Non-standard goods shelf intelligent design method and system based on parameterized model

Abstract

The invention discloses a non-standard goods shelf intelligent design method and system based on a parameterized model, and relates to the technical field of computer aided design, and the method comprises the following steps: collecting parameters, and carrying out abnormal value elimination and dimension unified processing to generate a standardized demand parameter set; defining core structure parameters and establishing a component association rule, and constructing a parameterized basic model through three-dimensional modeling; the storage rack structure is optimized by combining storage mechanics and material mechanics characteristics, stress concentration is reduced, and deformation under rated load is controlled; a simulation environment is built for statics and dynamics simulation, and a simulation test report is output; screening an optimal parameter combination to generate a detailed design scheme; and receiving user feedback, adjusting parameters and material selection in combination with production feasibility and cost budget, and repeating optimization simulation steps to generate a final scheme. Efficient design of the non-standard goods shelf is achieved through parametric modeling and intelligent optimization, the structure is safe and reliable, the material utilization rate is high, and the design quality and efficiency are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of computer-aided design technology, and in particular to a method and system for intelligent design of non-standard shelving based on parametric models. Background Technology

[0002] With the rapid development of the warehousing and logistics industry, the demand for warehouse space utilization from different industries is becoming increasingly diversified and personalized. Non-standard shelving, due to its ability to adapt to special warehousing environments, load-bearing requirements, and space constraints, is finding wider application in e-commerce, cold chain logistics, and manufacturing. Unlike standardized shelving, non-standard shelving requires customized design based on personalized parameters such as warehouse dimensions, cargo characteristics, and load-bearing requirements. Its structural form, material selection, and process requirements all need to be specifically adjusted. Currently, the demand for non-standard shelving in the warehousing industry is not only reflected in increased quantity but also in higher requirements for design precision, structural safety, material utilization, and design cycle. Traditional design models are no longer sufficient to meet the industry's development needs.

[0003] Traditional non-standard shelving design relies heavily on the experience of designers, with the design process primarily consisting of manual drawing and empirical calculations, resulting in several significant shortcomings. At the parameter processing level, the collection of requirement parameters lacks a standardized process, making it difficult to unify and integrate parameters from different sources and with different dimensions. This easily leads to parameter omissions or errors, causing the design to deviate from actual needs. At the model building level, fixed templates are often used for modification, lacking flexible parametric association mechanisms. Adjustments to component dimensions require repeated drawing modifications, resulting in low design efficiency and difficulty in ensuring compatibility between components. At the structural optimization level, simple strength checks are performed using empirical formulas without fully integrating the principles of materials mechanics and warehousing mechanics for systematic optimization. This easily leads to material redundancy or insufficient strength, affecting the safety and economy of shelving use. At the performance verification level, physical prototype testing is often relied upon, increasing design costs and extending the design cycle, and making it difficult to comprehensively simulate shelving performance under complex working conditions.

[0004] While existing computer-aided design tools have improved drafting efficiency to some extent, a complete intelligent design system for non-standard shelving has yet to be established. Some tools only support parametric modeling of single structures, lacking support for multi-module combinations and multi-objective optimization of non-standard shelving; structural optimization functions are limited to local parameter adjustments, failing to achieve coordinated optimization of topology and material usage; performance simulation is disconnected from the design process, and simulation results are difficult to directly feed back to model adjustments, resulting in low design iteration efficiency. Furthermore, cost estimation and production feasibility analysis are often performed independently during the design process, lacking real-time linkage with the design model, easily leading to situations where the design solution meets performance requirements but is too costly or cannot be mass-produced. As the structural complexity and application scenario complexity of non-standard shelving continue to increase, the shortcomings of traditional design methods and existing computer-aided design tools in terms of design accuracy, efficiency, safety, and economy become increasingly apparent. There is an urgent need for an intelligent design method and system based on parametric models to achieve standardization, intelligence, and efficiency throughout the entire non-standard shelving design process. Summary of the Invention

[0005] This invention proposes a non-standard shelving intelligent design method and system based on a parametric model to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a non-standard shelving intelligent design method based on a parametric model, comprising the following steps: Demand parameter collection and standardization steps: Collect four types of parameters for non-standard shelving: usage scenario, load, space constraints, and performance requirements through parameter input interface and external system interface; use mean filling to process missing data, remove outliers based on the 3σ principle, and convert the units of measurement through standardization algorithm to generate a standardized demand parameter set; Parametric model construction steps: Based on the standardized demand parameter set, define the core structural parameters of the non-standard shelving, establish parametric association rules for the shelving structural components, clarify the mapping relationship between the size parameters of each component and the demand parameters, and construct a parametric basic model of the non-standard shelving containing the core components through a 3D modeling engine; Structural optimization design steps: Based on the parametric basic model, combined with the principles of warehouse mechanics and the mechanical properties of materials, the topology of the rack structure is optimized. By adjusting the core structural parameters, the stress value in the stress concentration area is reduced, and the deformation range of the rack under rated load is controlled. Performance simulation verification steps: Build a professional shelf performance simulation test environment, perform static and dynamic simulations on the optimized parametric model, and output a simulation test report containing specific data and charts; Design scheme generation and output steps: Based on the simulation test results, select parameter combinations that meet the performance requirements and generate a non-standard shelving design scheme. The scheme includes 3D model drawings, component list, material specifications and assembly process description. The design scheme iteration and optimization steps are as follows: receive user feedback on the design scheme, combine production and processing requirements and cost budget standards, adjust the structural parameters and material selection of the parametric model, repeat the structural optimization and performance simulation verification steps until the final design scheme that meets user needs and actual application scenarios is generated.

[0007] Furthermore, it also includes a structural strength verification step, which verifies the strength of the core load-bearing components of the rack by constructing a structural strength safety factor calculation model, as shown in the following formula: ,in This is the structural strength safety factor; The yield strength of the material; This refers to the material quality coefficient. The structural reinforcement factor; This represents the maximum working stress of the core component. This is the load fluctuation coefficient; This is the temperature influence coefficient.

[0008] Furthermore, it also includes a material cost optimization step. Based on the structural parameters and material selection of the parametric model, combined with the material market price and processing loss rate, a correlation model between material usage and structural parameters is constructed. The material thickness is allocated according to the stress magnitude and distribution differences of each component of the rack, and a combination of high-strength and low-loss materials is adopted to optimize the material procurement plan.

[0009] Furthermore, in the parametric model construction step, a parameter-driven modular modeling approach is adopted, dividing the shelving into upright modules, beam modules, shelf modules, connector modules, and auxiliary accessory modules. Each module has independently set parametric control items, and standardized interfaces are established between modules through dimensional parameters and connection method parameters. At the same time, a version management mechanism for the parametric model is established to record the parameter adjustment history, adjustment reasons, and adjustment personnel information at different design stages.

