A facade renovation module material combination optimization method based on genetic algorithm and polycephalococcus algorithm

Abstract

The application provides a facade reconstruction module material combination optimization method based on a genetic algorithm and a polycephalocystis algorithm.The method comprises the following steps: step one, dividing material categories;step two, establishing a genetic algorithm model;step three, based on the obtained core cavity distribution scheme, generating a path in the core cavity distribution framework based on the polycephalocystis algorithm, so as to form the solid material part and other cavity parts, thereby providing support for the core filling material and reserving space for other filling materials; and step four, combination assembly.The advanced materials including phase change materials, air bags and the like and the thermal insulation materials are deeply combined with the intelligent algorithm, the integrated design of the external wall reconstruction module is realized, the requirements of the thermal insulation effect, the material utilization rate and the construction cost and the like are taken into account, and the method has remarkable popularization and application value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building energy saving and facade reconstruction, and particularly relates to a facade reconstruction module material combination optimization method based on a genetic algorithm and a polycephalococcus algorithm. BACKGROUND

[0002] Traditional facade reconstruction is mostly achieved by laying insulation boards or heat insulation coatings on the outer layer of the original facade in one direction to improve the thermal insulation performance, but these methods are static or passive, and it is difficult to adaptively adjust to changing climate conditions over a long period of time. The problems caused thereby include:

[0003] (1) Unable to effectively respond to seasonal and day-night temperature changes

[0004] In the context of climate change and future climate warming, traditional reconstruction methods lack dynamic adjustment capabilities.

[0005] (2) Ignoring the potential of "dynamic performance materials" in facade reconstruction

[0006] Most of the current common facade reconstruction schemes only focus on the thermal conductivity or thickness of traditional insulation materials, and fail to integrate materials with performance adjustment functions such as phase change materials (PCM) (heat absorption and release capacity) and air bags (chargeable and dischargeable characteristics) into "integrated reconstruction modules", thereby missing the opportunity to significantly improve energy efficiency by utilizing dynamic thermal regulation.

[0007] (3) Difficulty in balancing overheating risks and overall structural needs

[0008] Although some facade systems have attempted to introduce energy storage materials or ventilation layers in the surface layer, they lack systematic and intelligent optimization design, making it difficult to balance thermal insulation, heat dissipation, structural strength, and construction cost.

[0009] (4) Low technology integration and high construction complexity

[0010] Traditional reconstruction methods involve on-site scattered construction, with several materials being laid or attached in sequence, which is a tedious process and requires high technical skills of construction personnel. Once there is local damage or the need to replace materials, large-scale demolition and reinstallation are often required, resulting in high maintenance costs and a lack of flexible solutions for "renewal" or "mobility". SUMMARY

[0011] The present application aims to solve the problems in the prior art and provides a facade reconstruction module material combination optimization method based on a genetic algorithm and a polycephalococcus algorithm.

[0012] The application is realized by the following technical scheme, the application provides a facade reconstruction module material combination optimization method based on genetic algorithm and multiple head velvet bubble algorithm, the method comprises the following steps:

[0013] Step one: divide the reconstruction module into core performance components 1 and structures 2; the core performance components 1 are composed of core cavity parts 3 and core filling materials 4, wherein the core filling materials 4 include phase change materials and air bags with performance adjusting function materials; the structures 2 are composed of solid material parts 5, other cavity parts 6 and other filling materials 7, wherein the solid material parts 5 refer to structural materials, and the other filling materials 7 include thermal insulation foaming agents located in the other cavity parts 6;

[0014] Step two: establish a genetic algorithm model, take "heat transfer performance optimization, core material efficiency optimization and overall reliability" as the target, carry out multi-objective optimization on the position, size and distribution of the core cavity part 3, and obtain the optimal or approximate optimal core cavity distribution scheme; the core performance components 1 are composed of the core cavity part 3 and the core filling material 4;

[0015] Step three: based on the obtained core cavity distribution scheme, generate a path in the core cavity distribution framework based on the multiple head velvet bubble algorithm, so as to form the solid material part 5 and the other cavity part 6, thereby providing support for the core filling material 4 and reserving space for the other filling material 7, and the three together form the structure 2;

[0016] Step four: the core performance components 1 and the structure 2 jointly form the facade reconstruction module, so that the reconstruction module as a whole has thermal insulation performance, material utilization rate and structural stability.

