A method and system for assembling a frame design for a whole cell

Abstract

The present application relates to the technical field of frame design, and particularly relates to a frame design method and system for assembled whole-cell intercell, which comprises the following steps: obtaining demand information and a model library, wherein the demand information comprises multiple frame models of intercell, and the demand function, demand size and bearing requirement of each frame model; the model library comprises multiple component models; determining the connection sequence of each frame model according to the demand function, determining the component model combination of the corner of each frame model in turn according to the connection sequence, determining the component model combination in each frame model and the corresponding connection sequence according to the demand size, and obtaining the component model sequence of intercell; determining the geometric characteristics and material of the component model in the component model sequence according to the bearing requirement, and generating the frame structure of intercell; the present application can provide a quick and flexible design scheme according to the individualized customization demand of intercell.

Description

Technical Field

[0001] This invention relates to the field of frame design technology, specifically to a method and system for designing prefabricated, integrated cell-to-cell frames. Background Technology

[0002] Prefabricated modular cell laboratories are cell laboratories designed using modular technology and rapidly assembled from prefabricated component models. They are widely used in modern biotechnology fields such as cell biology research, genetic engineering, cell engineering, enzyme engineering, and fermentation engineering, providing researchers with an efficient, safe, and reliable working environment.

[0003] The prefabricated, modular cell culture room adopts a modular design concept, dividing the laboratory space into multiple independent frame models based on its functional requirements, such as changing rooms, buffer rooms, cell culture rooms, quality control rooms, sterilization rooms, washing rooms, liquid nitrogen storage rooms, clean rooms, solution preparation rooms, and internal corridors. These frame models can be combined and adjusted according to actual needs to achieve a flexible spatial layout.

[0004] However, traditional design methods are complex and inefficient, making it difficult to provide fast and flexible design solutions for the personalized customization needs between cells. There is an urgent need for an efficient and intelligent design approach to meet the needs of diverse application scenarios.

[0005] This invention aims to provide an efficient and low-cost framework design method that, through intelligent processing, enables personalized customization between cells, thereby improving assembly speed and accuracy. Summary of the Invention

[0006] To address the aforementioned issues, this invention provides a prefabricated, modular frame design method and system for cells, which can offer rapid and flexible design solutions to meet the personalized customization needs of cells.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] On one hand, embodiments of the present invention provide a method for designing a frame between assembled cells, the method comprising the following steps:

[0009] Acquire requirement information and a model library. The requirement information includes multiple framework models between cells, as well as the required functions, required dimensions, and load-bearing requirements of each framework model. The model library includes multiple component models.

[0010] Based on the required functions, determine the connection order of each frame model, determine the component model combination at the corner of each frame model in sequence according to the connection order, determine the component model combination and corresponding connection order inside each frame model according to the required size, and obtain the component model sequence between cells.

[0011] Based on the load-bearing requirements, determine the geometric features and materials of the component models in the component model sequence, and generate the intercellular framework structure.

[0012] Optionally, the step of determining the component model combinations at the corners of each frame model in the order of connection, and determining the component model combinations and corresponding connection order within each frame model according to the required dimensions of the frame model, includes:

[0013] Construct a graph model containing various framework models, using the component models at the corners of the framework models as nodes of the graph model, and the connections between the component models as edges of the graph model.

[0014] Obtain the interface type of each component model. Starting from the starting node, use the depth-first search algorithm to search for adjacent nodes that match the interface type in the order of connection, and record the nodes found each time and the connection relationship between the nodes until the end node is found, thus obtaining the node sequence.

[0015] The component models in the model library are divided into multiple model sets that correspond one-to-one with each frame model according to the interface size. The component model is selected from the model set as the initial child node between adjacent nodes in the corresponding frame model.

[0016] The number of initial child nodes between adjacent nodes is determined based on the required size, the sealing performance of adjacent component models is determined based on the difference in interface size, and an objective function to maximize the sealing performance is established based on the sealing performance of each adjacent component model in the cell.

