positive number; The average production quantity is represented by the average daily production quantity of individual employees in the production department.

[0010] As a preferred solution of the digital decision-making system based on project production data analysis described in the present invention, the calculation expression of the production line productivity is: ; in, Expressed as the productivity of any production line, Represents the current number of people, It is represented by the number of mobile people in the first time period, Expressed as average production quantity.

[0011] As a preferred solution of the digital decision-making system based on project production data analysis described in the present invention, wherein: the first decision is used to make a decision on the first production volume and the second production volume, and the logic of making the first decision on the target production plan based on the productivity includes: Calculate the sum of the productivity of each production line, record it as the second sum, obtain the current order quantity, calculate the multiples of the second sum, calculate the difference between the current order quantity and each multiple, record it as the first difference, select the first difference with the smallest absolute value, set the multiple corresponding to the first difference with the smallest absolute value as the target multiple, calculate the product of the target multiple and any productivity, set it as the first production volume of the corresponding production line, and assign the first difference with the smallest absolute value to the production line with the largest productivity value, and set the first difference with the smallest absolute value as the second production volume of the corresponding production line.

[0012] As a preferred solution of the digital decision-making system based on project production data analysis described in the present invention, the boundary conditions include: Employee satisfaction is distributed between 8 and 10; The delivery time is less than or equal to the current time limit; The qualified rate of finished products is between 80% and 100%; Among them, employee satisfaction is distributed between 0 and 10 and is obtained by employees' self-scoring. The current time limit is expressed as the longest delivery time of the current order. The finished product qualification rate is expressed as the ratio of the qualified number of finished products of each production line drawn according to the first ratio to the total number. When the employee satisfaction, delivery time and finished product qualification rate do not meet the boundary conditions, a maximum of two second decisions will be made.

[0013] As a preferred solution of the digital decision-making system based on project production data analysis described in the present invention, wherein: according to the first decision, simulated production is performed, and simulated employee satisfaction, simulated delivery time, and simulated finished product qualification rate are obtained, and the simulated employee satisfaction, simulated delivery time, and simulated finished product qualification rate are compared with the marginal conditions respectively. When the simulated employee satisfaction, simulated delivery time, and simulated finished product qualification rate are distributed within the boundary conditions, the second decision is not made; Otherwise, make the second decision.

[0014] As a preferred solution of the digital decision-making system based on project production data analysis described in the present invention, when making the second decision, the production line number that is not distributed in the boundary condition is obtained and set as the target production line; When the corresponding simulated employee satisfaction is not distributed within the boundary conditions, the first amount of production is allocated to the production line with the largest employee satisfaction value, and the employee satisfaction of the target production line after allocation is obtained. When the employee satisfaction after allocation is distributed within the boundary conditions, the allocation is stopped. When the employee satisfaction after allocation is not distributed within the boundary conditions, the first amount of the target production line's production after allocation is allocated to the production line with the second largest employee satisfaction value, and the allocation is stopped. When the corresponding delivery time is not distributed within the boundary conditions, the first amount of production is allocated to the production line with the smallest delivery time value, and the delivery time of the target production line after allocation is obtained. When the delivery time after allocation is distributed within the boundary conditions, the allocation is stopped. When the delivery time after allocation is not distributed within the boundary conditions, the first amount of the production of the target production line after allocation is allocated to the production line with the second smallest delivery time, and the allocation is stopped. When the corresponding finished product qualification rate is not distributed within the boundary conditions, the first quantity of production volume is configured to the production line with the largest finished product qualification rate value, and the finished product qualification rate of the target production line after configuration is obtained. When the finished product qualification rate after configuration is distributed within the boundary conditions, the configuration is stopped. When the finished product qualification rate after configuration is not distributed within the boundary conditions, the first quantity of the configured production volume of the target production line is configured to the production line with the second largest finished product qualification rate, and the configuration is stopped.

[0015] The application has the advantages that the production efficiency is improved, the production parameters and resource allocation are optimized, the energy consumption and raw material waste are reduced, the production process stability and consistency are improved by using big data analysis and optimization algorithm, the decision-making accuracy and reliability are improved based on data-driven decision-making mode, the market demand changes can be quickly responded, large-scale personalized customization is supported, and the production process intelligence, efficiency and greenness can be realized for enterprises. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A basic flow diagram of a digital decision-making system based on project production data analysis is provided for an embodiment of the application. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned objects, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the application, not all embodiments.

