MES system-based intelligent storage area management and control method and system

Abstract

The invention mainly relates to an intelligent storage area management and control method based on an MES system. The method comprises the steps that material consumption data and production progress data of a production line are acquired based on the MES system; the production progress data comprises the current progress and the predicted completion time of each work order; determining a real-time production material demand based on the material consumption data and the production progress data; based on the real-time production material demand and the production plan, determining a material storage scheme of an intelligent storage area; wherein the target production plan comprises starting time and ending time of a work order; determining a target production material carrying path based on the material storage scheme and the real-time production material demand; and based on the target production material carrying path, carrying the materials to production lines corresponding to the various materials by utilizing carrying equipment.

Description

Technical Field

[0001] This application relates to the technical field of warehouse management, and particularly to an intelligent warehouse area control method, system, device and storage medium based on the MES system. Background Art

[0002] With the development of industry and intelligent manufacturing, technologies such as the Internet of Things, cloud computing, and big data have gradually been applied to warehouse management systems. Traditional warehouse management methods are inefficient, prone to errors, and difficult to meet the high-efficiency, precision, and intelligent requirements of modern manufacturing. As a key technology bridging the enterprise's upper-layer ERP system and the underlying automated production equipment, the MES system can achieve comprehensive management and control of the production process. However, there is currently a lack of an effective method for deeply integrating the MES system and intelligent warehouse management in the market.

[0003] In view of this, this application provides an intelligent warehouse area control method, system, device and storage medium based on the MES system. Summary of the Invention

[0004] This application provides an intelligent warehouse area control method, device and storage medium based on the MES system, aiming to provide an intelligent warehouse area control method based on the MES system. By combining the MES system with intelligent warehouse equipment, efficient, precise, and intelligent management of the warehouse area is achieved, thereby improving warehouse management efficiency, reducing manual participation, and lowering operating costs.

[0005] In a first aspect, this application provides an intelligent warehouse area control method based on the MES system, and the method includes:

[0006] Obtaining the material consumption data and production progress data of the production line based on the MES system; the production progress data includes the current progress and the estimated completion time of each work order;

[0007] Determining the real-time production material requirements based on the material consumption data and the production progress data;

[0008] Determining the material storage plan for the intelligent warehouse area based on the real-time production material requirements and the production plan; wherein, the target production plan includes the start time and end time of the work order;

[0009] Determining the target production material handling path based on the material storage plan and the real-time production material requirements;

[0010] Based on the target production material handling path, using handling equipment to transport materials to the production lines corresponding to various materials.

[0011] In some embodiments, the method further includes:

[0012] Based on the production order progress information and the production plan, determine the predicted change in material requirements within a future time period;

[0013] Based on the predicted change in material requirements, update the real-time production material requirements;

[0014] Based on the updated real-time production material requirements and the production plan, determine the material storage plan for the intelligent warehousing area.

[0015] In some embodiments, the determining the material storage plan for the intelligent warehousing area based on the real-time production material requirements and the production plan includes:

[0016] Based on the real-time production material requirements, the production plan, and the production progress data, use a genetic algorithm to determine the material storage plan for the intelligent warehousing area.

[0017] In some embodiments, the using a genetic algorithm to determine the material storage plan for the intelligent warehousing area based on the real-time production material requirements, the production plan, and the production progress data includes:

[0018] Based on the real-time production material requirements, the production plan, and the production progress data, initialize a set of material storage plans; the set of material storage plans includes multiple material storage plans;

[0019] Use a fitness function to calculate a first score value for each material storage plan;

[0020] Based on the first score value of each material storage plan, determine the current candidate plan group; the current candidate plan group includes at least one material storage plan;

[0021] Perform crossover and / or mutation on the material storage plans within the current candidate plan group to obtain a first candidate plan group;

[0022] Use the fitness function to calculate a second score value for each material storage plan within the first candidate plan group;

[0023] Based on the second score value of each material storage plan, update the current candidate plan group, and iteratively execute the above steps until a preset condition is met;

[0024] When a first preset condition is met, determine the material storage plan corresponding to the maximum second score value as the material storage plan for the intelligent warehousing area.

