Production management method, device and equipment for building blocks and medium

Abstract

The application relates to a production management method, device and equipment for a building block toy and a medium, and belongs to the technical field of production management. The method comprises the following steps: acquiring a building block toy product model, wherein the building block toy product model comprises a plurality of virtual component packages, each virtual component package corresponds to a product function unit, and each virtual component package comprises at least two different physical part combination schemes; generating a production plan based on the building block toy product model, taking the virtual component package as a unit; acquiring current material data and current mold usage state information; determining a target combination scheme from the plurality of physical part combination schemes corresponding to each virtual component package based on the production plan, the current material data and the current mold usage state information; generating a production instruction based on the determined target combination scheme; and sending the production instruction to a corresponding production execution unit. The application has the effects of dynamically adjusting a production scheme according to real-time material and mold states and avoiding production line stagnation.

Description

Technical Field

[0001] This application relates to the technical field of production management, and in particular to a method, apparatus, equipment and medium for production management of building block toys. Background Technology

[0002] In existing production management systems for modular products or other discrete manufacturing industries, the traditional model combining material requirements planning (MRP) and manufacturing execution system (MES) is commonly used. This model typically relies on a fixed product bill of materials (BOM), where a final product corresponds to a specific list of physical parts.

[0003] However, the above model has revealed many inherent defects in actual operation: once the production plan is issued, the system will strictly lock and apply for specific parts according to the fixed bill of materials. Once an unplanned situation occurs, such as insufficient inventory of a certain part, or the key mold required to produce the part is under maintenance, the entire production work order will be forced to stop, affecting production efficiency and on-time order delivery rate.

[0004] Furthermore, when product designers are creating products, they mainly focus on product functions and appearance. Their design tools are isolated from the status of backend production resources. Designers often use combinations of parts that are currently in short supply or have risks without their knowledge. This causes the design to create potential production bottlenecks from the very beginning, forming a vicious cycle of "design-production-problems-design modification".

[0005] Therefore, existing technologies are essentially a rigid, "plan-centric" driving model that cannot adapt to the uncertainty prevalent in modern manufacturing environments and the urgent need for flexible, adaptive production. Summary of the Invention

[0006] To adapt to the uncertainty prevalent in modern manufacturing environments and the need for flexible, adaptive production, this application provides a production management method, apparatus, equipment, and medium for building block toys.

[0007] Firstly, this application provides a production management method for building block toys, employing the following technical solution:

[0008] A production management method for building block toys includes:

[0009] Obtain a building block toy product model, wherein the building block toy product model includes multiple virtual component packages, wherein each virtual component package corresponds to a product functional unit, and each virtual component package includes at least two different physical part combination schemes;

[0010] Based on the building block toy product model, a production plan is generated using virtual component packages as units;

[0011] Obtain current material data and current mold usage status information;

[0012] Based on the production plan, the current material data, and the current mold usage status information, a target combination scheme is determined from the multiple physical part combination schemes corresponding to each virtual component package;

[0013] Production instructions are generated based on the determined target combination scheme;

[0014] The production instruction is sent to the corresponding production execution unit.

[0015] By adopting the above technical solution, a product model of a building block toy containing multiple virtual component packages is obtained. Based on the product model, a production plan is generated with virtual component packages as units. Then, by combining the current material data and mold usage status information, the target combination scheme is determined and production instructions are generated. The production instructions are sent to the production execution unit, breaking the traditional rigid drive mode. This allows for flexible adjustment of parts combination according to actual production resources, effectively addressing uncertainties in production, improving production efficiency and on-time order delivery rate, and adapting to the modern manufacturing demand for flexible and adaptive production.

[0016] Optionally, determining a target combination scheme from multiple physical part combination schemes corresponding to each virtual component package based on the production plan, the current material data, and the current mold usage status information includes:

[0017] Determine material availability constraints based on the current material data;

[0018] Determine mold production capacity and capacity constraints based on the current mold usage status information;

[0019] Obtain real-time energy consumption data and manual shift information from the production line to determine energy and human resource constraints;

[0020] Using the material availability constraints, mold capacity and capability constraints, energy constraints, and human resource constraints as screening conditions, a set of feasible solutions is selected from the various physical part combination schemes of each virtual component package;

[0021] Each combination of feasible solutions in the set of feasible solutions is evaluated based on at least one preset optimization objective to obtain the evaluation result. The preset optimization objective includes at least one of production cost, production efficiency and material utilization rate.

[0022] The optimal combination scheme based on the evaluation results is selected from the set of feasible schemes as the target combination scheme.

[0023] By adopting the above technical solutions, and by comprehensively considering constraints such as material availability, mold capacity and capability, energy, and human resources, a set of feasible solutions is selected. Then, based on the preset optimization goals, the optimal target combination solution is selected, thereby more comprehensively and accurately determining the part combination solution that meets the actual production conditions, further improving the rationality and effectiveness of production management, and reducing production problems caused by resource constraints.