[0017] Further, in step two, the cavity size, position and distribution are determined by the center point coordinates, shape and size parameters respectively; the cavity volume is the total cavity volume, and the cavity number is the number of size parameters not equal to 0; the heat transfer coefficient is evaluated by using a simplified thermal resistance network in the rapid iteration stage; wherein the cavity position is the three-dimensional coordinates (x, y, z) of the core cavity in the module area; the cavity size is the size parameter r of the cavity; and the cavity number is the total cavity number a.

[0018] Further, in step two, the heat transfer coefficient target is evaluated by using a simplified thermal resistance network in the iteration stage, specifically, the entire module is divided into regular network units, divided into n layers along the heat transfer direction, each layer has m units, the layers are in series, and the layers are in parallel;

[0019] That is,

[0020]

[0021] Wherein:

[0022]

[0023] R total is the total thermal resistance of the whole module, n is the total number of layers along the heat flow direction, m is the number of units in each layer, R k.j is the thermal resistance of the jth unit in the kth layer;

[0024]

[0025] where d k,j is the thickness of the jth unit in the kth layer, λ k,j is the thermal conductivity of the jth unit in the kth layer, A k,j is the cross-sectional area of the jth unit in the kth layer;

[0026] Core material efficiency optimization, i.e. less core cavity volume:

[0027]

[0028] Overall reliability optimization, i.e. to minimize the number of cavities to avoid structural complexity and excessively high failure rate:

[0029] f3 = minimize (a)

[0030] The weighted sum formula is:

[0031]

[0032] where

[0033] Further, in step two, the iteration termination condition of the genetic algorithm includes stopping when the fitness function no longer significantly improves within a preset number of iterations, or stopping when the optimal solution set under a preset convergence criterion is obtained.

[0034] Further, in step three, the multi-headed jellyfish algorithm limits the growth and branching morphology by setting growth rules, boundary conditions and objective functions for the support path, to form a support skeleton that meets the installation requirements of the core performance component 1 in the cavity distribution range, while minimizing the occupation of structural solid materials and reserving space for thermal insulation material filling.

[0035] Further, in the multi-headed jellyfish algorithm,

[0036] Construct nodes and edges: consider the key positions in the optimized cavity distribution as a node set V; allow potential connections between nodes to form an edge set E; let G = (V, E) represent a graph, V is the node set, E is the available edge set, for each edge e ij ∈ E, define the pipe thickness as Dij ;

[0037] Set source / sink: In the module structure, the core cavity or support area is regarded as a "source" node, and the external area or other functional area is regarded as a "sink" node; or multiple source-sink pairs can be defined according to the material layout to be supported;

[0038] Iterative execution of the algorithm: initialize the thickness of all edges Set to a positive value; update the flow f according to the pressure difference ij :

[0039]

[0040]

[0041] f ij Flow of edge, p i , p j Respectively represent the "pressure" of nodes i, j, L ij Length; the two formulas derive the linear equations of p i , derive all node pressures p i And the flow of each edge; then update D ij :

[0042]

[0043] D ij Represents the "conductivity" or "thickness" of e ij , that is, the flow capacity; μ is the positive gain coefficient, which controls the speed of pipe growth; λ is the attenuation rate, which represents that the pipe will gradually shrink when not used; |f ij | represents the absolute flow, and the larger the flow, the "healthier" and thicker the pipe; if the flow is small or even zero, the pipe thickness will continuously decay until it disappears; after the final update is completed, perform the following on the path:

[0044]

[0045] Where Δt is the step size, δ is the pipe thickness threshold, and ε is the flow threshold for determining whether to retain the pipe;

[0046] Output the optimal path: After several iterations, the thickness of most pipes with small flow will gradually decrease and disappear; only the pipes with large flow are retained, thereby forming an optimal or approximately optimal "skeleton path" that can support the core performance component 1.

[0047] Further, in step four, the other filling material 7 and the solid material part 5 form an integral structure with the core cavity part 3 after being injected or foamed, to further reduce the heat transfer coefficient and improve the module strength.

