Design method of anti-falling external wall heat insulation structure

Abstract

The invention provides a design method of an anti-falling external wall thermal insulation structure, which belongs to the technical field of external wall thermal insulation design, and comprises the following steps: establishing an external wall thermal insulation system pasting index matrix, and quantifying the bonding performance of a material and a base layer; constructing a pasting stability matrix, and representing the influence of the environmental condition on the system stability; executing a simulation experiment, and calculating a stress distribution state; performing entity small test verification to obtain interface parameters; designing an application falling change function, and constructing a dynamic prediction model; calculating a falling probability matrix, and identifying a high-risk area; anchoring point arrangement is optimized, and the optimal position is determined; developing an interface enhancement layer to improve the strain adaptability; an optimal construction matrix is generated, and a complete anti-falling system is formed. Through systematic design and optimization, the technical problems that in the prior art, an external wall heat preservation system is insufficient in long-term stability and prone to falling off are solved, and the long-term stability of the heat preservation system is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of exterior wall thermal insulation design, and in particular relates to a design method for an anti-falling exterior wall thermal insulation structure. Background Art

[0002] Exterior wall insulation technology is a crucial component of building energy efficiency. Traditional exterior wall insulation systems primarily utilize a combination of adhesives and mechanical anchoring to secure insulation materials to building exterior walls. These systems typically utilize materials such as polystyrene boards and rock wool boards. The insulation panels are adhered to the wall substrate using a point-bonding or strip-bonding method, supplemented by mechanical fixings using plastic expansion anchors to enhance overall system stability.

[0003] However, traditional exterior wall insulation systems often experience failures during use, including delamination between the insulation layer and the base layer, warping of insulation panel edges, and cracking of the exterior finish. These issues are primarily due to long-term damage to the insulation system caused by environmental factors such as temperature cycling, alternating humidity and heat, and wind loads, as well as stress concentration caused by differences in physical and chemical properties between material interfaces. Existing design methods typically rely on empirical rules to determine the number and arrangement of anchor points, lacking systematic consideration of environmental conditions and material properties, making it difficult to address the risk of shedding under complex working conditions.

[0004] The greatest technical challenge facing current exterior wall insulation systems lies in the inability to accurately predict the spatiotemporal distribution of interface detachment risks under varying environmental conditions. This results in a lack of theoretical basis for the design of anti-detachment measures, hindering the quantitative assessment and optimization of the insulation system's long-term stability. Simply increasing the number of anchor points or improving bond strength often results in material waste or increases construction difficulty, without fundamentally resolving the detachment issue. In other words, existing technologies present a technical challenge: insufficient long-term stability and a high risk of detachment. Summary of the Invention

[0005] In view of this, the present invention provides a design method for an anti-falling exterior wall insulation structure, which can solve the technical problems in the prior art of insufficient long-term stability and easy falling of the exterior wall insulation system.

[0006] The present invention is implemented as follows: The present invention provides a design method for an anti-falling exterior wall insulation structure, which includes: establishing an application index matrix of an exterior wall insulation system; constructing an application stability matrix; performing simulation experiments, using finite element analysis software to simulate the stress distribution state of different material combinations under various environmental conditions; conducting physical small-scale test verification; designing an application fall-off change function; using an anti-falling stability equation group to calculate a fall-off probability matrix; applying a facility site selection algorithm to optimize the layout of anchor points; developing an interface reinforcement layer design; generating an optimal structural matrix, and integrating all parameter optimization results to form a comprehensive anti-falling insulation structural system including material selection, interface treatment, anchor layout, and construction technology, so as to achieve long-term stability of the exterior wall insulation system.

[0007] Among them, the establishment of the external wall insulation system pasting index matrix includes: by measuring the bonding strength, shear strength and interface contact area between different materials and the base layer, constructing a three-dimensional parameter space, and quantifying the bonding performance between the pasting material and the base layer; using the pasting index matrix formula to calculate the normal bonding strength, tangential shear strength and interface contact area ratio at different positions, and obtaining the insulation layer thickness parameters, material elastic modulus parameters, base layer surface state parameters and initial interface strength parameters.

[0008] Among them, the construction of the application stability matrix includes: collecting ambient temperature, humidity, wind load and substrate expansion coefficient data, and establishing a 4×4 matrix to characterize the stability performance of the exterior wall insulation system under different environmental conditions; using the application stability matrix formula to construct a matrix containing ambient temperature factors, humidity influence factors, wind load factors and material thermal expansion stress factors, and obtain temperature gradient parameters, humidity cycle amplitude parameters, temperature change rate parameters and wind pressure action coefficients.