[0024] Optionally, evaluating each combination of solutions in the feasible solution set based on at least one preset optimization objective includes:

[0025] Obtain the weight coefficient for each of the preset optimization objectives;

[0026] For the set of feasible solutions:

[0027] Calculate the individual evaluation value of each of the combined schemes on each preset optimization objective;

[0028] The comprehensive evaluation value of each combination scheme is calculated based on the individual evaluation value and the weight coefficient corresponding to the individual evaluation value, and the comprehensive evaluation value is used as the evaluation result.

[0029] By adopting the above technical solution, the weight coefficient of each preset optimization objective is obtained, the individual evaluation value of each combination scheme on each preset optimization objective is calculated, and then the comprehensive evaluation value is calculated by combining the weight coefficient as the evaluation result. Thus, the importance of different optimization objectives is comprehensively considered, making the evaluation result more in line with actual production needs, and providing a more scientific and reasonable basis for selecting the optimal objective combination scheme.

[0030] Optionally, calculating the individual evaluation value of each of the combined schemes on each preset optimization objective includes:

[0031] Given that the preset optimization target is production cost, the individual evaluation value is calculated based on the procurement cost and processing cost of all physical parts in the combined scheme;

[0032] When the preset optimization target is production efficiency, the individual evaluation value is calculated based on the processing time of all physical parts in the combined scheme;

[0033] When the preset optimization target is material utilization rate, the individual evaluation value is calculated based on the current inventory turnover rate of the materials used in the physical parts of the combination scheme.

[0034] By adopting the above technical solutions, individual evaluation values ​​are calculated based on corresponding actual data for different optimization objectives such as production cost, production efficiency, and material utilization rate. This makes the evaluation process more operable and accurate, and helps to more accurately measure the performance of each combination scheme on different optimization objectives.

[0035] Optionally, before evaluating each combination of feasible solutions in the set of feasible solutions based on at least one preset optimization objective, the method further includes:

[0036] Obtain historical production information, wherein the historical production information includes the actual cost, actual production duration and material consumption records of historical production tasks;

[0037] The historical production information is trained based on a machine learning model to predict the actual production performance of each combination scheme under the current mold usage state. The actual production performance includes predicted cost deviation, predicted efficiency deviation, and predicted material waste rate.

[0038] The predicted actual production performance is used as an additional optimization objective in the evaluation of the combined scheme. The additional optimization objective corresponds to a weight coefficient, and the weight coefficient corresponding to the additional optimization objective and the weight coefficient of the preset optimization objective are used together to calculate the comprehensive evaluation value.

[0039] By adopting the above technical solutions, and through historical data and machine learning models, the performance of combined solutions in actual production can be predicted more accurately, such as predicting cost deviations, efficiency deviations, and material waste rates. This makes the evaluation results more comprehensive and reliable, and further improves the quality of production management decisions.

[0040] Optionally, the determination of the weighting coefficients includes:

[0041] Real-time acquisition of production environment change parameters, including material price fluctuations, equipment failure rate, order urgency, and environmental energy consumption indicators;

[0042] The weighting coefficients are dynamically adjusted based on the production environment change parameters and optimization algorithm to make the comprehensive evaluation value adapt to real-time production conditions.

[0043] The dynamic adjustment includes: increasing the weighting coefficient of production costs when material price fluctuations exceed a threshold; and increasing the weighting coefficient of production efficiency when the urgency of orders reaches a preset level.

[0044] By adopting the above technical solutions, and by monitoring parameters such as material price fluctuations, equipment failure rates, order urgency, and environmental energy consumption in real time, the weighting coefficients are dynamically adjusted so that the comprehensive evaluation value can adapt to changes in production conditions in real time. This ensures that the optimal target combination solution can be selected under different production environments, thereby improving the flexibility and adaptability of production management.

[0045] Optionally, before obtaining the building block toy product model, the process may also include:

[0046] During the design phase of building block toy products, a virtual component package library is displayed to the user in the design interface. Each virtual component package in the virtual component package library is associated with multiple customizable parameters.

[0047] In response to the user's selection and combination of virtual component packages, the building block toy product model is generated;

[0048] In the product design stage of the building block toy, based on the current material data and the current mold usage status, the usability assessment of each virtual component package in the virtual component package library is carried out, and the assessment results are displayed to the user in a visual way using color coding, usability star rating or text prompts.

[0049] By adopting the above technical solutions, designers can understand the availability of production resources during the design phase. Visual prompts guide designers to select appropriate virtual component packages, avoiding production bottlenecks from the design source, reducing the vicious cycle of "design-production-problems-design modification", and improving design quality and production efficiency.

[0050] Secondly, this application provides a production management device for building block toys, which adopts the following technical solution:

[0051] A production management device for building block toys, comprising:

[0052] The first acquisition module is used to acquire a building block toy product model, wherein the building block toy product model includes multiple virtual component packages, wherein each virtual component package corresponds to a product functional unit, and each virtual component package includes at least two different physical part combination schemes;

[0053] The generation module is used to generate a production plan based on the building block toy product model, using virtual component packages as units;

[0054] The second acquisition module is used to acquire current material data and current mold usage status information;

[0055] The first determining module is used to determine a target combination scheme from multiple physical part combination schemes corresponding to each virtual component package based on the production plan, the current material data and the current mold usage status information;

[0056] The second determining module is used to generate production instructions based on the determined target combination scheme;

[0057] The sending module is used to send the production instructions to the corresponding production execution unit.