[0018] Embodiments, with reference to Figure 1 For an embodiment of the application, a digital decision-making system based on project production data analysis is provided, which includes an optimization module, a decision-making module and a compensation module. The optimization module optimizes the historical production scheme to obtain an optimization function set, and substitutes the historical production data corresponding to the historical production scheme into the optimization function set to obtain a target production scheme. The decision-making module calculates the productivity of each production line according to the production department information, and makes a first decision on the target production scheme according to the productivity. The compensation module sets boundary conditions and compensates the first decision according to the boundary conditions to obtain a second decision.

[0019] The application optimizes the production scheme and dynamically adjusts the production plan to significantly improve the production efficiency, optimizes the production parameters and resource allocation, reduces the energy consumption and raw material waste, uses big data analysis and optimization algorithm to improve the stability and consistency of the production process, uses data-driven decision-making mode to reduce the interference of human factors, improves the accuracy and reliability of decision-making, can quickly respond to market demand changes, supports large-scale personalized customization, and can help enterprises realize intelligent, efficient and green production process.

[0020] The historical production scheme is represented as a historical production mode, personnel allocation plan and material scheduling plan for producing products, and the production mode includes intelligent production mode, networked production mode, cloud manufacturing production mode, additive production mode and service-oriented production mode. The optimization elements include power consumption efficiency, pollutant gas emission, material cost and labor cost. The optimization purpose of the optimization elements is to reduce the optimization elements to the minimum value. The power consumption efficiency is expressed as the ratio of the power used to produce qualified products to the total power.

[0021] In specific implementation, by optimizing production models and material scheduling plans, production efficiency can be significantly improved, energy use can be optimized, material waste can be reduced, and labor costs can be lowered. Through intelligent and precise control, product consistency and quality can be improved, pollutant emissions can be reduced, and environmental risks can be reduced. This can bring significant economic and environmental benefits to enterprises and promote the development of the manufacturing industry towards intelligence, greenness, and efficiency.

[0022] The logic of optimizing historical production plans to obtain the target production plan includes: Each historical production plan is constructed into a plan set z i , the optimization objectives are expressed as A j , where i and j are both natural numbers; Construct a function set covering the optimization objectives, bring the numerical values ​​of the optimization objectives corresponding to each historical production plan into the function set to calculate each function value, perform weighted calculation on each function value, and obtain the first sum value corresponding to each historical production plan; The first value and the second value are set as the first threshold, each first sum value is compared with the first threshold, and the historical production plan corresponding to the first sum value distributed between the first value and the second value is set as the target production plan. The first value and the second value are respectively expressed as the arithmetic mean and geometric mean of each first sum value.

[0023] The calculation expression of the function set is: ; ; ; ; in, 、 、 and Together they form a set of functions, is the value of power consumption efficiency corresponding to each historical production plan, is the total amount of pollutant gas emitted, It is the sum of material cost and labor cost.

[0024] In the specific implementation, mathematical models and statistical methods are used to ensure the scientific nature and objectivity of the decision-making process, reduce the interference of human factors, and consider multiple optimization goals (such as energy consumption, cost, efficiency, etc.) at the same time to maximize the comprehensive benefits. The optimization goals are adjusted according to different production scenarios to adapt to various production modes. By optimizing energy consumption and material costs, production costs are significantly reduced. By reducing pollution emissions, the requirements of green manufacturing are met and the social responsibility of enterprises is enhanced. By optimizing production plans, production efficiency and resource utilization are improved, which not only improves the economic benefits of enterprises, but also enhances their competitiveness and sustainable development capabilities in the market.

[0025] Production department information includes production line number, number of people moving in the first time period, current number of people, and average production quantity; The number of employees who have moved in the first time period is expressed as the sum of the number of employees who have left and the number of employees who have joined in the first time period in the future. The sum of the number of employees who have left and the number of employees who have joined in the first time period in the future is an integer. The number of employees who have left is expressed as a negative number, and the number of employees who have joined is expressed as a positive number. The average production quantity is expressed as the average daily production quantity of individual employees in the production department.