[0048] Further, in step four, when the modified module is integrally installed on the building outer wall facade, it can be connected with the original wall through prefabricated parts, inserts or adhesive layers to form an integrated outer wall insulation modification system.

[0049] The application further provides an electronic device, including a memory and a processor, the memory stores a computer program, and the processor implements the steps of the facade modification module material combination optimization method based on the genetic algorithm and the polycephalococcus algorithm when executing the computer program.

[0050] The application further provides a computer readable storage medium for storing computer instructions, and the computer instructions implement the steps of the facade modification module material combination optimization method based on the genetic algorithm and the polycephalococcus algorithm when executed by a processor.

[0051] The application has the following beneficial effects:

[0052] (1) Dynamically cope with climate change and overheating risk

[0053] By introducing phase change materials or inflatable air bags and other variable performance components into the core cavity, the entire outer wall module can actively or passively absorb heat, release heat or adjust the internal gas content according to temperature changes, thereby significantly alleviating the overheating problem at high temperatures and reducing heat loss in low temperature environments.

[0054] (2) Multi-objective optimization and efficient material utilization

[0055] On the basis of multi-objective optimization of the genetic algorithm, the comprehensive control of the heat transfer coefficient, the cavity volume and the number is realized, which avoids the blind thickening of the thermal insulation material or the use of too much solid material, improves the thermal insulation efficiency and reduces material waste.

[0056] (3) Structural integrity and construction convenience

[0057] The branch path generated by the polycephalococcus algorithm provides reliable support for dynamic elements such as air bags and reserves reasonable space for the layout of other thermal insulation materials, which not only ensures the stability of the outer wall modification module, but also shortens the on-site construction period, realizes the convenience of integrated installation and later maintenance.

[0058] (4) Strong adaptability and broad application prospect

[0059] The integrated modification module proposed in the application can be customized according to different climate regions, building types or energy consumption targets, and can further integrate more new functional materials such as light-heat conversion films, renewable energy embedded components, etc., and has broad market promotion and application potential. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings in the following description only only the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.

[0061] Figure 1 The logic diagram for facade renovation module material combination;

[0062] Figure 2 The flow chart of the facade renovation module material combination optimization method based on genetic algorithm and multiple-head fuzzy celled fungus algorithm;

[0063] Figure 3 The schematic diagram of the overall effect;

[0064] Figure 4 The schematic diagram of the core cavity and core filling material;

[0065] Figure 5 The schematic diagram of the solid structure material part and other cavity / other filling material longitudinal section;

[0066] Figure 6 The schematic diagram of the solid structure material part and other cavity / other filling material transverse section.

[0067] List of reference signs: 1, core performance component, 2, structure, 3, core cavity part, 4, core filling material, 5, solid material part, 6, other cavity part, 7, other filling material. DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments only only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0069] In combination Figures 1-6 The present application proposes a facade renovation module material combination optimization method based on genetic algorithm and multiple-head fuzzy celled fungus algorithm, which is applied to building exterior wall integrated renovation. The method comprises the following steps:

[0070] Step one: divide the retrofit module into core performance component 1 and structure 2; core performance component 1 is composed of core cavity part 3 and core filling material 4, wherein core filling material 4 includes phase change material and air bag and other materials with performance adjustment function; structure 2 is composed of solid material part 5, other cavity part 6 and other filling material 7, wherein solid material part 5 refers to structural material including common structural materials such as wood and concrete, and other filling material 7 includes thermal insulation foaming agent located in other cavity part 6; core filling material includes but is not limited to air bag, phase change material, and components with performance adjustment function are suitable for this combination method;

[0071] Step two: establish a genetic algorithm model, and take "heat transfer performance optimization, core material efficiency optimization and overall reliability" as the target to optimize the position, size and distribution of the core cavity part 3, and obtain the optimal or approximate optimal core cavity distribution scheme; core performance component 1 is composed of core cavity part 3 and core filling material 4;

[0072] In step two, the size, position and distribution of the cavity are determined by the center point coordinates, shape and size parameters; the cavity volume is the total volume of the cavity, and the cavity number is the number of size parameters that is not 0; the heat transfer coefficient is evaluated by using a simplified thermal resistance network in the rapid iteration stage, so as to compare the advantages and disadvantages of different design schemes faster in the algorithm convergence process. For the final optimization or candidate scheme, a numerical simulation such as finite element simulation (Rhino / Grasshopper combined with third-party simulation plug-in) should be used for more comprehensive three-dimensional heat transfer analysis to obtain more accurate heat transfer coefficient. The cavity position is the three-dimensional coordinates (x, y, z) of the core cavity in the module area; the cavity size is the size parameter r of the cavity; and the cavity number is the total number of cavities a.

