Control method and control system of double-sided intelligent material cabinet

Abstract

The invention discloses a control method and a control system for a double-sided intelligent material cabinet, and belongs to the technical field of intelligent material management.The control method comprises the steps of obtaining a predicted material demand quantity based on production scheduling plan information and equipment state information; comparing the predicted material demand quantity with the real-time material inventory information so as to selectively trigger a purchasing strategy; generating an LED matrix indication scheme for the material cabinet bins based on the material storage scheme; on the basis of a role access control model and in combination with a multi-mode authentication mode, a bin cabinet door is controlled to limit material receiving; obtaining current production scheduling information and current production information, performing time conflict judgment and material conflict judgment on the current production scheduling information and the current production information based on an optimization algorithm, and generating a conflict processing strategy based on a judgment result; the effects of accurately identifying conflict types and dynamically generating a grading strategy according to current information are achieved, so that deep cooperation of material management and production scheduling is promoted, and production continuity and resource utilization efficiency are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent material management, and specifically refers to a control method and control system for a double-sided intelligent material cabinet. Background Art

[0002] Traditional material management adopts a manual method, with cumbersome recording methods, low efficiency, great influence of human factors, low accuracy, easy occurrence of forged data, waste of human resources, and high management and maintenance costs. As a result, it is very difficult to ensure the correctness of receiving, inspection and shipping, thus generating inventory and delaying delivery, further increasing costs. Moreover, the manual management method cannot provide managers with real-time, fast and accurate warehouse operation and inventory information.

[0003] An intelligent access cabinet for material management is a device that combines Internet of Things (IoT) and automation technologies, and is used to store and manage various materials, tools, equipment or other resources. Different from the traditional material management method, the intelligent access cabinet can realize the automatic identification, tracking, monitoring and management of the material access process through intelligent technologies, ensuring the safe, accurate and efficient management of materials, and having an inventory material management method superior to the traditional material management method.

[0004] Although the inventory material management method adopted by the existing intelligent access cabinets is superior to the traditional material management method, there are still problems such as low demand prediction accuracy before material preparation and lag in response to conflicts between tasks and new production scheduling plans during production. Among them, the problem of production scheduling conflicts is particularly prominent. According to industry statistics, about 35% of production delays are due to conflicts between production scheduling plans and resources (such as equipment occupancy, material shortages). In the prior art, production scheduling conflicts are mostly handled by manual adjustment or simple rule judgment, and these simple handling methods have two major defects. One is that the conflict classification is rough, without distinguishing conflict types and without analyzing the characteristics of material types, resulting in poor strategy adaptability. The other is that the conflict resolution decision is isolated, that is, the conflict resolution is not linked with real-time data such as the status of production equipment and supplier capabilities, and it is difficult to achieve global optimization. Summary of the Invention

[0005] In order to solve the problems existing in the inventory material management method of the intelligent material cabinet mentioned in the above background art, the present invention provides a control method and control system for a double-sided intelligent material cabinet.

[0006] The first invention object of the present application is achieved through the following technical solutions:

[0007] A control method for a double-sided intelligent material cabinet includes the steps of:

[0008] Obtain production scheduling plan information, real-time material inventory information and equipment status information, input them into a trained demand prediction model, and obtain the predicted material demand;

[0009] Compare the predicted material demand and the real-time material inventory information, and selectively trigger the procurement strategy based on the comparison result;

[0010] Obtain the storage information of the material cabinet. Based on the predicted material demand and the storage information of the material cabinet, generate a material storage plan for the double-sided intelligent material cabinet, and generate an LED matrix indication plan for the storage locations based on the material storage plan;

[0011] Obtain the work order data information, and control the opening and closing status of the storage location cabinet doors of the double-sided intelligent material cabinet by combining the work order data information, the role access control model, and the multi-modal authentication method;

[0012] Obtain the current production scheduling information and the current production information in real time. Based on the optimization algorithm, perform time conflict judgment and material conflict judgment on the current production scheduling information and the current production information, and generate a conflict handling strategy based on the results of the time conflict judgment and the material conflict judgment.

[0013] By adopting the above solution, obtain the production scheduling plan information, the real-time material inventory information, and the equipment status information, input them into the trained demand prediction model to obtain the predicted material demand, obtain the real-time material cabinet storage information about the double-sided intelligent material cabinet, compare the predicted material demand and the real-time material inventory information, selectively trigger the procurement strategy based on the comparison result, generate a material storage plan for the double-sided intelligent material cabinet based on the predicted material demand and the material cabinet storage information, generate an LED matrix indication plan for the storage locations of the double-sided intelligent material cabinet based on the material storage plan, obtain the work order data information, and control the opening and closing status of the storage location cabinet doors based on the role access control model and the work order data information, combined with the multi-modal authentication method of face recognition verification and work permit NFC chip verification, so as to restrict the personnel who can receive materials, obtain the current production scheduling information and the current production information in real time, perform time conflict judgment and material conflict judgment on the current production scheduling information and the current production information based on the optimization algorithm, and generate a conflict handling strategy based on the results of the time conflict judgment and the material conflict judgment; Through comparing the predicted material demand and the real-time material inventory information in this application, advance material preparation is carried out, and by obtaining the current production scheduling information and the current production information in real time, performing time conflict judgment and material conflict judgment on the current production scheduling information and the current production information based on the optimization algorithm, and generating a conflict handling strategy based on the results of the time conflict judgment and the material conflict judgment, the conflict type can be accurately identified, the effect of dynamically generating a hierarchical strategy based on the current information can be achieved, so as to promote the deep coordination of material management and production scheduling, and ensure production continuity and resource utilization efficiency.