[0017] A greedy algorithm is used to iteratively search for the initial child nodes of each edge. After the condition for stopping the iteration is met, the optimal combination of child nodes for each edge is obtained.

[0018] The optimal combination of child nodes for each edge is embedded into the corresponding node sequence to obtain the component model sequence between cells.

[0019] Optionally, determining the sealing performance of adjacent component models based on differences in interface dimensions includes:

[0020] The differences in interface dimensions are obtained, and the sealing performance of adjacent component models is determined using a sealing performance function; the sealing performance function is:

[0021]

[0022] Where S is the sealing performance index, with a value between 0 and 1; t1 and t2 are the thicknesses of two adjacent components at the interface, respectively; k is the thickness difference sensitivity coefficient; E is the elastic modulus of the material; σ is the stress concentration factor at the interface; L is the actual sealing gap width; and L0 is the standard sealing gap width in the design.

[0023] Optionally, the objective function for maximizing sealing performance based on the sealing performance of each adjacent component model in the intercellular space includes:

[0024] The sealing performance weight coefficient of adjacent nodes in the node sequence is set as the first weight. The first weight is adjusted based on the number of initial child nodes between the initial child node and the nearest node to obtain the second weight of the initial child node.

[0025] The average of the second weights of adjacent initial child nodes is used to obtain the sealing performance weight coefficients of the corresponding adjacent component models;

[0026] The objective function is constructed by weighting and combining the sealing performance of each adjacent component model in the intercellular space with the corresponding sealing performance weight coefficients.

[0027] Optionally, the step of determining the geometric features and materials of the component models in the component model sequence according to the load-bearing requirements, and generating the inter-cell framework structure, includes:

[0028] Obtain the material of the component model, and use stress analysis method to perform mechanical analysis on the component model in the component model sequence to obtain stress distribution data;

[0029] The thickness of each component model is determined based on load-bearing requirements and stress distribution data, and the intercellular framework structure is generated.

[0030] Based on the connection order, the various component models are combined and spliced ​​to generate the framework structure between cells.

[0031] Optionally, determining the thickness of each component model based on load-bearing requirements and stress distribution data includes:

[0032] The maximum load value that each component model can withstand and the stress distribution data under different working conditions are determined based on the load-bearing requirements.

[0033] By combining stress distribution data, we can analyze the stress concentration areas and stress magnitude gradients within each component model to obtain the deformation and deformation trend of each component model during the load-bearing process.

[0034] Based on the load-bearing requirements, stress distribution data, and deformation degree, an objective function is established, and the thickness is set as the variable to be solved. The objective function is to meet the load-bearing requirements, reduce the stress concentration, and control the deformation within the deformation threshold. The thickness range of each component model is determined step by step.

[0035] On the other hand, embodiments of the present invention provide a prefabricated, modular intercellular frame design system, comprising:

[0036] At least one processor;

[0037] At least one memory for storing at least one program;

[0038] When the at least one program is executed by the at least one processor, the at least one processor performs the method described above.

[0039] The beneficial effects of this invention are as follows: This invention discloses a prefabricated, integrated intercellular frame design method and system. By acquiring requirement information and a model library, this invention determines the connection order of each frame model according to the required functions, sequentially determines the component model combinations at the corners of each frame model according to the connection order, and determines the component model combinations and corresponding connection orders within each frame model according to the required dimensions, thus obtaining the intercellular component model sequence. This initially forms an intercellular frame structure that meets the required functions and dimensions, achieving rapid design and efficient production. The geometric features and materials of the component models in the component model sequence are determined according to load-bearing requirements, generating the intercellular frame structure, thereby optimizing the component model sequence, improving the stability and safety of the overall structure, and ensuring that the intercellular maintains excellent sealing performance and load-bearing capacity under various working conditions. This invention, by optimizing the design process and gradually meeting the requirement information, optimizes the design of the intercellular frame structure, providing rapid and flexible design solutions for the personalized customization needs of intercellular. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating a prefabricated, modular intercellular frame design method according to an embodiment of the present invention.