[0010] Furthermore, in the structural optimization design step, a multi-objective optimization algorithm is introduced, taking the maximization of structural strength, the maximization of material utilization, and the minimization of manufacturing cost as optimization objectives. The weight coefficients of each objective are calculated by combining the analytic hierarchy process with the priority of user needs, and the optimal combination of structural parameters is obtained through iterative calculation. During the optimization process, the slenderness ratio of columns and beams and the deflection of the floor slabs are strictly constrained.

[0011] Furthermore, in the performance simulation verification step, a rack-cargo coupled simulation model is established, considering the impact of cargo stacking method and center of gravity distribution on rack performance. The rack stress state under three typical working conditions—full load, empty load, and off-center load—is simulated. The off-center load condition is set as the cargo stacking height on one side exceeding that of other areas by 30%. The stress distribution cloud map, deformation data, and vibration frequency curve under each working condition are output. At the same time, the simulation results are compared with industry standards and design specifications item by item to generate a compliance assessment report.

[0012] Furthermore, in the iterative optimization step of the design scheme, a design scheme evaluation system is constructed, which comprehensively evaluates the scheme from five dimensions: structural safety, material cost, production feasibility, assembly convenience, and service life. Each dimension sets quantitative evaluation indicators and scoring standards. Design schemes with a comprehensive score of 85 points or higher are listed as the optimal schemes, while schemes with a score of less than 60 points need to be re-optimized structurally. At the same time, the weight of the evaluation indicators is dynamically adjusted based on user feedback and changes in actual application scenarios.

[0013] Furthermore, a system for intelligent design of non-standard shelving based on a parametric model includes the following modules: Demand parameter acquisition module: Configures a visual parameter input interface and a standardized external system interface. The external system interface supports data docking with ERP and WMS systems. It supports two acquisition methods: manual input by users and data import from external systems. The acquired parameters have a built-in data verification algorithm to automatically remove outliers and perform unit unification processing to generate a standardized set of demand parameters. Parametric modeling module: Built-in professional 3D modeling engine, defines the core structural parameters of the shelving and the rules for the association of components, builds a parametric basic model, supports parameter-driven adjustment and modular combination of the model, provides model version management function, and records parameter adjustment history, adjustment reasons and adjustment personnel information; Structural optimization module: Integrates warehouse mechanics and materials mechanics analysis algorithms to perform topology optimization on the rack structure. It introduces a multi-objective optimization algorithm to balance structural strength, material utilization and cost, and outputs the optimized combination of structural parameters through iterative calculation. Performance simulation module: Build a professional static and dynamic simulation environment, establish a rack-cargo coupled simulation model, support simulation testing of various working conditions, output simulation data and compliance assessment reports, and provide visualization display function of simulation results; Design scheme generation module: Based on simulation test results, the optimal parameter combination is selected to generate a design scheme, and CAD and STEP format files can be exported. Iterative optimization module: Receives user feedback, combines production feasibility and cost budget, adjusts model parameters and material selection, links with structure optimization module and performance simulation module to iterate the solution, provides real-time feedback on iteration progress, until the final design solution is generated; Data storage module: It uses a distributed database to store requirement parameters, model data, simulation results, design schemes and user feedback information, and is configured with a scheduled backup mechanism to support fast data query, retrieval and backup.

[0014] Furthermore, it also includes a cost estimation module. This module is based on the structural parameters, material selection, and processing technology of the parametric model, combined with real-time updated material market prices, processing fee standards, and transportation cost data, to build a cost estimation model, automatically calculate the total cost of the design scheme, and the total cost covers material costs, processing costs, transportation costs, and management costs, generating a detailed cost report. The cost data is updated in real time as the model parameters are adjusted.

[0015] Furthermore, it also includes a remote collaboration module, which supports multiple users to access and operate the design system online simultaneously. It sets three permission levels: administrator, designer, and user. It synchronizes design progress and feedback information through system pop-ups and email reminders, supports online sharing and version synchronization of design files, and enables collaborative design across multiple teams in different regions.

[0016] Compared with existing technologies, the beneficial effects of this invention are: At the level of demand parameter processing and model building, this invention effectively integrates multi-source heterogeneous demand parameters through standardized parameter acquisition processes and data verification mechanisms, avoiding parameter omissions or errors and laying a precise data foundation for design work. The parameter-driven modular modeling approach enables flexible combination and rapid adjustment of various shelving components. Standardized interfaces connect modules, ensuring component compatibility, significantly reducing repetitive drawing workload, and substantially improving design efficiency. The model version management mechanism supports traceability and rollback of the design process, facilitating design optimization and iteration, problem identification, and enhancing the standardization of design work.

[0017] At the structural optimization and performance verification level, this invention integrates the principles of warehouse mechanics and materials mechanics, combined with a multi-objective optimization algorithm, to achieve synergistic optimization of structural strength, material utilization, and manufacturing cost. While ensuring the safety of the rack structure, it reduces the use of redundant materials and improves the economic efficiency of the design. A professional performance simulation environment supports simulation testing of various typical working conditions. The rack-cargo coupled simulation model comprehensively reflects complex stress states, and the simulation results are visualized and compared in real time with industry standards, ensuring the compliance and reliability of the design scheme and avoiding the increased costs and extended cycles associated with physical prototype testing. The structural strength verification model further strengthens the safety of core components, mitigating structural risks from the design stage.

[0018] In terms of cost control and collaboration efficiency, the material cost optimization mechanism and cost estimation module of this invention are linked, real-time correlation with material market prices and processing requirements, dynamic calculation of the total cost of the design scheme, and support for cost comparison analysis of multiple schemes, providing a clear direction for cost optimization and achieving a balance between performance and cost. The remote collaboration module supports cross-regional multi-team collaborative design, with clear permission division and real-time information synchronization, reducing communication barriers and improving the iteration efficiency of design schemes and user satisfaction. The design scheme evaluation system quantitatively evaluates design quality from multiple dimensions, ensuring that the final scheme takes into account safety, economy, production feasibility, and ease of use.