[0058] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0059] An electronic device includes a processor and a memory, wherein the processor is coupled to the memory;

[0060] The processor is configured to execute a computer program stored in the memory, causing the electronic device to perform the method as described in any of the first aspects.

[0061] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0062] A computer-readable storage medium includes a computer program or instructions that, when executed on a computer, cause the computer to perform the method as described in any of the first aspects. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating a production management method for building block toys in the embodiments of this application.

[0064] Figure 2 This is a flowchart illustrating the sub-step S104 in the embodiments of this application.

[0065] Figure 3 This is a structural block diagram illustrating a production management device for building block toys in the embodiments of this application.

[0066] Figure 4 This is a structural block diagram illustrating an electronic device in the embodiments of this application. Detailed Implementation

[0067] The present application will be further described in detail below with reference to the accompanying drawings.

[0068] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0069] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0070] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0071] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0072] This application provides a production management method for building block toys. This method can be executed by an electronic device, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet computer, desktop computer, etc., but is not limited to these.

[0073] like Figure 1 As shown, a production management method for building block toys is described in the following main process flow (steps S101 to S105):

[0074] Step S101: Obtain a building block toy product model, wherein the building block toy product model includes multiple virtual component packages, wherein each virtual component package corresponds to a product functional unit, and each virtual component package includes at least two different physical part combination schemes;

[0075] In this embodiment, a virtual component package is a collection of components consisting of multiple physical parts, formed by geometrically and physically disassembling each product functional unit according to its shape, structure, and functional requirements. Each virtual component package has at least two different combinations of physical parts. These different combinations are achieved by using physical parts of different shapes, quantities, or connection structures, while maintaining the overall function, appearance, and interface compatibility of the product functional unit corresponding to the virtual component package. The relationship between the physical parts and the overall function and appearance is preset. When a virtual component package is acquired, multiple different combinations of physical parts are automatically generated based on the virtual component package.

[0076] In this embodiment, the building block toy product "doll" is used as an example for illustration. The building block toy product model includes multiple product functional units, such as head functional unit, torso main body functional unit, armor functional unit, arm functional unit, leg functional unit and weapon / object holding functional unit.

[0077] For the "Armor Functional Unit," the virtual component package is a collection of components designed to recreate the armor worn by the doll. The virtual component package can contain the following two different combinations of physical parts:

[0078] Option 1: It consists of a large printed part that is connected to the main body of the torso via snap fasteners.

[0079] Option 2: It consists of multiple small, plain-colored parts, such as armor plates and rope parts, which are assembled using mortise and tenon joints and then wrapped around the torso.

[0080] Although the physical parts used in the two schemes are completely different in shape, quantity and connection method, the overall appearance and function of the product functional units implemented by the two schemes are the same.

[0081] For the "arm functional unit", the virtual component package may include:

[0082] Option 1: Use a single, injection-molded movable arm component.

[0083] Option 2: Assemble an arm with the same range of motion using three separate parts: the upper arm, the elbow joint, and the lower arm.

[0084] Before obtaining the building block toy product model, the process includes: during the building block toy product design phase, displaying a virtual component package library to the user in the design interface, where each virtual component package in the virtual component package library is associated with multiple customizable parameters; generating a building block toy product model in response to the user's selection and combination of virtual component packages; and, during the building block toy product design phase, conducting a usability assessment of each virtual component package in the virtual component package library based on current material data and current mold usage status, and displaying the assessment results to the user in a visual manner using color coding, usability star ratings, or text prompts.

[0085] The design interface displays a virtual component package library to the user. Each virtual component package in the library is associated with multiple customizable parameters, such as color, size, and texture complexity. The electronic device responds to the user's selection and combination of virtual component packages to generate the final block toy product model.

[0086] Among them, the use of two-color nested outlines on the preview image of the building block toy product model ensures that the different colors of the products in the preview image still have high product / structure recognition even at low print resolution when printing paper product forms, thereby improving the convenience of subsequent sorting work. By superimposing the part color and the two-color outline image, a new image is automatically generated. Compared with the traditional system that generates preview images in real time, it has a lower load and runs more efficiently.

[0087] Electronic equipment acquires current material data and current mold usage status in real time. The current material data includes raw material information for producing physical parts, such as the inventory of ABS plastic granules of different colors. The current mold usage status includes the mold's operating status, such as whether it is in production, idle, under maintenance, number of uses, estimated remaining lifespan, and the busyness of future production schedules.

[0088] Usability assessment includes the following aspects:

[0089] Current material availability assessment: Calculate the inventory matching degree of the materials required for each physical part. For example, a red part requires red ABS plastic granules. Inventory matching degree = current available inventory / consumption per unit. If the inventory matching degree is higher than the preset threshold, it is determined that the current material is sufficient; if it is lower than the preset threshold, it is determined that the current material is insufficient. The preset threshold is determined based on the production quantity of the physical part.