[0073] In step two, the heat transfer coefficient target is evaluated by using a simplified thermal resistance network in the iteration stage, which is specifically divided into regular network units, divided into n layers along the heat transfer direction, each layer has m units, and the layers are in series and the layers are in parallel;

[0074] That is:

[0075]

[0076] Wherein:

[0077]

[0078] R total The total thermal resistance is the total thermal resistance of the whole module, n is the total number of layers of the module along the heat flow direction (usually perpendicular to the outer wall plane direction); m is the number of units in each layer; R k.j The thermal resistance of the jth unit in the kth layer is R

[0079]

[0080] wherein d k,j is the thickness (heat transfer path length) of the jth cell in the kth layer; λ k,j is the thermal conductivity of the jth cell in the kth layer; A k,j is the cross-sectional area of the jth cell in the kth layer;

[0081] Core material efficiency optimization, i.e. less core cavity volume:

[0082]

[0083] Overall reliability optimization, i.e. as few cavities as possible to avoid structural complexity and excessively high failure rates:

[0084] f3 = minimize (a)

[0085] The weighted sum formula is:

[0086]

[0087] wherein

[0088] In step two, the fitness function of the genetic algorithm model considers the heat transfer coefficient, cavity volume and cavity number by weighting, and the weights can be dynamically adjusted according to actual needs.

[0089] In step two, the iteration termination condition of the genetic algorithm includes stopping when the fitness function no longer significantly improves within a preset number of iterations, or stopping when the optimal solution set under a preset convergence standard is obtained.

[0090] Step three: based on the obtained core cavity distribution scheme, the Physarealm plug-in of Grasshopper generates paths in the core cavity distribution framework based on the multi-headed wool bubble algorithm to form the solid material part 5 and other cavity parts 6, thereby providing support for the core performance component 1 and reserving space for other filler materials 7, and the three together form a structure 2;

[0091] In step three, the multi-headed wool bubble algorithm limits the growth and branching morphology by setting the growth rules, boundary conditions and objective function of the support path, so as to form a support skeleton that meets the installation requirements of the core performance component 1 within the cavity distribution range, while minimizing the occupation of structural solid materials and reserving space for thermal insulation material filling.

[0092] In the multi-headed wool bubble algorithm,

[0093] Constructing Nodes and Edges: Key locations (core cavities, connection points, etc.) in the optimized cavity distribution are considered as a node set V; potential connections between nodes are allowed (within a certain range), forming an edge set E; let G = (V, E) represent a graph, where V is the node set (the cavity distribution optimized by the genetic algorithm), and E is the set of edges that can be connected (potential connections between nodes). For each edge e... ij ∈E, define the pipe thickness as D ij ;

[0094] Setting source / sink points: In the module structure, the core cavity or support area is regarded as the "source" node, and the external area or other functional area is regarded as the "sink" node; multiple source-sink pairs can also be defined according to the material layout to be supported.

[0095] Iterative execution algorithm: Initialize the thickness of all edges Set to a positive value; update the flow rate f based on the pressure difference. ij :

[0096]

[0097] f ij Let p be the flow of the edge. i ,p j L represents the "pressure" at nodes i and j, respectively. ij For length; the two equations yield p i The linear equations yield the pressure p at all nodes. i and the flow rates on each side; then update D according to the pipeline evolution equation. ij :

[0098]

[0099] D ij e ij The "conductivity" or "thickness" refers to the flow capacity; μ is the positive gain coefficient, controlling the rate of pipe growth; λ is the attenuation rate, indicating how the pipe gradually shrinks when not in use; |f ij | represents absolute flow rate. The higher the flow rate, the "healthier" and thicker the pipe; if the flow rate is low or even zero, the pipe thickness continuously decreases until it disappears. Because the growth path in this simulation needs to pursue both the shortest path and material conservation, while also considering minimum structural support and safety redundancy, the following steps are performed on the path after the final update:

[0100]

[0101] Where Δt is the step size, δ is the pipe thickness threshold, and ε is the flow threshold for determining whether the pipe should be retained;

[0102] Output optimal path: After several iterations, most of the small flow pipe thickness will gradually decrease and disappear; only the pipe with large flow is retained, thereby forming an optimal or near-optimal "skeleton path" that can support the core performance component 1.