[0019] This invention comprehensively covers the entire process of non-standard shelving design, realizing the standardization, intelligence and efficiency of design work. It effectively solves the problems of traditional design methods such as reliance on experience, low efficiency and unstable quality. It is adaptable to the non-standard shelving design needs of different industries and scenarios, and provides efficient and reliable design solutions for the warehousing and logistics industry. It has broad industry application value and promotion prospects. Attached Figure Description

[0020] Figure 1 This is a schematic block diagram of the intelligent design method for non-standard shelving based on a parametric model proposed in this invention; Figure 2 This is a schematic block diagram of the non-standard shelving intelligent design system based on a parametric model proposed in this invention; Figure 3 A scatter plot showing the relationship between the thickness of the laminate and the maximum working stress. Figure 4 This is a horizontal bar graph showing the deformation of the core components of the shelving under different working conditions. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0024] Reference Figures 1 to 4 A non-standard shelving intelligent design method based on a parametric model includes the following steps: Demand Parameter Collection and Standardization Steps: The following steps are taken: Use scenario parameters, load parameters, space constraint parameters, and performance requirement parameters for non-standard shelving are collected through parameter input interfaces and external system interfaces. Use scenario parameters cover storage type, environmental temperature and humidity, and ground load-bearing capacity. Load parameters include single-layer rated load, cargo dimensions, and number of stacking layers. Space constraint parameters involve available warehouse length, width, height, and aisle width. Performance requirement parameters include shelving lifespan, corrosion resistance level, and ease of assembly and disassembly. Missing data is processed using the mean imputation method, outliers are removed based on the 3σ principle, and parameters of different dimensions are converted to a unified range using a standardization algorithm to generate a standardized demand parameter set. Parametric model construction steps: Based on the standardized requirement parameter set, define the core structural parameters of non-standard shelving, including column height and thickness, beam length and width, shelf thickness and connector specifications, establish parametric association rules for shelving structural components, clarify the mapping relationship between the size parameters of each component and the requirement parameters, and construct a parametric basic model of non-standard shelving containing core components such as columns, beams, shelves and connectors through a 3D modeling engine. The model supports parameter-driven real-time adjustment. Structural optimization design steps: Based on the parametric basic model, combined with the principles of warehouse mechanics and the mechanical properties of materials, the topology of the rack structure is optimized. The core optimization directions are structural strength, stability and material utilization. By adjusting the core structural parameters, the stress value in the stress concentration area is reduced, so that the deformation of the rack under the rated load is within the allowable range. Performance simulation verification steps: Build a professional shelf performance simulation test environment, conduct static and dynamic simulations on the optimized parametric model. The static simulation simulates the structural stress distribution and deformation under the rated load, and the dynamic simulation simulates the vibration response and shock tolerance during the process of goods storage and retrieval, and output a simulation test report containing specific data and charts; Design scheme generation and output steps: According to the simulation test results, screen the optimal parameter combination that meets the performance requirements, generate a detailed design scheme for the non-standard shelf. The scheme includes 3D model drawings, part lists, material specifications, and assembly process descriptions, and supports exporting mainstream format files such as CAD and STEP for production and processing; Design scheme iterative optimization steps: Receive the feedback from users on the design scheme, combine the production and processing technology requirements and cost budget standards, adjust the structural parameters and material selection of the parametric model, and repeat the structural optimization and performance simulation verification steps until the final design scheme that meets the user's needs and actual application scenarios is generated.

[0025] In the present invention, it also includes a structural strength checking step. The strength of the core bearing components of the shelf is verified by constructing a structural strength safety factor calculation model. The formula is as follows: Where is the structural strength safety factor, and a value greater than or equal to 1.2 is judged as qualified; is the material yield strength, with the unit of MPa; is the material quality coefficient, with a value range of 0.9 - 1.0, which is determined according to the material batch quality inspection results; is the structural enhancement coefficient, with a value range of 1.0 - 1.5, which is determined by the structural reinforcement design measures; is the maximum working stress of the core component, with the unit of MPa, which is obtained through static simulation; is the load fluctuation coefficient, with a value range of 1.0 - 1.2, which is determined according to the uniformity of goods stacking; is the temperature influence coefficient, with a value range of 0.95 - 1.05, which is determined by the temperature change range of the storage environment.

[0026] In the present invention, it also includes a material cost optimization step. Based on the structural parameters and material selection of the parametric model, combined with the material market price and processing loss rate, construct an association model between material usage and structural parameters, differentially distribute the material thickness according to the force magnitude and distribution of each component of the shelf, reduce the use of redundant materials on the premise of meeting the strength requirements, adopt high-strength and low-loss material combinations, and at the same time consider the impact of material procurement batch on cost, optimize the material procurement plan, and reduce the overall manufacturing cost of the shelf.

[0027] In this invention, a parameter-driven modular modeling approach is adopted in the parametric model construction step. The shelving is divided into upright modules, beam modules, shelf modules, connector modules, and auxiliary accessory modules. Each module has independently set parametric control items. The modules are associated with each other through standardized interfaces based on dimensional parameters and connection method parameters, which supports flexible replacement and combination of modules. At the same time, a version management mechanism for the parametric model is established to record the parameter adjustment history, adjustment reasons, and adjustment personnel information at different design stages, which facilitates the traceability and rollback of design schemes.

[0028] In this invention, a multi-objective optimization algorithm is introduced in the structural optimization design step. The optimization objectives are to maximize structural strength, maximize material utilization, and minimize manufacturing cost. The weight coefficients of each objective are calculated by combining the analytic hierarchy process with the priority of user needs. The optimal combination of structural parameters is obtained through iterative calculation. During the optimization process, the slenderness ratio of the columns and beams and the deflection of the shelves are strictly constrained so that the rack structure fully meets the relevant requirements of the warehouse equipment design specifications.

[0029] In this invention, during the performance simulation verification step, a rack-cargo coupled simulation model is established, considering the impact of cargo stacking method and center of gravity distribution on rack performance. The stress state of the rack under three typical working conditions—full load, empty load, and off-center load—is simulated. The off-center load condition is set as the stacking height of cargo on one side exceeding that of other areas by 30%. The stress distribution cloud map, deformation data, and vibration frequency curve under each working condition are output. At the same time, the simulation results are compared with industry standards and design specifications item by item to generate a detailed compliance assessment report.

[0030] In this invention, during the iterative optimization of the design scheme, a design scheme evaluation system is constructed. This system comprehensively evaluates the design scheme from five dimensions: structural safety, material cost, production feasibility, assembly convenience, and service life. Each dimension has quantitative evaluation indicators and scoring standards. Structural safety is scored based on the achievement of the strength safety factor; material cost is scored based on the cost per unit load; production feasibility is scored based on the complexity of the processing technology; assembly convenience is scored based on assembly time; and service life is scored based on the fatigue resistance of the materials. Design schemes with a comprehensive score higher than 85 are considered the optimal schemes, while schemes with a score lower than 60 require structural optimization. Furthermore, the weights of the evaluation indicators are dynamically adjusted based on user feedback and changes in actual application scenarios.

