Intelligent optimization scheduling system and method based on multi-target production

Abstract

The invention discloses an intelligent optimal scheduling system and method based on multi-target production, relates to the technical field of intelligent scheduling optimization, and solves the problems that a scheduling scheme is difficult to adapt to the change of a production environment due to the fact that the scheduling scheme is generated by adopting a fixed weight or a static priority in the prior art. And the accuracy of the multi-target production scheduling scheme is low. A product comprehensive function is constructed according to product data, equipment data, order data and inventory data; generating a scheduling scheme according to the product comprehensive function and performing production scheduling according to the scheduling scheme; an alarm signal is generated according to equipment data, a product comprehensive function of multiple product production is constructed, multiple objective functions are combined through adaptive weights, the optimal state can be dynamically adjusted according to real-time production data, the accuracy of a production scheduling scheme is improved, meanwhile, the equipment and order states are monitored in real time, and the production scheduling efficiency is improved. An early warning signal can be sent in time, and the situation that equipment cannot work normally or orders are not processed in time is avoided.

Description

Technical Field

[0001] The present application belongs to the field of intelligent scheduling optimization technology, and specifically, is an intelligent optimization scheduling system and method based on multi-objective production. Background Art

[0002] Manufacturing production refers to the process of converting raw materials, parts or semi-finished products into final products through a series of process flows such as processing, assembly and testing. This process involves multiple links, including production planning, resource scheduling, equipment operation, quality control and logistics management. The core goal of manufacturing production is to complete product production efficiently and with high quality, while optimizing resource utilization, reducing costs and meeting customer needs. In modern industrial production, production scheduling is a key link to improve efficiency and reduce costs.

[0003] Traditional production scheduling methods are usually optimized based on a single objective, such as minimizing costs or maximizing output, and are difficult to cope with complex production environments with multiple objectives and multiple constraints. With the development of intelligent manufacturing, there are also existing technologies that implement scheduling solutions for multi-objective production, but they often use fixed weights or static priorities to generate scheduling solutions, making it difficult for scheduling solutions to adapt to changes in the production environment, resulting in low accuracy of multi-objective production scheduling solutions. Therefore, the scheduling system for multi-objective production still needs further improvement. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes an intelligent optimization scheduling system and method based on multi-objective production, which is used to solve the technical problem that the prior art often uses fixed weights or static priorities to generate scheduling plans, making it difficult for the scheduling plans to adapt to changes in the production environment, resulting in low accuracy of the multi-objective production scheduling plans.

[0005] To achieve the above-mentioned purpose, the first aspect of the present application provides an intelligent optimization scheduling system based on multi-objective production, comprising: a data acquisition module, a data analysis module, a scheduling module, an early warning module and a database;

[0006] The data acquisition module is used to acquire production data in real time through data acquisition equipment; the production data includes product data, equipment data, order data and inventory data;

[0007] The data analysis module: constructs a product comprehensive function based on product data, equipment data, order data and inventory data; generates a scheduling plan based on the product comprehensive function; and generates an alarm signal based on the equipment data;

[0008] The scheduling module performs production scheduling according to the scheduling plan;

[0009] The early warning module: makes prompts according to the alarm signal and contacts the management personnel.

[0010] Through the above steps, this application constructs a comprehensive product function for the production of multiple products. This function is composed of multiple objective functions through adaptive weights, and can dynamically adjust the optimal state according to real-time production data, thereby significantly improving the accuracy of the production scheduling plan; at the same time, the system monitors the equipment and order status in real time, and can issue early warning signals in time to effectively avoid situations where equipment cannot work normally or orders are not processed in a timely manner.

[0011] Furthermore, the product comprehensive function is constructed according to the product data, equipment data, order data and inventory data, including:

[0012] Obtain product data, equipment data, order data and inventory data; the product data includes product ID, production cost and production time; the equipment data includes equipment ID, equipment status and equipment historical working data; the order data includes order ID, order product quantity, order period, order customer level and order total; the inventory data includes inventory product ID and its corresponding inventory quantity;

[0013] Calculate order priority based on order period, order customer level and order total amount;

[0014] Generate several target quantitative functions based on production cost, production time, equipment status and order priority;