[0090] Current mold availability assessment: Determine the availability status of the mold required to produce each physical part. If the mold is in production or idle, it is determined to be available. If the mold is under maintenance, it is determined to be unavailable in the short term based on the estimated completion time of the maintenance. If the mold is scrapped or has a very low remaining lifespan, it is determined to be unavailable.

[0091] Evaluation of physical component assembly schemes:

[0092] Parts combination scheme feasibility: A physical parts combination scheme is considered feasible if and only if all physical parts constituting the physical parts combination scheme are assessed as available or sufficient in terms of material and mold dimensions.

[0093] Solution load assessment: Even if the component combination solution is feasible, it is still necessary to calculate the overall resource load. For example, solution A requires the use of a mold that is currently operating under high load, while solution B, which is functionally equivalent, uses an idle mold. In this case, the load assessment result of solution B is better.

[0094] Virtual component package evaluation:

[0095] Green / Five-star: The virtual component package has at least one feasible combination with low resource load; Yellow / Three-star: The virtual component package has feasible solutions, but all feasible solutions have some kind of resource shortage, such as molds about to be repaired, inventory at critical levels, or production efficiency not being optimal. In this case, a prompt message is generated that is available, but the delivery time may be extended; Red / One-star: The virtual component package has no feasible combination of parts under the current conditions. In this case, a prompt message is generated, such as all critical molds being damaged, or materials of a specific color being out of stock and having no alternatives.

[0096] Designers can gain a preliminary understanding of the producible physical assembly schemes for producing virtual component packages through the evaluation results.

[0097] Step S102: Generate a production plan based on the block toy product model, using virtual component packages as units;

[0098] In this embodiment, the building block toy product model is divided into multiple virtual component packages for independent production.

[0099] Step S103: Obtain current material data and current mold usage status information;

[0100] In this embodiment, the acquisition of material data includes:

[0101] Inbound Update: When a batch of ABS plastic granule raw materials arrives at the warehouse, the warehouse manager uses a barcode scanner to scan the barcode affixed to the material packaging. After scanning, the barcode scanner automatically parses the material information and stores it in the material database via wireless network. The material information includes the material code, batch number, and quantity.

[0102] Outbound Update: When the production workshop needs to collect raw materials according to the plan, the electronic equipment generates an electronic outbound slip according to the plan. The warehouse manager scans the barcode of the required materials again to pick and confirm the outbound shipment based on the outbound slip. After receiving the material outbound confirmation, the corresponding quantity is deducted from the material warehouse, such as material code: RAW-ABS-RED-001, quantity: -150kg.

[0103] Mold usage status information acquisition includes:

[0104] Electronic devices can retrieve or receive mold status information actively pushed by the MES in real time from the MES. The mold status information includes: mold identifier: such as mold ID#005; current status: such as running, standby, fault repair, planned maintenance; additional status information: such as the task number currently being produced, output, estimated completion time, fault code, etc.; production capacity information: such as the theoretical cycle time of the mold.

[0105] Step S104: Based on the production plan, current material data, and current mold usage status information, determine a target combination scheme from the multiple physical part combination schemes corresponding to each virtual component package;

[0106] Specifically, step S104 includes the following sub-steps (steps S1041 to S104):

[0107] Step S1041: Determine material availability constraints based on current material data;

[0108] In this embodiment, each physical component combination scheme is converted into a list of required raw materials. For example, for scheme A1 of virtual component package A, 100 parts X need to be produced. Each part X consumes 20 grams of red ABS plastic granules. The electronic device calculates the total raw material requirements of scheme A1 and compares them with the real-time inventory to form a constraint: scheme A1 is feasible when the current inventory of red ABS plastic granules is ≥2kg.

[0109] Step S1042: Determine mold production capacity and capacity constraints based on the current mold usage status information;

[0110] In this embodiment, the electronic device obtains the mold availability of the parts required for each combination scheme. For example, scheme A3 requires the use of molds #005 and #007. The electronic device obtains the usage status of the corresponding molds, such as mold #005 being in operation and its production capacity for the next 4 hours being booked; while mold #007 is in standby mode. This creates a constraint: scheme A3 is feasible when mold #007 is available and the production task can be arranged after the idle period of mold #005.

[0111] Step S1043: Obtain real-time energy consumption data and manual shift information of the production line to determine energy constraints and human resource constraints;

[0112] In this embodiment, the energy management system obtains the current electricity price and the current shift operators from the HR system, wherein the operators include, but are not limited to, junior operators, intermediate operators and senior operators.

[0113] Step S1044: Using material availability constraints, mold capacity and capability constraints, and energy and human resource constraints as screening conditions, select a set of feasible solutions from the various physical part combination schemes of each virtual component package.

