Workshop layout optimization method and device based on vertical model, equipment and medium

Abstract

The embodiment of the invention provides a workshop layout optimization method and device based on a vertical model, equipment and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring a basic mathematical model of a target workshop, and acquiring layout optimization demand information of the target workshop; based on a preset workshop digital simulation driving model, performing information analysis on the layout optimization demand information to obtain structured layout constraint information; performing constraint supplement on the structured layout constraint information to obtain layout constraint set information; based on a workshop digital simulation driving model, performing function conversion on the layout constraint set information to obtain a workshop layout extension constraint function; based on the workshop layout extension constraint function and the target workshop, performing model optimization on the basic mathematical model to obtain a dynamic workshop simulation model; and performing loop iteration optimization on the dynamic workshop simulation model to obtain workshop optimization layout information. According to the embodiment of the invention, the accuracy of workshop layout optimization can be improved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, equipment and medium for optimizing workshop layout based on a vertical model. Background Technology

[0002] Workshop layout optimization refers to actions taken to improve workshop performance by adjusting the position and layout of various components within the workshop. For example, in the electronics manufacturing industry, optimizing the layout of an electronics production workshop can improve production efficiency or reduce operating costs.

[0003] Currently, the common method for optimizing workshop layout based on vertical models involves professionals constructing a workshop simulation model based on the current layout of the workshop and the optimization goals. However, manually constructed workshop simulation models are often prone to oversights in constraints, resulting in inaccurate workshop layout optimization solutions. Therefore, improving the accuracy of workshop layout optimization has become an urgent technical problem to be solved. Summary of the Invention

[0004] The main objective of this application is to propose a workshop layout optimization method, apparatus, equipment, and medium based on a vertical model, aiming to improve the accuracy of workshop layout optimization.

[0005] To achieve the above objectives, a first aspect of this application proposes a workshop layout optimization method based on a vertical model, the method comprising: Obtain the basic mathematical model of the target workshop and obtain the layout optimization requirements of the target workshop; Based on the preset workshop simulation driving model, the layout optimization requirement information is parsed to obtain structured layout constraint information; The structured layout constraint information is supplemented to obtain the layout constraint set information; Based on the workshop simulation-driven model, the layout constraint set information is transformed by function to obtain the workshop layout extended constraint function; Based on the workshop layout extended constraint function and the target workshop, the basic mathematical model is optimized to obtain a dynamic workshop simulation model. The dynamic workshop simulation model is iteratively optimized to obtain optimized workshop layout information.

[0006] In some embodiments, the dynamic workshop simulation model includes a workshop layout optimization objective function and a workshop layout optimization constraint function; the iterative optimization of the dynamic workshop simulation model to obtain optimized workshop layout information includes: Based on the workshop layout optimization constraint function, a feasible solution is calculated for the workshop layout optimization objective function to obtain a candidate feasible solution set, wherein the candidate feasible solution set includes multiple candidate feasible solutions; Based on the dynamic workshop simulation model, the candidate feasible solutions are simulated to obtain the fitness of the candidate solutions; Based on the fitness of the candidate solutions, the set of candidate feasible solutions is iteratively updated to obtain an updated set of candidate solutions; The updated candidate solution set is compared for fitness to obtain the optimal solution of the objective function; The optimal solution of the objective function is mapped to the workshop layout to obtain the optimized workshop layout information.

[0007] In some embodiments, the step of performing simulation processing on the candidate feasible solutions based on the dynamic workshop simulation model to obtain the fitness of the candidate solutions includes: The dynamic workshop simulation model is replicated to obtain a replicated discrete event simulation model; Based on the candidate feasible solutions, the simulation model of the replicated discrete event is processed to obtain model performance data. The model performance data is quantified to obtain the fitness of the candidate solutions.

[0008] In some embodiments, the step of iteratively updating the candidate feasible solution set based on the candidate solution fitness to obtain an updated candidate solution set includes: Based on the fitness of the candidate solutions, the direction of candidate solution adjustment is determined; Based on the preset number of iterations and the adjustment direction of the candidate solutions, the candidate feasible solution set is adjusted to obtain the updated candidate solution set.

[0009] In some embodiments, the basic mathematical model includes a target workshop optimization function and a basic constraint function for workshop layout; the target workshop includes workshop components; the optimization of the basic mathematical model based on the extended constraint function for workshop layout and the target workshop to obtain a dynamic workshop simulation model includes: Based on the extended constraint function of the workshop layout, the basic constraint function of the workshop layout is updated to obtain the target constraint function of the workshop layout. Component modeling is performed on the workshop components to obtain component simulation models; The component simulation models are spliced ​​together to obtain a static workshop simulation model; Based on the workshop layout objective constraint function and the objective workshop optimization function, the static workshop simulation model is embedded to obtain the dynamic workshop simulation model.

[0010] In some embodiments, supplementing the structured layout constraint information to obtain layout constraint set information includes: Based on a pre-set multi-level indicator knowledge base, keyword matching is performed on the structured layout constraint information to obtain the first constraint extension information; Based on the multi-level indicator knowledge base, semantic matching is performed on the structured layout constraint information to obtain the second constraint extension information; The first constraint extension information and the second constraint extension information are merged to obtain the layout constraint set information.

[0011] In some embodiments, the layout optimization requirement information is parsed based on a preset workshop simulation-driven model to obtain structured layout constraint information, including: Based on the workshop simulation-driven model, key information is extracted from the layout optimization requirement information to obtain the constraint objects and quantification parameters. Based on the preset demand classification dimensions, the layout optimization demand information is classified to obtain multi-dimensional constraint information of workshop layout. Based on the constraint object and the quantification parameters, the multi-dimensional constraint information of the workshop layout is processed in a structured manner to obtain the structured layout constraint information.

