Customer order intelligent production scheduling system based on multi-source data analysis

Abstract

The invention provides a customer order intelligent production scheduling system based on multi-source data analysis, a product production line is used for producing various products, the product production line comprises a central control device and M production devices, the customer order intelligent production scheduling system is carried in the central control device, and the customer order intelligent production scheduling system comprises a data acquisition module used for acquiring order production scheduling multi-source data, the order scheduling multi-source data comprises N pieces of scheduling order data; the data processing module is used for carrying out preprocessing and feature extraction on the order production scheduling multi-source data and generating input data for optimizing production scheduling; and the intelligent production scheduling module is used for constructing an intelligent production scheduling model to carry out optimization solution, determining an order production scheduling queue, and controlling a product production line to carry out product production based on the order production scheduling queue. By introducing the reusability of production equipment, parallel production scheduling of multiple order queues is realized on the basis of multi-source data, so that the work efficiency of the production equipment is improved while order task allocation is ensured to be completed, and energy conservation and cost reduction are realized while production scheduling is optimized.

Description

Technical Field

[0001] The present application relates to the field of intelligent order scheduling, and specifically, to an intelligent scheduling system for customer orders based on multi-source data analysis. Background Art

[0002] With the deepening of globalization and digitalization, the market competition faced by enterprises is becoming increasingly fierce. How to efficiently and accurately meet customer order requirements has become an important manifestation of corporate competitiveness. Order scheduling is the core link of production management, and its optimization is directly related to production efficiency, resource utilization and customer satisfaction. However, the complexity, diversity and dynamics of the modern production environment pose severe challenges to traditional scheduling methods. In today's manufacturing environment, customer orders are usually manifested as multiple product types and complex workloads. This complexity is mainly reflected in the diversity of order requirements and the heterogeneity of production equipment. For example, the order requirements of different customers include significant differences in product types, production quantities and delivery cycles; the equipment in the production system usually has different performances, which makes equipment load distribution more complicated.

[0003] Traditional scheduling methods, such as first-in-first-out rules or heuristic methods based on fixed allocation, perform well in single-task types or simple production environments, but show obvious limitations in complex and dynamic production environments. Faced with the needs of modern production systems, new methods are urgently needed to adapt to system status changes in real time and optimize scheduling plans.

[0004] Applying multi-source data analysis to order scheduling is an important direction of scheduling optimization research. Through data-driven methods, the real-time status of the production system can be dynamically obtained and the model parameters can be updated to reflect the dynamic changes of the system. This combined method has achieved initial results in some studies, but its application is still limited to specific scenarios, such as the optimization of a single factory or a single-stage production system. For more complex production systems, further development and optimization are still needed. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide an intelligent scheduling system for customer orders based on multi-source data analysis. By introducing the reusability of production equipment, parallel scheduling of multiple order queues is realized based on multi-source data, so as to improve the work efficiency of production equipment while ensuring the completion of order task allocation, optimize production scheduling and achieve energy saving and cost reduction.

[0006] In order to achieve the above purpose, the embodiments of the present application are implemented in the following manner:

[0007] In a first aspect, an embodiment of the present application provides a customer order intelligent scheduling system based on multi-source data analysis, wherein the product production line is used to produce a variety of products, and includes a central control device and M production equipment, each of which is a dedicated device or a shared device, wherein the dedicated equipment only participates in a single production process of the product, and the shared equipment can participate in multiple production processes of a variety of products, and the customer order intelligent scheduling system is mounted in the central control device, including: a data acquisition module, used to acquire multi-source data for order scheduling, the multi-source data for order scheduling includes N scheduling order data, each scheduling order data includes product type, product quantity, order deadline, and production equipment and production process required for product production, and each scheduling order data is used for the production of a single product; a data processing module, used to pre-process and extract features of the multi-source data for order scheduling, and generate input data for optimizing scheduling; an intelligent scheduling module, used to construct an intelligent scheduling model based on the input data for optimization and solution, determine the order scheduling queue, and control the product production line for product production based on the order scheduling queue.

[0008] In combination with the first aspect, in a first possible implementation method of the first aspect, the data processing module is specifically used to: preprocess multi-source data of order scheduling; perform feature extraction on each production scheduling order data after preprocessing, and extract order features and production features of each production scheduling order data, the order features reflect the product production requirements for completing the production scheduling order data, and the production features reflect the production equipment, product processes, process sequence and production time required to complete the production scheduling order data; integrate the order features and production features of each production scheduling order data, and construct an order feature matrix as input data for optimizing scheduling.

[0009] In combination with the first possible implementation method of the first aspect, in the second possible implementation method of the first aspect, the production process includes the product process, process sequence and production time of the production equipment corresponding to the product type, and the data processing module is specifically used to: for each pre-processed production order data: extract the order number, product type, product quantity, and order deadline in this production order data, and generate a one-dimensional vector with a length of S as an order feature; perform one-hot encoding on the production equipment corresponding to the product type in this production order data, and expand the attributes corresponding to each production equipment in the one-hot encoding feature vector, and the extended attributes include product process, process sequence and production time, and complete the extended one-hot encoding feature vector based on the product process, process sequence and production time of the production equipment to obtain a one-dimensional vector with a length of 4M as a production feature; splice the order features and the production features to obtain the integrated features of each production order data; combine the integrated features of N production order data into an order feature matrix of (4M+S)×N.