[0015] By formula Construct product comprehensive function FC; where ω 1 ,ω 2 ,ω 3 and ω 4 is the dynamic weight coefficient, ω 1 ∈(α 1 , α 2 ),ω 2 ∈(α 3 , α 4 ),ω 3 ∈(α 5 , α 6 ),ω 4 ∈(α 7 , α 8 ); α 1 , α 2 , α 3 , α 4 , α 5 , α 6 , α 7 and α 8 is a constant, and α 1 , α 2 , α3 , α 4 , α 5 , α 6 , α 7 and α 8 ∈(0, 1); C is the quantitative function of minimizing production cost, DC is the unit cost; T is the quantitative function of minimizing production time, DT is the unit time; U is the quantitative function of maximizing equipment utilization, DU is the unit utilization; P is the quantitative function of maximizing order priority, DP is the unit priority.

[0016] Furthermore, the order priority is calculated according to the order period, order customer level and order total amount, including:

[0017] Obtain the order ID and its corresponding order period DQ, order customer level DKD and order total amount DZJ; the order customer level is assessed by experts based on the customer identity;

[0018] By formula Calculate order priority DY j ; Among them, β 1 , β 2 and β 3 is the weight coefficient, β 1 , β 2 and β 3 ∈(0, 1); DT is unit time, DD is unit level, and DJ is unit price.

[0019] This application calculates the order priorities corresponding to all order IDs through the above steps, and gives priority to high-priority order IDs when building product comprehensive functions to ensure that high-priority orders are processed first, which not only helps to improve customer satisfaction, but also further optimizes production efficiency and resource utilization, thereby achieving more efficient production scheduling.

[0020] Furthermore, the method generates several target quantitative functions according to production cost, production time, equipment status and order priority, including:

[0021] Obtain production costs, production time, equipment status, and order priorities for several products; the production costs include raw material costs Energy costs and human cost

[0022] By formula Construct a quantitative function C that minimizes production cost; where SL i Expressed as the production quantity of the i-th product;

[0023] By the formula T = max{ST i}Construct a function T that minimizes the production time; where ST i It is expressed as the production time of the i-th product;

[0024] By formula Construct the quantitative function U that maximizes equipment utilization; where YT k It is represented as the running time of the kth device, and ZT is represented as the total running time of all devices;

[0025] By formula Construct the maximum order priority quantitative function P; where WZ n It represents the order completion status corresponding to the high-priority order ID; GYDS represents the total number of high-priority orders; and the GYDS is generated according to the order priority.

[0026] Furthermore, the GYDS is generated by order priority, including:

[0027] Get the order priority DY corresponding to several order IDs j ;

[0028] Prioritize your order j Order IDs greater than the priority threshold are marked as high priority orders;

[0029] GYDS is obtained by summing the quantities corresponding to the order IDs labeled as high priority orders.

[0030] Furthermore, the dynamic weight coefficient is dynamically adjusted according to production data, including:

[0031] Obtain production data in real time;

[0032] Inputting production data into a weight estimation model to obtain a number of estimated dynamic weights; the weight estimation model is constructed by a machine learning model;

[0033] The dynamic weight coefficients are updated by assigning several estimated dynamic weights to their corresponding dynamic weight coefficients.

[0034] Furthermore, the weight estimation model is constructed through a machine learning model, including:

[0035] Obtaining some historical production data and some corresponding dynamic weights;

[0036] Divide a number of historical production data and their corresponding dynamic weights into training data, verification data and test data; perform data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set;

[0037] Select a machine learning model as the base model;

[0038] Train the basic model with the training set, and adjust the learning rate and hyperparameters on the validation set to obtain the pre-trained model;

[0039] By verifying the pre-trained model on the test set, we finally get the input production data and output a weight estimation model that estimates several dynamic weights.

[0040] Furthermore, generating a scheduling plan according to the product comprehensive function includes:

[0041] Obtain production data and product comprehensive functions in real time;

[0042] The production data and product comprehensive function are combined through the deep reinforcement learning model to obtain the optimal solution of the product comprehensive function, and then generate a scheduling plan based on it.