[0012] To achieve the above objectives, a second aspect of this application proposes a workshop layout optimization device based on a vertical model, the device comprising: The basic information acquisition module is used to acquire the basic mathematical model of the target workshop and the layout optimization requirements of the target workshop. The requirement information parsing module is used to parse the layout optimization requirement information based on the preset workshop simulation driving model to obtain structured layout constraint information. The constraint supplementation module is used to supplement the structured layout constraint information to obtain layout constraint set information; The constraint information conversion module is used to perform function conversion on the layout constraint set information based on the workshop simulation driving model to obtain the workshop layout extended constraint function; The mathematical model optimization module is used to optimize the basic mathematical model based on the workshop layout extended constraint function and the target workshop to obtain a dynamic workshop simulation model. The model iterative optimization module is used to perform iterative optimization of the dynamic workshop simulation model to obtain optimized workshop layout information.

[0013] To achieve the above objectives, a third aspect of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method of the first aspect described above.

[0014] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of the first aspect described above.

[0015] This application proposes a workshop layout optimization method, device, electronic equipment, and storage medium based on a vertical model. By acquiring the basic mathematical model and layout optimization requirements of the target workshop, the direction of workshop optimization can be clearly defined. Then, using a pre-constructed workshop simulation-driven model, the layout optimization requirements are parsed into structured layout constraints. These constraints are then supplemented to obtain a set of layout constraints, ensuring that the set covers both explicit user requirements and implicit industry indicators, thus guaranteeing the feasibility of the generated workshop layout scheme. Subsequently, based on the workshop simulation-driven model, the set of layout constraints is transformed to obtain an extended workshop layout constraint function. Based on this extended constraint function and the target workshop, the basic mathematical model is optimized, resulting in a dynamic workshop simulation model capable of simulating multiple scenarios in actual production. This reduces reliance on manual labor during modeling, thereby improving the efficiency of workshop model construction. Finally, the dynamic workshop simulation model is iteratively optimized, and the resulting optimized layout information is the optimal workshop layout scheme obtained through multiple rounds of verification, improving the accuracy of workshop layout optimization. Attached Figure Description

[0016] Figure 1 This is a flowchart of the workshop layout optimization method based on a vertical model provided in an embodiment of this application; Figure 2 yes Figure 1 The flowchart of step S102 in the document; Figure 3 yes Figure 1 The flowchart of step S103 in the process; Figure 4 yes Figure 1 The flowchart of step S105 in the process; Figure 5 yes Figure 1 The flowchart of step S106 in the process; Figure 6 yes Figure 5 The flowchart of step S502 in the document; Figure 7 yes Figure 5The flowchart of step S503 in the process; Figure 8 This is a schematic diagram of the structure of the workshop layout optimization device based on the vertical model provided in the embodiments of this application; Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0020] First, let's analyze some of the terms used in this application: Workshop Layout Optimization System: This system is a comprehensive auxiliary decision-making system integrating data acquisition, digital modeling, constraint control, intelligent optimization, and simulation verification. Its core function is to scientifically plan and optimize workshop layout schemes. The system first collects basic workshop data and optimization objectives. Using a modeling module, it transforms workshop components such as production equipment, auxiliary facilities, and functional areas into digital models, and integrates basic and extended constraints to construct a complete constraint system. Then, it generates multiple sets of candidate feasible solutions using intelligent optimization algorithms such as genetic algorithms and particle swarm optimization. Combined with a dynamic simulation module, it simulates the actual production scenarios of each candidate scheme and calculates the suitability of the scheme. Subsequently, through iterative updates, it selects the optimal solution and finally outputs an optimized scheme that includes intuitive layout drawings, performance evaluation reports, and constraint satisfaction proofs. It also supports real-time adjustment of scheme parameters and secondary simulations. This system not only replaces the subjective biases of traditional experience-based layouts but also proactively avoids layout conflicts and production risks, significantly improving the efficiency, compliance, and feasibility of workshop layout.

[0021] Artificial intelligence (AI) is a new branch of computer science that studies, develops, and applies theories, methods, technologies, and systems to simulate, extend, and expand human intelligence. It aims to understand the essence of intelligence and produce intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thought. Furthermore, AI utilizes digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results.

[0022] Workshop layout optimization refers to actions taken to improve workshop performance by adjusting the position and layout of various components within the workshop. For example, in the electronics manufacturing industry, optimizing the layout of an electronics production workshop can improve production efficiency or reduce operating costs.

[0023] Currently, the common method for optimizing workshop layout based on vertical models involves professionals constructing a workshop simulation model based on the current layout of the workshop and the optimization goals. However, manually constructed workshop simulation models are often prone to oversights in constraints, resulting in inaccurate workshop layout optimization solutions. Therefore, improving the accuracy of workshop layout optimization has become an urgent technical problem to be solved.

[0024] Based on this, embodiments of this application provide a workshop layout optimization method and apparatus, electronic device and storage medium based on a vertical model, aiming to improve the accuracy of workshop layout optimization.

[0025] The workshop layout optimization method, apparatus, electronic device, and storage medium based on a vertical model provided in this application are specifically described through the following embodiments. First, the workshop layout optimization method based on a vertical model in this application is described.

[0026] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0027] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0028] The workshop layout optimization method based on a vertical model provided in this application relates to the field of artificial intelligence technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the workshop layout optimization method based on a vertical model, but is not limited to the above forms.

[0029] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0030] Figure 1 This is an optional flowchart of a vertical model-based workshop layout optimization method provided in this application embodiment. The vertical model-based workshop layout optimization method can be applied to a workshop layout optimization system. Figure 1 The method may include, but is not limited to, steps S101 to S106.

[0031] Step S101: Obtain the basic mathematical model of the target workshop and obtain the layout optimization requirements of the target workshop; Step S102: Based on the preset workshop simulation driving model, the layout optimization requirement information is parsed to obtain structured layout constraint information. Step S103: Supplement the structured layout constraint information to obtain the layout constraint set information; Step S104: Based on the workshop simulation-driven model, perform function transformation on the layout constraint set information to obtain the workshop layout extended constraint function; Step S105: Based on the workshop layout extended constraint function and the target workshop, optimize the basic mathematical model to obtain the dynamic workshop simulation model; Step S106: Iteratively optimize the dynamic workshop simulation model to obtain optimized workshop layout information.