[0010] In combination with the first possible implementation method of the first aspect, in a third possible implementation method of the first aspect, the intelligent scheduling module is specifically used to: obtain the set production efficiency matrix of M production equipment within a unit time period; define the production task allocation function of each production equipment based on the order feature matrix and the set production efficiency matrix of each production equipment; construct the objective function to be optimized based on the production task allocation function; generate constraints based on the order feature matrix and the set production efficiency matrix of each production equipment; optimize and solve the objective function based on the constraints to obtain the order scheduling queue.

[0011] In combination with the third possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, the set production efficiency matrix of the M production equipment in a unit time period is:

[0012]

[0013] Among them, w 11 It represents the set production efficiency of the first production equipment for the first product process in the set of all product processes within a unit time period. M is the total number of production equipment, and V is the total number of processes of all product types supported by the product production line.

[0014] In combination with the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner of the first aspect, specifically for: setting the production efficiency of the k-th production equipment to w k =[w k1 ,w k2 ,…,w ki ,…,w kV ],w ki Define the production task allocation function δ of the kth production equipment as the set production efficiency of the ith product process in the set of all product processes within a unit time period k for:

[0015] δ k =[δ k1 ,δ k2 ,…,δ ki ,…,δ kV ],

[0016] Among them, δ ki It is the proportion of the set production efficiency of the kth production equipment when the i-th product process in the set of all product processes is allocated to the k-th production equipment within a unit time period.

[0017] In combination with the fifth possible implementation manner of the first aspect, in a sixth possible implementation manner of the first aspect, the intelligent production scheduling module is specifically used to: based on the order feature matrix, for the jth production scheduling order data: based on the start time node of the unit time period corresponding to the product process with the smallest process sequence in which the order number of the jth production scheduling order data first appears, determine the order start node ts of the jth production scheduling order data j , and, based on the end time node of the unit time period corresponding to the product process with the largest process sequence when completing the product quantity of the order number of the jth production order data, determine the order end node te of the jth production order data j ; The objective function to be optimized is constructed as:

[0018] minimizeF=A+B+C,

[0019]

[0020] Among them, F is the objective function to be optimized, A, B, and C are the three components of function F, M is the number of production equipment, and δ ki is the proportion of the set production efficiency of the kth production equipment when the i-th product process in the set of all product processes is allocated to the k-th production equipment in a unit time period, V is the total number of processes of all product types supported by the product production line, is the production efficiency of the kth production equipment in the lth effective unit period, T k is the total number of valid unit time periods of the kth production equipment. The valid unit time period indicates the unit time period from the time when the production equipment participates in the production of the products of these N production order data to the time when the production of the products of these N production order data ends. k is the efficiency weight value of the kth production equipment, N is the total number of production order data, ts j is the order start node of the jth production order data, te j is the order end node of the jth production order data, β is the adjustment factor, T is the total time to complete the production of N production order data under single order production, t j is the order deadline of the jth production order data, tz j is the order entry time of the jth production order data, γ j is the timely weight value of the j-th production scheduling order data.

[0021] In combination with the sixth possible implementation manner of the first aspect, in a seventh possible implementation manner of the first aspect, the intelligent production scheduling module is specifically used to generate the following constraints based on the order feature matrix and the set production efficiency matrix of each production equipment:

[0022] Equipment efficiency constraints:

[0023] Production time constraints:

[0024] In combination with the seventh possible implementation of the first aspect, in an eighth possible implementation of the first aspect, corresponding to the product processes of the same order number, the product process i of the later process sequence is produced by the production equipment x={x1,…,x p}, p∈[1,k] participate in production, and the product process r of the previous process sequence is produced by the production equipment y={y1,…,y q},q∈[1,k] participates in production, the intelligent scheduling module is also used to: generate product process constraints:

[0025]

[0026] in, is the number of production processes i allocated to production equipment x in a unit period b Time production equipment x b The ratio of production efficiency is set. For production equipment x b Set the production efficiency of product process i in unit time period, is the production efficiency of product process i in the lth effective unit period, is the number of production processes r assigned to production equipment y in a unit period c Time production equipment c The ratio of production efficiency is set. For production equipment c The production efficiency of product process r in a unit time period is set. is the sum of the production efficiency of product process r in the first (l-1) effective unit time periods, It is the sum of the production efficiency of product process i in the first (l-1) effective unit time periods.

[0027] In combination with the third possible implementation manner of the first aspect, in a ninth possible implementation manner of the first aspect, a genetic algorithm or a particle swarm algorithm is used to optimize and solve the objective function.