[0025] In some embodiments, the fitness function is:

[0026]

[0027] Among them, f(p i , s) is the score value calculated by the fitness function, Pi is the storage scheme of the i-th material, s represents the current iteration number, N is the number of material types in the candidate scheme group, d(m, s) is the distance between material m and the production line, t1(m, s) is the handling time required for material m, α is an adjustment coefficient used to balance the weights of distance and demand change, Δq m (t2) represents the consumption change of material m at the current time t2, which comes from the material consumption data.

[0028] In some embodiments, determining the target production material handling path based on the material storage scheme and the real-time production material demand includes:

[0029] Obtain the device status data of the handling equipment;

[0030] Based on the material storage scheme and the device status data, initialize the handling path graph; among them, the pheromone concentration of each path in the initialized handling path graph is the initial value;

[0031] Based on the initialized handling path graph, obtain the selection probability of each handling path;

[0032] Based on the real-time production material demand and the path length of each handling path, update the pheromone concentration; in the pheromone concentration update, the pheromone volatilization is negatively correlated with time, the pheromone increment is negatively correlated with the path length, and is positively correlated with the real-time production material demand;

[0033] Iteratively execute the above path selection and pheromone concentration update steps until the second preset condition is met;

[0034] Based on the selection probability of each handling path in the last iteration, determine the target production material handling path.

[0035] In some embodiments, the selection probability of each handling path is obtained through the following formula:

[0036]

[0037] Among them, P ij (t3) represents the selection probability of each handling path, τ ij (t3) is the pheromone concentration on path ij, d ij is the path length of path ij, α1 and β are weight parameters used to control the relative importance of pheromone and path distance, D m (t3) is the real-time production demand of material m corresponding to path ij, and λ is a coefficient used to control the influence of real-time production material demand.

[0038] In a second aspect, the present application provides an intelligent warehouse area control system based on an MES system, and the system includes:

[0039] An acquisition module, configured to acquire material consumption data and production progress data of a production line based on the MES system; the production progress data includes the current progress and the estimated completion time of each work order;

[0040] A first determination module, configured to determine real-time production material requirements based on the material consumption data and the production progress data;

[0041] A second determination module, configured to determine a material storage plan for the intelligent warehouse area based on the real-time production material requirements and a production plan; wherein, the target production plan includes the start time and the end time of the work order;

[0042] A third determination module, configured to determine a target production material handling path based on the material storage plan and the real-time production material requirements;

[0043] A handling module, configured to transport materials to the production lines corresponding to various types of materials by using handling equipment based on the target production material handling path.

[0044] In a third aspect, an electronic device is provided, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus;

[0045] The memory is used to store a computer program;

[0046] The processor, when executing the program stored on the memory, implements the steps of the intelligent warehouse area control method based on the MES system according to any one of the embodiments in the first aspect.

[0047] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the intelligent warehouse area control method based on the MES system according to any one of the embodiments in the first aspect are implemented.

[0048] The above technical solution provided by the embodiments of the present application has the following advantages compared with the prior art: By comprehensively analyzing the material storage plan, real-time production material requirements, and equipment status data, the handling path can be automatically optimized. The path selection not only considers the material requirements and path distance but also combines the operating status of the equipment, thus avoiding unnecessary detours or congestion. The intelligent path selection can significantly improve the material handling efficiency inside the warehouse, reducing the handling time and cost. At the same time, it can also dynamically adjust the handling path according to the real-time production material requirements, enabling the warehouse handling system to make timely adjustments according to the changes in production line requirements, thereby avoiding resource waste or delays caused by fluctuations in production demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present invention and, together with the specification, are used to explain the principles of the present invention.

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is a schematic flowchart of a method for controlling an intelligent warehousing area based on an MES system provided by an embodiment of the present application;

[0052] Figure 2 It is a schematic flowchart of a process for determining a material storage plan provided by an embodiment of the present application;

[0053] Figure 3 It is a schematic flowchart of a process for determining a target production material handling path provided by an embodiment of the present application;

[0054] Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0056] Figure 1The flowchart shows a method for controlling an intelligent warehousing area based on an MES system provided by an embodiment of the present application. In some embodiments, Figure 1 The process 100 shown can be executed by an electronic device with computing capabilities. For example, it can be executed by a computer device with an MES system or a warehouse management system. As Figure 1 shown, the process 100 may include the following operations.