[0032] Steps S101 to S106 of this embodiment, by acquiring the basic mathematical model and layout optimization requirement information of the target workshop, can clarify the direction of workshop optimization. Then, using a pre-built workshop simulation-driven model, the layout optimization requirement information is parsed into structured layout constraint information, and the structured layout constraint information is supplemented to obtain layout constraint set information. This layout constraint set information can cover the explicit needs of users and the implicit indicators of the industry, thereby ensuring the feasibility of the generated workshop layout scheme. Subsequently, based on the workshop simulation-driven model, the layout constraint set information is transformed into a function to obtain the workshop layout extended constraint function. Based on the workshop layout extended constraint function and the target workshop, the basic mathematical model is optimized to obtain a dynamic workshop simulation model that can simulate multiple scenarios in actual production. This reduces the reliance on manual labor in the modeling process, thereby improving the construction efficiency of the workshop model. Finally, the dynamic workshop simulation model is iteratively optimized. The obtained workshop optimized layout information is the optimal workshop layout scheme obtained in multiple rounds of verification, improving the accuracy of workshop layout optimization.

[0033] In step S101 of some embodiments, the target workshop refers to the production workshop that needs to be optimized in layout. For example, in the context of automobile manufacturing, the target workshop may be the automobile assembly workshop.

[0034] A basic mathematical model is a mathematical model constructed based on the physical parameters and basic equipment data of the target workshop. It includes information such as the spatial boundaries of the target workshop, equipment attributes, and workshop optimization objectives. For example, in a machining scenario, a basic mathematical model can be a model that includes information such as the length and width of the workshop, machine tool coordinates and dimensions.

[0035] Layout optimization requirements refer to various requirements put forward by users regarding the optimization of the target workshop layout. For example, in the production scenario of an electronic product workshop, the layout optimization requirements may be requirements put forward by the workshop director of the electronic product workshop, such as "the production capacity should reach 5,000 pieces per day, the equipment utilization rate should be maintained at 80%-90%, and the width of the fire passage should be ≥1.5 meters". It should be noted that the layout optimization requirements can be voice data or text data.

[0036] This application embodiment can obtain the physical parameters and basic equipment data of the target workshop through on-site measurement and data collection. Secondly, based on the physical parameters and basic equipment data, professionals can generate functional expressions for the spatial boundaries and equipment attributes of the target workshop. In addition, by converting the optimization objective of the target workshop proposed by the user into a function, a workshop optimization function can be obtained. Finally, by integrating the functional expressions for the spatial boundaries and equipment attributes of the target workshop with the workshop optimization function, a basic mathematical model of the target workshop can be obtained. Specifically, the method for on-site measurement and data collection of the target workshop can be to organize an engineering team to use laser rangefinders, CAD drawing tools, etc., to collect data such as the length, width, and height of the target workshop, the ground load-bearing capacity, and the location and size of doors and windows. At the same time, data such as the equipment model, length, width, and height dimensions, energy consumption parameters, operating space requirements, and minimum safe distance between equipment of each piece of equipment in the target workshop can be collected.

[0037] Furthermore, in this embodiment of the application, the required information for optimizing the layout of the target workshop can be obtained through interviews with workshop personnel, questionnaires, or on-site surveys, i.e., layout optimization requirement information.

[0038] In step S102 of some embodiments, the preset workshop digitization driving model refers to a pre-built model used to drive workshop digitization and simulation processing. Simulation processing can include requirements analysis, function transformation, etc. It should also be noted that the workshop digitization driving model is typically a vertical model specifically fine-tuned or specially trained, such as a large language model adapted to the workshop production and manufacturing field. Structured layout constraint information refers to layout constraint information with a clear structure. For example, in a food processing workshop scenario, when the structure of the layout constraint information is "constraint object, type, parameter, priority," the structured layout constraint information can be represented as "constraint object: sterilization equipment, type: safety class, parameter: distance from other equipment ≥ 2 meters, priority: mandatory."

[0039] In this embodiment, key information can be extracted from the layout optimization requirement information based on a pre-built workshop simulation-driven model. Then, the layout optimization requirement information is classified according to preset dimensions to obtain multi-dimensional constraint information. Finally, based on the extracted key information, the multi-dimensional constraint information is transformed into structured layout constraint information.

[0040] For details, please refer to Figure 2 In some embodiments, step S102 may include, but is not limited to, steps S201 to S203: Step S201: Based on the workshop simulation-driven model, extract key information from the layout optimization requirement information to obtain the constraint objects and quantitative parameters; Step S202: Based on the preset demand classification dimensions, the layout optimization demand information is classified to obtain multi-dimensional constraint information of the workshop layout; Step S203: Based on the constraint objects and quantification parameters, the multi-dimensional constraint information of the workshop layout is processed in a structured manner to obtain structured layout constraint information.

[0041] In step S201 of some embodiments, the constraint object refers to the specific object that is restricted or required in the layout optimization requirement information. It should be noted that the constraint object is the subject of the constraint rule. Therefore, the constraint object is usually equipment, area, performance index, etc. in the workshop. For example, in the scenario of automobile final assembly workshop, the constraint object is usually the final assembly line, parts storage area, production efficiency, etc.

[0042] Quantitative parameters refer to the specific numerical requirements for the constrained objects in the layout optimization requirements information. For example, in the scenario of logistics warehousing workshop, quantitative parameters can be 1.8 meters in the shelf spacing ≥ 1.8 meters and 5 times in the daily turnover rate ≥ 5 times.

[0043] In this embodiment, the user-provided layout optimization requirements (such as "daily production capacity of electronic workshop ≥ 8000 pieces, distance between SMT equipment and testing equipment ≥ 3 meters") can be input into a preset workshop simulation driving model. Then, the workshop simulation driving model can use semantic recognition algorithms and built-in industrial knowledge graphs to locate core terms such as equipment, area, and performance indicators in the layout optimization requirements as constraint objects, and then identify the numerical expressions in the layout optimization requirements as quantitative parameters.

[0044] In step S202 of some embodiments, the preset requirement classification dimension refers to a pre-defined standard used to classify layout optimization requirement information, such as constraint nature, urgency, scope of effect, etc.

[0045] Multi-dimensional constraint information for workshop layout refers to the layout constraints presented from different demand classification dimensions. It is important to know that each piece of information in the layout optimization demand information should have a clear classification attribute in any dimension.