[0028] Beneficial effects:

[0029] 1. In this solution, the product production line is used to produce multiple products, including a central control device and M production equipment. Each production equipment is a dedicated device or a shared device. The dedicated equipment only participates in a single production process of the product, and the shared equipment can participate in multiple production processes of multiple products. The customer order intelligent scheduling system is installed in the central control device, including: a data acquisition module, used to obtain multi-source data for order scheduling, the multi-source data for order scheduling includes N scheduling order data, each scheduling order data includes product type, product quantity, order deadline and production equipment and production process required for product production, and each scheduling order data is used for the production of a single product (through preliminary processing, the customer's multi-product order is split into a single product order to distinguish different product types, quantities, deadlines, etc.); a data processing module, used to pre-process and extract features of the multi-source data for order scheduling, and generate input data for optimizing scheduling; an intelligent scheduling module, used to build an intelligent scheduling model based on the input data for optimization and solution, determine the order scheduling queue, and control the product production line for product production based on the order scheduling queue. By introducing the reusability of production equipment, parallel scheduling of multiple order queues is realized based on multi-source data. The introduction of multi-source data analysis can obtain the status of the production system in real time, and dynamically adjust the scheduling plan based on these data, so as to better adapt to the complexity, diversity and dynamics of the modern production environment. By building an intelligent scheduling model, it is possible to comprehensively consider factors such as order demand, production equipment performance and production process, and optimize the allocation of production tasks. This not only helps to ensure that orders are completed on time, but also improves the work efficiency of production equipment, reduces resource waste, and achieves energy saving and cost reduction. The system can handle customer orders containing multiple product types and complex workloads. Through parallel scheduling, the overall order processing cycle is effectively shortened and customer satisfaction is improved. The system not only supports dedicated equipment, but also makes full use of shared equipment to participate in multiple production processes of multiple products, thereby improving the flexibility and responsiveness of the production line.

[0030] 2. When preprocessing and extracting features from multi-source data for order scheduling, the system not only extracts basic information about the order (such as order number, product type, product quantity, order deadline), but also goes deep into the production level and extracts production features related to production equipment, product process, process sequence, and production time. This comprehensive feature extraction method helps the system to understand order requirements and production conditions more accurately, providing a solid foundation for subsequent optimization and scheduling. Processing the input data into an order feature matrix can ensure the consistency of the input data (N data can be changed, (3M+S) remains constant), and can represent a large amount of order data in an efficient and compact way, which is not only convenient for subsequent calculations and analysis, but also helps to improve the operating efficiency and response speed of the system. Based on this matrix, the system can use advanced optimization algorithms (such as genetic algorithms, particle swarm algorithms, etc.) to find the optimal scheduling plan. This data-driven optimization method can fully consider various constraints (such as equipment efficiency, production time, etc.) to ensure the effectiveness and feasibility of the scheduling plan.

[0031] 3. By setting the production efficiency matrix for M production equipment in a unit time period, the system can accurately reflect the production efficiency of each production equipment in different product processes. This helps the system to fully consider the actual capacity of the production equipment when allocating production tasks and ensure the rationality of the allocation. Setting the production efficiency matrix also makes it easier for the system to monitor and optimize the performance of production equipment, thereby improving overall production efficiency. Based on the order feature matrix and the set production efficiency matrix of each production equipment, the production task allocation function is defined (including three parts of the optimization function. By minimizing the three components A, B, and C, the system can comprehensively consider multiple factors such as production efficiency, production time, and order deadline, so as to find the optimal production scheduling plan. Part A in the objective function takes into account the efficiency balance of the production equipment, which helps to avoid equipment overload or idleness; Part B considers the minimization of production time, which helps to reduce production costs; Part C considers the timely delivery of orders, which helps to improve customer satisfaction. In this way, factors such as the production efficiency of the production equipment, the task processing time of each order scheduling data, and the completion cycle of the entire order scheduling task can be comprehensively considered. Since the supply can be supplemented according to the situation and is highly affected by external influences and human intervention, it is directly assumed that the supply is sufficient and not considered. On the production line of the fully automatic process, the raw material supply can also be taken into consideration to jointly construct the production task allocation function), so that the system can flexibly adjust the production task allocation according to the actual situation, which helps to respond quickly to customer needs. The production task allocation function can also ensure the balanced distribution of production tasks among multiple production equipment, avoid overloading or idleness of some equipment, thereby improving resource utilization, and can take into account the continuity between the various processes of the task as much as possible, shorten the production cycle of each product, and avoid the situation where a large number of process parts are accumulated.

[0032] 4. Equipment efficiency constraints ensure that the production task allocation does not exceed the actual capacity of the production equipment, avoiding the risk of equipment overload and damage. Production time constraints ensure that the start and end times of the order are consistent with the actual production process, avoiding production delays and order backlogs. Product process constraints ensure the order and continuity of product processes with the same order number in the production process, which helps to ensure product quality and production efficiency. Based on the constraints, the objective function is optimized and solved, and the system can quickly find the optimal production scheduling plan. This helps enterprises improve production efficiency and reduce production costs. The optimization solution process can also consider multiple constraints and objective functions to generate a production scheduling plan that is more in line with actual needs. Through the order scheduling queue obtained by optimization, the system can guide the specific task allocation and time arrangement in the production process. This helps enterprises to realize the visualization and controllability of the production process and improve the level of refinement of production management. The production scheduling queue can also serve as an important basis for production planning, helping enterprises to reasonably arrange production resources and inventory, and reduce production risks and costs.