[0042] Figure 2 This is a schematic diagram of the structure of a prefabricated, modular intercellular frame design system according to an embodiment of the present invention. Detailed Implementation

[0043] The following will provide a clear and complete description of the concept, specific structure, and technical effects of the present invention in conjunction with embodiments and accompanying drawings, so as to fully understand the purpose, solution, and effects of the present invention. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0044] refer to Figure 1 ,like Figure 1 The image shows a prefabricated, modular intercellular frame design method provided by an embodiment of the present invention. The method includes the following steps:

[0045] S100, Obtain requirement information and model library. The requirement information includes multiple framework models between cells, as well as the required functions, required dimensions and load-bearing requirements of each framework model. The model library includes multiple component models.

[0046] Specifically, each component model has corresponding attribute information (geometric features and materials) and interface type. The frame design of the prefabricated cell assembly should be rationally functionally divided according to different functional requirements such as cell culture and cell experiments. Each frame model has corresponding required functions, and the divided frame models include aseptic operation area, incubation area, preparation area, storage area, and cleaning and disinfection area, and must adopt an aseptic environment design. Since the functional requirements of the cell culture laboratory may change as scientific research progresses, the frame design should have a certain degree of flexibility and variability to facilitate later modification and adjustment.

[0047] The component model is expressed in the form of a parametric model. One parametric model corresponds to the model number, material, and interface type of a component model. Multiple parametric models form a model library.

[0048] S200: Determine the connection order of each frame model according to the required functions, determine the component model combination of each frame model corner according to the connection order, determine the component model combination and corresponding connection order inside each frame model according to the required dimensions, and obtain the component model sequence between cells.

[0049] The component model combination includes multiple component models; specifically, a beam, column, and connector are selected from the model library according to the interface type to form a component model combination; the component model combination includes beams, columns, and connectors; the positional order of each frame model is determined according to the required functions, which serves as the connection order; first, the component models located at the corners in each frame are determined as adjacent component models, and then the frames are filled to obtain the internal component model combination.

[0050] S300 determines the geometric features and materials of the component models in the component model sequence according to the load-bearing requirements, and generates the frame structure between cells.

[0051] The structural design must ensure overall seismic performance and stability to prevent structural vibrations from affecting cell culture and experimental results. A reasonable frame structure needs to be designed based on the weight and usage requirements of the equipment within the cell culture chamber to ensure that the load-bearing capacity meets the equipment's operational needs.

[0052] Compared with other prefabricated structures, the prefabricated integrated cell frame design places greater emphasis on the rationality of spatial layout, structural stability, precision of airflow organization and pressure control, high requirements for sealing and cleanliness, and the perfection of equipment and facility integration, in order to meet the special needs of cell culture experiments.

[0053] In some embodiments, S200, the step of determining the component model combinations at the corners of each frame model in sequence according to the connection order, and determining the component model combinations and corresponding connection order within each frame model according to the required dimensions of the frame model, includes:

[0054] S210, construct a graph model containing each frame model, use the component models at the corners of the frame models as nodes of the graph model, and use the connections between the component models as edges of the graph model.

[0055] Specifically, a graph model is constructed for the framework model, treating each corner component model as a master node and the connections between component models as edges, thus creating a graph model that describes the connection structure of the framework model. In the graph model, the interface type, geometric features, and material of each node (component model) and the interface type of each edge (connection) are labeled. This graph model clearly defines the connection architecture of the entire framework model, laying the foundation for subsequently determining the combination of corner component models according to the connection order.

[0056] S220: Obtain the interface type of each component model. Starting from the starting node, use the depth-first search algorithm to search for adjacent nodes that match the interface type in the order of connection, and record the nodes found each time and the connection relationship between the nodes, until the end node is found, and obtain the node sequence.

[0057] Specifically, starting from the selected initial node (component model) in the graph model, the depth-first search algorithm is used to sequentially search for adjacent nodes (component models) along the edges (connections) according to the defined connection order. Each node (component model) found and its connection relationship are recorded, forming a progressively expanding sequence of corner component models. When all corner component models are included in the corner component model sequence and meet the specific connection requirements, the depth-first search algorithm is stopped, resulting in the final corner component model sequence.

