A digital decision system based on project production data analysis

Abstract

The application discloses a kind of digital decision system based on project production data analysis, it is related to the technical field of intelligent decision, including optimization module, decision module and compensation module, optimization function set is obtained by optimizing history production scheme, and target production scheme is obtained, the productivity of each production line is calculated, first decision is made, boundary condition is set, first decision is compensated, and second decision is obtained.The application significantly improves production efficiency by optimizing production scheme and dynamically adjusting production plan, optimizes production parameters and resource allocation, reduces energy consumption and raw material waste, improves the stability and consistency of the production process using big data analysis and optimization algorithms, reduces the interference of human factors based on data-driven decision-making, improves the accuracy and reliability of decision-making, can quickly respond to market demand changes, supports large-scale personalized customization, and can help enterprises achieve intelligent, efficient and green production processes.

Description

TECHNICAL FIELD

[0001] The application 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

[0002] In recent years, AI and ML technology has become the core force driving big data analysis, which can extract deep insights from massive data, identify potential patterns, trends and relationships, and help decision-makers make more accurate and efficient decisions. Through big data analysis, customers' needs and market dynamics can be understood in depth, and targeted strategies can be developed.

[0003] At present, in the Chinese invention patent with the publication number CN117829453A, an intelligent equipment production and management integrated system is disclosed. The method controls the operation of the production line through preset production parameters and programs, analyzes the collected production data, generates a production report, and makes decisions to adjust the production parameters and programs according to the production report. However, the related technology does not compare and optimize the actual effect and ideal production effect of the historical production scheme, lacks the utilization rate of historical data and the scientific nature of production, does not link the employee satisfaction to the production decision, lacks the humanistic care for employees, does not control the production decision according to the delivery limit and the quality of finished products, lacks the reliability of products, and is not conducive to customer stickiness, which has certain limitations. SUMMARY

[0004] The technical problem solved by the application is that the related technology does not compare and optimize the actual effect and ideal production effect of the historical production scheme, lacks the utilization rate of historical data and the scientific nature of production, does not link the employee satisfaction to the production decision, lacks the humanistic care for employees, does not control the production decision according to the delivery limit and the quality of finished products, lacks the reliability of products, and is not conducive to customer stickiness, which has certain limitations.

[0005] To solve the above technical problems, the application provides the following technical scheme: a digital decision-making system based on project production data analysis, comprising an optimization module, a decision-making module and a compensation module.

[0006] 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.

[0007] 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.

[0008] The compensation module sets a boundary condition, and compensates the first decision according to the boundary condition to obtain a second decision.

[0009] As a preferred scheme of the digital decision system based on project production data analysis, the historical production scheme is represented as a production mode, a personnel allocation plan and a material scheduling plan for producing a product, and the production mode includes intelligent production mode, networked production mode, cloud manufacturing production mode, additive production mode and service-oriented production mode.

[0010] The optimization elements include consumption power efficiency, emission of polluting gas, material cost and labor cost, and the optimization purpose of the optimization elements is represented as reducing the optimization elements to the minimum value, and the consumption power efficiency is represented as the ratio of power for producing qualified products to total power.

[0011] As a preferred scheme of the digital decision system based on project production data analysis, the logic for optimizing the historical production scheme to obtain the target production scheme includes:

[0012] Each historical production scheme is constructed as a scheme set z i The optimization purpose is represented as A j Wherein, i and j are natural numbers.

[0013] A function set covering the optimization purpose is constructed, the numerical value of the corresponding optimization purpose of each historical production scheme is brought into the function set for calculation to obtain each function value, and each function value is weighted to obtain a first sum value corresponding to each historical production scheme.

[0014] The first value and the second value are set as a first threshold, each first sum value is compared with the first threshold, and the historical production scheme corresponding to the first sum value distributed between the first value and the second value is set as the target production scheme, and the first value and the second value are respectively represented as the arithmetic mean and the geometric mean of each first sum value.

[0015] As a preferred scheme of the digital decision system based on project production data analysis, the calculation expression of the function set is:

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] Wherein, 、 、 and constitute a function set, is a value of the consumed power efficiency corresponding to each historical production scheme, is a value of the total amount of emitted pollution gas, is a sum value of the material cost and the labor cost.