[0031] This invention includes the following modules: Demand parameter acquisition module: Configures a visual parameter input interface and a standardized external system interface. The external system interface supports data docking with ERP and WMS systems. It supports two acquisition methods: manual input by users and data import from external systems. It collects parameters such as usage scenarios, load, space constraints and performance requirements. It has a built-in data verification algorithm to automatically remove outliers and perform unit unification processing to generate a standardized set of demand parameters. Parametric modeling module: Built-in professional 3D modeling engine, defines the core structural parameters of the shelving and the rules for the association of components, builds a parametric basic model, supports parameter-driven adjustment and modular combination of the model, provides model version management function, records the parameter adjustment history, adjustment reasons and adjustment personnel information, and supports model version traceability and rollback; Structural optimization module: Integrates warehouse mechanics and materials mechanics analysis algorithms to perform topology optimization on the rack structure. It introduces a multi-objective optimization algorithm to balance structural strength, material utilization and cost. Through iterative calculation, it outputs the optimized combination of structural parameters and clarifies the adjustment range and basis of each parameter. Performance simulation module: Build a professional static and dynamic simulation environment, establish a rack-cargo coupled simulation model, support simulation tests of various working conditions such as full load, no load and off-center load, output simulation data such as stress distribution, deformation, vibration response and compliance assessment report, and provide visualization display function of simulation results; Design scheme generation module: Based on simulation test results, the optimal parameter combination is selected to generate a detailed design scheme. The scheme includes 3D drawings, parts list, material specifications and assembly process. It supports export of mainstream file formats such as CAD and STEP to meet production and processing needs. Iterative optimization module: Receives user feedback, combines production feasibility and cost budget, adjusts model parameters and material selection, links with structure optimization module and performance simulation module to iterate the solution, provides real-time feedback on iteration progress, until the final design solution is generated; Data storage module: It uses a distributed database to store requirement parameters, model data, simulation results, design schemes and user feedback information, and is configured with a scheduled backup mechanism to support fast data query, retrieval and backup, ensure data security and integrity, and provide data access control functions.

[0032] This invention also includes a cost estimation module. Based on the structural parameters, material selection, and processing technology of the parametric model, combined with real-time updated material market prices, processing fee standards, and transportation cost data, the module constructs a cost estimation model and automatically calculates the total cost of the design scheme. The total cost includes material costs, processing costs, transportation costs, and management costs, generating detailed cost reports. The module supports cost comparison analysis of different material combinations and process schemes, provides cost optimization suggestions to users, and updates cost data in real time as model parameters are adjusted, ensuring the accuracy and timeliness of cost estimation.

[0033] This invention also includes a remote collaboration module that supports multiple users accessing and operating the design system online simultaneously. It sets three permission levels: administrator, designer, and user. Administrators have full operational permissions, designers can perform model design, parameter adjustment, and scheme generation, and users can view design schemes and submit text feedback. It provides real-time message notification functionality, synchronizing design progress and feedback information through system pop-ups and email reminders. It supports online sharing and version synchronization of design files, enabling collaborative design across multiple teams in different regions, and improving design and communication efficiency.

[0034] The following two examples further illustrate specific embodiments of the present invention: Example 1: Intelligent Design of Non-standard Shelving for E-commerce Cold Chain Warehouses This embodiment is applied to the cold chain warehousing center of an e-commerce company. The center primarily stores temperature-sensitive goods such as fresh food and pharmaceutical reagents. The storage space is a rectangular area measuring 80 meters long, 40 meters wide, and 12 meters high, with a floor load-bearing capacity of 3.5 tons per square meter. The storage environment temperature is controlled between -18℃ and 5℃, and the relative humidity is between 40% and 60%. Goods are packaged in standardized cold chain boxes, each measuring 60cm × 40cm × 30cm and weighing 25kg. The maximum stacking depth is four layers. The shelving is required to be corrosion-resistant, easy to assemble and disassemble, and have a service life of at least 10 years. Traditional design methods require 15 days to complete the design and suffer from low material utilization and poor structural adaptability. This invention, through intelligent design methods and systems, standardizes and automates the design process, improving design quality and efficiency.

[0035] I. System Deployment The system is deployed on a local server cluster in the cold chain warehousing center. The demand parameter acquisition module is configured with a visual parameter input interface and standardized external system interfaces, which connect with the warehouse management system and enterprise resource planning system to achieve data integration and support automatic parameter import. The parametric modeling module has a built-in professional 3D modeling engine with a pre-built library of basic parameters for core components such as columns, beams, shelves, and connectors, supporting modular combination and parameter-driven adjustment. The structural optimization module integrates warehousing mechanics analysis algorithms and multi-objective optimization algorithms, improving optimization efficiency through the multi-core computing power of the server cluster. The performance simulation module is equipped with static and dynamic simulation tools, supporting the adjustment of material performance parameters under cold chain conditions. The design scheme generation module supports the export of CAD and STEP format files, adapting to downstream production and processing equipment. The iterative optimization module is linked with the user feedback platform to receive design adjustment opinions in real time. The data storage module adopts a distributed database, dividing the storage into independent storage partitions for demand parameters, model data, simulation results, etc., and configuring a daily scheduled backup mechanism. The cost estimation module accesses the industry material price database to obtain real-time market prices of corrosion-resistant materials such as stainless steel and aluminum alloys; the remote collaboration module supports multi-terminal collaboration between the design team, warehouse management team, and manufacturers.

[0036] II. Implementation Steps Demand parameter collection and standardization steps: Load parameters such as cargo dimensions, weight, and stacking layers are imported through the warehouse management system. Spatial constraint parameters such as warehouse length, width, height, and floor load-bearing capacity are obtained through the enterprise resource planning system. Performance requirements parameters such as environmental temperature and humidity, corrosion resistance level, and service life are manually entered through the parameter input interface. The mean-filling method is used to handle three missing cargo stacking uniformity parameters. Two outlier floor load-bearing capacity anomalies exceeding reasonable ranges are removed based on the 3σ principle. A standardization algorithm is used to convert parameters of different dimensions to a range of 0 to 1, generating a standardized demand parameter set containing 28 core parameters.

[0037] Parametric model construction steps: Based on a standardized set of requirements parameters, define the core structural parameters as follows: column height 11.5 meters, thickness 8 mm; beam length 3.6 meters, width 120 mm; shelf thickness 5 mm; and bolt-type connectors. Establish parameter association rules for each component: column height is linked to available warehouse height; beam length is adapted to cargo dimensions and aisle width; and shelf thickness matches the rated load of a single layer. Adopt a modular modeling approach, dividing the shelving into column, beam, shelf, connector, and moisture-proof base modules. Each module establishes standardized interface associations through dimensional and connection parameters. Construct a parametric basic model using a 3D modeling engine, adjusting the column spacing to 1.2 meters and beam layer spacing to 0.4 meters to ensure compatibility with cold chain box stacking requirements. The model supports real-time parameter adjustment and automatic updates. Establish a model version management mechanism to record initial modeling parameters, adjustment history, and adjustment personnel information for easy traceability.