[0014] In a preferred example, the present application can be further configured as follows: The step of obtaining the production scheduling plan information, real-time material inventory information, and equipment status information, and inputting them into the trained demand prediction model to obtain the predicted material demand quantity includes the steps:

[0015] Obtain the production scheduling plan information and the equipment status information of the production equipment associated with the production scheduling plan information;

[0016] Input the equipment status information and the production scheduling plan information into the demand prediction model, so that the demand prediction model dynamically corrects the loss coefficient, and generates the predicted material demand quantity associated with the production scheduling plan information based on the loss coefficient.

[0017] By adopting the above solution, obtain the production scheduling plan information and the equipment status information of the production equipment associated with the production scheduling plan information, input the equipment status information and the production scheduling plan information into the demand prediction model, so that the demand prediction model dynamically corrects the loss coefficient, and generates the predicted material demand quantity associated with the production scheduling plan information based on the loss coefficient, ensuring that the materials meet the production requirements of the production scheduling plan and guaranteeing the continuity of production.

[0018] In a preferred example, the present application can be further configured as follows: The step of obtaining the work order data information and controlling the opening and closing status of the compartment cabinet door of the double-sided intelligent material cabinet in combination with the work order data information, role-based access control model, and multi-modal authentication method includes the steps:

[0019] Obtain the work order data information regarding the workshop production scheduling plan, and associate the employee information with the real-time inventory material information based on the work order data;

[0020] Based on the role-based access control model, allocate the material receiving permission in combination with the work order data information and the employee information;

[0021] Combined with the multi-modal authentication method of face recognition verification and work card NFC chip verification, determine whether the employee has the material receiving permission;

[0022] Based on the compartment control unit, control the opening and closing status of the compartment cabinet door, judge the operation behavior of the employee through image analysis, and trigger the video evidence collection of the material cabinet when there is a suspicious operation.

[0023] By adopting the above solution, work order data information regarding the workshop production scheduling plan is obtained, and employee information is associated with real-time inventory material information based on the work order data. Based on the role-based access control model, material collection permissions are assigned by combining the work order data information and employee information. By means of a multi-modal authentication method combining face recognition verification and work card NFC chip verification, it is judged whether an employee has the material collection permission. The opening and closing states of the storage cabinet doors are controlled based on the storage location control unit, and the operation behavior of the employee is judged through image analysis. When there is a suspicious operation, video evidence collection of the material cabinet is triggered, so as to realize the restriction of material collection, clearly record the material turnover and usage information, and avoid unclear losses caused by incorrect collection, over-collection, or missed collection of materials.

[0024] In a preferred example of the present application, it can be further configured as follows: The step of obtaining the current production scheduling information and the current production information in real time, judging the time conflict and material conflict between the current production scheduling information and the current production information based on an optimization algorithm, and generating a conflict handling strategy based on the results of the time conflict judgment and the material conflict judgment includes the steps of:

[0025] Obtain the current production scheduling information and the current production information in real time, compare the time intervals of the production cycles of the current production scheduling information and the current production information, and mark the time conflict types based on the time interval comparison results. The time conflict types include hard conflicts and soft conflicts;

[0026] Based on an optimization algorithm, judge the material conflict between the current production scheduling information and the current production information with time conflicts. When there is a material conflict, mark its material conflict type. The material conflict types include consumable material conflicts and durable material conflicts;

[0027] Group the current production scheduling information with both time conflicts and material conflicts based on the combination of conflict types, and generate corresponding conflict handling strategies based on the grouping results.

[0028] By adopting the above solution, the current production scheduling information and the current production information are obtained in real time, the time intervals of the production cycles of the current production scheduling information and the current production information are compared, and the time conflict types are marked based on the time interval comparison results. The time conflict types include hard conflicts and soft conflicts. Based on an optimization algorithm, judge the material conflict between the current production scheduling information and the current production information with time conflicts. When there is a material conflict, mark its material conflict type. The material conflict types include consumable material conflicts and durable material conflicts. Group the current production scheduling information with both time conflicts and material conflicts based on the combination of conflict types, and generate corresponding conflict handling strategies based on the grouping results, so as to realize the refined classification of production scheduling conflict problems, thereby adopting corresponding optimal solutions, and avoiding resource waste and reduced production efficiency caused by production scheduling plans conflicting with resources.

[0029] In a preferred example, the present application can be further configured as follows: The conflict handling strategy includes a material sharing handling strategy, a material substitution handling strategy, an emergency procurement handling strategy, and a loss prediction handling strategy. The step of dividing the current production scheduling information with both time conflicts and material conflicts into groups based on the combination of conflict types and generating corresponding conflict handling strategies based on the group division results includes the steps:

[0030] Compare the current production scheduling information with the current production information. When there are hard conflicts and consumable material conflicts, set it as a sharing handling group; when there are hard conflicts and durable material conflicts, set it as a substitution handling group; when there are soft conflicts and consumable material conflicts, set it as a procurement handling group; when there are soft conflicts and durable material conflicts, set it as a prediction handling group;

[0031] Generate a material sharing handling strategy for the current production scheduling information associated with the sharing handling group;

[0032] Generate a material substitution handling strategy for the current production scheduling information associated with the substitution handling group;

[0033] Generate an emergency procurement handling strategy for the current production scheduling information associated with the procurement handling group;

[0034] Generate a loss prediction handling strategy for the current production scheduling information associated with the prediction handling group.