[0103] Step four: the core performance component 1 and the structure 2 jointly form a facade renovation module, so that the renovation module as a whole has thermal insulation performance, material utilization rate and structural stability.

[0104] In step four, the other filling material 7 and the solid material part 5 form an integral structure with the core cavity part 3 after injection or foaming, so as to further reduce the heat transfer coefficient and improve the strength of the module.

[0105] In step four, when the renovation module as a whole is installed on the building facade, it can be connected with the original wall through a prefabricated part, an embedded part or an adhesive layer to form an integrated external wall thermal insulation renovation system.

[0106] The application also provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the facade renovation module material combination optimization method based on the genetic algorithm and the multi-head velvet bubble algorithm when executing the computer program.

[0107] The application also provides a computer readable storage medium for storing computer instructions, and the computer instructions implement the steps of the facade renovation module material combination optimization method based on the genetic algorithm and the multi-head velvet bubble algorithm when executed by a processor.

[0108] The memory in the embodiments of the present application can be a volatile memory or a nonvolatile memory, or can include both volatile and nonvolatile memory. Among them, the nonvolatile memory can be a read only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM can be used, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DRRAM). It is noted that the memory of the methods described herein is intended to include, but not be limited to, these and any other suitable types of memory.

[0109] In the above embodiments, all or part of the methods can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the methods can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes one or more available media sets. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as high-density digital video disc (DVD)), or semiconductor media (such as solid state disc (SSD)), etc.

[0110] In the implementation process, each step of the above method can be completed by the integrated logic circuit of hardware in the processor or the instruction in the form of software. The steps of the method disclosed in the embodiments of the present application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0111] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with a signal processing capability. In the implementation process, each step of the method embodiments can be completed by the integrated logic circuit of hardware or the instruction in the form of software in the processor. The processor mentioned above can be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in a random access memory, a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register or other mature storage medium in the art. The storage medium is located in the memory, and the processor reads the information in the memory, and combines the hardware to complete the steps of the above method.

[0112] The above describes in detail the facade reconstruction module material combination optimization method based on genetic algorithm and multiple head velvet bubble algorithm. The principle and implementation manner of the present application are described by using specific examples. The above embodiment is only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for optimizing the combination of materials for facade renovation modules based on genetic algorithms and multi-headed vesicle bacteria algorithms, characterized in that, The method includes the following steps: Step 1: Divide the modification module into core performance components (1) and structure (2); the core performance components (1) consist of core cavity parts (3) and core filling materials (4), wherein the core filling materials (4) include phase change materials and materials with performance adjustment functions for airbags; the structure (2) consists of solid material parts (5), other cavity parts (6) and other filling materials (7), wherein the solid material parts (5) refer to structural materials, and the other filling materials (7) include thermal insulation foaming agents located in other cavity parts (6); Step 2: Establish a genetic algorithm model with the goals of "heat transfer performance optimization, core material efficiency optimization, and overall reliability" to perform multi-objective optimization on the position, size and distribution of the core cavity part (3) to obtain the optimal or near-optimal core cavity distribution scheme; the core performance component (1) is composed of the core cavity part (3) and the core filling material (4); Step 3: Based on the obtained core cavity distribution scheme, a path is generated in the core cavity distribution framework based on the multi-headed velvet fungus algorithm to form the solid material part (5) and other cavity parts (6), thereby providing support for the core filling material (4) and reserving space for other filling materials (7), and the three together form the structure (2). Step 4: The core performance components (1) and the structure (2) together constitute the facade renovation module, so that the renovation module as a whole has thermal insulation performance, material utilization rate and structural stability.