[0043] Further, generating an alarm signal according to the device data includes:

[0044] Obtaining device data and several order priorities; the device data includes a device ID and historical device working data; the historical device working data includes historical device working hours and the working environment temperature and working environment humidity corresponding to the working hours;

[0045] By formula Calculate the damage index SSZ of several equipment k ; Among them, β 4 and β 5 is the weight coefficient, β 4 and β 5 ∈(0, 1); g is the proportionality coefficient, g∈(0, π / 2); LS km W is the historical working time of the kth device in the mth time period. km and S km They represent the working temperature and humidity of the kth device in the mth time period in history respectively; ZW and ZS represent the optimum working temperature and humidity; DW is the unit temperature, DS is the unit humidity, and DGT is the unit working time;

[0046] Determine the equipment damage index SSZ k Is it greater than the damage threshold? If yes, generate an equipment scrap alarm signal; if no, determine the equipment damage index SSZ k Is it greater than D times the damage threshold? If yes, generate an equipment maintenance warning signal; if no, do nothing; where D is the proportionality coefficient, D∈(0,1);

[0047] Determine whether the order priority is greater than the emergency processing threshold; if so, generate an order emergency processing prompt signal; if not, do nothing.

[0048] This application monitors equipment data and order data in real time. When equipment damage reaches a certain threshold, the system will immediately issue an alarm signal so that management personnel can repair it in time to prevent a decrease in production efficiency due to equipment problems. At the same time, the system tracks order data in real time and gives priority to high-priority orders, thereby improving customer satisfaction and overall work efficiency.

[0049] Another aspect of the present invention provides an intelligent optimization scheduling method based on multi-objective production, comprising:

[0050] S0: Acquire production data in real time; the production data includes product data, equipment data, order data and inventory data;

[0051] S1: Build product comprehensive function based on product data, equipment data, order data and inventory data;

[0052] S2: Generate a scheduling plan based on the product comprehensive function; perform production scheduling based on the scheduling plan;

[0053] S3: Generate an alarm signal based on the equipment data; make a prompt based on the alarm signal and contact the management personnel.

[0054] Compared with the prior art, the beneficial effects of this application are:

[0055] 1. This application constructs a product comprehensive function according to product data, equipment data, order data and inventory data; generates a scheduling plan according to the product comprehensive function; performs production scheduling according to the scheduling plan; generates an alarm signal according to the equipment data, and constructs a product comprehensive function for the production of multiple products. The product comprehensive function is composed of multiple objective functions through adaptive weights, and can dynamically adjust the optimal state according to real-time production data, thereby improving the accuracy of the production scheduling plan. At the same time, it monitors the equipment and order status in real time, and can issue early warning signals in time to avoid equipment malfunctioning or untimely order processing.

[0056] 2. This application dynamically adjusts the dynamic weight coefficients corresponding to each objective function through real-time production data, so that the weight coefficients corresponding to the objective function can be changed when the production data is in a specific situation, making the production data more in line with pre-set expectations, thereby improving the flexibility and adaptability of multi-objective scheduling.

[0057] 3. This application conducts real-time monitoring through equipment data and order data. When the equipment damage reaches a certain level, an alarm signal is issued to promptly remind management personnel to carry out repairs, thus avoiding the situation where production efficiency is reduced. At the same time, order data is monitored and high-priority order data is processed in a timely manner to improve customer satisfaction and work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] 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 or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 This is a schematic diagram of the principle of the intelligent optimization scheduling system based on multi-objective production of this application;

[0060] Figure 2 This is a flow chart of the intelligent optimization scheduling method based on multi-objective production in this application. DETAILED DESCRIPTION

[0061] The technical solution of the present application will be described clearly and completely in conjunction with the embodiments below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0062] See also Figure 1 , the first aspect of the present application provides an intelligent optimization scheduling system based on multi-objective production, including: a data acquisition module, a data analysis module, a scheduling module, an early warning module and a database;

[0063] Data acquisition module: obtain production data in real time through data acquisition equipment; production data includes product data, equipment data, order data and inventory data; data acquisition equipment includes various sensors, etc.;

[0064] Data analysis module: constructs a product comprehensive function based on product data, equipment data, order data and inventory data. The product comprehensive function is constructed to obtain the optimal scheduling method for the current state; generates a scheduling plan based on the product comprehensive function. The scheduling plan refers to a solution taken for the current production state to make the production results tend to a good state; generates an alarm signal based on the equipment data;

[0065] Scheduling module: performs production scheduling according to the scheduling plan;

[0066] Early warning module: Prompts are given based on alarm signals and management personnel are contacted. Alarm signals include equipment scrapping alarm signals and equipment maintenance alarm signals.