[0035] By adopting the above solution, compare the current production scheduling information with the current production information. When there are hard conflicts and consumable material conflicts, set it as a sharing handling group; when there are hard conflicts and durable material conflicts, set it as a substitution handling group; when there are soft conflicts and consumable material conflicts, set it as a procurement handling group; when there are soft conflicts and durable material conflicts, set it as a prediction handling group. Generate a material sharing handling strategy for the current production scheduling information associated with the sharing handling group, generate a material substitution handling strategy for the current production scheduling information associated with the substitution handling group, and generate an emergency procurement handling strategy for the current production scheduling information associated with the procurement handling group, realizing accurate identification of conflict types and dynamically generating hierarchical strategies, thereby ensuring the deep coordination between material management and production scheduling and guaranteeing production continuity and resource utilization efficiency.

[0036] In a preferred example, the present application can be further configured as follows: A control method for a double-sided intelligent material cabinet further includes the steps:

[0037] Obtain the weight information and three-dimensional model information of the material after return for detection;

[0038] Feed the detection results back to the trained loss prediction model in real time for material loss comparison, and selectively trigger maintenance strategies, procurement strategies, and abnormal feedback strategies.

[0039] In a preferred example, the present application can be further configured as follows: The step of obtaining the weight information and three-dimensional model information of the returned materials for inspection includes the steps of:

[0040] Generating actual loss information based on the weight information and three-dimensional model information of the returned materials;

[0041] The step of real-time feedback of the detection result to the trained loss prediction model for material loss comparison and selectively triggering maintenance strategies, procurement strategies, and abnormal feedback strategies includes the steps of:

[0042] Obtaining historical material loss information, and inputting the historical material loss information and equipment status information into the pre-trained loss prediction model to generate predicted material loss information;

[0043] Overlapping comparison of the actual loss information and the predicted loss information through a pre-set comparison processing rule;

[0044] Selectively triggering maintenance strategies and procurement strategies based on the overlapping comparison result, and triggering an abnormal feedback strategy when there is abnormal loss.

[0045] By adopting the above solution, actual loss information is generated based on the weight information and three-dimensional model information of the returned materials, historical material loss information is obtained, and the historical material loss information and equipment status information are input into the pre-trained loss prediction model to generate predicted material loss information. The actual loss information and the predicted loss information are overlapped and compared through a pre-set comparison processing rule. Based on the overlapping comparison result, maintenance strategies and procurement strategies are selectively triggered, and when there is non-standard loss, an abnormal feedback strategy is triggered. It is realized to judge whether there is an abnormality in the production equipment through the loss comparison result after the material return, and to ensure that there are still enough materials for production in the subsequent production scheduling plan, avoiding the problem of increasing the material loss rate due to the unobvious abnormality of the production equipment.

[0046] In a preferred example, the present application can be further configured as follows: A control method for a double-sided intelligent material cabinet further includes the steps of:

[0047] Obtaining the material supply information of each supplier corresponding to the material;

[0048] Constructing a weighted scoring model based on pre-set decision rules and weight distribution rules;

[0049] Selecting the optimal supplier for procurement based on the material supply information through the weighted scoring model.

[0050] In a preferred example, the present application can be further configured as follows: The step of obtaining the material supply information of each supplier corresponding to the material includes the steps of:

[0051] Obtain the material supply information of each supplier corresponding to the material that triggers the procurement strategy, and the lead time information corresponding to this material;

[0052] The step of selecting the optimal supplier for procurement based on the material supply information through the weighted scoring model includes the steps:

[0053] Score the material supply information of each supplier through the weighted scoring model to obtain supplier scoring information;

[0054] Compare the lead time information with the material supply information, and based on the urgency of the procurement strategy, obtain the corresponding material urgency information;

[0055] Select the optimal supplier through the weighted scoring model based on the material urgency information and the supplier scoring information.

[0056] By adopting the above solution, obtain the material supply information of each supplier corresponding to the material that triggers the procurement strategy, and the lead time information corresponding to this material, construct a weighted scoring model based on the preset decision rules and weight allocation rules, and score the material supply information of each supplier through the weighted scoring model to obtain supplier scoring information, compare the lead time information with the material supply information, and determine whether an emergency procurement processing strategy is triggered to obtain the corresponding material urgency level, and select a supplier through the weighted scoring model based on the material urgency level and the supplier scoring information, solving the problems of long logistics time and poor material quality existing in the traditional selection of a single supplier to supply materials, and the weighted scoring model can adjust the weighting ratio based on the material urgency level to dynamically adapt to the urgency level of the material requirements of different production scheduling plans.

[0057] The above-mentioned second invention object of the present application is achieved through the following technical solutions:

[0058] A control system for a double-sided intelligent material cabinet, including:

[0059] A prediction demand module, used to obtain production scheduling plan information, real-time material inventory information and equipment status information, input them into the trained demand prediction model, and obtain the predicted material demand;

[0060] An inventory comparison module, used to compare the predicted material demand and the real-time material inventory information, and selectively trigger the procurement strategy based on the comparison result;

[0061] A bin indication scheme generation module, used to obtain the material cabinet storage information, generate a material storage scheme for the double-sided intelligent material cabinet based on the predicted material demand and the material cabinet storage information, and generate an LED matrix indication scheme for the bins based on the material storage scheme;

[0062] The position control module is used to obtain work order data information, and combine the work order data information, role-based access control model, and multi-modal authentication method to control the opening and closing status of the position cabinet doors of the double-sided intelligent material cabinet;

[0063] The conflict handling module is used to obtain the current production scheduling information and current production information in real time, judge the time conflict and material conflict between the current production scheduling information and the current production information based on the optimization algorithm, and generate a conflict handling strategy based on the results of the time conflict judgment and material conflict judgment;

[0064] The loss analysis module is used to obtain the weight information and three-dimensional model information of the returned materials, perform inspections, and feedback the inspection results to the trained loss prediction model in real time for material loss comparison, and selectively trigger maintenance strategies, procurement strategies, and exception feedback strategies;

[0065] The material procurement module is used to obtain the material supply information of each supplier corresponding to the materials, construct a weighted scoring model based on the pre-set decision rules and weight allocation rules, and select the optimal supplier for procurement based on the material supply information through the weighted scoring model.