[0009] Among them, the execution of the simulation experiment includes: using finite element analysis software to simulate the stress distribution state of different material combinations under various environmental conditions, calculating the position and value of stress concentration points, and obtaining preliminary application stability data; using the stress distribution state equation to calculate the stress distribution of each point in space.

[0010] Among them, the physical small-scale test verification includes: installing the insulation system on the standard test wall according to different application schemes, simulating extreme temperature cycles, hot and humid cycles and wind pressure changes, monitoring interface stress changes and small displacements; using the interface roughness calculation formula to obtain the interface roughness coefficient, ultraviolet radiation intensity, freeze-thaw cycle number parameters, material aging rate parameters and interface damage accumulation coefficient.

[0011] Among them, the design of the application and shedding change function includes: based on simulation and test data, establishing a mathematical model of the change of the application state of the insulation material over time, introducing the time attenuation factor and the environmental sensitivity coefficient, and constructing a dynamic prediction model; using the application and shedding change function equation to calculate the application state parameters at different time points, and obtain the effectiveness parameters of the protective measures; the application and shedding change function is a mathematical function that describes the change law of the application state of the insulation material over time. By introducing the time attenuation factor and the environmental sensitivity coefficient, a prediction model of the change of the interface bonding strength with the change of environmental conditions and the passage of time is established.

[0012] Among them, the use of the anti-falling stability equation group to calculate the falling probability matrix includes: coupling the application stability matrix with the application falling change function to obtain the falling probability distribution of each node under different working conditions and identify high-risk areas; using the falling probability matrix calculation formula, combined with the interface stress distribution function and the time-related strength attenuation function, to calculate the falling probability of each node; the falling probability matrix is a probability distribution matrix that characterizes the possibility of falling of each node under different working conditions, obtained by coupling the application stability matrix with the application falling change function, and is used to identify high-risk areas and guide the design of anti-falling measures.

[0013] Among them, the application of facility site selection algorithm to optimize the layout of anchor points includes: mapping the detachment probability matrix into a node weight graph, determining the optimal position of the anchor point by solving the minimum weight covering problem, and considering the minimum distance constraint between anchor points, boundary effect influencing factors and structural stress concentration areas to generate an anchor optimization layout diagram; using the minimum weight covering problem objective function and constraint conditions to obtain the anchor point number density parameters, anchor depth parameters, anchor tensile strength parameters and anchor distribution uniformity coefficient.

[0014] Among them, the development of the interface reinforcement layer design includes: adding an interface reinforcement layer with flexible transition characteristics between the thermal insulation material and the base layer, adjusting the thickness and material composition of the interface reinforcement layer, and improving the strain adaptability of the overall system; using the interface reinforcement layer flexibility characteristic calculation formula to optimize the comprehensive strain capacity of the interface reinforcement layer.

[0015] Among them, the application index matrix is a mathematical expression describing the bonding performance between the insulation material and the base layer. By measuring the normal bonding strength, tangential shear strength and interface contact area ratio at different positions, a three-dimensional parameter space is constructed, which is used to quantitatively characterize the degree of interface bonding of the materials; the application stability matrix is a mathematical model that characterizes the structural stability of the exterior wall insulation system under different environmental conditions. The application stability matrix includes the ambient temperature factor, humidity influence factor, wind load factor and material thermal expansion stress factor, forming a 4×4 order matrix structure; the optimal construction matrix is an anti-falling insulation system design scheme formed by comprehensively optimizing all parameters. The optimal construction matrix includes the best material combination, interface treatment method, anchoring arrangement scheme and construction process requirements, forming a complete set of exterior wall insulation anti-falling structural system.

[0016] The fall stability equations are a mathematical model for calculating the fall probability matrix. These anti-fall stability equations include the interface stress distribution equation, the environmental response equation, the time decay equation, and the anchoring equation. The facility site selection algorithm is a mathematical method for optimizing anchor point placement. This algorithm divides the insulation system surface into a discrete node grid, assigning each node a weight calculated based on the fall probability matrix. The algorithm then solves a combinatorial optimization problem to find the minimum number of anchor point locations required to effectively cover all high-risk areas.