[0026] The productivity of the production line is calculated as: ; in, Expressed as the productivity of any production line, Represents the current number of people, It is represented by the number of mobile people in the first time period, Expressed as average production quantity.

[0027] In practice, we optimize staffing and production planning to ensure optimal production operations. For example, we can timely replenish manpower based on employee turnover to avoid production delays caused by staff shortages. By accurately predicting staff turnover, we can reduce costs caused by insufficient or redundant staff. We can also optimize production plans to reduce resource waste caused by uneven production. Through data analysis, we can optimize production processes and reduce quality fluctuations caused by staff changes.

[0028] The first decision is used to determine the first production volume and the second production volume. The logic for making the first decision on the target production plan based on the productivity includes: Calculate the sum of the productivity of each production line, record it as the second sum, obtain the current order quantity, calculate the multiples of the second sum, calculate the difference between the current order quantity and each multiple, record it as the first difference, select the first difference with the smallest absolute value, set the multiple corresponding to the first difference with the smallest absolute value as the target multiple, calculate the product of the target multiple and any productivity, set it as the first production volume of the corresponding production line, and assign the first difference with the smallest absolute value to the production line with the largest productivity value, and set the first difference with the smallest absolute value as the second production volume of the corresponding production line.

[0029] In specific implementation, by optimizing production plans, we ensure that the production department operates in the best condition and improve production efficiency. By accurately predicting production demand, we reduce resource waste caused by production imbalance and reduce production costs. By optimizing production processes, we reduce quality problems caused by production fluctuations and improve product quality. By quickly adjusting production plans, we increase the response speed to market changes.

[0030] Boundary conditions include: Employee satisfaction is distributed between 8 and 10; The delivery time is less than or equal to the current time limit; The qualified rate of finished products is between 80% and 100%; Among them, employee satisfaction is distributed between 0 and 10 and is obtained by employees' self-scoring. The current time limit is expressed as the longest delivery time of the current order. The finished product qualification rate is expressed as the ratio of the qualified number of finished products of each production line drawn according to the first ratio to the total number. When employee satisfaction, delivery time and finished product qualification rate do not meet the boundary conditions, at most two second decisions will be made.

[0031] In specific implementation, by setting clear boundary conditions, we ensure that production decisions are made under the premise of satisfying employee satisfaction, delivery time and product quality, avoiding suboptimal solutions caused by single-target optimization. By ensuring that employee satisfaction is at a high level (8-10), we improve employee work enthusiasm and loyalty, reduce staff turnover, and by constraining the finished product qualification rate and delivery time, ensure that the company can deliver high-quality products on time, enhance customer satisfaction and market competitiveness. The secondary decision-making mechanism allows adjustments when the initial decision does not meet the boundary conditions, improving the system's adaptability to complex production environments.

[0032] According to the first decision, simulated production is performed, and simulated employee satisfaction, simulated delivery time, and simulated finished product qualification rate are obtained. The simulated employee satisfaction, simulated delivery time, and simulated finished product qualification rate are compared with the marginal conditions. If the simulated employee satisfaction, simulated delivery time, and simulated finished product qualification rate are distributed in the marginal conditions, the second decision is not made. Otherwise, make the second decision.

[0033] When making the second decision, the production line number that is not distributed in the boundary conditions is obtained and set as the target production line; When the corresponding simulated employee satisfaction is not distributed within the boundary conditions, the first amount of production is allocated to the production line with the largest employee satisfaction value, and the employee satisfaction of the target production line after allocation is obtained. When the employee satisfaction after allocation is distributed within the boundary conditions, the allocation is stopped. When the employee satisfaction after allocation is not distributed within the boundary conditions, the first amount of the target production line's production after allocation is allocated to the production line with the second largest employee satisfaction value, and the allocation is stopped. When the corresponding delivery time is not distributed within the boundary conditions, the first amount of production is allocated to the production line with the smallest delivery time value, and the delivery time of the target production line after allocation is obtained. When the delivery time after allocation is distributed within the boundary conditions, the allocation is stopped. When the delivery time after allocation is not distributed within the boundary conditions, the first amount of the production of the target production line after allocation is allocated to the production line with the second smallest delivery time, and the allocation is stopped. When the corresponding finished product qualification rate is not distributed within the boundary conditions, the first quantity of production volume is configured to the production line with the largest finished product qualification rate value, and the finished product qualification rate of the target production line after configuration is obtained. When the finished product qualification rate after configuration is distributed within the boundary conditions, the configuration is stopped. When the finished product qualification rate after configuration is not distributed within the boundary conditions, the first quantity of the configured production volume of the target production line is configured to the production line with the second largest finished product qualification rate, and the configuration is stopped.