[0066] In summary, a control method and control system for a double-sided intelligent material cabinet of the present application include at least one of the following beneficial technical effects:

[0067] 1. In the present application, based on the role-based access control model, combined with the multi-modal authentication method of face recognition verification and work card NFC chip verification, the opening and closing status of the position cabinet doors is controlled, thereby restricting the personnel who can receive materials. The time conflict and material conflict between the current production scheduling information and the current production information are judged based on the optimization algorithm, and a conflict handling strategy is generated based on the results of the time conflict judgment and material conflict judgment, realizing the accurate identification of conflict types and the effect of dynamically generating hierarchical strategies based on the current information, thereby promoting the deep coordination of material management and production scheduling, and ensuring production continuity and resource utilization efficiency. Description of the Drawings

[0068] Figure 1 It is a flowchart of an embodiment of a control method and control system for a double-sided intelligent material cabinet of the present application;

[0069] Figure 2 It is an implementation flowchart of step S40 in an embodiment of a control method and control system for a double-sided intelligent material cabinet of the present application;

[0070] Figure 3 It is an implementation flowchart of step S53 in an embodiment of a control method and control system for a double-sided intelligent material cabinet of the present application;

[0071] Figure 4This is a flowchart showing one implementation of step S60 in the control method and control system of a double-sided intelligent material cabinet according to this application;

[0072] Figure 5 This is a flowchart showing one implementation of step S70 in the control method and control system of a double-sided intelligent material cabinet according to this application. Detailed implementation manner

[0073] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0074] In one embodiment, as Figure 1 shown, this application discloses a control method for a double-sided intelligent material cabinet, which specifically includes the following steps:

[0075] S10: Obtain production scheduling plan information, real-time material inventory information, and equipment status information, input them into a trained demand prediction model, and obtain predicted material demand quantities;

[0076] In this embodiment, the production scheduling plan information is a material demand schedule including production elements such as process sequence, equipment allocation, and time nodes. The real-time inventory information is real-time material quantity, location, and status data such as available quantity, locked quantity, and batch number-related information. The equipment status information is the operating status, fault code, and energy consumption parameter operation data of the equipment. The demand prediction model is a machine learning model for predicting future material demands, and the predicted material demand quantities are the predicted material quantities and material type information required for production.

[0077] Specifically, obtain production scheduling plan information, real-time material inventory information, and equipment status information, input them into a trained demand prediction model, and obtain the predicted material quantities and material type information required for production.

[0078] S20: Compare the predicted material demand quantities with the real-time material inventory information, and selectively trigger a procurement strategy based on the comparison result;

[0079] In this embodiment, the real-time material inventory information is the actual quantity of the current inventory, including materials in transit and those retained in the warehouse.

[0080] Specifically, compare the predicted material demand quantities with the real-time material inventory information, and selectively trigger a procurement strategy based on the comparison result.

[0081] S30: Obtain the storage information of the material cabinet. Based on the predicted material demand and the storage information of the material cabinet, generate a material storage plan for the double-sided intelligent material cabinet, and generate an LED matrix indication plan for the storage positions based on the material storage plan;

[0082] In this embodiment, the storage information of the material cabinet is to record the physical states of each storage position of the double-sided intelligent material cabinet. The material storage plan is an arrangement plan for storing the materials corresponding to the predicted material demand in the intelligent material cabinet. The LED matrix indication plan is a plan for indicating the material position and status through ways such as light color and dynamic flashing.

[0083] Specifically, obtain the storage information of the material cabinet. Based on the predicted material demand and the storage information of the material cabinet, generate a material storage plan for the double-sided intelligent material cabinet, and generate an LED matrix indication plan for the storage positions based on the material storage plan. Improve the operation efficiency of workers through LED indication, realize dynamic inventory management, intelligent layout optimization and automated operation guidance, and improve efficiency and reduce human errors.

[0084] S40: Obtain the work order data information, and combine the work order data information, the role-based access control model, and the multi-modal authentication method to control the opening and closing states of the storage position cabinet doors of the double-sided intelligent material cabinet;

[0085] In this embodiment, the work order data information includes production order data such as production task instructions, process routes, bills of materials, and quality requirements. The role-based access control model is an access control mechanism for allocating permissions based on employee roles. The storage position cabinet door is the cabinet door set outside the storage position of the intelligent material cabinet.

[0086] Specifically, obtain the work order data information. Based on the role-based access control model, through the dual authentication methods of face recognition verification and work permit NFC chip verification, thereby control the opening and closing states of the storage position cabinet doors, and thus the material collection operations of workers.

[0087] S50: Real-time obtain the current production scheduling information and the current production information. Based on the optimization algorithm, perform time conflict judgment and material conflict judgment on the current production scheduling information and the current production information, and generate a conflict handling strategy based on the results of the time conflict judgment and the material conflict judgment.