[0114] In this embodiment, the feasible solution set refers to the set of combination solutions that meet all real-time constraints. All initial combination solutions for each virtual component package are screened. For example, the initial combination solutions for virtual component package A are A1, A2, A3, and A4. After screening: A1 is eliminated due to insufficient red ABS raw materials, A2 is eliminated due to mold failure, and A4 is eliminated due to excessive energy consumption and lack of senior operators. Finally, solution A3 successfully passes all constraint checks and is added to the feasible solution set.

[0115] Step S1045: Evaluate each combination of feasible solutions in the set of feasible solutions based on at least one preset optimization objective to obtain the evaluation result. The preset optimization objective includes at least one of production cost, production efficiency and material utilization rate.

[0116] Specifically, obtain the weight coefficient of each preset optimization objective; for the set of feasible solutions: calculate the individual evaluation value of each combined solution on each preset optimization objective; calculate the comprehensive evaluation value of each combined solution based on the individual evaluation value and the weight coefficient corresponding to the individual evaluation value, and use the comprehensive evaluation value as the evaluation result.

[0117] The calculation of individual evaluation values ​​for each combination scheme under each preset optimization objective includes: calculating individual evaluation values ​​based on the procurement and processing costs of all physical parts in the combination scheme when the preset optimization objective is production cost; calculating individual evaluation values ​​based on the processing time of all physical parts in the combination scheme when the preset optimization objective is production efficiency; and calculating individual evaluation values ​​based on the current inventory turnover rate of the materials used by the physical parts in the combination scheme when the preset optimization objective is material utilization rate.

[0118] In this embodiment, a quantitative score is calculated for each combination scheme on each optimization objective, for example, 0-1 points or 0-100 points, where a higher quantitative score indicates that the combination scheme performs better on the preset optimization objective.

[0119] The individual evaluation value of production cost: Total cost of the solution = Σ(purchase cost of each physical part + processing cost of each physical part). Scoring: Among all feasible solutions, the solution with the lowest total cost gets the highest score in this item, such as 100 points, and the solution with the highest cost gets the lowest score, such as 0 points. Other solutions are scored by linear interpolation based on cost. The lower the cost, the higher the score.

[0120] Individual evaluation value of production efficiency: Total processing time of the scheme = Σ (standard processing time of each physical part); Scoring: The scheme with the shortest total processing time gets the highest score, and the scheme with the longest total processing time gets the lowest score. Other schemes are scored by linear interpolation of time, that is, the shorter the time, the higher the score.

[0121] Material utilization rate evaluation value: Based on the current inventory turnover rate of the materials used in the physical parts of the combination scheme, priority is given to using materials with low turnover rate, i.e., stagnant inventory, which can accelerate capital return and reduce warehousing costs. For example, scheme A uses a large amount of materials with high current inventory and low turnover, while the functionally equivalent scheme B uses materials that need to be newly purchased. In this case, scheme A has a higher score in material utilization rate. Score = Σ(value of parts used * (1 - turnover rate)), where turnover rate = usage cost for preset time period / average inventory cost. Usage cost for preset time period: the total cost of the material being used or issued within the preset time period. Average inventory cost: the average inventory cost within the preset time period. The preset time period can be 1 month or 1 year, without specific limitation.

[0122] Before evaluating each combination of feasible solutions based on at least one preset optimization objective, the process includes: acquiring historical production information, which includes the actual cost, actual production time, and material consumption records of historical production tasks; training a machine learning model on the historical production information to predict the actual production performance of each combination solution under the current mold usage state, whereby the actual production performance includes predicted cost deviation, predicted efficiency deviation, and predicted material waste rate; and using the predicted actual production performance as an additional optimization objective in the evaluation of the combination solutions, whereby the additional optimization objective corresponds to a weight coefficient, and the weight coefficient corresponding to the additional optimization objective and the weight coefficient of the preset optimization objective are used together to calculate the comprehensive evaluation value.

[0123] In this embodiment, a machine learning model is used to train historical data. The machine learning model includes, but is not limited to, regression models and random forests. The machine learning model learns the deviation patterns between the plan and the actual situation.

[0124] Predicting actual production performance: For each feasible solution, the machine learning model is preset based on the current mold status, material batch and other information to obtain the predicted cost deviation, predicted efficiency deviation and predicted material waste rate.

[0125] Cost robustness raw value = 1 - predicted cost deviation rate; efficiency robustness raw value = 1 - predicted efficiency deviation rate; material robustness raw value = 1 - predicted material waste rate. Each robustness score = [(current scenario raw value - worst raw value among all scenarios) / (best raw value among all scenarios - worst raw value among all scenarios)] × 100, such as scenario A: 0.92 (best); scenario B: 0.85; scenario C: 0.78 (worst).

[0126] Option A (optimal): [(0.92-0.78) / (0.92-0.78)]×100=100 points;

[0127] Option C (worst): [(0.78-0.78) / (0.92-0.78)]×100=0 points

[0128] Option B: [(0.85-0.78) / (0.92-0.78)]×100=[0.07 / 0.14]×100=50 points.