[0057] Step 101: Obtain the material consumption data and production progress data of the production line based on the MES system.

[0058] The production progress data includes the current progress and the estimated completion time of each work order.

[0059] The MES system (Manufacturing Execution System) is a management platform that connects production plans and actual production, used to track and control the production process in real time and collect various data on the production line. For example, the MES system can collect information such as the working status of the production line, equipment operation, and material consumption through sensors and barcode scanners.

[0060] Material consumption data refers to information such as the quantity, type, and consumption time of raw materials, components, semi-finished products, etc. used in each process during the production process. For example, if a production line produces mobile phone cases and 200 grams of plastic raw materials are consumed for every 100 mobile phone cases produced, the material consumption data may include information such as "mobile phone case production line, 200 grams of plastic consumed for 100 mobile phone cases".

[0061] Production progress data includes the actual production progress and the estimated completion time of each work order, used to reflect the status of current production activities. For example, the production progress data of a work order may be "Work order A, currently 70% completed, estimated completion time is March 10, 2025".

[0062] In some embodiments, relevant production line data is extracted from the MES system, including the material consumption records of each production line and the production progress of each work order. Through the database interface or real-time data transmission mechanism of the MES system, the real-time data of each production line is obtained.

[0063] Step 102: Determine the real-time production material requirements based on the material consumption data and the production progress data.

[0064] Real-time production material requirements refer to the quantity of materials required by each production work order at the current moment based on the current production progress and material consumption situation. For example, if the production progress data shows that work order A is 50% completed and the required materials are 100 units, then the real-time production material requirement is 50 units.

[0065] In some embodiments, the electronic device may calculate the material requirements for each work order based on the material consumption data and production progress data, and then deduce the actual quantity of materials required at the current moment.

[0066] Step 103: Determine the material storage plan for the intelligent storage area based on the real-time production material requirements and the production plan.

[0067] Among them, the target production plan includes the start time and end time of the work order.

[0068] The production plan refers to the overall arrangement of the production process, including the production start time, end time, and required materials for each work order. For example, the production plan may stipulate that "Work order A will start on March 1, 2025, end on March 5, 2025, and the required raw materials include 100 units of raw material X".

[0069] The material storage plan is a warehousing management plan determined based on the real-time material requirements and the production plan, including the storage location, storage quantity, and storage order of the materials, etc. For example, the material storage plan may stipulate that "Raw material X is stored in area A of the warehouse, and the storage quantity is 200 units". Another example, if a production plan requires 100 units of raw material A and 200 units of raw material B, the material storage plan may stipulate that raw material A is stored in area A and raw material B is stored in area B.

[0070] In some embodiments, the storage requirements and the best storage locations of each material in the storage area may be calculated based on the real-time production material requirements and the production plan to obtain the material storage plan.

[0071] In some embodiments, the electronic device may determine the material storage plan through the operations shown in the following embodiments.

[0072] S10: Determine the predicted change in material requirements within a future time period based on the production work order progress information and the production plan.

[0073] The production work order progress information refers to the current progress status of the work order during the production process, including the work order completion percentage, the time used, the remaining time, etc. For example, the progress information of work order A may be "70% completed, and the estimated completion time is March 10, 2025".

[0074] The production plan refers to the overall arrangement of production activities, including the production start time, end time, and quantity of required materials for the work order, etc. For example, the production plan may stipulate that "Work order B will start on March 1, 2025, end on March 5, 2025, and the required raw materials include 100 units of raw material X".

[0075] Predicting changes in material requirements refers to predicting the changes in material requirements over a period of time in the future based on the current production progress information and production plan, including increases or decreases in material requirements. For example, if the progress of a work order lags behind, it may lead to a need for more materials in the future; if the work order is completed ahead of schedule, it may lead to a decrease in demand.

[0076] In some embodiments, the changes in material requirements in the future can be predicted by analyzing the progress information of production work orders and the production plan.