[0017] The present invention quantitatively describes the interface state, environmental response and time evolution of the exterior wall insulation system by establishing a multi-dimensional parameter matrix and a dynamic prediction model, thereby achieving accurate prediction of the risk of shedding and optimal design of anti-shedding measures. By introducing the application index matrix and the application stability matrix, the present invention systematically solves the problem of difficult quantification of interface performance in traditional methods, so that the bonding performance between the insulation material and the base layer can be accurately characterized. At the same time, by establishing an anti-shedding stability equation group and a shedding probability matrix, the problem of inaccurate shedding risk prediction is solved, high-risk areas can be identified and the anchoring arrangement can be optimized in a targeted manner. In addition, by designing the application shedding change function and the interface enhancement layer, the problem of the inability to predict long-term stability in traditional methods is effectively solved. The present invention realizes the transition from empirical design to theoretical design, solves the technical problems of insufficient long-term stability and easy shedding in the existing exterior wall insulation system, provides a full life cycle safety guarantee for the exterior wall insulation system, and greatly improves the stability and service life of the building exterior wall insulation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the method of the present invention.

[0019] Figure 2 This is a schematic structural diagram of the exterior wall insulation system in Example 2.

[0020] Figure 3 This is a schematic diagram of the connection arrangement in Example 3.

[0021] Figure 4 This is a schematic diagram of the front dimensions of the insulation board. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0023] like Figure 1 FIG. 1 is a flow chart of a method for designing an anti-falling exterior wall insulation structure provided by the present invention, and the method comprises the following steps:

[0024] S01. Establish an application index matrix for the exterior wall insulation system. By measuring the bond strength, shear strength, and interface contact area between different materials and the base layer, a three-dimensional parameter space is constructed to quantify the bonding performance between the application material and the base layer. In this step, the application index matrix formula is used to calculate the normal bond strength, tangential shear strength, and interface contact area ratio at different locations, and the insulation layer thickness parameters, material elastic modulus parameters, base layer surface state parameters, and initial interface strength parameters are obtained.

[0025] S02. Construct a coating stability matrix, collect data on ambient temperature, humidity, wind load, and substrate expansion coefficient, and establish a 4×4 matrix to characterize the stability performance of the exterior wall insulation system under different environmental conditions. In this step, the coating stability matrix formula is used to construct a matrix containing the ambient temperature factor, humidity influence factor, wind load factor, and material thermal expansion stress factor, and obtain the temperature gradient parameter, humidity cycle amplitude parameter, temperature change rate parameter, and wind pressure effect coefficient.

[0026] S03. Perform simulation experiments, using finite element analysis software to simulate the stress distribution state of different material combinations under various environmental conditions, calculate the location and value of stress concentration points, and obtain preliminary application stability data; in this step, the stress distribution state equation is used to calculate the stress distribution at each point in space;

[0027] S04. Conduct a physical test to verify the installation of the insulation system according to different application schemes on a standard test wall, simulate extreme temperature cycles, heat and humidity cycles, and wind pressure changes, and monitor interface stress changes and small displacements. In this step, the interface roughness calculation formula is used to obtain the interface roughness coefficient, ultraviolet radiation intensity, freeze-thaw cycle number parameter, material aging rate parameter, and interface damage accumulation coefficient.

[0028] S05. Design a patch shedding change function. Based on simulation and pilot test data, establish a mathematical model for how the insulation material's patch state changes over time. Introduce a time attenuation factor and an environmental sensitivity coefficient to construct a dynamic prediction model. In this step, use the patch shedding change function equation to calculate the patch state parameters at different time points and obtain the effectiveness parameters of the protective measures.

[0029] S06. Calculate the shedding probability matrix using the anti-shedding stability equations, couple the application stability matrix with the application shedding change function to obtain the shedding probability distribution of each node under different working conditions, and identify high-risk areas. In this step, the shedding probability matrix calculation formula is used in combination with the interface stress distribution function and the time-dependent strength attenuation function to calculate the shedding probability of each node.

[0030] S07. Apply the facility site selection algorithm to optimize the layout of anchor points. Map the dropout probability matrix to a node weight graph. Determine the optimal location of the anchor points by solving the minimum weight covering problem. Consider the minimum distance constraint between anchor points, boundary effect factors, and structural stress concentration areas to generate an optimized anchor layout diagram. In this step, the minimum weight covering problem objective function and constraints are used to obtain the anchor point density parameter, anchor depth parameter, anchor tensile strength parameter, and anchor distribution uniformity coefficient.