[0033] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 A schematic diagram of an intelligent scheduling system for customer orders based on multi-source data analysis provided in an embodiment of the present application.

[0036] Icons: 10-Customer order intelligent scheduling system; 11-Data acquisition module; 12-Data processing module; 13-Intelligent scheduling module; 20-Production equipment; 21-Special equipment; 22-Shared equipment; 30-Central control device. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0038] See also Figure 1 , Figure 1A schematic diagram of a customer order intelligent production scheduling system based on multi-source data analysis provided in an embodiment of the present application. In this embodiment, the product production line is used to produce a variety of products, including a central control device and M production equipment, each of which is a dedicated device or a shared device, and the dedicated device only participates in a single production process of the product (i.e., only processes one product process), and the shared device can participate in multiple production processes of multiple products (i.e., can process multiple product processes, generally processing product processes of multiple different products, rather than different product processes of the same product), and the customer order intelligent production scheduling system is mounted in the central control device.

[0039] The customer order intelligent scheduling system includes a data acquisition module, a data processing module and an intelligent scheduling module.

[0040] The data acquisition module is used to obtain multi-source data for order scheduling. The multi-source data for order scheduling includes N pieces of scheduling order data. Each piece of scheduling order data includes product type, product quantity, order deadline, and production equipment and production process required for product production. Each piece of scheduling order data is used for the production of a single product.

[0041] In this embodiment, the customer order can be a single product order or a complex customer order containing multiple products and corresponding delivery time. In order to deal with complex customer orders, it is necessary to pre-process such customer orders and decompose the customer orders into single product orders. For example, if a customer order requires product A, 20,000 pieces, and a delivery time before December 31, 2024, requires product B, 50,000 pieces, and a delivery time before December 31, 2024, and requires product C, 100,000 pieces, and a delivery time before March 31, 2025, it can be split into three single product orders. Each single product order can form a production scheduling order data (including product type, product quantity, order deadline, and production equipment and production process required for product production) and enter it into the system. The system can obtain order production differentially according to the deadline, for example, according to the production schedule divided by week (or day) for scheduling, obtain multi-source data of order scheduling that needs to be completed in a week, so as to carry out task planning.

[0042] Based on this, the data acquisition module can acquire multi-source data of order scheduling, and the multi-source data of order scheduling includes N pieces of scheduling order data.

[0043] Afterwards, the data processing module can preprocess and extract features from multi-source order scheduling data to generate input data for optimizing scheduling.

[0044] Exemplarily, the data processing module may pre-process the multi-source data of order scheduling, such as deduplication, outlier processing, etc., to ensure that each production order data in the multi-source data of order scheduling remains accurate and reliable.

[0045] After preprocessing, the data processing module can perform feature extraction on each preprocessed production order data, and extract the order characteristics and production characteristics of each production order data. The order characteristics reflect the product production requirements for completing the production order data, and the production characteristics reflect the production equipment, product processes, process sequence and production time required to complete the production order data.

[0046] Specifically, for each production order data after preprocessing:

[0047] The data processing module can extract the order number, product type, product quantity, and order deadline from the production order data to generate a one-dimensional vector of length S as the order feature. For example, the order number occupies 1 dimension, the product type occupies 1 dimension, the product quantity occupies 1 dimension, the order deadline occupies 1 dimension, and the order entry time occupies 1 dimension. Then, the order feature is a one-dimensional vector of length 5.

[0048] The data processing module can perform one-hot encoding on the production equipment corresponding to the product type in this production order data (for example, the entire production line has 46 production equipment that can participate in product production) (the corresponding value of the production equipment that needs to participate in product production is 1, and the corresponding value of the production equipment that does not need to participate in product production is 0), and expand the attributes corresponding to each production equipment in the one-hot encoded feature vector (each original dimension is expanded to 4 dimensions). The extended attributes include product process (the processing required to complete the production of the product), process sequence (used to indicate the order of the product process in the production of products of this product type) and production time (the time it takes to complete a single product in this product process), and the extended one-hot encoded feature vector is completed based on the product process, process sequence and production time of the production equipment to obtain a one-dimensional vector with a length of 4M as a production feature.

[0049] Afterwards, the data processing module can concatenate the order features and production features to obtain the integrated features of each production scheduling order data (a one-dimensional vector with a length of (4M+S)), and then combine the integrated features of N production scheduling order data into an order feature matrix of (4M+S)×N. In this way, the input data for optimizing production scheduling can be obtained.

[0050] After obtaining the input data, the intelligent production scheduling module can build an intelligent production scheduling model based on the input data for optimization and solution, determine the order scheduling queue, and control the product production line for product production based on the order scheduling queue.

[0051] For example, the intelligent production scheduling module can obtain the set production efficiency matrix of M production equipment in a unit time period. The set production efficiency matrix of M production equipment in a unit time period (pre-set, set according to the efficiency of the product process processed by each production equipment, for example, the efficiency of a certain production equipment in a certain product process is to complete the processing of 15 products in each unit time period) is:

[0052]

[0053] Among them, w 11 It represents the set production efficiency of the first production equipment for the first product process in the set of all product processes within a unit time period, M is the total number of production equipment, and V is the total number of processes of all product types supported by the product production line (for example, 278 product processes).