[0038] Structural optimization design steps: A multi-objective optimization algorithm is introduced, with the optimization objectives being maximizing structural strength, maximizing material utilization, and minimizing manufacturing cost. Using the analytic hierarchy process (AHP) combined with user requirement priorities, the weights for structural strength (0.4), material utilization (0.3), and manufacturing cost (0.3) are determined. Based on the principles of warehouse mechanics and material mechanical properties, topology optimization is performed on the rack structure. The column cross-section shape is adjusted to rectangular, and reinforcement plates are added to connect the beams and columns to reduce the stress value in stress concentration areas. During the optimization process, the column slenderness ratio is constrained to not exceed 150, and the shelf deflection is constrained to not exceed 1 / 500 of the span to ensure compliance with warehouse equipment design specifications. The optimal combination of structural parameters is obtained through iterative calculations, with the column thickness adjusted to 9mm, the beam width adjusted to 130mm, and the shelf thickness kept constant at 5mm.

[0039] Structural strength verification steps: The strength of the beams and columns is verified using a structural strength safety factor calculation model. The formula is as follows: Made of 304 stainless steel. Material quality coefficient The value is taken as 0.95 based on the batch test results; structural reinforcement coefficient. The reinforcement plate is increased to 1.3; the maximum working stress of the column is obtained through static simulation. Maximum working stress of the beam The goods are stacked relatively evenly. Take 1.05; the temperature variation in the cold chain environment is small. Take 1.0. Calculate the column strength safety factor S = 205 × 0.95 × 1.3 / (120 × 1.05 × 1.0) = 205 × 1.235 / 126 = 253.175 / 126 ≈ 2.01, and the beam strength safety factor S = 205 × 0.95 × 1.3 / (105 × 1.05 × 1.0) = 253.175 / 110.25 ≈ 2.29. Both are greater than the qualified standard of 1.2, and the structural strength meets the requirements.

[0040] Material cost optimization steps: Based on the optimized structural parameters, a correlation model between material usage and structural parameters is constructed. According to the stress conditions of each component, the columns are made of 9mm thick 304 stainless steel, the beams are made of 130mm wide and 9mm thick stainless steel, the shelves are made of 5mm thick stainless steel, and the moisture-proof base is made of aluminum alloy. Considering real-time market prices, the unit price of 304 stainless steel is 2.8 yuan / kg, and the unit price of aluminum alloy is 2.2 yuan / kg, with a processing loss rate calculated at 5%. Through differentiated material allocation, while meeting strength requirements, the material usage in non-stressed areas of the shelves is reduced. Simultaneously, the procurement batch size is optimized; columns and beams are purchased in whole pieces to reduce cutting losses. The total material cost is reduced by 12% compared to the initial plan.

[0041] Performance simulation verification steps: A performance simulation test environment under cold chain conditions was built, and a rack-cargo coupled simulation model was established, considering the offset of the cargo stacking center of gravity and the anti-slip characteristics of the cold chain box. Three working conditions were simulated: full load, empty load, and off-center load. The off-center load condition was set so that the stacking height of goods on one side exceeded that of other areas by 30%. Static simulation results showed that the maximum deformation of the rack under full load was 3.2mm, located in the middle of the top beam, which is within the allowable range; the stress distribution was uniform, with no obvious stress concentration areas. Dynamic simulation simulated the impact of forklifts storing and retrieving goods. The vibration frequency was concentrated in the 5-8Hz range, and no resonance phenomenon was observed, indicating that the impact resistance met the requirements. The simulation results were compared item by item with the "Design Code for Warehouse Racking" GB / T27924-2011, and a compliance assessment report was generated. All indicators met the standard requirements.

[0042] Design scheme generation and output steps: Based on simulation test results and cost optimization analysis, the optimal parameter combination is selected to generate a detailed design scheme. The scheme includes 3D model drawings, a parts list, material specifications, and assembly process instructions, clearly defining the dimensional parameters, material types, and connection methods of each component. It supports exporting CAD format files for production and processing, and simultaneously generates a BOM list that is synchronized to the enterprise resource planning system, facilitating material procurement and production planning.

[0043] Design iteration and optimization steps: Feedback from the warehouse management team was received, requesting the addition of anti-collision feet for the shelves and mounting interfaces for signage. Based on production feasibility and cost budget, the parametric model was adjusted, adding 12mm thick anti-collision foot modules to the bottom of the uprights and pre-drilling holes for signage mounting on the sides of the beams. The structural optimization and performance simulation verification steps were repeated, ensuring the strength and safety factor still met requirements, and the cost increase was controlled within 3%. A design evaluation system was constructed, scoring the design scheme from five dimensions: structural safety, material cost, production feasibility, assembly convenience, and service life. A comprehensive score of 92 points was selected as the optimal scheme, generating the final design scheme.

[0044] III. Effect Verification Table 1 Comparison of the effects of different design methods in Example 1 Evaluation indicators Traditional design methods The intelligent design method of this invention Advantages Design cycle 15 days 3 days Higher design efficiency Material utilization rate 65% 88% Less material waste Structural safety factor 1.3-1.5 2.0-2.3 Superior structural safety Manufacturing costs higher lower Better economical User satisfaction 75% 96% More adaptable to demand Table 1 clearly demonstrates the significant advantages of this invention in the design of non-standard shelving for e-commerce cold chain warehousing. Traditional design methods rely on manual experience, with a design cycle of up to 15 days, material utilization of only 65%, significant material redundancy, and structural safety factors at the lower limit of acceptable levels. Furthermore, improper parameter coordination results in a user satisfaction rate of only 75%. This invention, through standardized parameter collection and parametric modeling, shortens the design cycle to 3 days, significantly improving design efficiency. The synergistic effect of multi-objective optimization algorithms and material cost optimization mechanisms increases material utilization to 88%, effectively reducing waste. Structural strength verification and performance simulation ensure a high level of structural safety, providing greater security. The cost optimization scheme reduces manufacturing costs while meeting the special requirements of cold chain environments, such as corrosion resistance and ease of assembly and disassembly, increasing user satisfaction to 96%. This solution is fully adaptable to the special working conditions of cold chain warehousing, providing reliable protection for the safe storage of temperature-sensitive goods and significantly improving the efficiency of warehouse space utilization and operational economy.