[0046] Before classifying the layout optimization requirements, this application embodiment needs to clarify the requirements classification dimensions and set the classification attributes for each requirements classification dimension. For example, the classification attributes for constraints are safety, efficiency, space, etc., and the classification attributes for urgency are mandatory, important, and recommended.

[0047] After obtaining the requirement classification dimensions and the classification attributes of each requirement classification dimension, the embodiments of this application can match each constraint information in the layout optimization requirement information with the classification attributes of the requirement classification dimensions based on the semantic information of the layout optimization requirement information. For example, in the requirement classification dimension of constraint nature, the layout optimization requirement information "fire lane width ≥ 1.2 meters" can be clearly understood through semantic understanding to involve production safety. Therefore, the layout optimization requirement information "fire lane width ≥ 1.2 meters" is matched with the safety category classification attribute in the constraint nature, so the layout optimization requirement information "fire lane width ≥ 1.2 meters" can be classified as a safety constraint. The above classification process is repeated until each constraint information in the layout optimization requirement information has been classified, and the multi-dimensional constraint information of the workshop layout can be obtained.

[0048] In step S203 of some embodiments, the layout optimization requirement information can be adjusted by taking the constraint object, quantitative parameters, and multi-dimensional constraint information of workshop layout as core elements according to the pre-set structured format. For example, when the pre-set structured format is a combination of fields such as constraint object, constraint type, urgency level, scope of action, and quantitative parameters, the layout optimization requirement information can be organized as: constraint object: fire lane width, constraint type: safety type, urgency level: mandatory, scope of action: global, quantitative parameter: ≥1.2 meters.

[0049] Steps S201 to S203 of this embodiment, based on the workshop simulation-driven model, extract key information from the layout optimization requirement information to obtain constraint objects and quantification parameters. This avoids ambiguity or omission of constraint information in the layout optimization requirement information. Secondly, based on preset requirement classification dimensions, the layout optimization requirement information is classified to obtain multi-dimensional constraint information of the workshop layout, which avoids the problem of chaotic layout optimization requirement information. Finally, based on constraint objects and quantification parameters, the multi-dimensional constraint information of the workshop layout is structured to obtain structured layout constraint information, which improves the standardization of constraint information and reduces distortion and loss of constraint information during transmission, thereby ensuring the accuracy of the generated constraint function.

[0050] In step S103 of some embodiments, the layout constraint set information refers to a set of constraints that covers the user's requirements and industry-specified requirements. For example, in a chemical production workshop scenario, the layout constraint set information may be a set of various constraints, such as equipment explosion-proof spacing, personnel passage width, and production efficiency indicators.

[0051] This application embodiment can, based on a preset multi-level indicator knowledge base, first perform keyword matching on the structured layout constraint information to obtain the first constraint extension information, then perform semantic matching to obtain the second constraint extension information, and finally merge the first and second constraint extension information to obtain the layout constraint set information.

[0052] For details, please refer to Figure 3 In some embodiments, step S103 may include, but is not limited to, steps S301 to S303: Step S301: Based on the preset multi-level indicator knowledge base, perform keyword matching on the structured layout constraint information to obtain the first constraint extension information; Step S302: Based on the multi-level indicator knowledge base, perform semantic matching on the structured layout constraint information to obtain the second constraint extension information; Step S303: Merge the first constraint extension information and the second constraint extension information to obtain the layout constraint set information.

[0053] In step S301 of some embodiments, the preset multi-level indicator knowledge base refers to a pre-constructed database that is divided into levels and contains various layout constraint-related indicators. It should be noted that the levels in the multi-level indicator knowledge base include general industrial standard layer, industry specification layer, enterprise process layer, etc. For example, in the scenario of automotive parts production workshop, the multi-level indicator knowledge base may be a database containing national fire protection standards, automotive industry equipment spacing specifications, and enterprise internal logistics path requirements.

[0054] The first constraint extension information refers to the constraint extension content obtained from the multi-level indicator knowledge base that directly corresponds to the keywords of the structured layout constraint information. For example, in the scenario of a machining workshop, if the keyword of the structured layout constraint information is equipment utilization rate, then the first constraint extension information can be directly related information from the multi-level indicator knowledge base, such as CNC machine tool utilization rate ≥85% and milling machine maintenance interval ≤7 days.

[0055] This application embodiment can establish mapping rules between keywords and a pre-built multi-level indicator knowledge base. For example, the keyword "fire lane" can be directly associated with fire-related indicators in the multi-level indicator knowledge base, and equipment utilization rate can be associated with production efficiency-related indicators in the multi-level indicator knowledge base. Furthermore, keywords are extracted from the structured layout constraint information to obtain constraint core keywords. Then, according to the above mapping rules, the constraint information corresponding to the constraint core keywords in the multi-level indicator knowledge base can be queried to form the first constraint extension information.

[0056] In step S302 of some embodiments, the second constraint extension information refers to constraint extension content obtained from a multi-level indicator knowledge base that is semantically related to the structured layout constraint information. For example, in a medical device manufacturing workshop scenario, if the semantics of the structured layout constraint information is to ensure aseptic production, the second constraint extension information may be hidden information from the multi-level indicator knowledge base, such as an isolation zone between aseptic and non-aseptic areas ≥3 meters and an air exchange rate of the ventilation system ≥15 times / hour. It should be noted that the second constraint extension information is supplementary to the first constraint extension information.

[0057] In this embodiment, semantic word segmentation and intent recognition are first performed on each structured layout constraint information to obtain the information intent of each structured layout constraint information. Then, based on the information intent, topic similarity matching and scene association matching are performed on each indicator in the multi-level indicator knowledge base. In this way, constraint information that has semantic association with the structured layout constraint information in the multi-level indicator knowledge base can be found, namely the second constraint extension information.

[0058] In step S303 of some embodiments, duplicate constraint information is deleted by comparing the first constraint extension information and the second constraint extension information. For example, if both the first constraint extension information and the second constraint extension information contain the fire lane width ≥ 1.2 meters, any constraint information with a fire lane width ≥ 1.2 meters is deleted, while retaining one constraint information with a fire lane width ≥ 1.2 meters. Furthermore, the first constraint extension information and the second constraint extension information with the duplicate constraint information deleted are subjected to structuring processing to obtain structured layout constraint information.