[0021] As a preferred scheme of the digital decision-making system based on project production data analysis, the production department information comprises a production line number, a first time period flow number, a current number of people, and an average production quantity.

[0022] The first time period flow number is expressed as a sum value of a first time period future number of people leaving and a first time period future number of people entering, wherein the sum value of the first time period future number of people leaving and the first time period future number of people entering is an integer, the first time period future number of people leaving is expressed as a negative number, and the first time period future number of people entering is expressed as a positive number.

[0023] The average production quantity is expressed as an average value of a daily production quantity of each employee in the production department.

[0024] As a preferred scheme of the digital decision-making system based on project production data analysis, a calculation expression of the production line productivity is:

[0025] ;

[0026] wherein, is expressed as the productivity of any production line, is expressed as the current number of people, is expressed as the first time period flow number, is expressed as the average production quantity.

[0027] As a preferred scheme of the digital decision-making system based on project production data analysis, the first decision is used to make a decision on a first production quantity and a second production quantity, and a logic of making the first decision on the target production scheme according to the productivity comprises:

[0028] Sum of the productivity of each production line is calculated as a second sum value, current order quantity is obtained, each multiple value of the second sum value is calculated, a difference value between the current order quantity and each multiple value is calculated as a first difference value, the first difference value with the smallest absolute value is selected, the multiple corresponding to the first difference value with the smallest absolute value is set as a target multiple, a product of the target multiple and any productivity is calculated and set as a first production quantity of the corresponding production line, the first difference value with the smallest absolute value is distributed to the production line with the largest productivity value, and the first difference value with the smallest absolute value is set as a second production quantity of the corresponding production line.

[0029] As a preferred scheme of the digital decision-making system based on project production data analysis, the boundary conditions include:

[0030] The employee satisfaction is distributed between 8 and 10;

[0031] The delivery time is less than or equal to the current time limit;

[0032] The finished product qualified rate is distributed between 80% and 100%;

[0033] The employee satisfaction is distributed between 0 and 10, and is obtained by self-scoring of the employee, the current time limit is represented by the longest delivery time of the current order, the finished product qualified rate is represented by the ratio of the qualified number to the total number of the finished products of each production line extracted according to the first proportion, and the employee satisfaction, the delivery time and the finished product qualified rate do not meet the boundary conditions, and the second decision-making is performed at most twice.

[0034] As a preferred scheme of the digital decision-making system based on project production data analysis, the simulation production is performed according to the first decision-making, and the simulated employee satisfaction, the simulated delivery time and the simulated finished product qualified rate are obtained, the simulated employee satisfaction, the simulated delivery time and the simulated finished product qualified rate are compared with the boundary conditions respectively, and when the simulated employee satisfaction, the simulated delivery time and the simulated finished product qualified rate are distributed in the boundary conditions, the second decision-making is not performed.

[0035] Otherwise, the second decision-making is performed.

[0036] As a preferred scheme of the digital decision-making system based on project production data analysis, when the second decision-making is performed, the production line number not distributed in the boundary conditions is obtained and set as a target production line.

[0037] When the corresponding analog employee satisfaction does not distribute in the boundary condition, the first number of production quantity is configured to the production line with the maximum employee satisfaction value, and the employee satisfaction of the configured target production line is obtained, when the configured employee satisfaction distributes in the boundary condition, the configuration is stopped, when the configured employee satisfaction does not distribute in the boundary condition, the first number of the configured production quantity of the target production line is configured to the production line with the second maximum employee satisfaction value, and the configuration is stopped;

[0038] When the corresponding delivery time does not distribute in the boundary condition, the first number of production quantity is configured to the production line with the minimum delivery time value, and the delivery time of the configured target production line is obtained, when the configured delivery time distributes in the boundary condition, the configuration is stopped, when the configured delivery time does not distribute in the boundary condition, the first number of the configured production quantity of the target production line is configured to the production line with the second minimum delivery time, and the configuration is stopped;

[0039] When the corresponding delivery time does not distribute in the boundary condition, the first number of production quantity is configured to the production line with the minimum delivery time value, and the delivery time of the configured target production line is obtained, when the configured delivery time distributes in the boundary condition, the configuration is stopped, when the configured delivery time does not distribute in the boundary condition, the first number of the configured production quantity of the target production line is configured to the production line with the second minimum delivery time, and the configuration is stopped;