[0034] In specific implementation, by dynamically adjusting production volume, production resources are ensured to be allocated to the production lines that need them most, thereby improving overall production efficiency, improving employee satisfaction, avoiding employee dissatisfaction caused by excessive work or unreasonable arrangements, thereby improving employee work enthusiasm and loyalty, increasing the probability of on-time delivery of orders, enhancing customer satisfaction and market competitiveness, improving the qualified rate of finished products, ensuring that product quality meets standards, reducing defective rates and rework costs, and making dynamic adjustments based on real-time data to ensure the scientific and rationality of decision-making, reduce interference from human factors, and make at most two adjustments to avoid excessive skewed distribution of production quantity due to subjective factors such as employee ratings. Clarifying the distribution of production quantity is beneficial for the finance department to adjust salaries according to the different workloads of employees on different production lines, and is beneficial to encouraging employee work enthusiasm.

[0035] The present invention significantly improves production efficiency by optimizing production plans and dynamically adjusting production plans, optimizes production parameters and resource allocation, reduces energy consumption and waste of raw materials, utilizes big data analysis and optimization algorithms to improve the stability and consistency of the production process, and based on a data-driven decision-making model, reduces interference from human factors, improves the accuracy and reliability of decision-making, can quickly respond to changes in market demand, support large-scale personalized customization, and can help enterprises realize intelligent, efficient and green production processes.

[0036] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0037] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that modifications or equivalent replacements of the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A digital decision-making system based on project production data analysis, characterized in that: Including optimization module, decision module and compensation module; The optimization module optimizes the historical production plan to obtain an optimization function set, substitutes the historical production data corresponding to the historical production plan into the optimization function set, and obtains the target production plan; The decision module calculates the productivity of each production line based on the production department information, and makes a first decision on the target production plan based on the productivity; The compensation module sets boundary conditions, compensates the first decision according to the boundary conditions, and obtains a second decision.

2. A digital decision-making system based on project production data analysis according to claim 1, characterized in that: The historical production plan is represented by the production mode, personnel allocation plan and material scheduling plan used to produce the product in the past. The production mode includes intelligent production mode, networked production mode, cloud manufacturing production mode, additive production mode and service-oriented production mode; The optimization elements include power consumption efficiency, pollutant gas emission, material cost and labor cost. The optimization purpose of the optimization elements is to reduce the optimization elements to the minimum value. The power consumption efficiency is expressed as the ratio of the power used to produce qualified products to the total power.

3. The digital decision-making system based on project production data analysis according to claim 1, characterized in that: The logic of optimizing historical production plans to obtain the target production plan includes: Each historical production plan is constructed into a plan set z i , the optimization objectives are expressed as A j , where i and j are both natural numbers; Construct a function set covering the optimization objectives, bring the numerical values ​​of the optimization objectives corresponding to each historical production plan into the function set to calculate each function value, perform weighted calculation on each function value, and obtain the first sum value corresponding to each historical production plan; The first value and the second value are set as the first threshold, each first sum value is compared with the first threshold, and the historical production plan corresponding to the first sum value distributed between the first value and the second value is set as the target production plan, and the first value and the second value are respectively expressed as the arithmetic mean and geometric mean of each first sum value.

4. A digital decision-making system based on project production data analysis as claimed in claim 3, characterized in that: The calculation expression of the function set is: ； ； ； ； in, 、 、 and Together they form a set of functions, is the value of power consumption efficiency corresponding to each historical production plan, is the total amount of pollutant gas emitted, It is the sum of material cost and labor cost.