[0059] Steps S301 to S303 as shown in the embodiments of this application involve keyword matching of structured layout constraint information based on a preset multi-level indicator knowledge base to obtain first constraint extension information, and semantic matching of structured layout constraint information based on the multi-level indicator knowledge base to obtain second constraint extension information. Then, the first constraint extension information and the second constraint extension information are merged to ensure that the obtained layout constraint set information covers both the easily searchable specification requirements in the multi-level indicator knowledge base and the difficult-to-find specification requirements in the multi-level indicator knowledge base, thereby improving the comprehensiveness of the layout constraint set information.

[0060] In step S104 of some embodiments, the workshop layout extension constraint function refers to constraint information expressed in function form. For example, in the scenario of a chemical production workshop, the workshop layout extension constraint function can be a function representation of various constraints such as equipment explosion-proof distance, personnel passage width, and production efficiency indicators.

[0061] According to the embodiment of this application, the constraint information can be transformed by function according to the classification attribute of each constraint information in the layout constraint set information based on the function transformation function of the workshop simulation driving model. For example, for spatial constraint equipment spacing ≥ 2 meters, the workshop simulation driving model can transform it into a geometric distance function ||x1-x2||≥2, where x1 and x2 are the abscissas of the center points of the equipment with equipment number 1 and 2 in the target workshop, respectively.

[0062] In step S105 of some embodiments, the dynamic workshop simulation model refers to a model that includes constraint rules and optimization objective functions and can be directly used for simulation operation to simulate multiple scenarios in actual production. The multiple scenarios may include equipment failure, demand fluctuations, and other conditions.

[0063] In this embodiment, the constraint functions contained in the basic mathematical model can be updated according to the workshop layout extended constraint function to obtain the target constraint function. Then, the workshop components in the target workshop are modeled and spliced ​​into a static workshop simulation model. Finally, the target constraint function and the target workshop optimization function are embedded in the static workshop simulation model to obtain a dynamic workshop simulation model.

[0064] For details, please refer to Figure 4 In some embodiments, the basic mathematical model includes a target workshop optimization function and a basic constraint function for workshop layout. The target workshop includes workshop components. Step S105 may include, but is not limited to, steps S401 to S404: Step S401: Based on the extended constraint function of the workshop layout, update the basic constraint function of the workshop layout to obtain the target constraint function of the workshop layout. Step S402: Model the workshop components to obtain the component simulation model; Step S403: Perform model splicing on the component simulation model to obtain a static workshop simulation model; Step S404: Based on the workshop layout target constraint function and the target workshop optimization function, the static workshop simulation model is embedded to obtain the dynamic workshop simulation model.

[0065] In step S401 of some embodiments, the target shop optimization function refers to the functional representation of the optimization objective of the target shop.

[0066] The basic constraint function for workshop layout refers to a mathematical function that only includes the core explicit requirements of workshop layout, such as equipment size and basic workshop space requirements.

[0067] Workshop components refer to the various functional units that make up the target workshop, such as production equipment, auxiliary facilities, and functional areas.

[0068] The workshop layout objective constraint function is a mathematical function that integrates the basic constraints expressed by the basic constraint function of the workshop layout and the extended constraints expressed by the extended constraint function of the workshop layout.

[0069] In this embodiment, the constraint information represented by the basic constraint function and the extended constraint function of the workshop layout can be compared. The constraint information not covered by the basic constraint function of the workshop layout in the extended constraint function can be retained, and the retained constraint function can be added to the basic constraint function of the workshop layout to obtain the target constraint function of the workshop layout.

[0070] In step S402 of some embodiments, the component simulation model refers to a digital model that can simulate the function and state of workshop components.

[0071] In this embodiment, the mathematical scheme represented by the workshop layout target constraint function can be transformed into detailed information for each workshop component using a large language model. This information includes, for example, dimensions, weight, operating power, operating space requirements, and location information. It is important to note that this detailed information for the workshop components can be described using a structured JSON format to ensure clear and orderly organization of the information, facilitating the rapid and accurate extraction of necessary information. Furthermore, using a structured JSON format to describe the detailed information of the workshop components ensures the integrity of the information, thereby improving the accuracy of workshop modeling. Further, after obtaining the detailed information of the workshop components in JSON format, a component simulation model can be obtained by calling a pre-built model component library. Specifically, based on the detailed information of the workshop components in JSON format, the component model corresponding to the workshop component, i.e., the component simulation model, can be accurately selected from the model component library.

[0072] In step S403 of some embodiments, a static workshop simulation model refers to a simulation model that only includes the spatial layout and basic physical properties of workshop components and cannot simulate the dynamic operating state of workshop components. For example, in a textile workshop scenario, a static workshop simulation model may be a digital model that only presents the spatial location of looms, winding machines, and finished product stacking areas and cannot simulate the fabric production flow.

[0073] In this embodiment, the component simulation models of each workshop component can be connected according to the spatial position relationship and production process logic relationship between each workshop component in the target workshop to obtain a static workshop simulation model.

[0074] In step S404 of some embodiments, a workshop simulation constraint model can be obtained by converting the workshop layout target constraint function into a rule module and then embedding the rule module into a static workshop simulation model. Furthermore, a dynamic workshop simulation model can be obtained by associating the target workshop optimization function with the built-in evaluation module in the workshop simulation constraint model.

[0075] Steps S401 to S404, as illustrated in this embodiment, update the basic constraint function of the workshop layout based on the extended constraint function to obtain the target constraint function. This retains the constraint information represented by the basic constraint function and supplements the constraint information represented by the extended constraint function, solving the problem of incomplete coverage of the basic constraint function. Based on this, component modeling is performed on the workshop components to obtain component simulation models. These simulation models are then combined to obtain a static workshop simulation model, avoiding layout deviations caused by the disconnect between the model and the target workshop. Finally, based on the target constraint function and the target workshop optimization function, the static workshop simulation model is embedded to obtain a dynamic workshop simulation model. This allows the model to transition from static to dynamic, enabling real-time calculation of optimization indicators such as production efficiency and cost, providing observable numerical indicators for workshop layout optimization.