[0040] The beneficial effects of the present application are: through optimizing the production scheme and dynamically adjusting the production plan, the production efficiency is significantly improved, the production parameters and resource allocation are optimized, the energy consumption and raw material waste are reduced, the data analysis and optimization algorithm are utilized to improve the stability and consistency of the production process, the data-driven decision mode is used to reduce the interference of human factors and improve the accuracy and reliability of the decision, the market demand changes can be quickly responded, the large-scale personalized customization can be supported, and the intelligent, efficient and green production process can be realized for enterprises. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 A basic flow diagram of a digital decision system based on project production data analysis is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments.

[0043] Embodiments, refer to Figure 1For an embodiment of the present application, a digital decision-making system based on project production data analysis is provided, including an optimization module, a decision-making module and a compensation module;

[0044] 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;

[0045] 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;

[0046] The compensation module sets boundary conditions and compensates the first decision according to the boundary conditions to obtain a second decision.

[0047] The present application significantly improves production efficiency by optimizing production schemes and dynamically adjusting production plans, optimizes production parameters and resource allocation, reduces energy consumption and material waste, uses big data analysis and optimization algorithms to improve the stability and consistency of the production process, reduces the interference of human factors based on a data-driven decision-making mode, improves the accuracy and reliability of the decision, can quickly respond to changes in market demand, supports large-scale personalized customization, and can help enterprises realize intelligent, efficient and green production processes.

[0048] The historical production scheme is represented as a historical production mode for producing products, a personnel allocation plan and a material scheduling plan, the production mode includes intelligent production mode, networked production mode, cloud manufacturing production mode, additive production mode and service type production mode;

[0049] The optimization elements include power consumption efficiency, emission of pollutants, material cost and labor cost, and the optimization purpose of the optimization elements is to reduce the optimization elements to the minimum value, and the power consumption efficiency is represented as the ratio of the power used for producing qualified products to the total power.

[0050] In specific implementation, by optimizing the production mode and the material scheduling plan, the production efficiency is significantly improved, the energy use is optimized, the material waste is reduced and the labor cost is reduced, by intelligent and precise control, the product consistency and quality are improved, the pollutant emission is reduced, the environmental risk is reduced, which can bring significant economic and environmental benefits to enterprises, and promote the development of manufacturing industry towards intelligent, green and efficient direction.

[0051] The logic of optimizing the historical production scheme to obtain the target production scheme includes;

[0052] Each historical production scheme is constructed as a scheme set z i The optimization purpose is represented as A j Wherein, i and j are natural numbers;

[0053] The function set for optimization purposes is constructed, the corresponding numerical values of the optimization purposes of each historical production scheme are brought into the function set for calculation to obtain each function value, and each function value is weighted and calculated to obtain the first sum value corresponding to each historical production scheme;

[0054] The first value and the second value are set as the first threshold value, each first sum value is compared with the first threshold value, and the historical production scheme corresponding to the first sum value distributed between the first value and the second value is set as the target production scheme. The first value and the second value respectively represent the arithmetic mean and the geometric mean of each first sum value.

[0055] The calculation expression of the function set is:

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] Among them, 、 、 and together constitute a function set, is the value of the consumption power efficiency corresponding to each historical production scheme, is the value of the total amount of emission of polluted gas, is the sum of the material cost and the labor cost.

[0061] In specific implementation, through mathematical models and statistical methods, the scientificity and objectivity of the decision-making process are ensured, human factors are reduced, multiple optimization objectives (such as energy consumption, cost, efficiency, etc.) are considered, the maximization of comprehensive benefits is realized, the optimization objectives are adjusted according to different production scenarios, multiple production modes are adapted, the production cost is significantly reduced through optimization of energy consumption and material cost, the requirements of green manufacturing are met through reduction of pollution emission, the social responsibility of enterprises is improved, the production efficiency and resource utilization rate are improved through optimization of production schemes, not only the economic benefits of enterprises are improved, but also their competitiveness and sustainable development ability in the market are enhanced.