[0088] In this embodiment, the optimization algorithm is an algorithm for solving production scheduling conflicts and resource allocation problems. The time conflict is a contradiction generated during the execution of the production plan due to overlapping or closely connected time arrangements of different processes, tasks, or equipment. The material conflict is a conflict caused by unreasonable material allocation.

[0089] Specifically, the current production scheduling information and the current production information are obtained in real time, and time conflict judgment and material conflict judgment are performed on the current production scheduling information and the current production information based on an optimization algorithm. For situations with different time conflict judgments and different material conflict judgment results, different conflict handling strategies are generated.

[0090] In one embodiment, step S10 includes the steps of:

[0091] S11: Obtain the production scheduling plan information and the equipment status information of the production equipment associated with the production scheduling plan information;

[0092] S12: Input the equipment status information and the production scheduling plan information into the demand forecasting model, so that the demand forecasting model dynamically corrects the loss coefficient, and generates the predicted material demand quantity associated with the production scheduling plan information based on the loss coefficient.

[0093] In this embodiment, the loss coefficient is the conversion ratio of the material from the theoretical demand to the actual consumption in production.

[0094] Specifically, obtain the production scheduling plan information and the equipment status information of the production equipment associated with the production scheduling plan information, input the equipment status information and the production scheduling plan information into the demand forecasting model, so that the demand forecasting model dynamically corrects the loss coefficient, and generates the predicted material demand quantity associated with the production scheduling plan information based on the loss coefficient.

[0095] In one embodiment, step S30 includes:

[0096] S31: Obtain the storage information of the material cabinet, and generate a material storage plan for the double-sided intelligent material cabinet through a preset multi-objective optimization algorithm based on the predicted material demand quantity and the storage information of the material cabinet;

[0097] S32: Generate an LED matrix indication plan for the storage positions of the double-sided intelligent material cabinet based on the material storage plan.

[0098] In this embodiment, the multi-objective optimization algorithm is an algorithm that balances objectives such as the material access efficiency, the cabinet space utilization rate of the double-sided intelligent material cabinet, and safety.

[0099] Specifically, the storage information of the double-sided intelligent material cabinet, such as the material position and quantity, is obtained in real time. Based on the predicted material demand quantity and the storage information of the material cabinet, through a preset multi-objective optimization algorithm, considering rules such as giving priority to high-frequency materials, weight adaptation, and safety isolation, a material storage plan for the double-sided intelligent material cabinet is generated. An LED matrix indication plan for the storage positions of the double-sided intelligent material cabinet is generated based on the material storage plan. By comparing the demand and inventory in real time, the dynamic and accurate control of the inventory materials is realized, avoiding the risks of over-purchasing or out-of-stock, and the LED matrix assists the workers in operation, which can significantly improve the operation efficiency.

[0100] In one embodiment, as Figure 2 shown, step S40 includes:

[0101] S41: Obtain work order data information, and associate employee information with real-time inventory material information based on the work order data;

[0102] S42: Based on the role-based access control model, allocate material collection permissions by combining work order data information and employee information;

[0103] S43: Combine multi-modal authentication methods of face recognition verification and work permit NFC chip verification to determine whether an employee has the right to collect materials;

[0104] S44: Control the opening and closing state of the bin cabinet door based on the bin control unit, judge the operation behavior of the employee through image analysis, and trigger video evidence collection of the material cabinet when there is a suspicious operation.

[0105] In this embodiment, the bin control unit is a hardware module for controlling the opening and closing of the material cabinet door, and image analysis is to use computer vision technology to monitor the operation behavior in real time and detect abnormal actions.

[0106] Specifically, obtain work order data information about the workshop production schedule, associate employee information with real-time inventory material information based on the work order data, allocate material collection permissions by combining work order data information and employee information based on the role-based access control model, combine multi-modal authentication methods of face recognition verification and work permit NFC chip verification to determine whether an employee has the right to collect materials, control the opening and closing state of the bin cabinet door based on the bin control unit, judge the operation behavior of the employee through image analysis, and trigger video evidence collection of the material cabinet when there is a suspicious operation.

[0107] In one embodiment, step S50 includes:

[0108] S51: Obtain the current production schedule information and the current production information in real time, compare the time intervals of the production cycles of the current production schedule information and the current production information, and mark the time conflict types based on the time interval comparison results. The time conflict types include hard conflicts and soft conflicts;

[0109] S52: Based on the optimization algorithm, judge the material conflict of the current production schedule information and the current production information with time conflicts. When there is a material conflict, mark its material conflict type. The material conflict types include consumable material conflicts and durable material conflicts;

[0110] S53: Divide the current production schedule information with both time conflicts and material conflicts into groups based on the combination of conflict types, and generate corresponding conflict handling strategies based on the group division results.

[0111] In this embodiment, a hard conflict is an irreconcilable time overlap. For example, two production plans are assigned to the same device and their time windows overlap. A soft conflict is an adjacent production plan that can be negotiated and adjusted. A consumable material is a material that requires key attention to demand forecasting, inventory replenishment strategies, and cost control. A durable material is a material that requires attention to service life, maintenance plans, and fault prevention to avoid production interruptions caused by equipment or tool failures. The conflict handling strategy is to automatically match a preset solution according to the combination of conflict types.

[0112] Specifically, the current production scheduling plan and the actual production progress are obtained in real time. Two types of time conflicts are marked through comparison on the timeline, and the material conflict types of the materials with time conflicts are judged. The tasks with both time and material conflicts are grouped, and a preset solution is automatically matched according to the combination of conflict types.