[0058] S230: Divide the component models in the model library into multiple model sets that correspond one-to-one with each frame model according to the interface size, and select the component model from the model set as the initial child node between adjacent nodes in the corresponding frame model.

[0059] Specifically, the various framework models are arranged in connection order, and the component models in the model library are sorted in descending order according to interface size, and then divided into multiple model sets that correspond one-to-one with each framework model. Component models are selected from the model sets corresponding to the framework models as child nodes between adjacent nodes in the framework model. The selected component models are combined into multiple combinations, and the edges between adjacent nodes are determined as multiple child node combinations of the edges.

[0060] S240: Determine the number of initial child nodes between adjacent nodes based on the required size, determine the sealing performance of adjacent component models based on the difference in interface size, and establish an objective function to maximize the sealing performance based on the sealing performance of each adjacent component model in the cell.

[0061] Specifically, the sealing performance of adjacent component models is determined based on the difference in interface size; the sealing performance between cells reflects the sealing performance of each frame model.

[0062] S250 uses a greedy algorithm to iteratively search for the initial child nodes of each edge. After the condition for stopping the iteration is met, the optimal combination of child nodes for each edge is obtained.

[0063] Specifically, by analyzing the differences in interface dimensions between adjacent component models, their sealing performance parameters are calculated, and an objective function is constructed to maximize the inter-cell sealing performance. An optimization algorithm is used to solve the objective function, selecting the optimal combination of component models to ensure that the overall sealing performance of the frame model reaches its best state. During the optimization process, the synergistic effect between interface size matching and sealing performance is considered, the objective function parameters are refined, and the component model combination is iteratively adjusted until the preset sealing standard is met, ensuring that the frame model maintains high-efficiency sealing performance even in complex environments.

[0064] The conditions for stopping iteration include the change in the objective function value being less than a change threshold, or the number of iterations reaching a threshold. Specifically, for each edge e, an empty child node combination S_e is initialized; all initial child nodes i on edge e are traversed, and the objective function value f(S_e, i) after adding child node i to the current combination S_e is calculated; if the objective function value is better than the objective function value f(S_e) of the current combination S_e, then i is added to S_e; the above steps are repeated until the stopping condition is met.

[0065] S260 embeds the optimal combination of child nodes of each edge into the corresponding node sequence to obtain the component model sequence between cells.

[0066] In some embodiments, S240, determining the sealing performance of adjacent component models based on differences in interface dimensions includes:

[0067] The differences in interface dimensions are obtained, and the sealing performance of adjacent component models is determined using a sealing performance function; the sealing performance function is:

[0068]

[0069] Where S is the sealing performance index, with a value between 0 and 1; t1 and t2 are the thicknesses of two adjacent components at the interface, respectively; k is the thickness difference sensitivity coefficient; E is the elastic modulus of the material; σ is the stress concentration factor at the interface; L is the actual sealing gap width; and L0 is the standard sealing gap width in the design.

[0070] It should be noted that a larger value of S indicates better sealing performance; the thickness difference sensitivity coefficient k reflects the degree of influence of thickness difference on sealing performance and needs to be determined based on specific materials and working conditions; the elastic modulus E of the material reflects the stiffness characteristics of the material under stress; and the stress concentration factor σ at the interface considers the influence of interface geometry and load distribution on sealing performance. The sealing performance function comprehensively considers the influence of factors such as interface size differences, material properties, and stress concentration on sealing performance. When the thickness difference between two components is large, the sealing performance will decrease; the higher the elastic modulus of the material, the better the sealing performance; the larger the stress concentration factor, the worse the sealing performance; and the closer the sealing gap width is to the design standard, the better the sealing performance.

[0071] In some embodiments, S240, establishing an objective function to maximize sealing performance based on the sealing performance of each adjacent component model in the intercellular space includes:

[0072] S241, set the sealing performance weight coefficient of adjacent nodes in the node sequence as the first weight value, and adjust the first weight value based on the number of initial child nodes between the initial child node and the nearest node to obtain the second weight value of the initial child node;

[0073] S242, average the second weights of adjacent initial child nodes to obtain the sealing performance weight coefficients of the corresponding adjacent component models;

[0074] S243, the sealing performance of each adjacent component model in the intercellular space and the corresponding sealing performance weight coefficient are weighted and combined to construct the objective function.