5. The digital decision-making system based on project production data analysis according to claim 1, characterized in that: The production department information includes the production line number, the number of people moving in the first time period, the current number of people and the average production quantity; The number of employees who have moved during the first time period is represented by the sum of the number of employees who have resigned and the number of employees who have joined in the first time period in the future, wherein the sum of the number of employees who have resigned and the number of employees who have joined in the first time period in the future is an integer, the number of employees who have resigned is represented by a negative number, and the number of employees who have joined is represented by a positive number; The average production quantity is represented by the average daily production quantity of individual employees in the production department.

6. The digital decision-making system based on project production data analysis according to claim 1, characterized in that: The productivity of the production line is calculated as: ； in, Expressed as the productivity of any production line, Represents the current number of people, It is represented by the number of mobile people in the first time period, Expressed as average production quantity.

7. A digital decision-making system based on project production data analysis according to claim 6, characterized in that: The first decision is used to make a decision on the first production volume and the second production volume. The logic of making the first decision on the target production plan according to the productivity includes: Calculate the sum of the productivity of each production line, record it as the second sum, obtain the current order quantity, calculate the multiples of the second sum, calculate the difference between the current order quantity and each multiple, record it as the first difference, select the first difference with the smallest absolute value, set the multiple corresponding to the first difference with the smallest absolute value as the target multiple, calculate the product of the target multiple and any productivity, set it as the first production volume of the corresponding production line, and assign the first difference with the smallest absolute value to the production line with the largest productivity value, and set the first difference with the smallest absolute value as the second production volume of the corresponding production line.

8. The digital decision-making system based on project production data analysis according to claim 1, characterized in that: The boundary conditions include: Employee satisfaction is distributed between 8 and 10; The delivery time is less than or equal to the current time limit; The qualified rate of finished products is between 80% and 100%; Among them, employee satisfaction is distributed between 0 and 10 and is obtained by employees' self-scoring. The current time limit is expressed as the longest delivery time of the current order. The finished product qualification rate is expressed as the ratio of the qualified number of finished products of each production line drawn according to the first ratio to the total number. When the employee satisfaction, delivery time and finished product qualification rate do not meet the boundary conditions, a maximum of two second decisions will be made.

9. The digital decision-making system based on project production data analysis according to claim 8, characterized in that: According to the first decision, simulated production is performed, and simulated employee satisfaction, simulated delivery time, and simulated finished product qualification rate are obtained. The simulated employee satisfaction, simulated delivery time, and simulated finished product qualification rate are compared with the marginal conditions. If the simulated employee satisfaction, simulated delivery time, and simulated finished product qualification rate are distributed in the marginal conditions, the second decision is not made. Otherwise, make the second decision.

10. A digital decision-making system based on project production data analysis according to claim 9, characterized in that: When making the second decision, the production line number that is not distributed in the boundary conditions is obtained and set as the target production line; When the corresponding simulated employee satisfaction is not distributed within the boundary conditions, the first amount of production is allocated to the production line with the largest employee satisfaction value, and the employee satisfaction of the target production line after allocation is obtained. When the employee satisfaction after allocation is distributed within the boundary conditions, the allocation is stopped. When the employee satisfaction after allocation is not distributed within the boundary conditions, the first amount of the target production line's production after allocation is allocated to the production line with the second largest employee satisfaction value, and the allocation is stopped. When the corresponding delivery time is not distributed within the boundary conditions, the first amount of production is allocated to the production line with the smallest delivery time value, and the delivery time of the target production line after allocation is obtained. When the delivery time after allocation is distributed within the boundary conditions, the allocation is stopped. When the delivery time after allocation is not distributed within the boundary conditions, the first amount of the production of the target production line after allocation is allocated to the production line with the second smallest delivery time, and the allocation is stopped. When the corresponding finished product qualification rate is not distributed within the boundary conditions, the first quantity of production volume is configured to the production line with the largest finished product qualification rate value, and the finished product qualification rate of the target production line after configuration is obtained. When the finished product qualification rate after configuration is distributed within the boundary conditions, the configuration is stopped. When the finished product qualification rate after configuration is not distributed within the boundary conditions, the first quantity of the configured production volume of the target production line is configured to the production line with the second largest finished product qualification rate, and the configuration is stopped.

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