[0113] In one embodiment, the conflict handling strategy includes a material sharing handling strategy, a material substitution handling strategy, an emergency procurement handling strategy, and a loss prediction handling strategy. As Figure 3 shown, step S53 includes:

[0114] S531: Compare the current production scheduling information with the current production information. When there are hard conflicts and consumable material conflicts, set them as a sharing handling group. When there are hard conflicts and durable material conflicts, set them as a substitution handling group. When there are soft conflicts and consumable material conflicts, set them as a procurement handling group. When there are soft conflicts and durable material conflicts, set them as a prediction handling group;

[0115] S532: Generate a material sharing handling strategy for the current production scheduling information associated with the sharing handling group;

[0116] S533: Generate a material substitution handling strategy for the current production scheduling information associated with the substitution handling group;

[0117] S534: Generate an emergency procurement handling strategy for the current production scheduling information associated with the procurement handling group;

[0118] S535: Generate a loss prediction handling strategy for the current production scheduling information associated with the prediction handling group.

[0119] In this embodiment, the sharing handling group is a task group formed due to the simultaneous existence of hard conflicts and consumable material conflicts, which needs to be resolved through resource coordination. The substitution handling group is a task group formed due to the simultaneous existence of hard conflicts and durable material conflicts, which needs to be resolved through material replacement or equipment adjustment. The procurement handling group is a task group formed due to the simultaneous existence of soft conflicts and consumable material conflicts, which needs to replenish materials through emergency procurement. The prediction handling group is a task group formed due to the simultaneous existence of soft conflicts and durable material conflicts, which needs to optimize material allocation through loss prediction.

[0120] Specifically, a material sharing processing strategy is generated for the current scheduling information associated with the shared processing group, that is, the task priority and the available material quantity are obtained, and based on the task priority and the available material quantity, the maximum-minimum fairness algorithm is used to proportionally allocate the real-time available material quantity, and at the same time, an emergency procurement processing strategy is adopted;

[0121] A material substitution processing strategy is generated for the current scheduling information associated with the alternative processing group, that is, based on the knowledge graph and the graph neural network algorithm, alternative materials with similar functional attributes are retrieved for substitution use, and at the same time, an emergency procurement processing strategy is adopted;

[0122] An emergency procurement processing strategy is generated for the current scheduling information associated with the procurement processing group, that is, the predicted material demand quantity is obtained based on the demand prediction model, and the suppliers are screened and preferentially selected based on the weighted scoring model after correcting the weighted proportion corresponding to the material supply timeliness;

[0123] A loss prediction processing strategy is generated for the current scheduling information associated with the prediction processing group, that is, a demand prediction model that dynamically corrects the loss coefficient based on the device sensor information is used to predict the remaining life of the durable material, and the maintenance strategy and the emergency procurement processing strategy are selectively adopted by comparing the remaining life of the durable material with the production safety threshold.

[0124] In one embodiment, a control method for a double-sided intelligent material cabinet further includes the steps:

[0125] S60: Obtain the weight information and three-dimensional model information of the material after return, and perform inspections, and the inspection results are fed back to the trained loss prediction model in real time for material loss comparison, and the maintenance strategy, procurement strategy, and abnormal feedback strategy are selectively triggered.

[0126] In this embodiment, the weight information of the material after return is the actual weight value collected by the weighing device in the intelligent material cabinet when the material is returned, which reflects the actual loss of the material. The three-dimensional model information is the geometric data such as the shape and size of the material obtained through three-dimensional scanning or CAD modeling technology in the intelligent material cabinet, which is used to detect physical losses such as material deformation and wear. The loss prediction model is a machine learning model trained based on historical loss data such as weight change and three-dimensional model deviation. The maintenance strategy is a repair plan formulated for abnormal material losses caused by equipment or processes, such as equipment calibration and process parameter adjustment. The procurement strategy is a supplementary procurement plan triggered according to the material shortage risk, including emergency procurement and batch procurement. The abnormal feedback strategy is to trigger an alarm and give feedback when unexpected losses are detected.

[0127] Specifically, for the equipment within the storage location of the intelligent material cabinet, the returned materials are weighed and three-dimensionally scanned to obtain weight deviation and deformation data. The detection results are input into the loss prediction model to determine whether it exceeds the normal loss range. If the loss is abnormal, a maintenance strategy is triggered. If the inventory is insufficient, a procurement strategy is triggered to generate an emergency order. If non-standard loss is detected, an abnormal feedback strategy is triggered and a log is recorded.

[0128] S70: Obtain the material supply information of each supplier corresponding to the material, construct a weighted scoring model based on the preset decision rules and weight assignment rules, and select the optimal supplier for procurement through the weighted scoring model based on the material supply information.

[0129] In this embodiment, the material supply information is a supplier material list containing data such as supplier qualification, delivery cycle, price, and quality grade. The decision rules and weight assignment rules are the scoring criteria for supplier selection. The weighted scoring model is a mathematical model that assigns weights to each supplier based on the decision rules, calculates the comprehensive score, and ranks them.

[0130] Specifically, obtain the material supply information of each supplier, calculate the weighted scores for each supplier based on the preset rules (such as quality weight 40%, price weight 30%, logistics weight 30%), sort by the comprehensive score, and select the supplier with the highest score to execute the procurement.

[0131] In one embodiment, as Figure 4 shown, step S60 includes:

[0132] S61: Generate actual loss information based on the weight information and three-dimensional model information of the returned materials;

[0133] S62: Obtain the historical material loss information, and input the historical material loss information and equipment status information into the pre-trained loss prediction model to generate the predicted material loss information;

[0134] S63: Overlap and compare the actual loss information and the predicted loss information through the preset comparison processing rules;

[0135] S64: Selectively trigger the maintenance strategy and procurement strategy based on the overlap comparison result, and trigger the abnormal feedback strategy when there is abnormal loss.