[0075] It should be noted that by optimizing the objective function, the overall sealing performance is improved, ensuring the stability and reliability of the component model in practical applications. Precise adjustment of each parameter is key to achieving efficient sealing. Through detailed analysis of the interactions between parameters, the objective function for sealing performance is further optimized to ensure its adaptability under complex working conditions. Precise control of thickness differences, elastic modulus, and stress concentration factor maximizes the sealing effect and improves the overall durability and safety of the component.

[0076] In some embodiments, S300, determining the geometric features and materials of the component models in the component model sequence according to the load-bearing requirements, and generating the inter-cell framework structure, includes:

[0077] S310: Obtain the material of the component model, and use the stress analysis method to perform mechanical analysis on the component models in the component model sequence to obtain stress distribution data;

[0078] Specifically, the load-bearing requirements between cells are obtained, and materials with appropriate strength, stiffness, toughness, and other mechanical properties are selected based on the mechanical performance requirements. This ensures that the component model possesses excellent resistance to deformation under load conditions, meeting the stability requirements of the frame structure. By using stress analysis software to perform detailed simulations of the component model, its stress response under different loads is evaluated, ensuring that each component maintains structural integrity and functional stability under extreme conditions. Combining material properties, the geometric design of the components is optimized to further improve the overall load-bearing capacity and durability of the frame structure.

[0079] S320 determines the thickness of each component model based on load requirements and stress distribution data, and generates the intercellular framework structure;

[0080] For each component model in the component model sequence, determine its geometric features based on load-bearing requirements. Define each component model in the component model sequence. Refine the thickness of each component model.

[0081] S330 combines and splices the various component models according to the connection order to generate the intercellular framework structure.

[0082] By simulating the assembly and splicing process of various component models, the gaps and connection strengths during the splicing process are precisely controlled, generating a framework structure between cells that provides guidance for actual installation, ensuring the tightness and consistency of the overall structure. Simulating the mechanical response under different working conditions verifies the stability and reliability of the frame structure, further optimizing the connection methods and improving the overall load-bearing capacity. Careful adjustment of the fit precision between each component ensures that the frame structure maintains excellent performance during long-term use.

[0083] In some embodiments, S320, determining the thickness of each component model based on load-bearing requirements and stress distribution data includes:

[0084] S321, determine the maximum load value that each component model can withstand and the stress distribution data under different working conditions according to the load-bearing requirements.

[0085] Specifically, through detailed mechanical analysis, the various external forces faced by the overall structure are quantified, clarifying the load that each component needs to bear at different positions and times, providing a precise stress basis for subsequent optimization of thickness and materials.

[0086] S322, combined with stress distribution data, analyze the stress concentration areas and stress magnitude gradients within each component model to obtain the deformation and deformation trend of each component model during the load-bearing process.

[0087] Specifically, stress analysis software is used to locate critical parts that may cause premature failure of components due to excessive stress, so that the thickness and material of these parts can be optimized and adjusted in a targeted manner to ensure the reliability and safety of the components in actual operation.

[0088] S323 establishes an objective function based on load-bearing requirements, stress distribution data, and deformation degree, sets thickness as the variable to be solved, and takes meeting load-bearing requirements, reducing stress concentration, and controlling deformation within deformation threshold as the objective function, and gradually determines the thickness range of each component model.

[0089] Specifically, the thickness is set as the variable to be solved. After establishing the objective function, the load-bearing requirements, stress distribution, and deformation degree are the constraints related to the thickness variable. The objective function is made to meet the constraints by adjusting the thickness.

[0090] First, the objective function is constructed based on the load-bearing requirements, stress distribution, and degree of deformation.

[0091] Let the bearing pressure on the component be P, the stress concentration factor be K, the deformation be δ, the deformation threshold be δmax, and the component thickness be t (the variable to be solved).