[0129] Production robustness score = (cost robustness score × W1) + (efficiency robustness score × W2) + (material robustness score × W3), where W1 is the weight of the cost robustness score, W2 is the weight of the efficiency robustness score, and W3 is the material robustness score. W1, W2, and W3 are preset.

[0130] In this embodiment, the comprehensive evaluation value = (production cost score × Wc) + (production efficiency score × We) + (material utilization rate score × Wm) + (production robustness score × Wr), where Wc is the weighting coefficient corresponding to production cost, We is the weighting coefficient of production efficiency, Wm is the weighting coefficient of material utilization rate, and Wr is the weighting coefficient of production robustness, and the sum of Wc, We, Wm and Wr is 1.

[0131] The determination of weighting coefficients includes: real-time acquisition of production environment change parameters, including material price fluctuations, equipment failure rates, order urgency, and environmental energy consumption indicators; dynamic adjustment of weighting coefficients based on production environment change parameters and optimization algorithms to adapt the comprehensive evaluation value to real-time production conditions; dynamic adjustment includes: increasing the weighting coefficient of production costs when material price fluctuations exceed a threshold; and increasing the weighting coefficient of production efficiency when order urgency reaches a preset level.

[0132] In this embodiment, dynamic adjustment includes: increasing the weighting coefficient Wc of production costs when material price fluctuations exceed a threshold; for example, when the market price of ABS plastic granules rises by more than 10%, Wc is automatically adjusted from 0.3 to 0.5 to emphasize cost control; increasing the weighting coefficient We of production efficiency when order urgency reaches a preset level; for example, when the order delivery period is shortened to within 24 hours, We is adjusted from 0.4 to 0.6 to prioritize production speed; increasing the weighting coefficient Wr of production robustness when equipment failure rate increases; for example, when the mold failure rate exceeds 20% of the historical average, Wr is adjusted from 0.1 to 0.3 to select a more reliable solution; and reducing the weighting coefficient We of production efficiency and increasing the weighting of material utilization rate Wm when environmental energy consumption indicators are limited, such as during peak electricity price periods, to optimize energy use.

[0133] The optimization algorithm can employ rule-based adaptive adjustment or reinforcement learning models to continuously optimize the weight coefficients based on historical adjustment effects, ensuring that the comprehensive evaluation value reflects changes in the production environment in real time.

[0134] Step S1046: Select the optimal combination scheme from the set of feasible schemes as the target combination scheme.

[0135] The combination scheme with the highest comprehensive evaluation value is selected as the target combination scheme, so as to adapt to the actual production situation while meeting the functional, appearance and interface requirements.

[0136] Step S105: Generate production instructions based on the determined target combination scheme;

[0137] Production instructions include: parts list and BOM, mold call instructions, and material delivery instructions.

[0138] Step S106: Send the production instruction to the corresponding production execution unit.

[0139] In this embodiment, the production instructions generated in step S105 are automatically distributed to different execution units through interfaces such as the Manufacturing Execution System and Enterprise Resource Planning: Material Warehouse: receives material delivery instructions and begins material preparation; Injection Molding Workshop: receives mold call instructions and part production parameters, and arranges mold installation and production.

[0140] Figure 2 This application provides a structural block diagram of a production management device 200 for building block toys. Figure 2 As shown, the production management device 200 for building block toys mainly includes:

[0141] The first acquisition module 201 is used to acquire a building block toy product model, wherein the building block toy product model includes multiple virtual component packages, wherein each virtual component package corresponds to a product functional unit, and each virtual component package includes at least two different physical part combination schemes;

[0142] Generation module 202 is used to generate a production plan based on the block toy product model, using virtual component packages as units;

[0143] The second acquisition module 203 is used to acquire current material data and current mold usage status information;

[0144] The first determining module 204 is used to determine a target combination scheme from multiple physical part combination schemes corresponding to each virtual component package based on the production plan, current material data and current mold usage status information;

[0145] The second determining module 205 is used to generate production instructions based on the determined target combination scheme;

[0146] The sending module 206 is used to send production instructions to the corresponding production execution unit.

[0147] As an optional implementation of this embodiment, the second determining module 205 includes:

[0148] The first determination submodule is used to determine material availability constraints based on current material data;

[0149] The second determination submodule is used to determine the mold production capacity and capacity constraints based on the current mold usage status information;

[0150] The third determination submodule is used to obtain real-time energy consumption data and manual shift information of the production line, and to determine energy constraints and human resource constraints.

[0151] As a submodule, it is used to select a set of feasible solutions from multiple physical part combinations in each virtual component package using material availability constraints, mold capacity and capability constraints, and energy and human resource constraints as screening conditions.

[0152] The evaluation submodule is used to evaluate each combination of feasible solutions in the set of feasible solutions based on at least one preset optimization objective, and obtain the evaluation result. The preset optimization objective includes at least one of production cost, production efficiency and material utilization rate.

[0153] As a submodule, it is used to select the optimal combination scheme from the set of feasible schemes as the target combination scheme.