[0136] In this embodiment, the actual loss information is the data of the actual loss degree of the material, the historical material loss information is the material loss records accumulated in the past production cycles, covering normal loss, abnormal loss, and the corresponding production conditions. The comparison processing rules are the preset standardization rules, which are used to determine whether the difference between the actual loss and the predicted loss reaches the condition for triggering the strategy.

[0137] Specifically, the returned materials are weighed and 3D scanned to extract weight deviation and deformation data. The historical loss records and equipment status data of the same period are input into the pre-trained loss prediction model to generate a material loss prediction curve for the next N weeks. The actual loss is compared with the predicted value according to preset rules (such as absolute error ≤3% is normal). If the error exceeds the threshold and the trend is abnormal (such as an increase in loss rate for three consecutive weeks), the maintenance strategy is triggered and the equipment status is checked. If the predicted loss exceeds the inventory safety line and there is no abnormality, the procurement strategy is triggered to generate a replenishment order. When abnormal loss that is not predicted by the model is detected, abnormal feedback is given.

[0138] In one embodiment, if Figure 5 As shown, step S70 includes:

[0139] S71: Obtaining material supply information of each supplier corresponding to the material that triggers the procurement strategy, and material preparation time information corresponding to the material;

[0140] S72: constructing a weighted scoring model based on preset decision rules and weight distribution rules, and scoring each material supply information of each supplier through the weighted scoring model to obtain supplier scoring information;

[0141] S73: Compare the material preparation time information with the material supply information, and determine whether the trigger is an emergency procurement processing strategy to obtain the corresponding material urgency;

[0142] S74: Select suppliers based on material urgency and supplier rating information using a weighted scoring model.

[0143] In this embodiment, the material preparation time information is the preparation time required from placing an order to the actual arrival of the material, and the material urgency is the priority divided according to the material preparation time and the urgency of production demand.

[0144] Specifically, obtain the list of materials that triggers the procurement strategy, simultaneously extract the material supply information of each supplier, score the supplier based on preset rules, compare the material preparation time with the production plan deadline, and determine whether the triggered emergency procurement processing strategy is an emergency procurement strategy, thereby generating the corresponding material urgency. Combine the material urgency with the supplier score through a weighted scoring model to select the optimal supplier.

[0145] The second object of the invention is achieved by the following technical solutions:

[0146] A control system for a double-sided intelligent material cabinet, comprising:

[0147] The demand forecasting module is used to obtain production scheduling information, real-time material inventory information and equipment status information, and input them into the trained demand forecasting model to obtain the predicted material demand;

[0148] Inventory comparison module, which is used to compare the predicted material demand and real-time material inventory information, and selectively trigger procurement strategies based on the comparison results;

[0149] Bin location indication scheme generation module, which is used to obtain the storage information of the material cabinet, generate a material storage scheme for the double-sided intelligent material cabinet based on the predicted material demand and the storage information of the material cabinet, and generate an LED matrix indication scheme for the bin location based on the material storage scheme;

[0150] Bin location control module, which is used to obtain the work order data information, and control the opening and closing states of the bin doors of the double-sided intelligent material cabinet in combination with the work order data information, the role access control model, and the multi-modal authentication method;

[0151] Conflict handling module, which is used to obtain the current production scheduling information and current production information in real time, judge the time conflict and material conflict between the current production scheduling information and the current production information based on the optimization algorithm, and generate a conflict handling strategy based on the results of the time conflict judgment and the material conflict judgment;

[0152] Loss analysis module, which is used to obtain the weight information and 3D model information of the returned materials, conduct inspections, and feed the inspection results back to the trained loss prediction model in real time for material loss comparison, and selectively trigger maintenance strategies, procurement strategies, and abnormal feedback strategies;

[0153] Material procurement module, which is used to obtain the material supply information of each supplier corresponding to the materials, construct a weighted scoring model based on the pre-set decision rules and weight distribution rules, and select the optimal supplier for procurement based on the material supply information through the weighted scoring model.

[0154] The above describes the present invention and its implementation manners. Such a description is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention. The actual structure is not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, creatively design a structural manner and an embodiment similar to the technical solution, they shall fall within the protection scope of the present invention.

Claims

1. A control method for a double-sided intelligent material cabinet, the double-sided intelligent material cabinet comprising a plurality of bins, characterized in that: Includes steps: Obtain production scheduling information, real-time material inventory information, and equipment status information, and input them into the trained demand forecasting model to obtain the predicted material demand; Compare the predicted material demand and real-time material inventory information, and selectively trigger procurement strategies based on the comparison results; Obtain the storage information of the material cabinet, generate a material storage plan for the double-sided intelligent material cabinet based on the predicted material demand and the storage information of the material cabinet, and generate an LED matrix indication plan for the storage position based on the material storage plan; Obtain work order data information, and combine the work order data information, role access control model and multimodal authentication method to control the switch status of the bin door of the double-sided intelligent material cabinet; The current scheduling information and the current production information are obtained in real time, and time conflict and material conflict judgments are made on the current scheduling information and the current production information based on the optimization algorithm, and a conflict handling strategy is generated based on the results of the time conflict judgment and the material conflict judgment.