[0031] S08. Develop an interface reinforcement layer design. Add an interface reinforcement layer with flexible transition characteristics between the thermal insulation material and the base layer. Adjust the thickness and material composition of the interface reinforcement layer to improve the strain adaptability of the entire system. In this step, use the calculation formula for the flexibility characteristics of the interface reinforcement layer to optimize the comprehensive strain capacity of the interface reinforcement layer.

[0032] S09. Generate an optimal structural matrix, integrate all parameter optimization results, and form a comprehensive anti-sloughing and thermal insulation structural system including material selection, interface treatment, anchoring arrangement, and construction technology to achieve long-term stability of the exterior wall insulation system. In this step, the optimal structural matrix formula is used, combined with the additional stability coefficient provided by the anchoring system, to generate the final anti-sloughing and thermal insulation structural system.

[0033] The adhesion index matrix is a mathematical expression that describes the bonding performance between the insulation material and the base layer. By measuring the normal bond strength, tangential shear strength, and interface contact area ratio at different positions, a three-dimensional parameter space is constructed to quantitatively characterize the degree of material interface bonding.

[0034] The application stability matrix is a mathematical model that characterizes the structural stability of the exterior wall insulation system under different environmental conditions. The application stability matrix includes parameters such as the ambient temperature factor, humidity influence factor, wind load factor, and material thermal expansion stress factor, forming a 4×4 matrix structure.

[0035] The application shedding change function is a mathematical function that describes the change of the application state of the thermal insulation material over time. By introducing the time attenuation factor and the environmental sensitivity coefficient, a prediction model for the change of the interface bonding strength with the change of environmental conditions and time is established.

[0036] The shedding probability matrix is calculated by coupling the application stability matrix with the application shedding change function. It is a probability distribution matrix that represents the probability of shedding of each node under different working conditions. It is used to identify high-risk areas and guide the design of anti-shedding measures.

[0037] The optimal structural matrix is a design scheme for the anti-sloughing insulation system formed by comprehensively optimizing all parameters. The optimal structural matrix includes the best material combination, interface treatment method, anchoring arrangement scheme, and construction process requirements, forming a complete set of exterior wall insulation and anti-sloughing structural system;

[0038] The anti-falling stability equation group is a mathematical model for calculating the falling probability matrix, and the anti-falling stability equation group includes an interface stress distribution equation, an environmental response equation, a time decay equation, and an anchoring effect equation;

[0039] Among them, the interface stress distribution equation is used to calculate the stress distribution state of each point on the interface between the thermal insulation layer and the base layer. The input includes the thermal insulation layer thickness parameter, the material elastic modulus parameter, the temperature gradient parameter, the interface roughness coefficient, and the base layer surface state parameter. The thermal insulation layer thickness parameter is obtained from step S01, the material elastic modulus parameter is obtained from step S01, the temperature gradient parameter is obtained from step S02, the interface roughness coefficient is obtained from step S04, and the base layer surface state parameter is obtained from step S01. The output is the interface stress distribution function, which is used to calculate the shedding probability matrix in step S06;

[0040] The environmental response equation is used to evaluate the degree of influence of environmental factors on interface stability. The input includes a humidity cycle amplitude parameter, a temperature change rate parameter, a wind pressure effect coefficient, an ultraviolet radiation intensity, and a freeze-thaw cycle number parameter. The humidity cycle amplitude parameter is obtained from step S02, the temperature change rate parameter is obtained from step S02, the wind pressure effect coefficient is obtained from step S02, the ultraviolet radiation intensity is obtained from step S04, and the freeze-thaw cycle number parameter is obtained from step S04. The output is a comprehensive environmental impact coefficient, which is used to calculate the time decay equation.

[0041] Among them, the time decay equation is used to predict the degradation law of material interface performance over time, and the input includes the initial interface strength parameter, the environmental impact comprehensive coefficient, the material aging rate parameter, the interface damage accumulation coefficient, and the protective measure effectiveness parameter. The initial interface strength parameter is obtained from step S01, the environmental impact comprehensive coefficient is obtained from the environmental response equation, the material aging rate parameter is obtained from step S04, the interface damage accumulation coefficient is obtained from step S04, and the protective measure effectiveness parameter is obtained from step S05. The output is a time-dependent strength decay function, which is used to calculate the shedding probability matrix in step S06;

[0042] The anchoring action equation is used to calculate the contribution of the auxiliary anchoring system to the overall stability. The input includes the anchor point density parameter, the anchor depth parameter, the anchor tensile strength parameter, the anchor distribution uniformity coefficient, and the insulation board size parameter. The anchor point density parameter is obtained from step S07, the anchor depth parameter is obtained from step S07, the anchor tensile strength parameter is obtained from step S07, the anchor distribution uniformity coefficient is obtained from step S07, and the insulation board size parameter is obtained from step S01. The output is the additional stability coefficient provided by the anchoring system, which is used to generate the optimal construction matrix in step S09.