[0062] The production department information includes the production line number, the number of people flowing in the first time period, the current number of people, and the average production quantity;

[0063] The first time period flow number is represented by the sum of the number of employees leaving and the number of employees entering the first time period in the future, wherein the sum of the number of employees leaving and the number of employees entering the first time period in the future is an integer, the number of employees leaving is represented by a negative number, and the number of employees entering is represented by a positive number;

[0064] The average production quantity is represented by the average of the production quantity of each employee in the production department per day.

[0065] The calculation expression of the production capacity of the production line is:

[0066] ;

[0067] wherein, is represented by the production capacity of any one production line, is represented by the current number of employees, is represented by the first time period flow number, is represented by the average production quantity.

[0068] In specific implementation, by optimizing personnel allocation and production plan, the production department is ensured to run in the best state. For example, according to the employee flow, the manpower is supplemented in time to avoid production delay due to personnel shortage, the cost waste due to insufficient or redundant personnel is reduced by accurately predicting the employee flow, the production plan is optimized to reduce resource waste due to uneven production, the production process is optimized by data analysis to reduce quality fluctuations due to personnel changes.

[0069] The first decision is used to make decisions on the first production quantity and the second production quantity, and the logic of making the first decision on the target production plan according to the production capacity includes:

[0070] The sum of the production capacities of each production line is calculated and denoted as a second sum value, the current order quantity is obtained, each multiple value of the second sum value is calculated, the difference between the current order quantity and each multiple value is calculated and denoted as a first difference value, the first difference value with the smallest absolute value is selected, the multiple corresponding to the first difference value with the smallest absolute value is set as a target multiple, the product of the target multiple and any production capacity is calculated and set as the first production quantity of the corresponding production line, the first difference value with the smallest absolute value is allocated to the production line with the largest production capacity value, and the first difference value with the smallest absolute value is set as the second production quantity of the corresponding production line.

[0071] In specific implementation, by optimizing production plan, the production department is ensured to run in the best state, the production efficiency is improved, the resource waste due to uneven production is reduced by accurately predicting production demand, the production cost is reduced, the quality problem due to production fluctuation is reduced by optimizing production process, the product quality is improved, and the response speed to market changes is improved by quickly adjusting production plan.

[0072] The boundary conditions include:

[0073] The employee satisfaction is distributed between 8 and 10;

[0074] The delivery time is less than or equal to the current time limit;

[0075] The finished product qualification rate is distributed between 80% and 100%;

[0076] The employee satisfaction is distributed between 0 and 10, obtained by self-scoring of the employees, the current time limit is represented as the longest delivery time of the current order, the finished product qualification rate is represented as the ratio of the qualified number to the total number of finished products of each production line according to the first proportion, and the employee satisfaction, the delivery time and the finished product qualification rate do not meet the boundary conditions, and the second decision is made at most twice.

[0077] In specific implementation, by setting clear boundary conditions, it is ensured that the production decision is made under the premise of meeting the employee satisfaction, the delivery time and the product quality, avoiding suboptimal solution caused by single target optimization, by ensuring that the employee satisfaction is at a high level (8-10), the work enthusiasm and loyalty of the employees are improved, and the personnel turnover is reduced, by constraining the finished product qualification rate and the delivery time, it is ensured that the enterprise can deliver high-quality products on time, and the customer satisfaction and market competitiveness are enhanced, the second decision mechanism allows adjustment when the initial decision does not meet the boundary conditions, and the adaptability of the system to complex production environment is improved.

[0078] According to the first decision, the simulated production is simulated, and the simulated employee satisfaction, the simulated delivery time and the simulated finished product qualification rate are obtained, the simulated employee satisfaction, the simulated delivery time and the simulated finished product qualification rate are compared with the boundary conditions, and when the simulated employee satisfaction, the simulated delivery time and the simulated finished product qualification rate are distributed in the boundary conditions, the second decision is not made;

[0079] Otherwise, the second decision is made.