[0054] After obtaining the set production efficiency matrix of M production equipment in a unit time period, the intelligent production scheduling module can define the production task allocation function of each production equipment based on the order feature matrix and the set production efficiency matrix of each production equipment.

[0055] Exemplarily, the intelligent production scheduling module can determine the set production efficiency parameters of each production equipment from the set production efficiency matrix. For example, the set production efficiency of the kth production equipment is:

[0056] w k =[w k1 ,w k2 ,…,w ki ,…,w kV ], (2)

[0057] Among them, w ki Set the production efficiency of the k-th production equipment for the i-th product process in the set of all product processes within a unit time period.

[0058] Based on this, the intelligent production scheduling module can define the production task allocation function δ of the kth production equipment: k for:

[0059] δ k =[δ k1 ,δ k2 ,…,δ ki ,…,δ kV ], (3)

[0060] Among them, δ ki It is the proportion of the set production efficiency of the kth production equipment when the i-th product process in the set of all product processes is allocated to the k-th production equipment within a unit time period.

[0061] After determining the production task allocation function, the intelligent production scheduling module can construct the objective function to be optimized based on the production task allocation function.

[0062] Exemplarily, based on the order feature matrix, for the j-th production scheduling order data:

[0063] The intelligent production scheduling module can determine the order start node ts of the jth production scheduling order data based on the start time node of the unit time period corresponding to the product process with the smallest process sequence that appears for the first time when the order number of the jth production scheduling order data appears. j , and, based on the end time node of the unit time period corresponding to the product process with the largest process sequence when completing the product quantity of the order number of the jth production order data, determine the order end node te of the jth production order data j .

[0064] Then, the objective function to be optimized is constructed as:

[0065] minimize F=A+B+C, (4)

[0066]

[0067] Where minimize means to minimize the function, F is the objective function to be optimized, A, B, and C are the three components of function F, M is the number of production equipment, and δ ki is the proportion of the set production efficiency of the kth production equipment when the i-th product process in the set of all product processes is allocated to the k-th production equipment in a unit time period, V is the total number of processes of all product types supported by the product production line, is the production efficiency of the kth production equipment in the lth effective unit period, T k is the total number of valid unit time periods of the kth production equipment. The valid unit time period indicates the unit time period from the time when the production equipment participates in the production of the products of these N production order data to the time when the production of the products of these N production order data ends. k is the efficiency weight value of the kth production equipment (used to measure the efficiency weights of different production equipment. The greater the power consumption and the more obvious the energy-saving effect of improving efficiency on the equipment, the greater the weight), N is the total number of production order data, ts j is the order start node of the jth production order data, te j is the order end node of the jth production order data, β is the adjustment factor (used to be set according to the production cycle of various product types of product production lines, so that part B occupies a reasonable weight. In this embodiment, β is between 0.1 and 0.7), T is the total time to complete the production of N production order data in the case of single order production, tj is the order deadline of the jth production order data, tz j is the order entry time of the jth production order data, γ j is the timely weight value of the jth production order data (it can measure the importance of the order. For example, if an order is urgent and important, an urgent order can be set so that the order is like the γ corresponding to the jth production order data). j The value is larger, for example, 10 times the weight of a regular order).

[0068] After constructing the objective function to be optimized, the intelligent scheduling module can generate constraints based on the order feature matrix and the set production efficiency matrix of each production equipment.

[0069] First, the intelligent production scheduling module can generate the following constraints based on the order feature matrix and the set production efficiency matrix of each production equipment:

[0070] Equipment efficiency constraints:

[0071]

[0072] Production time constraints:

[0073]

[0074] Equipment efficiency constraints are used to constrain the tasks assigned to production equipment so that they do not exceed their set production efficiency in each unit time period, while production time constraints are used to constrain the start and end times of product processes to avoid task overdue.

[0075] In addition, complex product process constraints need to be taken into account. For product processes with the same order number, the product process i of the next process order is produced by production equipment x = {x1,…,x p}, p∈[1,k] participate in production, and the product process r of the previous process sequence is produced by the production equipment y={y1,…,y q},q∈[1,k] participate in production, then the intelligent production scheduling module can generate product process constraints:

[0076]

[0077] in, is the number of production processes i allocated to production equipment x in a unit period b Time production equipment x b The ratio of setting production efficiency, For production equipment x b Set the production efficiency of product process i in unit time period, is the production efficiency of product process i in the lth effective unit period, is the number of production processes r assigned to production equipment y in a unit period c Time production equipment c The ratio of setting production efficiency, For production equipment c The production efficiency of product process r in a unit time period is set. is the sum of the production efficiency of product process r in the first (l-1) effective unit time periods, It is the sum of the production efficiency of product process i in the first (l-1) effective unit time periods.

[0078] Product process constraints are used to constrain the process sequence of product processes to avoid crossing process sequences.