[0067] In this embodiment, a product comprehensive function is constructed based on product data, equipment data, order data and inventory data, including:

[0068] Obtain product data, equipment data, order data and inventory data; product data includes product ID, production cost and production time; equipment data includes equipment ID, equipment status and equipment historical working data; order data includes order ID, order product quantity, order period, order customer level and order total; inventory data includes inventory product ID and its corresponding inventory quantity;

[0069] Order priority is calculated based on order deadline, order customer level and order total. Order priority refers to the priority supply of orders. The higher the priority of an order, the higher the priority of the order, the higher the priority of the production and supply of the required products.

[0070] Generate several target quantitative functions based on production cost, production time, equipment status and order priority; the target quantitative function is to make the scheduling plan move towards a certain goal, such as optimizing cost, optimizing equipment utilization, etc.;

[0071] By formula Construct product comprehensive function FC; where ω 1 ,ω 2 ,ω 3 and ω 4 is the dynamic weight coefficient, ω 1 ∈(α 1 , α 2 ),ω 2 ∈(α 3 , α 4 ),ω 3 ∈(α 5 , α 6 ),ω 4 ∈(α 7 , α 8 ); α 1 , α 2 , α 3 , α 4 , α 5 , α 6 , α 7 and α 8 is a constant, and α 1 , α 2 , α 3 , α 4 , α 5 , α 6 , α 7 and α 8∈(0, 1), the specific value is set according to experience, and the constant is set to make the dynamic weight coefficient adjustable within a certain range to avoid the situation where the objective function corresponding to a certain dynamic weight coefficient does not work; C is the quantitative function for minimizing production cost, DC is the unit cost; T is the quantitative function for minimizing production time, DT is the unit time; U is the quantitative function for maximizing equipment utilization, DU is the unit utilization; P is the quantitative function for maximizing order priority, DP is the unit priority.

[0072] This embodiment constructs a comprehensive product function through the above steps, aiming to schedule multiple products with multiple objectives, ensure that the scheduling between multiple products meets the predetermined objectives, improve the performance of the multi-objective production system, and thus improve the company's interests.

[0073] In this embodiment, the order priority is calculated according to the order period, the order customer level and the order total amount, including:

[0074] Get the order ID and its corresponding order period DQ, order customer level DKD and order total DZJ; the order customer level is assessed by experts based on the customer's identity;

[0075] By formula Calculate order priority DY j ; Among them, β 1 , β 2 and β 3 is the weight coefficient, β 1 , β 2 and β 3 ∈(0, 1), the specific value is set according to experience. When considering more factors such as order deadline, the corresponding weight coefficient can be set larger. When considering more factors such as order customer level, the corresponding weight coefficient can be set larger. In this implementation, the weight coefficient is set to β 1 >β 2 >β 3 ; DT is unit time, DD is unit level, DJ is unit price, and the specific values ​​are set based on experience; the shorter the order period, the sooner the order needs to be produced; the higher the order customer level, the sooner the corresponding multiple products need to be produced; the higher the order total amount, the more priority can be given to producing the corresponding products first, so the order priority increases accordingly.

[0076] This embodiment calculates the order priorities corresponding to all order IDs through the above steps, takes high-priority order IDs into account when constructing the product comprehensive function, and gives priority to high-priority order IDs, which can not only improve customer satisfaction, but also optimize production efficiency and resource utilization.

[0077] In this embodiment, several target quantization functions are generated according to production cost, production time, equipment status and order priority, including:

[0078] Get production costs, production time, equipment status and order priority for several products; production costs include raw material costs Energy costs and human cost

[0079] By formula Construct a quantitative function C that minimizes production cost; where SL i It is expressed as the production quantity of the i-th product; its purpose is to schedule the production process so that the production cost is kept at the lowest cost;

[0080] By the formula T = max{ST i}Construct a function T that minimizes the production time; where ST i It is expressed as the production time of the i-th product. There is a condition that the production time of the i-th product must be less than the order deadline corresponding to its order to ensure that the product can be produced within the order deadline.

[0081] By formula Construct the quantitative function U that maximizes equipment utilization; where YT k It is represented as the operating time of the kth device, and ZT is represented as the total time of all devices. Each device can only produce one product. The quantification function of maximizing equipment utilization is to ensure the maximum equipment utilization while completing orders and saving energy consumption;

[0082] By formula Construct the maximum order priority quantitative function P; where WZ n It is represented by the order completion status corresponding to the high-priority order ID; GYDS is represented by the total number of high-priority orders; GYDS is generated by order priority; the purpose of maximizing the order priority quantization function is to give priority to high-priority orders and ensure that high-priority order products can be produced as soon as possible.