[0080] When the second decision is made, the production line number not distributed in the boundary conditions is obtained, and is set as the target production line;

[0081] When the corresponding simulated employee satisfaction is not distributed in the boundary conditions, the first number of production capacity is configured to the production line with the largest employee satisfaction value, and the employee satisfaction of the configured target production line is obtained, when the configured employee satisfaction is distributed in the boundary conditions, the configuration is stopped, and when the configured employee satisfaction is not distributed in the boundary conditions, the first number of the configured production capacity of the target production line is configured to the production line with the second largest employee satisfaction value, and the configuration is stopped;

[0082] When the corresponding delivery time length does not distribute the boundary condition, the first number of production quantity is configured to the production line with the minimum delivery time length value, and the delivery time length of the configured target production line is obtained, when the configured delivery time length distributes the boundary condition, the configuration is stopped, when the configured delivery time length does not distribute the boundary condition, the first number of the configured production quantity of the target production line is configured to the production line with the second smallest delivery time length, and the configuration is stopped;

[0083] When the corresponding finished product qualified rate does not distribute the boundary condition, the first number of production quantity is configured to the production line with the maximum finished product qualified rate value, and the finished product qualified rate of the configured target production line is obtained, when the configured finished product qualified rate distributes the boundary condition, the configuration is stopped, when the configured finished product qualified rate does not distribute the boundary condition, the first number of the configured production quantity of the target production line is configured to the production line with the second largest finished product qualified rate, and the configuration is stopped.

[0084] In specific implementation, by dynamically adjusting production quantity, production resources are ensured to be allocated to the most needed production line, overall production efficiency is improved, employee satisfaction is improved, employee dissatisfaction caused by excessive work or unreasonable arrangement is avoided, thereby improving employee work enthusiasm and loyalty, improving order on-time delivery probability, enhancing customer satisfaction and market competitiveness, improving finished product qualified rate, ensuring product quality to meet standards, reducing scrap rate and rework cost, based on real-time data for dynamic adjustment, ensuring the scientificity and rationality of decision-making, reducing human factors interference, at most adjusting twice, avoiding that production quantity allocation is too inclined due to subjective factors such as employee score, and the allocation of production quantity is clear, which is beneficial to the financial department to adjust the salary according to the different workloads of employees of different production lines, and is beneficial to encouraging employee work enthusiasm.

[0085] The present application significantly improves production efficiency by optimizing production scheme and dynamically adjusting production plan, optimizes production parameters and resource allocation, reduces energy consumption and material waste, uses big data analysis and optimization algorithm to improve production process stability and consistency, based on data-driven decision-making mode, reduces human factors interference, improves decision-making accuracy and reliability, can quickly respond to market demand changes, supports large-scale personalized customization, and can help enterprises realize intelligent, efficient and green production process.

[0086] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (or computer- readable storage media) having computer-usable program code embodied in the medium. The medium may Figure 1 one or more functions specified in the flow or flows and / or blocks Figure 1 one or more functions specified in the flow or flows and / or blocks

[0087] It should be noted that the above-mentioned embodiments are only used to illustrate but not to limit the technical solutions of the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A digitalized decision system based on project production data analysis, characterized by, The optimization module, the decision module and the compensation module are included. The optimization module optimizes the historical production scheme to obtain an optimized function set, and substitutes the historical production data corresponding to the historical production scheme into the optimized function set to obtain a target production scheme. The decision 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 a boundary condition, compensates the first decision according to the boundary condition, and obtains a second decision. The logic of optimizing the historical production scheme to obtain the target production scheme includes: constructing each historical production scheme as a scheme set z i optimization purposes are respectively represented as A j wherein i and j are both natural numbers; A function set covering the optimization purpose is constructed, the numerical value of the corresponding optimization purpose of each historical production scheme is brought into the function set for calculation to obtain each function value, and each function value is weighted to obtain a first sum value corresponding to each historical production scheme. The first value and the second value are set as a first threshold value, each first sum value is compared with the first threshold value, and the historical production scheme corresponding to the first sum value distributed between the first value and the second value is set as the target production scheme, wherein the first value and the second value represent the arithmetic mean value and the geometric mean value of each first sum value respectively. The first decision is used to make a decision on the first production quantity and the second production quantity, and the logic of making the first decision on the target production scheme according to the productivity includes: A sum value of the productivity of each production line is calculated and recorded as a second sum value, a current order quantity is obtained, each multiple value of the second sum value is calculated, a difference value between the current order quantity and each multiple value is calculated and recorded as a first difference value, the first difference value with the smallest absolute value is selected, a multiple corresponding to the first difference value with the smallest absolute value is set as a target multiple, a product of the target multiple and any productivity is calculated and set as the first production quantity of the corresponding production line, the first difference value with the smallest absolute value is distributed to the production line with the largest productivity value, and the first difference value with the smallest absolute value is set as the second production quantity of the corresponding production line; When the second decision is made, a production line number not distributed in the boundary condition is obtained and set as a target production line; When the corresponding simulated employee satisfaction does not distribute in the boundary condition, the first quantity of production quantity is configured to the production line with the largest employee satisfaction value, and the employee satisfaction of the target production line after configuration is obtained, when the employee satisfaction after configuration distributes in the boundary condition, the configuration is stopped, and when the employee satisfaction after configuration does not distribute in the boundary condition, the first quantity of the production quantity after configuration of the target production line is configured to the production line with the second largest employee satisfaction value, and the configuration is stopped. When the corresponding delivery time does not distribute in the boundary condition, the first quantity of production quantity is configured to the production line with the smallest delivery time value, and the delivery time of the target production line after configuration is obtained, when the delivery time after configuration distributes in the boundary condition, the configuration is stopped, and when the delivery time after configuration does not distribute in the boundary condition, the first quantity of the production quantity after configuration of the target production line is configured to the production line with the second smallest delivery time, and the configuration is stopped. When the corresponding product qualification rate is not distributed in the boundary condition, the first number of production quantity is configured to the production line with the largest product qualification rate value, and the product qualification rate of the configured target production line is obtained, when the configured product qualification rate is distributed in the boundary condition, the configuration is stopped, and when the configured product qualification rate is not distributed in the boundary condition, the first number of the configured production quantity of the target production line is configured to the production line with the second largest product qualification rate, and the configuration is stopped.