[0079] Based on this, the intelligent production scheduling module can optimize and solve the objective function based on the constraints to obtain the order production scheduling queue. In this embodiment, this model is suitable for using a genetic algorithm (or a variant of a genetic algorithm) or a particle swarm algorithm to find the optimal solution, output the product process and task allocation of each production equipment in each unit time period, and thus obtain the order production scheduling queue.

[0080] Since the optimization algorithm process is not the focus of this solution, only the general process is introduced here. Taking the genetic algorithm as an example, when optimizing and solving the objective function in the customer order intelligent scheduling system based on multi-source data analysis, the specific process can be carried out according to the following steps:

[0081] (1) Initialize the population

[0082] Population size setting: First, determine the size of the population in the genetic algorithm, that is, the number of initial solutions.

[0083] Coding method: Select a suitable coding method to represent each solution (individual). In this embodiment, a real number coding method can be used to represent the production task allocation and product process information of each unit time period.

[0084] Initial population generation: Randomly generate an initial population that meets the constraints, and each individual represents a possible production scheduling plan.

[0085] (2) Fitness evaluation

[0086] Objective function calculation: For each individual, the corresponding objective function value is calculated based on the input data such as the order feature matrix and the production efficiency matrix set by the production equipment. The objective function value reflects the comprehensive performance of the production scheduling plan, including production efficiency, production time, order deadline and other aspects.

[0087] Fitness value determination: Determine the fitness value of each individual based on the objective function value. In genetic algorithms, the fitness value is used to measure the quality of an individual in a population, and is usually inversely proportional to the objective function value (i.e., the smaller the objective function value, the higher the fitness value).

[0088] (3) Select operation

[0089] Selection strategy: According to the fitness value of the individual, select some excellent individuals (individuals with high fitness values) as parents to generate the next generation.

[0090] (4) Crossover operation

[0091] Crossover method: Perform a crossover operation (such as two-point crossover) on the selected parent individuals to generate new individuals (offspring).

[0092] Crossover probability: Set a crossover probability to control the execution frequency of the crossover operation.

[0093] (5) Mutation Operation

[0094] Mutation method: Perform mutation operations (such as replacement) on offspring individuals to introduce new genetic information and increase the diversity of the population.

[0095] Mutation probability: Set a mutation probability to control the execution frequency of the mutation operation.

[0096] (6) Iterative Update

[0097] Generate a new generation of population: merge the offspring individuals obtained after selection, crossover and mutation operations with the elite individuals to form a new generation of population.

[0098] Termination condition judgment: Check whether the termination condition is met (such as reaching the maximum number of iterations, the fitness value reaches the preset threshold, etc.). If the termination condition is met, the optimal solution is output; otherwise, return to step 2 and continue iterative update.

[0099] (7) Output the optimal solution

[0100] Optimal solution decoding: Convert the optimal solution (individual) code into a specific production scheduling plan and output it to obtain an order production scheduling queue. This order production scheduling queue can be based on production equipment or product orders (i.e. production scheduling order data). This embodiment takes the former as an example, but is not limited to it.

[0101] In summary, the embodiment of the present application provides a customer order intelligent scheduling system based on multi-source data analysis. The product production line is used to produce multiple products, including a central control device and M production equipment. Each production equipment is a dedicated device or a shared device. The dedicated equipment only participates in a single production process of the product, and the shared equipment can participate in multiple production processes of multiple products. The customer order intelligent scheduling system is installed in the central control device, including: a data acquisition module, used to acquire multi-source data for order scheduling, the multi-source data for order scheduling includes N scheduling order data, each scheduling order data includes product type, product quantity, order deadline, and production equipment and production process required for product production, and each scheduling order data is used for the production of a single product (through preliminary processing, the customer's multi-product order is split into a single product order to distinguish different product types, quantities, deadlines, etc.); a data processing module, used to pre-process and extract features of the multi-source data for order scheduling, and generate input data for optimizing scheduling; an intelligent scheduling module, used to construct an intelligent scheduling model based on the input data for optimization and solution, determine the order scheduling queue, and control the product production line for product production based on the order scheduling queue. By introducing the reusability of production equipment, parallel scheduling of multiple order queues is realized based on multi-source data. The introduction of multi-source data analysis can obtain the status of the production system in real time, and dynamically adjust the scheduling plan based on these data, so as to better adapt to the complexity, diversity and dynamics of the modern production environment. By building an intelligent scheduling model, it is possible to comprehensively consider factors such as order demand, production equipment performance and production process, and optimize the allocation of production tasks. This not only helps to ensure that orders are completed on time, but also improves the work efficiency of production equipment, reduces resource waste, and achieves energy saving and cost reduction. The system can handle customer orders containing multiple product types and complex workloads. Through parallel scheduling, the overall order processing cycle is effectively shortened and customer satisfaction is improved. The system not only supports dedicated equipment, but also makes full use of shared equipment to participate in multiple production processes of multiple products, thereby improving the flexibility and responsiveness of the production line.