[0077] In some embodiments, AI algorithms can also be used. For example, the progress information of production work orders and the production plan are input into a machine learning model to obtain the predicted changes in material requirements.

[0078] S11. Update the real-time production material requirements based on the predicted changes in material requirements.

[0079] Updating material requirements means adjusting the current real-time material requirements according to the predicted changes in material requirements to ensure the matching of material supply and production demand. For example, if the prediction shows that an additional 20 units of raw materials are needed for a work order in the future, the real-time production material requirements can be increased by 20 units accordingly.

[0080] In some embodiments, the real-time material requirements are dynamically adjusted and updated according to the predicted changes in material requirements.

[0081] S12. Determine the material storage plan for the intelligent warehousing area based on the updated real-time production material requirements and the production plan.

[0082] The updated real-time production material requirements refer to the real-time production material requirements updated according to the predicted changes in material requirements. The updated real-time production material requirements are dynamic and continuously adjusted according to the production progress, work order progress, and predicted changes.

[0083] In some embodiments, the material storage plan for the intelligent warehousing area can be re-evaluated and determined according to the updated real-time production material requirements and the production plan, including adjustments to the types, quantities, storage locations, and storage orders of materials.

[0084] In some embodiments, determining the material storage plan for the intelligent warehousing area based on the real-time production material requirements and the production plan may further include: using a genetic algorithm to determine the material storage plan for the intelligent warehousing area based on the real-time production material requirements, the production plan, and the production progress data.

[0085] The genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanisms. Through operations such as population reproduction, selection, and mutation, it gradually approaches the optimal solution. In the determination of the material storage plan, the genetic algorithm can be used to search for the optimal storage plan, considering multiple variables such as storage space, material requirements, production schedule, etc. The genetic algorithm can select the optimal solution from multiple initial material storage plans. For example, the genetic algorithm can optimize the storage layout of materials according to material requirements, storage area space, and storage order.

[0086] For a detailed description of using the genetic algorithm to determine the material storage plan, please refer to the following text Figure 2 description.

[0087] Step 104, based on the material storage plan and the real-time production material requirements, determine the target production material handling path.

[0088] The material handling path refers to the path that needs to be passed from the material storage location in the warehouse area to the production line. The selection of the path involves factors such as the warehouse area, transportation equipment, path selection, etc. For example, the handling path from warehouse area A to production line B can be A area -> C area -> B production line.

[0089] The target production material handling path refers to the optimal handling path planned for each material based on the production plan, material requirements, and storage plan in the warehouse area. For example, in order to avoid congestion and delays, the target production material handling path may stipulate that raw material X is transported from warehouse area A to production line 1.

[0090] In some embodiments, the electronic device can plan the optimal handling path according to the real-time material requirements, material storage plan, and production line location. For example, path optimization algorithms (such as ant colony algorithm or genetic algorithm, etc.) can be used to determine the shortest or optimal path.

[0091] In some embodiments, the electronic device can use the ant colony algorithm to determine the optimal handling path. For a detailed description of using the ant colony algorithm to determine the optimal handling path, please refer to Figure 3 and its description.

[0092] Step 105, based on the target production material handling path, use the handling equipment to transport the materials to the production lines corresponding to various materials.

[0093] The handling equipment refers to the mechanical equipment used to transport materials between the warehouse and the production line, such as automatic guided vehicle (AGV), conveyor belt, forklift, etc. The automatic guided vehicle can automatically drive in the warehouse and transport the materials from the storage area to the production line.

[0094] In some embodiments, according to the target production material handling path, handling equipment can be scheduled to perform material handling tasks between different storage areas and production lines, so as to transport the material storage area to the production line.

[0095] Figure 2 It is a schematic flowchart of a process for determining a material storage plan provided by an embodiment of the present application. In some embodiments, Figure 2 The process 200 shown can be executed by an electronic device. As Figure 2 shown, the process 200 may include the following operations.

[0096] Step 201, initialize a set of material storage plans based on the real-time production material requirements, the production plan, and the production progress data.

[0097] The set of material storage plans includes multiple material storage plans.

[0098] In some embodiments, the electronic device can generate multiple material storage plans according to the real-time production material requirements, the production plan, and the production progress data. Each material storage plan contains information such as different material storage locations and storage quantities.