[0045] Example 2: Intelligent Design of Non-standard Shelving for Heavy Goods Storage in Manufacturing This embodiment is applied to a parts storage workshop of a heavy machinery manufacturing company. The workshop stores heavy goods such as engine blocks and gearbox housings, with individual items weighing 500-1500 kg and dimensions ranging from 120cm×80cm×60cm to 180cm×100cm×80cm, with a maximum stacking depth of two layers. The storage area is 60 meters long, 30 meters wide, and 9 meters high, with a ground load-bearing capacity of 5 tons per square meter. The shelving must possess high strength, high stability, and a service life of at least 15 years, and must support collaborative design by multiple teams across different regions. Traditional design methods suffer from long design cycles, incomplete performance verification, and low collaboration efficiency. This invention utilizes an intelligent design system to achieve efficient and precise design of heavy-duty non-standard shelving, meeting the heavy goods storage needs of the manufacturing industry.

[0046] I. System Deployment The system adopts a combined cloud and local deployment approach. The external system interface configured in the requirement parameter acquisition module connects with the enterprise's product lifecycle management system to automatically acquire parameters such as the 3D model, weight, and dimensions of the goods. It also allows designers to manually supplement site constraints and performance requirements. The parametric modeling module is deployed on a cloud server, supporting simultaneous online operation by multiple users. It includes a built-in parameter library specifically for heavy-duty racks, containing mechanical performance parameters of high-strength steel and standard connector specifications. The structural optimization module employs a distributed computing architecture, leveraging cloud computing power to accelerate multi-objective optimization iterations. The performance simulation module utilizes professional static and dynamic simulation software, supporting simulations of complex working conditions such as impact and vibration of heavy goods. The design scheme generation module supports exporting CAD and IGES format files conforming to industrial manufacturing standards, adapting to CNC machining equipment. The iterative optimization module is linked to the feedback system in the production workshop, receiving processing feasibility feedback in real time. The data storage module uses a geographically distributed, multi-active database to ensure the security of design data and fast cross-regional access. The cost estimation module connects to the real-time price database of the steel industry to dynamically obtain the market prices of high-strength carbon steel and alloy steel; the remote collaboration module supports cross-regional collaboration among design teams, process departments, and manufacturers, and sets up a three-level permission system.

[0047] II. Implementation Steps Requirements parameter collection and standardization steps: The 3D models, weight, dimensions, and other load parameters of 12 types of heavy goods were imported through the product lifecycle management system. The heaviest goods weighed 1500 kg, with maximum dimensions of 180cm × 100cm × 80cm. Spatial constraint parameters such as the length, width, height, and floor load-bearing capacity of the storage area were obtained through the workshop management system. Performance requirements parameters such as shelf life, stability requirements, and collaborative design requirements were manually entered. The mean-filling method was used to process four missing goods center-of-gravity coordinate parameters. One outlier in floor load-bearing capacity was removed based on the 3σ principle. A standardization algorithm was used to convert parameters of different dimensions to a unified range, generating a standardized requirements parameter set containing 32 core parameters.

[0048] Parametric model construction steps: Based on a standardized set of requirements parameters, define the core structural parameters as follows: column height 8.5 meters, thickness 16 mm; beam length 4.5 meters, width 200 mm; shelf thickness 12 mm; and high-strength bolt assemblies for connectors. Establish component parameter association rules: column height adapts to available warehouse height and the number of stacking layers; beam length is determined based on the maximum cargo size and aisle width; and shelf thickness matches the maximum weight of a single item. Adopt a parameter-driven modular modeling approach, dividing the shelving into column modules, beam modules, shelf modules, high-strength connector modules, and anti-slip mat modules. Each module is associated through standardized interfaces, supporting flexible module replacement. Construct a parametric basic model using a 3D modeling engine, setting the column spacing to 2.0 meters and the beam layer spacing to 1.2 meters to ensure suitability for storing and stacking heavy cargo. Establish a model version management mechanism to record the parameter adjustment history, reasons for adjustments, and personnel information at each design stage, supporting version synchronization and traceability across geographically dispersed teams.

[0049] Structural optimization design steps: A multi-objective optimization algorithm is introduced, with the optimization objectives of maximizing structural strength, maximizing material utilization, and minimizing manufacturing cost. Based on the priority of enterprise needs, the weights are determined as follows: structural strength 0.5, material utilization 0.2, and manufacturing cost 0.3. Based on the principles of materials mechanics and warehouse mechanics, topology optimization is performed on the rack structure. H-shaped steel sections are used for the uprights, and reinforcing ribs are added at the connection between the beams and uprights. Support beams are installed at the bottom of the shelves to reduce stress concentration. During the optimization process, the slenderness ratio of the uprights is constrained to not exceed 120, and the deflection of the shelves is constrained to not exceed 1 / 800 of the span, complying with heavy-duty rack design specifications. The optimal combination of structural parameters is obtained through iterative calculations: the upright thickness is adjusted to 18mm, the beam width is adjusted to 220mm, the shelf thickness remains at 12mm, and the spacing between support beams is set to 0.8 meters.

[0050] Structural strength verification steps: The strength of the core load-bearing components is verified using a structural strength safety factor calculation model, the formula is as follows: It is made of Q355 high-strength carbon steel. Material quality coefficient The structural reinforcement coefficient is set at 0.98 based on batch testing results. Due to the addition of reinforcing ribs and supporting beams, a value of 1.4 was used; the maximum working stress of the column was obtained through static simulation. Maximum working stress of the beam Heavy cargo is difficult to stack and generally results in uneven stacking. Take 1.15; the workshop ambient temperature fluctuates little. We take 1.02. The strength safety factor for the column is calculated as S = 355 × 0.98 × 1.4 / (180 × 1.15 × 1.02) = 355 × 1.372 / 209.4 = 487.06 / 209.4 ≈ 2.33, and the strength safety factor for the beam is S = 355 × 0.98 × 1.4 / (165 × 1.15 × 1.02) = 487.06 / 191.295 ≈ 2.55. Both meet the qualification standard of greater than 1.2, and the structural strength fully guarantees the safety of heavy cargo storage.

[0051] Material cost optimization steps: Based on the optimized structural parameters, a correlation model between material usage and structural parameters is constructed. According to the stress magnitude of each component, Q355 high-strength carbon steel is used for the columns and beams, wear-resistant alloy steel plates are used for the shelves, and high-strength bolt sets are selected for the connectors. Considering real-time material market prices, the unit price of Q355 carbon steel is 1.8 yuan / kg, the unit price of wear-resistant alloy steel plate is 3.5 yuan / kg, and the unit price of high-strength bolts is 8 yuan / set. The processing loss rate is calculated at 8%. Material thickness is allocated according to the differentiated stress distribution; the columns and beams adopt a thickened design, and the material specifications of non-core load-bearing components are optimized. At the same time, the procurement batch and processing technology are optimized, and laser cutting is used instead of traditional cutting to reduce material waste. The total cost is reduced by 15% compared to the initial plan.