2. The control method of a double-sided intelligent material cabinet according to claim 1, characterized in that: The step of obtaining production scheduling information, real-time material inventory information and equipment status information, and inputting them into the trained demand forecasting model to obtain the predicted material demand includes the following steps: Obtain production schedule information and equipment status information of production equipment associated with the production schedule information; The equipment status information and production scheduling information are input into the demand forecasting model, so that the demand forecasting model can dynamically correct the loss coefficient and generate the predicted material demand associated with the production scheduling information based on the loss coefficient.

3. The control method of a double-sided intelligent material cabinet according to claim 1, characterized in that: The step of acquiring the current production scheduling information and the current production information in real time, performing time conflict judgment and material conflict judgment on the current production scheduling information and the current production information based on the optimization algorithm, and generating a conflict handling strategy based on the results of the time conflict judgment and the material conflict judgment includes the following steps: Acquire current production scheduling information and current production information in real time, compare the time intervals of the production cycle of the current production scheduling information and the current production information, and mark the time conflict type based on the time interval comparison result, wherein the time conflict type includes hard conflict and soft conflict; Based on the optimization algorithm, material conflicts are judged between the current production scheduling information and the current production information that have time conflicts. When material conflicts occur, the material conflict types are marked. The material conflict types include consumable material conflicts and durable material conflicts. The current production scheduling information with both time conflicts and material conflicts is divided into groups based on the combination of conflict types, and the corresponding conflict handling strategy is generated based on the group division results.

4. The control method of a double-sided intelligent material cabinet according to claim 3 is characterized in that: The conflict handling strategies include material sharing handling strategies, material substitution handling strategies, emergency purchase handling strategies, and loss prediction handling strategies. The steps of grouping the current production scheduling information with both time conflicts and material conflicts based on the combination of conflict types, and generating corresponding conflict handling strategies based on the grouping results, include the steps of: Compare the current scheduling information with the current production information. When there is a hard conflict or a consumable material conflict, set it as a shared processing group; when there is a hard conflict or a durable material conflict, set it as an alternative processing group; when there is a soft conflict or a consumable material conflict, set it as a procurement processing group; when there is a soft conflict or a durable material conflict, set it as a forecast processing group; Generate material shared processing strategies for the current scheduling information associated with the shared processing group; Generate material alternative processing strategies for the current scheduling information associated with the alternative processing group; Generate an emergency procurement processing strategy for the current production scheduling information associated with the procurement processing group; Generate a loss forecast processing strategy for the current scheduling information associated with the forecast processing group.

5. The control method of a double-sided intelligent material cabinet according to claim 1, characterized in that: Also includes the steps: Obtain the weight information and 3D model information of the returned materials and conduct inspection; The detection results are fed back to the trained loss prediction model in real time to compare material losses and selectively trigger maintenance strategies, procurement strategies, and abnormal feedback strategies.

6. The control method of a double-sided intelligent material cabinet according to claim 5, characterized in that: The step of obtaining the weight information and three-dimensional model information of the returned material and performing detection comprises the following steps: Generate actual loss information based on the weight information and 3D model information of the returned materials; The step of feeding back the detection results to the trained loss prediction model in real time for material loss comparison and selectively triggering the maintenance strategy, procurement strategy and abnormal feedback strategy includes the following steps: Obtain historical material loss information, and input the historical material loss information and equipment status information into a pre-trained loss prediction model to generate material prediction loss information; Perform overlapping comparison of actual loss information and predicted loss information through pre-set comparison processing rules; The maintenance strategy and procurement strategy are selectively triggered based on the overlapping comparison results, and the abnormal feedback strategy is triggered when there is abnormal loss.

7. The control method of a double-sided intelligent material cabinet according to claim 1, characterized in that: Also includes the steps: Obtain material supply information corresponding to each supplier; Construct a weighted scoring model based on pre-set decision rules and weight allocation rules; The best supplier is selected for procurement based on material supply information through a weighted scoring model.

8. The control method of a double-sided intelligent material cabinet according to claim 7, characterized in that: The step of obtaining material supply information corresponding to each supplier of the material includes the steps of: Obtain the material supply information of each supplier corresponding to the material that triggers the procurement strategy, as well as the material preparation time information corresponding to the material; The step of selecting the best supplier for procurement based on material supply information through a weighted scoring model includes the following steps: Score each supplier's material supply information through a weighted scoring model to obtain supplier scoring information; Compare the material preparation time information with the material supply information, and obtain the corresponding material urgency information based on the urgency of the procurement strategy; The optimal supplier is selected based on material urgency information and supplier rating information through a weighted scoring model.

9. A control system for a double-sided intelligent material cabinet, characterized in that: include: The demand forecasting module is used to obtain production scheduling information, real-time material inventory information and equipment status information, and input them into the trained demand forecasting model to obtain the predicted material demand; Inventory comparison module, used to compare predicted material demand and real-time material inventory information, and selectively trigger procurement strategies based on the comparison results; A bin indication scheme generation module is used to obtain the storage information of the material cabinet, generate a material storage scheme for the double-sided intelligent material cabinet based on the predicted material demand and the storage information of the material cabinet, and generate an LED matrix indication scheme for the bin based on the material storage scheme; The bin control module is used to obtain work order data information, and control the switch status of the bin door of the double-sided intelligent material cabinet by combining the work order data information, the role access control model and the multi-modal authentication method; The conflict handling module is used to obtain the current scheduling information and the current production information in real time, perform time conflict judgment and material conflict judgment on the current scheduling information and the current production information based on the optimization algorithm, and generate a conflict handling strategy based on the results of the time conflict judgment and the material conflict judgment.

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