[0099] Initialization means that these plans can be randomly generated or generated according to certain basic rules (such as storing materials with large demand in easily accessible locations first).

[0100] Step 202, calculate the first score value of each material storage plan using a fitness function.

[0101] The fitness function is a standard for measuring the quality of a material storage plan. In a genetic algorithm, the "score" of each storage plan is calculated through the fitness function, and this score determines the probability of the plan being selected during the selection and evolution process. For example, if a storage plan scores high in terms of warehouse space utilization and material access efficiency, its fitness score will also be high.

[0102] In some embodiments, the electronic device can apply the fitness function to each material storage plan to calculate its first score value.

[0103] Exemplarily, the fitness function can be shown as the following formula (1).

[0104]

[0105] Where f(p i, s) is the score value calculated by the fitness function, Pi is the storage scheme of the i-th material, s represents the current iteration number, N is the number of material types in the candidate solution group, d(m, s) is the distance between material m and the production line, t1(m, s) is the handling time required for material m, α is an adjustment coefficient used to balance the weights of distance and demand change, Δq m (t2) represents the consumption change of material m at the current time t2, which comes from the material consumption data.

[0106] Step 203: Determine the current candidate solution group based on the first score values of each material storage scheme.

[0107] The current candidate solution group includes at least one material storage scheme.

[0108] The candidate solution group refers to a set of several material storage schemes selected according to the fitness function in a certain generation of the genetic algorithm. These schemes represent the current optimization direction of the genetic algorithm. For example, assume that after one selection and crossover operation in the genetic algorithm, 3 candidate storage schemes are generated, and these three candidate storage schemes can be used for further evaluation and optimization.

[0109] The first score value and the second score value are scores calculated by the fitness function and are used to evaluate the quality of the material storage scheme. The first score value is calculated for the preliminary candidate solution group, while the second score value is calculated for the new generation of solutions after crossover and mutation operations.

[0110] In some embodiments, the electronic device can select material storage schemes with higher fitness according to the first score value to form the current candidate solution group. The selected schemes can be based on the ranking, proportion or random method of the scores. For example, assume that the fitness of Scheme 1 is 70, the fitness of Scheme 2 is 60, and the fitness of Scheme 3 is 85. Select Scheme 1 and Scheme 3 with higher fitness to enter the current candidate solution group.

[0111] Step 204: Perform crossover and / or mutation on the material storage schemes in the current candidate solution group to obtain the first candidate solution group.

[0112] In some embodiments, the electronic device can use the crossover operation (swap the material storage positions of Scheme 1 and Scheme 3) and / or the mutation operation (randomly adjust the storage positions) to generate new candidate solutions.

[0113] The crossover operation can generate new combinations, and the mutation operation can produce new layouts.

[0114] For example, crossover: Material X of Scheme 1 is stored in the front row of Area A, and Material Y is stored in the back row of Area B; Material X of Scheme 3 is stored in Area C, and Material Y is stored in Area D. After crossover, Scheme 4 may be that Material X is stored in Area A and Material Y is stored in Area D.

[0115] Another example, mutation: Scheme 2 may mutate to Material X being stored in Area A and Material Y being stored in Area C.

[0116] Step 205, use the fitness function to calculate the second score value of each material storage scheme in the first candidate scheme group.

[0117] For example, if the fitness score of the initial storage scheme A is 80 (the first score value), and after crossover and mutation, the generated new scheme B has a score of 90 (the second score value), then Scheme B is better in the current generation.

[0118] Step 206, update the current candidate scheme group based on the second score value of each material storage scheme, and iteratively execute the above steps until a preset condition is met.

[0119] According to the principle of the genetic algorithm, operations such as crossover, mutation, and fitness evaluation will continue to be iteratively executed. Each iteration will generate a new candidate scheme group and calculate a new score value.

[0120] After each iteration, select the scheme with a higher fitness value for the next round of crossover and mutation. Each round of iteration can continue to optimize the material storage scheme based on the updated scheme.