[0052] Performance simulation verification steps: A performance simulation test environment for heavy-duty racking was built, and a rack-cargo coupled simulation model was established, considering cargo center of gravity shift, stacking method, and impact load during forklift access. Three working conditions were simulated: full load, empty load, and off-center load. The off-center load condition was set as 1500kg of heavy cargo stacked on one side and 500kg of light cargo stacked on the other side. Static simulation results showed that the maximum deformation of the racking under full load was 4.5mm, located in the middle of the top beam, meeting the deflection constraint requirements; the stress distribution was uniform, and the reinforcing ribs effectively dispersed the stress at the connection points. Dynamic simulation simulated the impact of a forklift picking up cargo. The maximum impact load was 1.3 times the weight of the cargo. The racking vibration response decayed rapidly without resonance, indicating good structural stability. The simulation results were compared with the "Design Code for Heavy-Duty Racking" GB / T30674-2014, generating a compliance assessment report. All indicators met the standard requirements.

[0053] Design scheme generation and output steps: Based on simulation test results and cost optimization analysis, the optimal parameter combination is selected to generate a detailed design scheme. The scheme includes 3D model drawings, a parts list, material specifications, assembly processes, and installation and commissioning instructions, clearly defining the dimensional parameters, material types, connection methods, and installation sequence of each component. It supports exporting CAD and IGES format files for use with CNC machining equipment, and simultaneously generates production process cards and quality inspection standards, which are synchronized to the production management system to guide production processing and quality inspection.

[0054] The design scheme iterative optimization steps are as follows: Feedback from the process department and manufacturer is received via a remote collaboration module, prompting requests to optimize the beam connection method and simplify the installation process. The parametric model is adjusted, optimizing the bolt connection between the beam and column to a mortise and tenon + bolt composite connection, reducing the number of bolts used and shortening installation time. The structural optimization and performance simulation verification steps are repeated; the strength safety factor remains unchanged, and installation time is reduced by 20%. A design scheme evaluation system is established, with a comprehensive score of 90 points, classifying it as the optimal scheme. The final design scheme is synchronized to all relevant teams via the remote collaboration module, achieving seamless integration of design, process, and production.

[0055] III. Effect Verification Table 2 Comparison of the effects of different design methods in Example 2 Evaluation indicators Traditional design methods The intelligent design method of this invention Advantages Design cycle 20 days 4 days Higher design efficiency Material utilization rate 60% 85% Less material waste Structural safety factor 1.4-1.6 2.3-2.6 Superior structural stability Collaboration efficiency lower higher Smoother cross-team collaboration Production adaptability generally good Greater processing feasibility Table 2 data visually demonstrates the significant advantages of this invention in the design of non-standard racking for heavy cargo storage in the manufacturing industry. Traditional design methods have a design cycle of up to 20 days, material utilization rate of only 60%, structural safety factor within the acceptable range, and cross-regional collaboration relies on email transmission and offline meetings, resulting in low efficiency and limited production adaptability due to the disconnect between design and production. This invention, through cloud-based parametric modeling and distributed computing, shortens the design cycle to 4 days, significantly improving design efficiency; multi-objective optimization and cost optimization mechanisms reduce material redundancy, increasing material utilization rate to 85% and reducing manufacturing costs; structural strength verification and multi-condition simulation ensure a high level of structural safety factor and superior stability; the remote collaboration module enables real-time collaboration among multiple teams across regions, significantly improving collaboration efficiency; the design scheme is deeply adapted to the production process, resulting in good production adaptability. This solution fully meets the high-strength and high-stability requirements of heavy cargo storage, providing reliable support for the safe storage and efficient turnover of heavy machinery parts, and significantly improving the warehousing management level and production operation efficiency of manufacturing enterprises.

[0056] The two embodiments respectively cover two typical scenarios: e-commerce cold chain and heavy cargo warehousing in manufacturing. They detail the technical aspects of the entire process, including parameter acquisition, model building, structural optimization, and performance simulation, and fully integrate the formula calculations and system modules of this invention. They also supplement technical solutions that were not fully described in detail, such as cold chain environment adaptation, heavy cargo impact simulation, and cross-regional collaboration. All technical aspects do not exceed the scope of this invention. The practicality and versatility of the solution in different scenarios are verified through tabular data, providing a feasible, end-to-end intelligent solution for non-standard shelving design.

[0057] Reference Figure 3The scatter plot reveals a negative correlation between shelf thickness and maximum working stress. As shelf thickness increases, the maximum working stress decreases, and the rate of decrease gradually slows. Due to the lighter weight of goods in cold chain warehousing, the maximum stress is generally lower than in heavy machinery warehousing. When the thickness increases from 4mm to 5mm, the stress decreases by 10MPa in cold chain scenarios and by 5MPa in heavy machinery scenarios, representing the most significant reduction. Beyond 6mm, the stress reduction is less than 5MPa, indicating that excessively increasing thickness has limited effect on reducing stress and instead increases material costs. Based on this principle, this invention selects a 5mm shelf thickness for cold chain warehousing and a 12mm thickness for heavy machinery warehousing, ensuring that stress remains within a safe range while avoiding material redundancy. This demonstrates the precision of the material selection in this invention and achieves an optimal balance between structural performance and cost.

[0058] Reference Figure 4 This horizontal bar chart illustrates the differences in deformation of the rack uprights and beams under five typical operating conditions, intuitively reflecting the impact of different conditions on the rack structure. The deformation is smallest under the unloaded condition, with uprights only 0.3mm and beams 0.5mm; the deformation is largest under the 50% eccentric load condition, with uprights 3.8mm and beams 6.2mm. This is because the eccentric load leads to uneven stress distribution, and the beams, as the main load-bearing components, generally deform more than the uprights. The performance simulation module of this invention accurately simulates the deformation under all operating conditions, and all values ​​are controlled within the allowable range of the warehouse equipment design specifications. For example, the beam deflection must be ≤ 1 / 500 of the span. Although the deformation is higher under the 50% eccentric load, it still meets the specifications. This chart allows for accurate identification of high-risk operating conditions, enabling targeted reinforcement of the beam structure during the design phase. It also verifies the comprehensive coverage of complex operating conditions by the performance simulation module of this invention, ensuring the structural stability of the rack under all operating conditions.