[0083] The GYDS in this embodiment is generated by order priority, including:

[0084] Get the order priority DY corresponding to several order IDs j ;

[0085] Prioritize your order j Order IDs greater than the priority threshold are marked as high priority orders; the priority threshold is set based on experience;

[0086] GYDS is obtained by summing the quantities corresponding to the order IDs labeled as high priority orders.

[0087] The dynamic weight coefficient in this embodiment is dynamically adjusted according to production data, including:

[0088] Obtain production data in real time;

[0089] Input the production data into the weight estimation model to obtain a number of estimated dynamic weights; the weight estimation model is constructed through a machine learning model;

[0090] The dynamic weight coefficients are updated by assigning several estimated dynamic weights and their corresponding dynamic weight coefficients. When estimating the dynamic weight coefficients, the dynamic weight coefficient corresponding to each objective function will only fluctuate within its fixed range and will not exceed its fixed range.

[0091] The weight estimation model in this embodiment is constructed through a machine learning model, including:

[0092] Obtaining some historical production data and some corresponding dynamic weights;

[0093] Divide a number of historical production data and their corresponding dynamic weights into training data, verification data and test data; perform data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set; the ratio of the training set, the test set and the verification set is 7:2:1;

[0094] Select a machine learning model as the basic model; the machine learning model includes a neural network model, etc.;

[0095] Train the basic model with the training set, and adjust the learning rate and hyperparameters on the validation set to obtain the pre-trained model;

[0096] By verifying the pre-trained model on the test set, we finally get the input production data and output a weight estimation model that estimates several dynamic weights.

[0097] This embodiment dynamically adjusts the dynamic weight coefficients corresponding to each objective function through real-time production data, so that it can flexibly adjust the weight distribution when the production data is in a specific situation, so that the production data is closer to the pre-set expected value, which can improve the flexibility and adaptability of multi-objective scheduling and ensure that the production scheduling plan can better cope with complex and changing production environments.

[0098] In this embodiment, the scheduling scheme is generated according to the product comprehensive function, including:

[0099] Obtain production data and product comprehensive functions in real time;

[0100] The production data and product comprehensive function are used through the deep reinforcement learning model to obtain the optimal solution of the product comprehensive function, and use it to generate a scheduling plan; the deep reinforcement learning model is a pre-trained model that can effectively obtain the optimal solution of the product comprehensive function and use it to generate a scheduling plan.

[0101] This embodiment uses a deep reinforcement learning model to obtain the optimal solution of the product comprehensive function and generates a scheduling plan for multi-objective production, thereby improving the intelligence level of scheduling decisions.

[0102] In this embodiment, generating an alarm signal according to device data includes:

[0103] Obtain equipment data and several order priorities; equipment data includes equipment ID and equipment historical working data; equipment historical working data includes equipment historical working hours and the corresponding working environment temperature and working environment humidity;

[0104] By formula Calculate the damage index SSZ of several equipment k ; Among them, β 4 and β 5 is the weight coefficient, β 4 and β 5 ∈(0, 1), the specific value is set according to experience. When the damage caused by temperature to the device during operation is greater than the humidity factor, β 4 >β 5 In this embodiment, β 4 and β 5 are both set to 0.5, that is, the influence of the two on the damage of the equipment within the equipment operation time range is consistent; g is the proportionality coefficient, g∈(0,π / 2), and the specific value is set according to experience. In this embodiment, g is set to make the equipment damage index SSZ k ∈(0,1);LS km W is the historical working time of the kth device in the mth time period. km and S km They represent the working temperature and working humidity of the kth device in the mth time period of history respectively; ZW and ZS represent the optimum working temperature and the optimum working humidity; DW is the unit temperature, DS is the unit humidity, and DGT is the unit working time. The specific values ​​are set according to experience. In this embodiment, DW can be set to 1°C, DS to 1%, and DGT to 1 day; when the device is not in operation, considering that temperature and humidity will not have much impact on the damage of the device, that is, the device damage index is 0 at this moment; once the device is in operation, temperature and humidity will have an adverse effect on the damage of the device. Therefore, as the operating time increases and the temperature and humidity deviate from the optimum working temperature and humidity, the device damage index will increase accordingly;

[0105] Determine the equipment damage index SSZ k Is it greater than the damage threshold? If yes, generate an equipment scrap alarm signal; if no, determine the equipment damage index SSZ k Is it greater than D times the damage threshold? If yes, generate an equipment maintenance warning signal; if no, do nothing; the damage threshold is set based on experience; where D is the proportionality coefficient, D∈(0,1), and the specific value is set based on experience. In this embodiment, D is set to 0.6;

[0106] Determine whether the order priority is greater than the emergency processing threshold; if yes, generate an order emergency processing prompt signal; if no, do nothing; the emergency processing threshold is set based on experience.