[0121] For example, assume that after several iterations, there are schemes A, B, C, and D in the current candidate scheme group, and their fitness scores are 80, 85, 90, and 92 respectively. After the next round of iteration, the new schemes may be Scheme D and Scheme E, with fitness values of 95 and 93. Since Scheme D has the highest fitness, Scheme D will continue to participate in the optimization in the next round.

[0122] The preset condition can be that the number of iterations reaches a preset number, such as 100 times, 50 times, 1000 times, etc., or the fitness value reaches a certain threshold, such as the score value reaches 98 points, 99 points, etc.

[0123] Step 207, when the first preset condition is met, determine the material storage scheme corresponding to the largest second score value as the material storage scheme of the intelligent storage area.

[0124] The electronic device can apply the material storage scheme finally determined by the genetic algorithm to the intelligent storage area and adjust the material storage location and method according to this scheme.

[0125] Figure 3This is a schematic flowchart for determining the handling path of target production materials provided by an embodiment of the present application. In some embodiments, Figure 3 the process shown can be executed by an electronic device. As Figure 3 shown, process 300 may include the following operations.

[0126] Step 301, obtain the device status data of the handling device.

[0127] The device status data refers to the real-time operating status of the handling device, including parameters such as the health status of the device (whether it is faulty), the current load (whether it is full or empty), the location (the specific location of the device in the warehouse), the battery power, and the working hours.

[0128] In some embodiments, the status data of all current handling devices can be obtained through a warehouse management system (WMS) or a device monitoring system.

[0129] Step 302, initialize the handling path graph based on the material storage plan and the device status data.

[0130] Among them, the pheromone concentration of each path in the initialized handling path graph is set to an initial value.

[0131] The handling path graph is a graphical structure that describes the feasible paths between the starting point and the target point of the handling device in the warehouse and their related information. Each path in the path graph represents a feasible route in the warehouse, and the weight (or distance, time, cost, etc.) of the path is an important part of the path graph.

[0132] For example, in the path graph of the warehouse, there may be two paths between area A and area B: one is a longer but unobstructed path, and the other is a shorter path but passing through some crowded areas.

[0133] In some embodiments, the storage locations in the material storage plan and the device status data can be used to initialize the handling path graph inside the warehouse. When initializing, each path of the path graph will be assigned an initial "pheromone concentration". The initial value of the pheromone concentration is usually set to a unified standard value, indicating the "importance" or "attractiveness" of the path. In the path graph, the connection relationship of the paths and the initial pheromone concentration of the paths will reflect the current operating status of the device and the warehouse layout.

[0134] Step 303, obtain the selection probability of each handling path based on the initialized handling path graph.

[0135] The selection probability refers to the probability that a certain path is selected in each path selection process. The selection probability can be calculated based on factors such as the "pheromone concentration" of the path and the "distance" or "cost" of the path. For example, assuming that the initial pheromone concentration of path 1 is 0.8 and the concentration of path 2 is 0.2, then when selecting a path, the selection probability of path 1 may be higher.

[0136] For each handling path, according to the initialized path map, calculate the selection probability of each path. The selection probability is proportional to the "pheromone concentration" of the path and the "distance" or "transportation cost" of the path. Paths with shorter lengths or lower costs will have a higher selection probability. By calculating the selection probability of each path, it is determined which path the device should select for material handling in a specific task.

[0137] In some embodiments, the selection probability of each handling path can be obtained through the following formula (2):

[0138]

[0139] Where, P ij (t3) represents the selection probability of each handling path, τ ij (t3) is the pheromone concentration on path ij, d ij is the path length of path ij, α1 and β are weight parameters used to control the relative importance of pheromone and path distance, D m (t3) is the real-time production demand of material m corresponding to path ij, and λ is a coefficient used to control the influence of real-time production material demand.

[0140] Step 304, update the pheromone concentration based on the real-time production material demand and the path lengths of each handling path.

[0141] In the update of the pheromone concentration, pheromone evaporation is negatively correlated with time, the pheromone increment is negatively correlated with the path length, and is positively correlated with the real-time production material demand.

[0142] Adjust the "pheromone concentration" of each path according to the real-time production material demand. If a certain path needs to handle a large amount of materials or has a shorter path length, the pheromone concentration of this path will increase, otherwise it will decrease.