[0059] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A non-standard shelving intelligent design method based on a parametric model, characterized in that, Includes the following steps: Demand parameter collection and standardization steps: Collect four types of parameters for non-standard shelving: usage scenario, load, space constraints, and performance requirements through parameter input interface and external system interface; use mean filling to process missing data, remove outliers based on the 3σ principle, and convert the units of measurement through standardization algorithm to generate a standardized demand parameter set; Parametric model construction steps: Based on the standardized demand parameter set, define the core structural parameters of the non-standard shelving, establish parametric association rules for the shelving structural components, clarify the mapping relationship between the size parameters of each component and the demand parameters, and construct a parametric basic model of the non-standard shelving containing the core components through a 3D modeling engine; Structural optimization design steps: Based on the parametric basic model, combined with the principles of warehouse mechanics and the mechanical properties of materials, the topology of the rack structure is optimized. By adjusting the core structural parameters, the stress value in the stress concentration area is reduced, and the deformation range of the rack under rated load is controlled. Performance simulation verification steps: Build a professional shelf performance simulation test environment, perform static and dynamic simulations on the optimized parametric model, and output a simulation test report containing specific data and charts; Design scheme generation and output steps: Based on the simulation test results, select parameter combinations that meet the performance requirements and generate a non-standard shelving design scheme. The scheme includes 3D model drawings, component list, material specifications and assembly process description. The design scheme iteration and optimization steps are as follows: receive user feedback on the design scheme, combine production and processing requirements and cost budget standards, adjust the structural parameters and material selection of the parametric model, repeat the structural optimization and performance simulation verification steps until the final design scheme that meets user needs and actual application scenarios is generated.

2. The intelligent design method for non-standard shelving based on a parametric model according to claim 1, characterized in that, It also includes a structural strength verification step, which verifies the strength of the core load-bearing components of the rack by constructing a structural strength safety factor calculation model, as shown in the following formula: ,in This is the structural strength safety factor; The yield strength of the material; This refers to the material quality coefficient. The structural reinforcement factor; This represents the maximum working stress of the core component. This is the load fluctuation coefficient; This is the temperature influence coefficient.

3. The intelligent design method for non-standard shelving based on a parametric model according to claim 1, characterized in that, It also includes a material cost optimization step, which involves constructing a correlation model between material usage and structural parameters based on the structural parameters and material selection of the parametric model, combined with material market prices and processing loss rates, allocating material thickness according to the stress magnitude and distribution differences of each component of the rack, and using a combination of high-strength and low-loss materials to optimize the material procurement plan.

4. The intelligent design method for non-standard shelving based on a parametric model according to claim 1, characterized in that, In the parametric model construction step, a parameter-driven modular modeling approach is adopted, dividing the rack into upright modules, beam modules, shelf modules, connector modules, and auxiliary accessory modules. Each module has independently set parametric control items, and standardized interfaces are established between modules through dimensional parameters and connection method parameters. At the same time, a version management mechanism for the parametric model is established to record the parameter adjustment history, adjustment reasons, and adjustment personnel information at different design stages.

5. The intelligent design method for non-standard shelving based on a parametric model according to claim 1, characterized in that, In the structural optimization design step, a multi-objective optimization algorithm is introduced, taking the maximization of structural strength, the maximization of material utilization, and the minimization of manufacturing cost as optimization objectives. The weight coefficients of each objective are calculated by combining the analytic hierarchy process with the priority of user needs, and the optimal combination of structural parameters is obtained through iterative calculation. During the optimization process, the slenderness ratio of columns and beams and the deflection of the floor slabs are strictly constrained.

6. The intelligent design method for non-standard shelving based on a parametric model according to claim 1, characterized in that, In the performance simulation verification step, a rack-cargo coupled simulation model is established, considering the impact of cargo stacking method and center of gravity distribution on rack performance. The rack stress state under three typical working conditions of full load, empty load and off-center load is simulated. The off-center load condition is set as the stacking height of cargo on one side exceeds that of other areas by 30%. The stress distribution cloud map, deformation data and vibration frequency curve under each working condition are output. At the same time, the simulation results are compared with industry standards and design specifications item by item to generate a compliance assessment report.

7. The intelligent design method for non-standard shelving based on a parametric model according to claim 1, characterized in that, In the iterative optimization of design schemes, an evaluation system for design schemes is constructed, which comprehensively evaluates the schemes from five dimensions: structural safety, material cost, production feasibility, assembly convenience, and service life. Quantitative evaluation indicators and scoring standards are set for each dimension. Design schemes with a comprehensive score of 85 or higher are listed as the optimal schemes, while schemes with a score of less than 60 need to be re-optimized. At the same time, the weights of the evaluation indicators are dynamically adjusted based on user feedback and changes in actual application scenarios.

8. A system applying the intelligent design method for non-standard shelving based on a parametric model as described in any one of claims 1-7, characterized in that, Includes the following modules: Demand parameter acquisition module: Configures a visual parameter input interface and a standardized external system interface. The external system interface supports data docking with ERP and WMS systems. It supports two acquisition methods: manual input by users and data import from external systems. The acquired parameters have a built-in data verification algorithm to automatically remove outliers and perform unit unification processing to generate a standardized set of demand parameters. Parametric modeling module: Built-in professional 3D modeling engine, defines the core structural parameters of the shelving and the rules for the association of components, builds a parametric basic model, supports parameter-driven adjustment and modular combination of the model, provides model version management function, and records parameter adjustment history, adjustment reasons and adjustment personnel information; Structural optimization module: Integrates warehouse mechanics and materials mechanics analysis algorithms to perform topology optimization on the rack structure. It introduces a multi-objective optimization algorithm to balance structural strength, material utilization and cost, and outputs the optimized combination of structural parameters through iterative calculation. Performance simulation module: Build a professional static and dynamic simulation environment, establish a rack-cargo coupled simulation model, support simulation testing of various working conditions, output simulation data and compliance assessment reports, and provide visualization display function of simulation results; Design scheme generation module: Based on simulation test results, the optimal parameter combination is selected to generate a design scheme, and CAD and STEP format files can be exported. Iterative optimization module: Receives user feedback, combines production feasibility and cost budget, adjusts model parameters and material selection, links with structure optimization module and performance simulation module to iterate the solution, provides real-time feedback on iteration progress, until the final design solution is generated; Data storage module: It uses a distributed database to store requirement parameters, model data, simulation results, design schemes and user feedback information, and is configured with a scheduled backup mechanism to support fast data query, retrieval and backup.

9. The system of a non-standard shelving intelligent design method based on a parametric model according to claim 8, characterized in that, It also includes a cost estimation module, which is based on the structural parameters, material selection and processing technology of the parametric model, combined with real-time updated material market prices, processing fee standards and transportation cost data, to build a cost estimation model, automatically calculate the total cost of the design scheme, the total cost covers material costs, processing costs, transportation costs and management costs, and generate a detailed cost report. The cost data is updated in real time as the model parameters are adjusted.

10. The system of a non-standard shelving intelligent design method based on a parametric model according to claim 8, characterized in that, It also includes a remote collaboration module, which supports multiple users to access and operate the design system online at the same time. It sets three permission levels: administrator, designer and user. It synchronizes design progress and feedback information through system pop-ups and email reminders, supports online sharing and version synchronization of design files, and enables collaborative design across multiple teams in different regions.

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