[0102] When preprocessing and extracting features from multi-source data for order scheduling, the system not only extracts basic information about the order (such as order number, product type, product quantity, order deadline), but also goes deep into the production level and extracts production features related to production equipment, product process, process sequence, and production time. This comprehensive feature extraction method helps the system understand order requirements and production conditions more accurately, providing a solid foundation for subsequent optimization and scheduling. Processing the input data into an order feature matrix can ensure the consistency of the input data (N data items are variable, (3M+S) remains constant), and can represent a large amount of order data in an efficient and compact way, which is not only convenient for subsequent calculations and analysis, but also helps to improve the operating efficiency and response speed of the system. Based on this matrix, the system can use advanced optimization algorithms (such as genetic algorithms, particle swarm algorithms, etc.) to find the optimal scheduling plan. This data-driven optimization method can fully consider various constraints (such as equipment efficiency, production time, etc.) to ensure the effectiveness and feasibility of the scheduling plan.

[0103] By setting the production efficiency matrix for M production equipment in a unit time period, the system can accurately reflect the production efficiency of each production equipment in different product processes. This helps the system to fully consider the actual capacity of the production equipment when allocating production tasks and ensure the rationality of the allocation. Setting the production efficiency matrix also makes it easier for the system to monitor and optimize the performance of production equipment, thereby improving overall production efficiency. Based on the order feature matrix and the set production efficiency matrix of each production equipment, the production task allocation function is defined (including three parts of the optimization function. By minimizing the three components A, B, and C, the system can comprehensively consider multiple factors such as production efficiency, production time, and order deadline, so as to find the optimal production scheduling plan. Part A in the objective function takes into account the efficiency balance of the production equipment, which helps to avoid equipment overload or idleness; Part B considers the minimization of production time, which helps to reduce production costs; Part C considers the timely delivery of orders, which helps to improve customer satisfaction. In this way, factors such as the production efficiency of the production equipment, the task processing time of each order scheduling data, and the completion cycle of the entire order scheduling task can be comprehensively considered. Since the supply can be supplemented according to the situation and is highly affected by external influences and human intervention, it is directly assumed that the supply is sufficient and not considered. On the production line of the fully automatic process, the raw material supply can also be taken into consideration to jointly construct the production task allocation function), so that the system can flexibly adjust the production task allocation according to the actual situation, which helps to respond quickly to customer needs. The production task allocation function can also ensure the balanced distribution of production tasks among multiple production equipment, avoid overloading or idleness of some equipment, thereby improving resource utilization, and can take into account the continuity between the various processes of the task as much as possible, shorten the production cycle of each product, and avoid the situation where a large number of process parts are accumulated.

[0104] Equipment efficiency constraints ensure that the production task allocation does not exceed the actual capacity of the production equipment, avoiding the risk of equipment overload and damage. Production time constraints ensure that the start and end times of the order are consistent with the actual production process, avoiding production delays and order backlogs. Product process constraints ensure the order and continuity of product processes with the same order number in the production process, which helps to ensure product quality and production efficiency. Based on the constraints, the system can quickly find the optimal production scheduling plan by optimizing the objective function. This helps enterprises improve production efficiency and reduce production costs. The optimization solution process can also consider multiple constraints and objective functions to generate a production scheduling plan that better meets actual needs. Through the order scheduling queue obtained by optimization, the system can guide the specific task allocation and time arrangement in the production process. This helps enterprises to realize the visualization and controllability of the production process and improve the refinement level of production management. The production scheduling queue can also serve as an important basis for production planning, helping enterprises to reasonably arrange production resources and inventory, and reduce production risks and costs.

[0105] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0106] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A customer order intelligent production scheduling system based on multi-source data analysis, characterized in that: The product production line is used to produce a variety of products, including a central control device and M production equipment. Each production equipment is a dedicated device or a shared device. Dedicated equipment only participates in a single production process of a product, and shared equipment can participate in multiple production processes of multiple products. The customer order intelligent scheduling system is installed in the central control device, including: The data acquisition module is used to acquire multi-source data of order scheduling. The multi-source data of order scheduling includes N pieces of scheduling order data. Each piece of scheduling order data includes product type, product quantity, order deadline, and production equipment and production process required for product production. Each piece of scheduling order data is used for the production of a single product. The data processing module is used to preprocess and extract features from multi-source data of order scheduling and generate input data for optimizing scheduling; The intelligent production scheduling module is used to build an intelligent production scheduling model based on input data for optimization and solution, determine the order scheduling queue, and control the product production line for product production based on the order scheduling queue.

2. The customer order intelligent production scheduling system based on multi-source data analysis according to claim 1 is characterized in that: Data processing module, specifically used for: Pre-process multi-source data for order scheduling; Perform feature extraction on each preprocessed production order data to extract order features and production features of each production order data. The order features reflect the product production requirements for completing the production order data, and the production features reflect the production equipment, product process and production time required to complete the production order data. Integrate the order characteristics and production characteristics of each production scheduling order data, and construct an order characteristic matrix as input data for optimizing production scheduling.