[0154] In this optional embodiment, the evaluation submodule is specifically used for:

[0155] Obtain the weight coefficients for each preset optimization objective; for the set of feasible solutions: calculate the individual evaluation value of each combined solution on each preset optimization objective; calculate the comprehensive evaluation value of each combined solution based on the individual evaluation value and the weight coefficients corresponding to the individual evaluation value, and use the comprehensive evaluation value as the evaluation result.

[0156] In this optional embodiment, the evaluation submodule is further specifically used for:

[0157] When the preset optimization objective is production cost, the individual evaluation value is calculated based on the procurement cost and processing cost of all physical parts in the combination scheme; when the preset optimization objective is production efficiency, the individual evaluation value is calculated based on the processing time of all physical parts in the combination scheme; when the preset optimization objective is material utilization rate, the individual evaluation value is calculated based on the current inventory turnover rate of the materials used by the physical parts in the combination scheme.

[0158] As an optional implementation of this embodiment, the production management device 200 for building block toys further includes:

[0159] The information acquisition module is used to acquire historical production information before evaluating each combination of solutions in the feasible solution set based on at least one preset optimization objective. The historical production information includes the actual cost, actual production time and material consumption records of historical production tasks.

[0160] The training module is used to train historical production information based on machine learning models to predict the actual production performance of each combination scheme under the current mold usage state. The actual production performance includes predicted cost deviation, predicted efficiency deviation and predicted material waste rate.

[0161] As a module, it is used to incorporate the predicted actual production performance as an additional optimization objective into the evaluation of the combined scheme. The additional optimization objective corresponds to a weight coefficient, and the weight coefficient corresponding to the additional optimization objective and the weight coefficient of the preset optimization objective are used together to calculate the comprehensive evaluation value.

[0162] As an optional implementation of this embodiment, the production management device 200 for building block toys further includes:

[0163] The adjustment module is used to acquire production environment change parameters in real time, including material price fluctuations, equipment failure rates, order urgency, and environmental energy consumption indicators. Based on the production environment change parameters and optimization algorithms, the module dynamically adjusts the weight coefficients to adapt the comprehensive evaluation value to real-time production conditions. The dynamic adjustment includes: increasing the weight coefficient of production costs when material price fluctuations exceed a threshold; and increasing the weight coefficient of production efficiency when order urgency reaches a preset level.

[0164] As an optional implementation of this embodiment, the production management device 200 for building block toys further includes:

[0165] The display module is used to show the virtual component package library to the user in the design interface before obtaining the product model of the building block toy, during the product design stage of the building block toy. Each virtual component package in the virtual component package library is associated with multiple customizable parameters.

[0166] The response module is used to respond to the user's selection and combination of virtual component packages and generate a building block toy product model. In the building block toy product design stage, based on the current material data and the current mold usage status, the usability assessment of each virtual component package in the virtual component package library is performed, and the assessment results are displayed to the user in a visual way using color coding, usability star rating or text prompts.

[0167] The functional modules in the embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. If the function is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of a production management method for building block toys according to various embodiments of this application.

[0168] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0169] Figure 3 This is a structural block diagram of an electronic device 300 provided in an embodiment of this application. (See diagram below.) Figure 3 As shown, the electronic device 300 includes a memory 301, a processor 302, and a communication bus 303; the memory 301 and the processor 302 are connected via the communication bus 303. The memory 301 stores a production management method for building block toys, which can be loaded and executed by the processor 302, as provided in the above embodiment.

[0170] The memory 301 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 301 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the production management method for building block toys provided in the above embodiments. The data storage area may store data involved in the production management method for building block toys provided in the above embodiments.

[0171] Processor 302 may include one or more processing cores. Processor 302 executes instructions, programs, code sets, or instruction sets stored in memory 301, and calls data stored in memory 301 to perform various functions and process data as described in this application. Processor 302 may be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of processor 302 may also be other types, and this application embodiment does not specifically limit the specific implementation.

[0172] The communication bus 303 may include a path for transmitting information between the aforementioned components. The communication bus 303 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 303 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double arrow, but this does not mean that there is only one bus or one type of bus.

[0173] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, a method for production management of building block toys.

[0174] In this embodiment, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), staging random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0175] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0176] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A production management method for building block toys, characterized in that, include: Obtain a building block toy product model, wherein the building block toy product model includes multiple virtual component packages, wherein each virtual component package corresponds to a product functional unit, and each virtual component package includes at least two different physical part combination schemes; Based on the building block toy product model, a production plan is generated using virtual component packages as units; Obtain current material data and current mold usage status information; Based on the production plan, the current material data, and the current mold usage status information, a target combination scheme is determined from the multiple physical part combination schemes corresponding to each virtual component package; Production instructions are generated based on the determined target combination scheme; The production instruction is sent to the corresponding production execution unit; The process of determining a target combination scheme from multiple physical part combination schemes corresponding to each virtual component package based on the production plan, the current material data, and the current mold usage status information includes: Determine material availability constraints based on the current material data; Determine mold production capacity and capacity constraints based on the current mold usage status information; Obtain real-time energy consumption data and manual shift information from the production line to determine energy and human resource constraints; Using the material availability constraints, mold capacity and capability constraints, energy constraints, and human resource constraints as screening conditions, a set of feasible solutions is selected from the various physical part combination schemes of each virtual component package; Each combination of feasible solutions in the set of feasible solutions is evaluated based on at least one preset optimization objective to obtain the evaluation result. The preset optimization objective includes at least one of production cost, production efficiency and material utilization rate. The optimal combination scheme based on the evaluation results is selected from the set of feasible schemes as the target combination scheme.