[0143] Pheromone evaporation is negatively correlated with time: As time goes by, the pheromone concentration on the path will gradually decrease to reflect the reduced usage frequency of the path.

[0144] The pheromone increment is negatively correlated with the path length: The increment of the shorter path is larger.

[0145] The pheromone increment is positively correlated with the real-time production material demand: the greater the demand, the greater the pheromone increment of the selected path.

[0146] Step 305, iteratively execute the above path selection and pheromone concentration update steps until the second preset condition is met.

[0147] The second preset condition is the stopping condition in the ant colony algorithm, which can be reaching a certain number of iterations or the pheromone concentration reaching a certain threshold. For example, assume that the number of iterations reaches 50 times, or the change in the selection probability of a certain path is less than a certain preset threshold (such as 0.01).

[0148] In each iteration, execute the path selection and pheromone concentration update steps until the second preset condition is met.

[0149] Step 306, determine the target production material handling path based on the selection probabilities of each handling path in the last iteration.

[0150] The target production material handling path refers to, after several rounds of iteration, determining the final optimal path as the actual execution route for material handling according to the selection probabilities of each handling path. This path not only considers the real-time demand of the materials but also the efficiency of the path (such as time, distance, cost, etc.). For example, assume there are two handling paths: Path 1 and Path 2, the selection probability of Path 1 is 0.7, and the selection probability of Path 2 is 0.3. Finally, Path 1 can be selected as the target production material handling path.

[0151] After all iterations are completed, the target production material handling path can be determined according to the selection probabilities of each path. The path with the highest selection probability is selected as the target path.

[0152] After that, tasks can be assigned to the handling equipment according to the selected path, and the actual material handling operation can start.

[0153] Some embodiments of the present application also provide an intelligent warehouse area control system based on the MES system. The system includes:

[0154] An acquisition module, configured to acquire the material consumption data and production progress data of the production line based on the MES system; the production progress data includes the current progress and the estimated completion time of each work order;

[0155] A first determination module, configured to determine the real-time production material demand based on the material consumption data and the production progress data;

[0156] A second determination module, configured to determine the material storage plan for the intelligent warehouse area based on the real-time production material demand and the production plan; wherein, the target production plan includes the start time and end time of the work order;

[0157] A third determination module, configured to determine a target production material handling path based on the material storage plan and the real-time production material requirement;

[0158] A handling module, configured to, based on the target production material handling path, use a handling device to transport materials to production lines corresponding to various types of materials.

[0159] As Figure 4 shown, an embodiment of the present application provides an electronic device, including a processor 111, a communication interface 112, a memory 113, and a communication bus 114. Among them, the processor 111, the communication interface 112, and the memory 113 complete mutual communication through the communication bus 114.

[0160] The memory 113 is used to store a computer program;

[0161] In an embodiment of the present application, when the processor 111 is used to execute the program stored on the memory 113, it implements the method for intelligent warehouse area control based on the MES system provided by any one of the foregoing method embodiments, including:

[0162] Obtaining material consumption data and production progress data of a production line based on the MES system; the production progress data includes the current progress and the estimated completion time of each work order;

[0163] Determining the real-time production material requirement based on the material consumption data and the production progress data;

[0164] Determining a material storage plan for the intelligent warehouse area based on the real-time production material requirement and the production plan; wherein, the target production plan includes the start time and the end time of the work order;

[0165] Determining a target production material handling path based on the material storage plan and the real-time production material requirement;

[0166] Based on the target production material handling path, using a handling device to transport materials to production lines corresponding to various types of materials.

[0167] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method for intelligent warehouse area control based on the MES system provided by any one of the foregoing method embodiments.

[0168] It should be noted that, in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0169] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather should be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. An intelligent storage area management and control method based on MES system, characterized in that: The method comprises: Obtaining material consumption data and production progress data of the production line based on the MES system; the production progress data includes the current progress and estimated completion time of each work order; Determining real-time production material requirements based on the material consumption data and the production progress data; Based on the real-time production material demand and production plan, determine the material storage plan of the intelligent storage area; wherein the target production plan includes the start time and end time of the work order; Determine a target production material transportation path based on the material storage plan and the real-time production material demand; Based on the target production material handling path, materials are transported to production lines corresponding to various types of materials using handling equipment.