[0076] In step S106 of some embodiments, the workshop optimization layout information refers to the scheme information containing the optimal layout of the workshop, wherein the optimal layout of the workshop may include the optimal coordinates of the equipment, logistics route planning, performance verification report, etc.

[0077] The embodiments of this application can use an optimization algorithm to calculate the set of candidate feasible solutions for a dynamic workshop simulation model, and then calculate the performance data of the dynamic workshop simulation model under each candidate feasible solution in the set of candidate feasible solutions to determine the optimal solution. Finally, based on the optimal solution, the layout information of the target workshop, i.e., the optimized layout information of the workshop, can be determined. The optimization algorithm can be a genetic algorithm, a particle swarm optimization algorithm, or an ant colony optimization algorithm, etc.

[0078] For details, please refer to Figure 5 In some embodiments, the dynamic workshop simulation model includes a workshop layout optimization objective function and a workshop layout optimization constraint function, and step S106 may include, but is not limited to, steps S501 to S505: Step S501: Based on the workshop layout optimization constraint function, calculate the feasible solution of the workshop layout optimization objective function to obtain a candidate feasible solution set, wherein the candidate feasible solution set includes multiple candidate feasible solutions; Step S502: Based on the dynamic workshop simulation model, simulate the candidate feasible solutions to obtain the fitness of the candidate solutions. Step S503: Based on the fitness of candidate solutions, iteratively update the set of candidate feasible solutions to obtain an updated set of candidate solutions; Step S504: Perform fitness comparison on the updated candidate solution set to obtain the optimal solution of the objective function; Step S505: Map the optimal solution of the objective function to the workshop layout to obtain the optimized workshop layout information.

[0079] In step S501 of some embodiments, the workshop layout optimization objective function refers to a quantitative mathematical function used to measure the effect of workshop layout optimization. For example, in the scenario of an electronic assembly workshop, the workshop layout optimization objective function can be a composite function that maximizes equipment utilization and minimizes material handling costs.

[0080] The workshop layout optimization constraint function is a quantitative mathematical function that defines the layout boundary conditions of the target workshop. The layout boundary can be dimensions such as space, safety, and compliance.

[0081] The candidate feasible solution set refers to a set of multiple layout schemes that meet the constraints. It is important to know that the candidate feasible solution set is the initial scheme pool for the iterative optimization of the dynamic workshop simulation model.

[0082] A candidate feasible solution refers to a single layout scheme in the set of candidate feasible solutions. It is important to know that each candidate feasible solution corresponds to a specific set of workshop component locations and area division schemes.

[0083] In this embodiment of the application, the quantization boundary can be determined according to the workshop layout optimization constraint function in the dynamic workshop simulation model. Then, according to the selected optimization algorithm, multiple workshop layout parameters that satisfy the quantization boundary are randomly generated, i.e. candidate feasible solutions. Furthermore, the generated candidate feasible solutions are integrated to obtain a set of candidate feasible solutions.

[0084] In step S502 of some embodiments, the fitness of a candidate solution refers to a quantitative index that measures the quality of a candidate feasible solution. It should be noted that the higher the fitness of a candidate solution, the closer the target workshop layout scheme represented by the candidate feasible solution is to the optimization target.

[0085] This application embodiment obtains a replicated discrete event simulation model by replicating a dynamic workshop simulation model, then obtains performance data by running the model based on candidate feasible solutions, and finally obtains the fitness of candidate solutions by combining the workshop layout optimization objective function quantification data.

[0086] For details, please refer to Figure 6 In some embodiments, step S502 may include, but is not limited to, steps S601 to S603: Step S601: Copy the dynamic workshop simulation model to obtain a copied discrete event simulation model; Step S602: Based on the candidate feasible solutions, perform model running processing on the simulation model of the replicated discrete events to obtain model performance data; Step S603: Quantize the model performance data to obtain the fitness of candidate solutions.

[0087] In step S601 of some embodiments, copying the discrete event simulation model refers to a copy of the simulation model that can be used independently for performance testing of candidate feasible solutions.

[0088] The embodiments of this application can obtain a replicated discrete event simulation model that is completely consistent with the dynamic workshop simulation model by copying parameters and components from the dynamic workshop simulation model.

[0089] In step S602 of some embodiments, model performance data refers to various data reflecting workshop production performance, such as production capacity and equipment utilization rate, output after the discrete event simulation model is run.

[0090] In this embodiment of the application, after inputting the workshop layout scheme represented by the candidate feasible solution into the above-mentioned replicated discrete event simulation model, the replicated discrete event simulation model can perform simulation operation according to the input information. Furthermore, by calculating indicators such as production capacity and equipment utilization rate of the running replicated discrete event simulation model, model performance data can be obtained.

[0091] In step S603 of some embodiments, the fitness value represented by each model performance data can be obtained by mapping each model performance data to the fitness space. Further, according to the weight ratio of each model performance data in the objective function of workshop layout optimization, all fitness values ​​are weighted and summed to obtain the fitness of candidate solutions.

[0092] Steps S601 to S603 as shown in the embodiments of this application involve replicating the dynamic workshop simulation model to obtain a replicated discrete event simulation model. Then, based on candidate feasible solutions, the replicated discrete event simulation model is run to obtain model performance data. Furthermore, the model performance data is quantified to obtain the fitness of candidate solutions, which can improve the calculation speed of candidate solution fitness, thereby improving the speed of workshop layout optimization.

[0093] In step S503 of some embodiments, updating the candidate solution set refers to the set containing newly generated and retained candidate feasible solutions. It should be noted that the overall fitness of the updated candidate solution set is higher than that of the candidate feasible solution set.

[0094] In this embodiment, the adjustment direction of the candidate solution can be determined based on the fitness of the candidate solution, and then the set of candidate feasible solutions can be adjusted in combination with the preset number of iterations and the adjustment direction to obtain an updated set of candidate solutions.

[0095] For details, please refer to Figure 7 In some embodiments, step S503 may include, but is not limited to, steps S701 to S702: Step S701: Determine the adjustment direction of candidate solutions based on their fitness. Step S702: Based on the preset number of iterations and the direction of candidate solution adjustment, the candidate feasible solution set is adjusted to obtain an updated candidate solution set.