2. The method according to claim 1, characterized in that, The evaluation of each combination of solutions in the feasible solution set based on at least one preset optimization objective includes: Obtain the weight coefficient for each of the preset optimization objectives; For the set of feasible solutions: Calculate the individual evaluation value of each of the combined schemes on each preset optimization objective; The comprehensive evaluation value of each combination scheme is calculated based on the individual evaluation value and the weight coefficient corresponding to the individual evaluation value, and the comprehensive evaluation value is used as the evaluation result.

3. The method according to claim 2, characterized in that, The calculation of the individual evaluation value of each of the combined schemes on each preset optimization objective includes: Given that the preset optimization target is production cost, the individual evaluation value is calculated based on the procurement cost and processing cost of all physical parts in the combined scheme; When the preset optimization target is production efficiency, the individual evaluation value is calculated based on the processing time of all physical parts in the combined scheme; When the preset optimization target is material utilization rate, the individual evaluation value is calculated based on the current inventory turnover rate of the materials used in the physical parts of the combination scheme.

4. The method according to claim 2, characterized in that, Before evaluating each combination of feasible solutions in the set of feasible solutions based on at least one preset optimization objective, the process includes: Obtain historical production information, wherein the historical production information includes the actual cost, actual production duration and material consumption records of historical production tasks; The historical production information is trained based on a machine learning model to predict the actual production performance of each combination scheme under the current mold usage state. The actual production performance includes predicted cost deviation, predicted efficiency deviation, and predicted material waste rate. The predicted actual production performance is used as an additional optimization objective in the evaluation of the combined scheme. The additional optimization objective corresponds to a weight coefficient, and the weight coefficient corresponding to the additional optimization objective and the weight coefficient of the preset optimization objective are used together to calculate the comprehensive evaluation value.

5. The method according to claim 4, characterized in that, The determination of the weighting coefficients includes: Real-time acquisition of production environment change parameters, including material price fluctuations, equipment failure rate, order urgency, and environmental energy consumption indicators; The weighting coefficients are dynamically adjusted based on the production environment change parameters and optimization algorithm to make the comprehensive evaluation value adapt to real-time production conditions. The dynamic adjustment includes: increasing the weighting coefficient of production costs when material price fluctuations exceed a threshold; and increasing the weighting coefficient of production efficiency when the urgency of orders reaches a preset level.

6. The method according to claim 1, characterized in that, Before obtaining the building block toy product model, the process also includes: During the design phase of building block toy products, a virtual component package library is displayed to the user in the design interface. Each virtual component package in the virtual component package library is associated with multiple customizable parameters. In response to the user's selection and combination of virtual component packages, the building block toy product model is generated; In the design phase of the building block toy product, based on the current material data and the current mold usage status, the usability of each virtual component package in the virtual component package library is evaluated, and the evaluation results are displayed to the user in a visual manner using color coding, usability star rating, or text prompts.

7. A production management device for building block toys, characterized in that, include: The first acquisition module is used to acquire a building block toy product model, wherein the building block toy product model includes multiple virtual component packages, wherein each virtual component package corresponds to a product functional unit, and each virtual component package includes at least two different physical part combination schemes; The generation module is used to generate a production plan based on the building block toy product model, using virtual component packages as units; The second acquisition module is used to acquire current material data and current mold usage status information; The first determining module is used to determine a target combination scheme from multiple physical part combination schemes corresponding to each virtual component package based on the production plan, the current material data and the current mold usage status information; The second determining module is used to generate production instructions based on the determined target combination scheme; The sending module is used to send the production instructions to the corresponding production execution unit; The first determination submodule is used to determine material availability constraints based on current material data; The second determination submodule is used to determine the mold production capacity and capacity constraints based on the current mold usage status information; The third determination submodule is used to obtain real-time energy consumption data and manual shift information of the production line, and to determine energy constraints and human resource constraints. As a submodule, it is used to select a set of feasible solutions from multiple physical part combinations in each virtual component package using material availability constraints, mold capacity and capability constraints, and energy and human resource constraints as screening conditions. The evaluation submodule is used to evaluate each combination of feasible solutions in the set of feasible solutions based on at least one preset optimization objective, and obtain the evaluation result. The preset optimization objective includes at least one of production cost, production efficiency and material utilization rate. As a submodule, it is used to select the optimal combination scheme from the set of feasible schemes as the target combination scheme.

8. An electronic device, characterized in that, It includes a processor and a memory, wherein the processor is coupled to the memory; The processor is configured to execute a computer program stored in the memory, causing the electronic device to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It includes a computer program or instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1 to 6.

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