[0107] See also Figure 2 Another aspect of the present application provides an intelligent optimization scheduling method based on multi-objective production, including:

[0108] S0: Real-time acquisition of production data; production data includes product data, equipment data, order data and inventory data;

[0109] S1: Build product comprehensive function based on product data, equipment data, order data and inventory data;

[0110] S2: Generate a scheduling plan based on the product comprehensive function; perform production scheduling based on the scheduling plan;

[0111] S3: Generate an alarm signal based on the equipment data; make a prompt based on the alarm signal and contact the management personnel.

[0112] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0113] The working principle of this application is as follows: by acquiring production data in real time; the production data includes product data, equipment data, order data and inventory data; constructing a product comprehensive function according to the product data, equipment data, order data and inventory data; generating a scheduling plan according to the product comprehensive function; performing production scheduling according to the scheduling plan; generating an alarm signal according to the equipment data; making a prompt according to the alarm signal and contacting the management personnel, constructing a product comprehensive function for the production of multiple products, and the product comprehensive function is composed of multiple objective functions combined by adaptive weights, which can dynamically adjust the optimal state according to the real-time production data, thereby improving the accuracy of the production scheduling plan, and at the same time monitoring the equipment and order status in real time, being able to issue early warning signals in time to avoid the equipment from not being able to work properly or the order being processed in time, thereby avoiding the problem that the prior art often uses fixed weights or static priorities to generate scheduling plans, making it difficult for the scheduling plans to adapt to changes in the production environment, resulting in low accuracy of multi-objective production scheduling plans.

[0114] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. Intelligent optimization scheduling system based on multi-objective production, characterized by: include: Data collection module, data analysis module, scheduling module, early warning module and database; The data acquisition module is used to acquire production data in real time through data acquisition equipment; the production data includes product data, equipment data, order data and inventory data; The data analysis module: constructs a product comprehensive function based on product data, equipment data, order data and inventory data; generates a scheduling plan based on the product comprehensive function; and generates an alarm signal based on the equipment data; The scheduling module performs production scheduling according to the scheduling plan; The early warning module: makes prompts according to the alarm signal and contacts the management personnel.

2. The intelligent optimization scheduling system based on multi-objective production according to claim 1 is characterized in that: The product comprehensive function is constructed according to product data, equipment data, order data and inventory data, including: Obtain product data, equipment data, order data and inventory data; the product data includes product ID, production cost and production time; the equipment data includes equipment ID, equipment status and equipment historical working data; the order data includes order ID, order product quantity, order period, order customer level and order total; the inventory data includes inventory product ID and its corresponding inventory quantity; Calculate order priority based on order period, order customer level and order total amount; Generate several target quantitative functions based on production cost, production time, equipment status and order priority; By formula Construct a comprehensive product function FC; where ω1, ω2, ω3 and ω4 are dynamic weight coefficients, ω1∈(α1, α2), ω2∈(α3, α4), ω3∈(α5, α6), ω4∈(α7, α8); α1, α2, α3, α4, α5, α6, α7 and α8 are constants, and α1, α2, α3, α4, α5, α6, α7 and α8∈(0, 1); C is a quantitative function for minimizing production cost, DC is unit cost; T is a quantitative function for minimizing production time, DT is unit time; U is a quantitative function for maximizing equipment utilization, DU is unit utilization; P is a quantitative function for maximizing order priority, DP is unit priority.

3. The intelligent optimization scheduling system based on multi-objective production according to claim 2 is characterized in that: The order priority is calculated based on the order period, order customer level and order total amount, including: Obtain the order ID and its corresponding order period DQ, order customer level DKD and order total amount DZJ; the order customer level is assessed by experts based on the customer identity; By formula Calculate order priority DY j ; Among them, β1, β2 and β3 are weight coefficients, β1, β2 and β3∈(0,1); DT is unit time, DD is unit level, and DJ is unit price.