[0096] In step S701 of some embodiments, the candidate solution adjustment direction refers to the specific direction in which the candidate feasible solution needs to be optimized and improved based on the numerical value of the candidate solution fitness and the performance shortcomings corresponding to the candidate solution fitness, such as insufficient production capacity or excessive cost. For example, in the scenario of a machining workshop, if the candidate solution fitness of a certain candidate feasible solution indicates that the material handling distance is too long, resulting in high cost and low fitness, then the candidate solution adjustment direction of the candidate feasible solution can be to reduce the parameter representing the distance between the CNC machine tool and the material temporary storage area.

[0097] According to the embodiments of this application, the performance shortcomings of a candidate feasible solution can be determined based on the pre-constructed mapping relationship between fitness and performance quality, as well as the fitness of the candidate feasible solution in each dimension. Furthermore, the adjustment direction can be derived in reverse based on the performance shortcomings to obtain the adjustment direction of the candidate solution.

[0098] In step S702 of some embodiments, the preset number of iterations refers to the upper limit of the total number of times the candidate feasible solution set is adjusted and optimized before the workshop layout optimization.

[0099] In this embodiment of the application, after clarifying the direction of candidate solution adjustment, the candidate feasible solution set is adjusted according to the direction of candidate solution adjustment to obtain the adjusted candidate feasible solution set. Further, the above steps S601 to S603 are repeated on the adjusted candidate feasible solution set until the number of adjustments to the candidate feasible solution set reaches the upper limit of the number of iterations, at which point the adjustment of the candidate feasible solution set is stopped and an updated candidate solution set is obtained.

[0100] Steps S701 to S702 shown in the embodiments of this application determine the adjustment direction of candidate solutions based on the fitness of candidate solutions, which avoids the problem of blind trial and error in the process of solving the optimal solution in the simulation model and improves the efficiency of workshop layout optimization. Furthermore, based on the preset number of iterations and the adjustment direction of candidate solutions, the candidate feasible solution set is adjusted to obtain an updated candidate solution set, which avoids the waste of resources caused by over-optimization.

[0101] In step S504 of some embodiments, the optimal solution of the objective function refers to the function solution that satisfies the constraints represented by the workshop layout optimization constraint function and has the highest fitness among the candidate solutions.

[0102] This application embodiment can obtain the fitness of the updated solution by calculating the fitness of all solutions in the updated candidate solution set. Furthermore, the fitness of all updated solutions is sorted in descending order, and the solution with the highest fitness is found, which is the optimal solution of the objective function. It should be noted that if there are multiple candidate solutions with the same highest fitness, the performance data of the discrete event simulation model under each solution is further compared, and the solution with better performance on the core indicators is selected as the optimal solution of the objective function. The core indicators can be capacity, cost or utilization rate.

[0103] In step S505 of some embodiments, the operating parameters, spatial positions, etc. of each workshop component in the target workshop can be determined according to the optimal solution of the objective function. Then, based on the operating parameters, spatial positions, etc. of each workshop component, a new workshop layout scheme, i.e., workshop optimized layout information, can be generated.

[0104] Steps S501 to S505, as shown in the embodiments of this application, calculate feasible solutions for the objective function of workshop layout optimization based on the workshop layout optimization constraint function to obtain a set of candidate feasible solutions. Then, based on a dynamic workshop simulation model, simulate the candidate feasible solutions to obtain the fitness of the candidate solutions. This solves the problem of subjective ambiguity caused by the traditional workshop modeling process, which can only evaluate workshop performance based on experience. This makes the optimization of workshop layout more accurate. Furthermore, based on the fitness of the candidate solutions, the set of candidate feasible solutions is iteratively updated to obtain an updated set of candidate solutions. The fitness of the updated set of candidate solutions is compared to obtain the optimal solution of the objective function. The optimal solution of the objective function is then mapped to the workshop layout, so that the obtained workshop optimization layout information is the optimal workshop layout scheme obtained in multiple rounds of verification, thus improving the accuracy of workshop layout optimization.

[0105] This application clarifies the direction of workshop optimization by acquiring the basic mathematical model and layout optimization requirements of the target workshop. Then, using a pre-built workshop simulation-driven model, the layout optimization requirements are parsed into structured layout constraints. These constraints are then supplemented to obtain a set of layout constraints, ensuring that the set covers both explicit user requirements and implicit industry indicators, thus guaranteeing the feasibility of the generated workshop layout scheme. Subsequently, based on the workshop simulation-driven model, the layout constraint set is transformed to obtain an extended workshop layout constraint function. Based on this extended constraint function and the target workshop, the basic mathematical model is optimized, resulting in a dynamic workshop simulation model that can simulate multiple scenarios in actual production. This reduces reliance on manual labor during modeling, improving the efficiency of workshop model construction. Finally, the dynamic workshop simulation model is iteratively optimized, and the resulting optimized layout information is the optimal workshop layout scheme obtained through multiple rounds of verification, improving the accuracy of workshop layout optimization.

[0106] Please see Figure 8 This application also provides a workshop layout optimization device based on a vertical model, which can implement the above-mentioned workshop layout optimization method based on a vertical model. The device includes: The basic information acquisition module 801 is used to acquire the basic mathematical model of the target workshop and the layout optimization requirements of the target workshop. The demand information parsing module 802 is used to parse the layout optimization demand information based on the preset workshop simulation driving model to obtain structured layout constraint information. The constraint supplementation module 803 is used to supplement the structured layout constraint information to obtain the layout constraint set information. The constraint information conversion module 804 is used to perform function conversion on the layout constraint set information based on the workshop simulation-driven model to obtain the workshop layout extended constraint function. The mathematical model optimization module 805 is used to optimize the basic mathematical model based on the workshop layout extended constraint function and the target workshop to obtain a dynamic workshop simulation model. The model iteration optimization module 806 is used to perform iterative optimization of the dynamic workshop simulation model to obtain optimized workshop layout information.

[0107] The specific implementation of the vertical model-based workshop layout optimization device is basically the same as the specific implementation of the vertical model-based workshop layout optimization method described above, and will not be repeated here.