2. A digitalized decision system based on project production data analysis as claimed in claim 1 wherein: The historical production scheme is represented as a historical production mode, a personnel allocation plan and a material scheduling plan for producing a product, the production mode includes intelligent production mode, networked production mode, cloud manufacturing production mode, additive production mode and service type production mode; The optimization elements include power consumption efficiency, emission of pollutant gas, material cost and labor cost, and the optimization purpose of the optimization element is to reduce the optimization element to the minimum value, and the power consumption efficiency is represented as the ratio of the power for producing a product with qualified quality to the total power.

3. A digitalized decision system based on project production data analysis as claimed in claim 1 wherein: The calculation expression of the function set is: ； ； ； ； wherein, , , and together constitute a function set, is a value of the consumed power efficiency corresponding to each historical production plan, is a value of the total amount of emitted pollution gas, is a sum value of the material cost and the labor cost.

4. The digitalized decision system based on project production data analysis of claim 1, wherein: The production department information includes production line number, first time period flow number, current number and average production quantity; The first time period flow number is represented as the sum of the number of employees leaving and the number of employees entering in the future first time period, wherein the sum of the number of employees leaving and the number of employees entering in the future first time period is an integer, the number of employees leaving is represented as a negative number, and the number of employees entering is represented as a positive number; The average production quantity is represented as the average value of the production quantity of each employee in the production department per day.

5. The digitalized decision system based on project production data analysis as claimed in claim 1 wherein: The calculation expression of the production capacity of the production line is: ； wherein, is expressed as productivity of any one production line, is expressed as current number of people, is expressed as number of people flowing in the first time period, is expressed as average production quantity.

6. The digitalized decision system based on project production data analysis of claim 1, wherein: The boundary condition includes: The employee satisfaction is distributed between 8 and 10; The delivery time is less than or equal to the current time limit; The product qualification rate is distributed between 80% and 100%; The employee satisfaction is distributed between 0 and 10, and is obtained by the employees themselves, the current time limit is represented as the longest delivery time of the current order, the product qualification rate is represented as the ratio of the qualified number to the total number of products of each production line extracted according to the first proportion, and the employee satisfaction, delivery time and product qualification rate do not meet the boundary condition, and the second decision is made at most twice.

7. A digitalized decision system based on project production data analysis as claimed in claim 6 wherein: According to the first decision, the simulation production is carried out, and the simulation employee satisfaction, simulation delivery time and simulation product qualification rate are obtained, the simulation employee satisfaction, simulation delivery time and simulation product qualification rate are compared with the boundary condition respectively, when the simulation employee satisfaction, simulation delivery time and simulation product qualification rate are distributed in the boundary condition, the second decision is not made; Otherwise, the second decision is made.

Images