2. The method according to claim 1, characterized in that The method further comprises: Determining predicted material demand changes in a future time period based on the production work order progress information and the production plan; Based on the predicted material demand changes, updating the real-time production material demand; Based on the updated real-time production material demand and the production plan, a material storage plan for the intelligent storage area is determined.

3. The method according to claim 1, characterized in that Determining a material storage plan for the intelligent storage area based on the real-time production material demand and production plan includes: Based on the real-time production material demand, the production plan and the production progress data, a material storage plan for the intelligent storage area is determined using a genetic algorithm.

4. The method according to claim 3, characterized in that The method of determining the material storage plan of the intelligent storage area by using a genetic algorithm based on the real-time production material demand, the production plan and the production progress data includes: Initializing a set of material storage plans based on the real-time production material demand, the production plan and the production progress data; the set of material storage plans includes multiple material storage plans; Calculate the first score value of each material storage plan using the fitness function; Based on the first score value of each material storage solution, determine a current candidate solution group; the current candidate solution group includes at least one material storage solution; Crossover and / or mutation of the material storage solutions in the current candidate solution group are performed to obtain a first candidate solution group; Calculating a second score value of each material storage solution in the first candidate solution group using the fitness function; Based on the second score value of each material storage solution, the current candidate solution group is updated, and the above steps are iteratively performed until a preset condition is met; When the first preset condition is met, the material storage plan corresponding to the largest second score value is determined as the material storage plan of the intelligent storage area.

5. The method according to claim 4, characterized in that The fitness function is: Among them, f(p i ,s) is the score value calculated by the fitness function, Pi is the i-th material storage solution, s represents the current number of iterations, N is the number of material types in the candidate solution group, d(m,s) is the distance between material m and the production line, t1(m,s) is the time required for the transportation of material m, α is the adjustment coefficient, which is used to balance the weight of distance and demand transformation, Δq m (t2) represents the consumption change of material m at the current time t2, which comes from the material consumption data.

6. The method according to claim 1, characterized in that The determining of the target production material transportation path based on the material storage plan and the real-time production material demand includes: Obtain equipment status data of handling equipment; Initialize a transport path map based on the material storage plan and the equipment status data; wherein the pheromone concentration of each path in the initial transport path map is an initial value; Based on the initialized transport path graph, obtaining a selection probability of each transport path; Based on the real-time production material demand and the path length of each transport path, the pheromone concentration is updated; in the pheromone concentration update, pheromone volatilization is negatively correlated with time, pheromone increment is negatively correlated with path length, and positively correlated with the real-time production material demand; Iteratively executing the above path selection and pheromone concentration update steps until the second preset condition is met; Based on the selection probability of each transport path in the last iteration, the target production material transport path is determined.

7. The method according to claim 6, characterized in that The selection probability of each transport path is obtained by the following formula: Among them, P ij (t3) represents the selection probability of each transport path, τ ij (t3) is the pheromone concentration on path ij, d ij is the path length of path ij, α1 and β are weight parameters used to control the relative importance of pheromone and path distance, D m (t3) is the real-time production demand of material m corresponding to path ij, and λ is the coefficient used to control the impact of real-time production material demand.

8. An intelligent storage area management and control system based on MES system, characterized in that: The system comprises: An acquisition module is used to acquire material consumption data and production progress data of the production line based on the MES system; the production progress data includes the current progress and estimated completion time of each work order; A first determination module, configured to determine real-time production material requirements based on the material consumption data and the production progress data; A second determination module is used to determine a material storage plan for the intelligent storage area based on the real-time production material demand and the production plan; wherein the target production plan includes the start time and end time of the work order; A third determination module is used to determine a target production material transportation path based on the material storage plan and the real-time production material demand; The transport module is used to transport the materials to the production lines corresponding to the various types of materials using transport equipment based on the target production material transport path.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the steps of the intelligent storage area management and control method based on the MES system as described in any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the intelligent warehouse area management and control method based on the MES system as described in any one of claims 1 to 7 are implemented.

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