[0108] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described vertical model-based workshop layout optimization method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0109] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and called and executed by the processor 901 to implement the vertical model-based shop layout optimization method of the embodiments of this application. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0110] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described vertical model-based workshop layout optimization method.

[0111] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0112] The workshop layout optimization method, device, electronic device, and storage medium based on a vertical model provided in this application embodiment obtain the basic mathematical model of the target workshop, acquire the layout optimization requirement information of the target workshop, parse the layout optimization requirement information based on a preset workshop simulation-driven model to obtain structured layout constraint information, supplement the structured layout constraint information to obtain layout constraint set information, perform function transformation on the layout constraint set information based on the workshop simulation-driven model to obtain the workshop layout extended constraint function, optimize the basic mathematical model based on the workshop layout extended constraint function and the target workshop to obtain a dynamic workshop simulation model, and perform iterative optimization on the dynamic workshop simulation model to obtain the optimized workshop layout information.

[0113] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0114] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0115] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0117] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0118] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, or indirect coupling or communication connection between the apparatus or units, and may be electrical, mechanical, or other forms.

[0120] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0122] If the integrated unit is implemented as a software functional unit 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 all 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 multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0123] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for optimizing a plant layout based on a vertical model, characterized in that, The method comprises: acquiring a basic mathematical model of a target workshop, and acquiring layout optimization demand information of the target workshop; performing information analysis on the layout optimization demand information based on a preset workshop numerical simulation driving model to obtain structured layout constraint information; performing constraint supplementing on the structured layout constraint information to obtain layout constraint set information; performing function conversion on the layout constraint set information based on the workshop numerical simulation driving model to obtain a workshop layout expansion constraint function; performing model optimization on the basic mathematical model based on the workshop layout expansion constraint function and the target workshop to obtain a dynamic workshop simulation model; performing cyclic iteration optimization on the dynamic workshop simulation model to obtain workshop optimization layout information.

2. The method of claim 1, wherein, The dynamic workshop simulation model comprises a workshop layout optimization objective function and a workshop layout optimization constraint function; the cyclic iteration optimization on the dynamic workshop simulation model to obtain the workshop optimization layout information comprises: performing feasible solution calculation on the workshop layout optimization objective function based on the workshop layout optimization constraint function to obtain a candidate feasible solution set, wherein the candidate feasible solution set comprises a plurality of candidate feasible solutions; performing simulation processing on the candidate feasible solutions based on the dynamic workshop simulation model to obtain candidate solution fitness; performing iteration updating on the candidate feasible solution set based on the candidate solution fitness to obtain an updated candidate solution set; performing fitness comparison on the updated candidate solution set to obtain an objective function optimal solution; performing workshop layout mapping on the objective function optimal solution to obtain the workshop optimization layout information.

3. The method of claim 2, wherein, The simulation processing on the candidate feasible solutions based on the dynamic workshop simulation model to obtain candidate solution fitness comprises: performing model copying on the dynamic workshop simulation model to obtain a copied discrete event simulation model; performing model running processing on the copied discrete event simulation model based on the candidate feasible solutions to obtain model performance data; performing quantitative processing on the model performance data to obtain the candidate solution fitness.

4. The method of claim 2, wherein, The iteration updating on the candidate feasible solution set based on the candidate solution fitness to obtain an updated candidate solution set comprises: determining a candidate solution adjustment direction based on the candidate solution fitness; performing candidate solution adjustment on the candidate feasible solution set based on a preset iteration number and the candidate solution adjustment direction to obtain the updated candidate solution set.

5. The method of claim 1, wherein, The basic mathematical model comprises a target workshop optimization function and a workshop layout basic constraint function; the target workshop comprises a workshop component; the model optimization on the basic mathematical model based on the workshop layout expansion constraint function and the target workshop to obtain a dynamic workshop simulation model comprises: performing constraint updating on the workshop layout basic constraint function based on the workshop layout expansion constraint function to obtain a workshop layout target constraint function; performing component modeling on the workshop component to obtain a component simulation model; performing model splicing on the component simulation model to obtain a static workshop simulation model; Based on the workshop layout target constraint function and the target workshop optimization function, function embedding is performed on the static workshop simulation model to obtain the dynamic workshop simulation model.

6. The method according to any one of claims 1 to 5, characterized in that, The constraint supplementing on the structured layout constraint information comprises: Based on the preset multi-level index knowledge base, keyword matching is performed on the structured layout constraint information to obtain first constraint expansion information; Based on the multi-level index knowledge base, semantic matching is performed on the structured layout constraint information to obtain second constraint expansion information; The information merging on the first constraint expansion information and the second constraint expansion information obtains the layout constraint set information.

7. The method according to any one of claims 1 to 5, characterized in that, The information parsing on the layout optimization demand information based on the preset workshop numerical simulation driving model obtains structured layout constraint information, which comprises: Based on the workshop numerical simulation driving model, key information extraction is performed on the layout optimization demand information to obtain constraint objects and quantization parameters; Based on the preset demand classification dimension, information classification is performed on the layout optimization demand information to obtain workshop layout multi-dimensional constraint information; Based on the constraint objects and the quantization parameters, the structured processing is performed on the workshop layout multi-dimensional constraint information to obtain the structured layout constraint information.

8. A plant layout optimization apparatus based on a vertical model, characterized by, The device comprises: a basic information acquisition module configured to acquire a basic mathematical model of a target workshop and acquire layout optimization demand information of the target workshop; a demand information parsing module configured to perform information parsing on the layout optimization demand information based on a preset workshop numerical simulation driving model to obtain structured layout constraint information; a constraint condition supplementing module configured to perform constraint supplementing on the structured layout constraint information to obtain layout constraint set information; a constraint information converting module configured to perform function conversion on the layout constraint set information based on the workshop numerical simulation driving model to obtain a workshop layout expansion constraint function; a mathematical model optimization module configured to perform model optimization on the basic mathematical model based on the workshop layout expansion constraint function and the target workshop to obtain a dynamic workshop simulation model; a model iterative optimization module configured to perform cyclic iterative optimization on the dynamic workshop simulation model to obtain workshop optimization layout information.

9. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the vertical model-based workshop layout optimization method in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the vertical model-based workshop layout optimization method in any one of claims 1 to 7.

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