3. The customer order intelligent production scheduling system based on multi-source data analysis according to claim 2 is characterized in that: The production process includes the product process, process sequence and production time of the production equipment corresponding to the product type, and the data processing module is specifically used for: For each production order data after preprocessing: Extract the order number, product type, product quantity, and order deadline from the production scheduling order data to generate a one-dimensional vector of length S as the order feature; Perform one-hot encoding on the production equipment corresponding to the product type in this production order data, and expand the attributes corresponding to each production equipment in the one-hot encoding feature vector. The extended attributes include product process, process sequence and production time. The extended one-hot encoding feature vector is completed based on the product process, process sequence and production time of the production equipment to obtain a one-dimensional vector with a length of 4M as the production feature; Combine order features and production features to obtain the integrated features of each production order data; The integrated features of N production scheduling order data are combined into an order feature matrix of (4M+S)×N.

4. The customer order intelligent production scheduling system based on multi-source data analysis according to claim 2 is characterized in that: Intelligent production scheduling module, specifically used for: Obtain the set production efficiency matrix of M production equipment in a unit time period; Based on the order feature matrix and the set production efficiency matrix of each production equipment, define the production task allocation function of each production equipment; Based on the production task allocation function, construct the objective function to be optimized; Generate constraints based on the order feature matrix and the set production efficiency matrix of each production equipment; The objective function is optimized and solved based on the constraints to obtain the order scheduling queue.

5. The customer order intelligent production scheduling system based on multi-source data analysis according to claim 4 is characterized in that: The set production efficiency matrix of M production equipment in a unit time period is: Among them, w 11 It represents the set production efficiency of the first production equipment for the first product process in the set of all product processes within a unit time period. M is the total number of production equipment, and V is the total number of processes of all product types supported by the product production line.

6. The customer order intelligent production scheduling system based on multi-source data analysis according to claim 5 is characterized in that: Intelligent production scheduling module, specifically used for: The set production efficiency of the kth production equipment is w k =[w k1 ,w k2 ,…,w ki ,…,w kV ],w ki Define the production task allocation function δ of the kth production equipment as the set production efficiency of the ith product process in the set of all product processes within a unit time period k for: d k =[δ k1 ,d k2 ,…,d ki ,…,d kV ], Among them, δ ki It is the proportion of the set production efficiency of the kth production equipment when the i-th product process in the set of all product processes is allocated to the k-th production equipment within a unit time period.

7. The customer order intelligent production scheduling system based on multi-source data analysis according to claim 6 is characterized in that: Intelligent production scheduling module, specifically used for: Based on the order feature matrix, for the j-th production order data: Based on the start time node of the unit time period corresponding to the product process with the smallest process sequence when the order number of the j-th production scheduling order data first appears, determine the order start node ts of the j-th production scheduling order data j , and, based on the end time node of the unit time period corresponding to the product process with the largest process sequence when completing the product quantity of the order number of the jth production order data, determine the order end node te of the jth production order data j ; The objective function to be optimized is constructed as: CF=A+B+C, Among them, F is the objective function to be optimized, A, B, and C are the three components of function F, M is the number of production equipment, and δ ki is the proportion of the set production efficiency of the kth production equipment when the i-th product process in the set of all product processes is allocated to the k-th production equipment in a unit time period, V is the total number of processes of all product types supported by the product production line, is the production efficiency of the kth production equipment in the lth effective unit period, T k is the total number of valid unit time periods of the kth production equipment. The valid unit time period indicates the unit time period from the time when the production equipment participates in the production of the products of these N production order data to the time when the production of the products of these N production order data ends. k is the efficiency weight value of the kth production equipment, N is the total number of production order data, ts j is the order start node of the jth production order data, te j is the order end node of the jth production order data, β is the adjustment factor, T is the total time to complete the production of N production order data under single order production, t j is the order deadline of the jth production order data, tz j is the order entry time of the jth production order data, γ j is the timely weight value of the j-th production scheduling order data.

8. The customer order intelligent production scheduling system based on multi-source data analysis according to claim 7 is characterized in that: Intelligent production scheduling module, specifically used for: Based on the order feature matrix and the set production efficiency matrix of each production equipment, the following constraints are generated: Equipment efficiency constraints: Production time constraints:

9. The customer order intelligent production scheduling system based on multi-source data analysis according to claim 8 is characterized in that: For the product process with the same order number, the product process i of the next process order is produced by the production equipment x={x1,…,x p }, p∈[1,k] participate in production, and the product process r of the previous process sequence is produced by the production equipment y={y1,…,y q },q∈[1,k] participates in production, intelligent scheduling module, and is also used for: Generate product process constraints: in, is the number of production processes i allocated to production equipment x in a unit period b Time production equipment x b The ratio of production efficiency is set. For production equipment x b Set the production efficiency of product process i in unit time period, is the production efficiency of product process i in the lth effective unit period, is the number of production processes r assigned to production equipment y in a unit period c Time production equipment c The ratio of production efficiency is set. For production equipment c The production efficiency of product process r in a unit time period is set. is the sum of the production efficiency of product process r in the first (l-1) effective unit time periods, It is the sum of the production efficiency of product process i in the first (l-1) effective unit time periods.

10. The customer order intelligent production scheduling system based on multi-source data analysis according to claim 4 is characterized in that: Genetic algorithm or particle swarm algorithm is used to optimize the objective function.

Images