[0092] To meet the load-bearing requirements, a load-bearing safety factor n is introduced, ensuring that the load-bearing capacity C satisfies C ≥ P*n. The load-bearing capacity C is a function related to the thickness t, and the load-bearing capacity function is C = f1(t) = λ1*t. 2 λ1 is the first constant coefficient.

[0093] To reduce stress concentration, the stress concentration factor K is related to the thickness t, and the stress concentration function is expressed as K = f2(t) = λ2 / [(2d+t)]. 2 -(2dt) 2 ], λ2 is the second constant coefficient. The goal is to minimize the stress concentration factor K, so the coefficient 1 / K is introduced into the objective function.

[0094] Regarding controlling the deformation amount within the deformation threshold, it is known that the deformation amount δ is a function of the thickness t, and the deformation amount function is δ=f3(t)=λ3 / t. 3λ3 is the third constant coefficient. To ensure that δ≤δmax, (δmax-δ) is introduced into the objective function. 2 When δ approaches or equals δmax, the value of this term will increase, prompting the objective function to optimize in the direction of reducing the deformation δ.

[0095] Taking all the above into consideration, the constructed objective function (F(t)) can be expressed as:

[0096]

[0097] Among them, w1, w2, and w3 are weighting coefficients used to adjust the importance of each item in the objective function, and can be reasonably set according to specific engineering needs and priorities.

[0098] Perform mathematical calculations on the established objective function (F(t)).

[0099] Differentiate f1(t), f2(t), and f3(t). Based on the differentiation results, use optimization algorithms (such as gradient descent) to find the initial range of thickness t that makes the objective function F(t) reach an extreme value (minimum or suitable value).

[0100] Next, the component model was simulated and analyzed using finite element analysis software. The component model was established in the simulation software, and parameters such as the component's geometry and material properties were accurately set. Different values ​​of the thickness t were selected within the initial range for simulation.

[0101] For each selected thickness value, a load-bearing analysis is performed to obtain the relationship between the actual load-bearing pressure Psim and the load-bearing capacity Csim, verifying whether the load-bearing requirement Csim ≥ Psim * n is met. Simultaneously, the stress distribution is analyzed to obtain the actual stress concentration factor Ksim, checking whether it meets the requirement of reducing stress concentration (whether Ksim is less than a certain set acceptable stress concentration value). Furthermore, the deformation δsim is measured to check whether δsim ≤ δmax is satisfied.

[0102] Based on the simulation analysis results, the initial value range is adjusted. If, at a certain thickness value, one or more of the requirements for load-bearing capacity, stress concentration, or deformation control are not met, the interval containing that thickness value is excluded, further narrowing the thickness value range. Through multiple iterative processes of mathematical calculations and simulation analyses, the thickness value range is continuously adjusted and narrowed. After each simulation analysis, the parameters or forms of the relevant functions (f1(t), f2(t), f3(t)) in the mathematical calculations are updated based on the results, and mathematical calculations are performed again to obtain a new thickness value range, which is then verified through simulation analysis.

[0103] After several iterations, the most suitable thickness range for each component model was finally determined, so that the component can simultaneously meet the load-bearing requirements, reduce stress concentration, and control the deformation within the deformation threshold within this range. The thickness range was finally determined, and any thickness value within this range can ensure that the performance of the component meets the various set requirements.

[0104] This invention improves sealing performance and load-bearing capacity while simplifying the assembly process through optimized design of the intercellular framework structure. Precise analysis and optimized combination of each component model achieves high efficiency and stability, ensuring reliable operation between cells and extending service life and safety.

[0105] refer to Figure 2 This invention also provides a prefabricated, modular intercellular frame design system, comprising:

[0106] At least one processor;

[0107] At least one memory for storing at least one program;

[0108] When the at least one program is executed by the at least one processor, the at least one processor performs the method described above.

[0109] The content of the above method embodiments is applicable to this embodiment. The specific functions implemented in this embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. Therefore, they will not be repeated here.