4. The intelligent optimization scheduling system based on multi-objective production according to claim 2 is characterized in that: The method generates several target quantitative functions according to production cost, production time, equipment status and order priority, including: Obtain production costs, production time, equipment status, and order priorities for several products; the production costs include raw material costs Energy costs and human cost By formula Construct a quantitative function C that minimizes production cost; where SL i Expressed as the production quantity of the i-th product; By the formula T = max{ST i }Construct a function T that minimizes the production time; where ST i It is expressed as the production time of the i-th product; By formula Construct the quantitative function U that maximizes equipment utilization; where YT k It is represented as the running time of the kth device, and ZT is represented as the total running time of all devices; By formula Construct the maximum order priority quantitative function P; where WZ n It represents the order completion status corresponding to the high-priority order ID; GYDS represents the total number of high-priority orders; and the GYDS is generated according to the order priority.

5. The intelligent optimization scheduling system based on multi-objective production according to claim 4 is characterized in that: The GYDS is generated by order priority, including: Get the order priority DY corresponding to several order IDs j ; Prioritize your order j Order IDs greater than the priority threshold are marked as high priority orders; GYDS is obtained by summing the quantities corresponding to the order IDs labeled as high priority orders.

6. The intelligent optimization scheduling system based on multi-objective production according to claim 2 is characterized in that: The dynamic weight coefficient is dynamically adjusted according to production data, including: Obtain production data in real time; Inputting production data into a weight estimation model to obtain a number of estimated dynamic weights; the weight estimation model is constructed by a machine learning model; The dynamic weight coefficients are updated by assigning several estimated dynamic weights to their corresponding dynamic weight coefficients.

7. The intelligent optimization scheduling system based on multi-objective production according to claim 6 is characterized in that: The weight estimation model is constructed through a machine learning model, including: Obtaining some historical production data and some corresponding dynamic weights; Divide a number of historical production data and their corresponding dynamic weights into training data, verification data and test data; perform data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set; Select a machine learning model as the base model; Train the basic model with the training set, and adjust the learning rate and hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally get the input production data and output a weight estimation model that estimates several dynamic weights.

8. The intelligent optimization scheduling system based on multi-objective production according to claim 1 is characterized in that: The generating of the scheduling plan according to the product comprehensive function includes: Obtain production data and product comprehensive functions in real time; The production data and product comprehensive function are combined through the deep reinforcement learning model to obtain the optimal solution of the product comprehensive function, and then generate a scheduling plan based on it.

9. The intelligent optimization scheduling system based on multi-objective production according to claim 1 is characterized in that: The generating of an alarm signal according to the equipment data comprises: Obtaining device data and several order priorities; the device data includes a device ID and historical device working data; the historical device working data includes historical device working hours and the working environment temperature and working environment humidity corresponding to the working hours; By formula Calculate the damage index SSZ of several equipment k ; Among them, β4 and β5 are weight coefficients, β4 and β5∈(0,1); g is the proportional coefficient, g∈(0,π / 2); LS km W is the historical working time of the kth device in the mth time period. km and S km They represent the working temperature and humidity of the kth device in the mth time period in history respectively; ZW and ZS represent the optimum working temperature and humidity; DW is the unit temperature, DS is the unit humidity, and DGT is the unit working time; Determine the equipment damage index SSZ k Is it greater than the damage threshold? If yes, generate an equipment scrap alarm signal; if no, determine the equipment damage index SSZ k Is it greater than D times the damage threshold? If yes, generate an equipment maintenance warning signal; if no, do nothing; where D is the proportionality coefficient, D∈(0,1); Determine whether the order priority is greater than the emergency processing threshold; if so, generate an order emergency processing prompt signal; if not, do nothing.

10. An intelligent optimization scheduling method based on multi-objective production, applied to an intelligent optimization scheduling system based on multi-objective production according to any one of claims 1 to 9, characterized in that: include: S0: Acquire production data in real time; the production data includes product data, equipment data, order data and inventory data; S1: Build product comprehensive function based on product data, equipment data, order data and inventory data; S2: Generate a scheduling plan based on the product comprehensive function; Carry out production scheduling according to the scheduling plan; S3: Generate an alarm signal based on the equipment data; make a prompt based on the alarm signal and contact the management personnel.

Images