2. The method according to claim 1, characterized in that, In step two, the size, location, and distribution of cavities are determined by the center point coordinates, shape, and size parameters, respectively; the cavity volume is the total cavity volume, and the number of cavities is the number of cavities with non-zero size parameters; in the rapid iteration phase, a simplified thermal resistance network is used to evaluate the relative heat transfer performance; where the cavity location is the three-dimensional coordinates (x, y, z) of the core cavity in the module region; the cavity size is the size parameter r of the cavity; and the number of cavities is the total number of cavities a.

3. The method according to claim 2, characterized in that, In step two, the heat transfer coefficient target is evaluated using a simplified thermal resistance network during the iteration phase. Specifically, the entire module is divided into regular network units, divided into n layers along the heat transfer direction, with m units in each layer. The units are connected in series between layers and in parallel within layers. Right now: in: R total R represents the overall total thermal resistance, n is the total number of layers in the module along the heat flow direction; m is the number of units in each layer; k.j Let J be the thermal resistance of the j-th unit in the k-th layer; Where, d k,j λ is the thickness of the j-th unit in the k-th layer; k,j A is the thermal conductivity of the j-th unit in the k-th layer; k,j Let be the cross-sectional area of ​​the j-th unit in the k-th layer; Core material efficiency optimization means less core cavity volume: Overall reliability optimization means minimizing the number of cavities to avoid structural complexity and excessively high failure rates. f3 = minimize(a) The weighted summation formula is: in 4. The method according to claim 1, characterized in that, In step two, the iteration termination conditions of the genetic algorithm include stopping when the fitness function no longer significantly improves within a preset number of iterations, or stopping when the optimal solution set under a preset convergence criterion is obtained.

5. The method according to claim 1, characterized in that, In step three, the multi-headed velvet fungus algorithm limits the growth and branching morphology by setting the growth rules, boundary conditions and objective functions of the support path, so as to form a support skeleton that meets the installation requirements of the core performance component (1) within the cavity distribution range, while minimizing the occupation of structural material and reserving space for insulation material filling.

6. The method according to claim 5, characterized in that, In the multi-headed vesicular bacteria algorithm, Constructing Nodes and Edges: Key locations in the optimized cavity distribution are considered as a set of nodes V; potential connections are allowed between nodes, forming an edge set E; let G = (V, E) represent a graph, where V is the set of nodes and E is the set of edges that can be connected. For each edge e... ij ∈E, define the pipe thickness as D ij ; Setting source / sink points: In the module structure, the core cavity or support area is regarded as the "source" node, and the external area or other functional area is regarded as the "sink" node; multiple source-sink pairs can also be defined according to the material layout to be supported. Iterative execution algorithm: Initialize the thickness of all edges Set to a positive value; update the flow rate f based on the pressure difference. ij : f ij Let p be the flow of the edge. i ,p j L represents the "pressure" at nodes i and j, respectively. ij For length; the two equations yield p i The linear equations yield the pressure p at all nodes. i and the flow rates on each side; then update D according to the pipeline evolution equation. ij : D ij e ij The "conductivity" or "thickness" refers to the flow capacity; μ is the positive gain coefficient, controlling the rate of pipe growth; λ is the attenuation rate, indicating how the pipe gradually shrinks when not in use; |f ij | represents absolute flow rate. The higher the flow rate, the "healthier" and thicker the pipe; if the flow rate is low or even zero, the pipe thickness continuously decreases until it disappears; after the final update is completed, the following is executed on the path: Where Δt is the step size, δ is the pipe thickness threshold, and ε is the flow threshold for determining whether the pipe should be retained; Output the optimal path: After several iterations, the thickness of most pipes with low flow will gradually decrease and disappear; only pipes with high flow will be retained, thus forming the optimal or near-optimal "skeleton path" that can support the core performance component (1).

7. The method according to claim 1, characterized in that, In step four, the other filling material (7) and the solid material part (5) form an integral structure with the core cavity part (3) after injection or foaming, so as to further reduce the heat transfer coefficient and improve the module strength.

8. The method according to claim 1, characterized in that, In step four, when the modification module is installed as a whole on the exterior wall of the building, it can be connected to the original wall through prefabricated parts, inserts or adhesive layers to form an integrated exterior wall insulation modification system.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium for storing computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-8.

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