[0110] Although the description of this disclosure has been quite detailed and particularly focused on several of the described embodiments, it is not intended to limit itself to any of these details or embodiments or any particular embodiment, but should be considered as effectively covering the intended scope of this disclosure by referring to the appended claims and taking into account the broad possible interpretations of these claims provided by the prior art. Furthermore, the foregoing description of this disclosure with respect to embodiments foreseeable by the inventors is intended to provide a useful description, and non-substantial modifications to this disclosure that have not yet been foreseen may still represent equivalent modifications.

Claims

1. A method for designing a frame between prefabricated, modular cells, characterized in that, The method includes the following steps: Acquire requirement information and a model library. The requirement information includes multiple framework models between cells, as well as the required functions, required dimensions, and load-bearing requirements of each framework model. The model library includes multiple component models. Based on the required functions, determine the connection order of each frame model, determine the component model combination at the corner of each frame model in sequence according to the connection order, determine the component model combination and corresponding connection order inside each frame model according to the required size, and obtain the component model sequence between cells. Based on the load-bearing requirements, determine the geometric features and materials of the component models in the component model sequence, and generate the framework structure between cells; The process of determining the component model combinations at the corners of each frame model according to the connection order, and determining the component model combinations and corresponding connection order within each frame model based on the required dimensions of the frame model, includes: Construct a graph model containing various frame models, take the component models at the corners of the frame models as the main nodes of the graph model, and take the connections between the component models at the corners as the edges of the graph model. Obtain the interface type of the component model of each corner. Starting from the starting main node, use the depth-first search algorithm to search for adjacent main nodes that match the interface type in the order of connection, and record the main nodes found each time and the connection relationship between the main nodes, until the ending main node is found, and obtain the main node sequence. The component models in the model library are divided into multiple model sets that correspond one-to-one with each frame model according to the interface size. The component model is selected from the model set as the initial child node between adjacent main nodes in the corresponding frame model. The number of initial child nodes between adjacent master nodes is determined based on the required size. The sealing performance of adjacent component models is determined based on the difference in interface size. An objective function for maximizing sealing performance is established based on the sealing performance of each adjacent component model in the cell. A greedy algorithm is used to iteratively search for the initial child nodes of each edge. After the iteration stops, the optimal combination of child nodes for each edge is obtained. Specifically, for each edge e, an empty combination of child nodes S_e is initialized. All initial child nodes i on edge e are traversed, and the objective function value f(S_e, i) for maximizing sealing performance after adding the initial child node i to the current combination of child nodes S_e is calculated. If the objective function value f(S_e, i) for maximizing sealing performance is better than the objective function value f(S_e) of the current combination of child nodes S_e, then i is added to S_e. The above steps are repeated until the stopping condition is met. The optimal combination of child nodes for each edge is embedded into the corresponding master node sequence to obtain the component model sequence between cells.

2. The method according to claim 1, characterized in that, The step of determining the geometric features and materials of the component models in the component model sequence according to the load-bearing requirements, and generating the intercellular framework structure, includes: Obtain the material of the component model, and use stress analysis method to perform mechanical analysis on the component model in the component model sequence to obtain stress distribution data; The thickness of each component model is determined based on load-bearing requirements and stress distribution data; Based on the component model sequence, the individual component models are combined and spliced ​​to generate the framework structure between cells.

3. The method according to claim 2, characterized in that, The determination of the thickness of each component model based on load-bearing requirements and stress distribution data includes: Determine the maximum load value that each component model can withstand and the stress distribution data under different working conditions based on the load-bearing requirements; By combining stress distribution data, we can analyze the stress concentration areas and stress magnitude gradients within each component model to obtain the deformation and deformation trend of each component model during the load-bearing process. Based on the load-bearing requirements, stress distribution data, and deformation degree, an objective function is established, and the thickness is set as the variable to be solved. The objective function is to meet the load-bearing requirements, reduce the stress concentration, and control the deformation within the deformation threshold. The thickness range of each component model is determined step by step.

4. A prefabricated, modular intercellular frame design system, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor performs the method as described in any one of claims 1 to 3.

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