[0043] Among them, the facility site selection algorithm is a mathematical method to optimize the layout of anchor points. By dividing the surface of the insulation system into discrete node grids, each node is assigned a weight value calculated based on the shedding probability matrix, and the combinatorial optimization problem of finding the minimum number of anchor point locations so that all high-risk areas are effectively covered is solved.

[0044] The specific implementation of the above steps is described in detail below.

[0045] The specific implementation method of step S01 is to first perform high-precision three-dimensional laser scanning on the surface of the exterior wall base to obtain the microscopic morphology data of the base surface, and then measure the normal bond strength, tangential shear strength and actual contact area between different insulation materials and the base under standard test conditions (temperature 23±2°C, relative humidity 50±5%). The measurement process uses material mechanics micro-area probe technology, uniformly selects 10×10 grid points on the sample surface, and measures each point 5 times and takes the average value to ensure data reliability. The three sets of data obtained are substituted into the application index matrix formula to construct a three-dimensional parameter representation space, where the matrix element A ijIt represents the comprehensive application performance index of the i-th material on the j-th base layer. The weighted summation method is used for calculation, with the normal bonding strength weighted 0.4, the tangential shear strength weighted 0.35, and the contact area ratio weighted 0.25 to obtain the comprehensive application index. The key parameters obtained include the thickness parameter of the thermal insulation layer (generally in the range of 50 to 200 mm), the elastic modulus parameter of the material (3 to 15 MPa for hard foam and 0.5 to 5 MPa for mineral wool), the surface state parameter of the base layer (roughness coefficient 0.5 to 4.5, the larger the rougher), and the initial interface strength parameter (minimum not less than 0.3 MPa). The purpose of this step is to establish a quantitative application performance evaluation system, objectively characterize the initial application state of different material combinations, and provide basic data support for the subsequent anti-shedding structure design.

[0046] The specific implementation method of step S02 is to continuously collect the ambient temperature, humidity, and wind speed data of the installation site for the past five years through a multi-site meteorological data acquisition system, and build a regional microclimate model by combining the building height, orientation, and surrounding terrain factors. The principal component analysis (PCA) method is used to process multidimensional meteorological data, extract the main influencing factors, and use the extreme value statistical method to determine the design value of each environmental parameter. Then, based on the material thermophysical property test, the thermal expansion coefficient, hygroscopicity, and mechanical response parameters of each insulation material are obtained. A 4×4 order matrix is constructed using the application stability matrix formula, where the matrix element M ij It represents the stability index of the jth insulation system under the i-th environmental condition. The diagonal elements represent the influence of a single factor, and the off-diagonal elements represent the coupling effect between factors. The matrix is constructed based on the principle of thermodynamic equilibrium and the stress-strain relationship of the material, taking into account the environmental temperature factor (design value -30 ~ +80 ° C), humidity factor (relative humidity cycle range 20% ~ 95%), wind load factor (maximum wind pressure 2.5kPa), material thermal expansion stress factor (linear expansion coefficient difference does not exceed 15 × 10 -6 Key parameters obtained from this process include the temperature gradient parameter (generally critical value 0.8°C / mm), the humidity cycle amplitude parameter (critical relative humidity change rate 15% / h), the temperature change rate parameter (critical value 8°C / h), and the wind pressure effect coefficient (height correction factor 1.0 to 2.5). The purpose of this step is to construct an environmental-material coupled response model to evaluate the impact of external environmental changes on the stability of the insulation system and provide an environmental adaptability basis for anti-shedding design.

[0047] The specific implementation method of step S03 is to first establish a three-dimensional digital model of the multi-layer composite structure of the insulation system, whose geometric dimensions are consistent with the actual project, including the wall base layer, interface layer, insulation layer and finishing layer. Finite element analysis software (such as ANSYS or ABAQUS) is used to discretize the geometric model into a high-density hexahedral unit grid, and the thickness direction of the insulation layer is divided into at least 10 layers of grid to capture the temperature gradient effect. The material property setting adopts a nonlinear constitutive relationship, considering elastic-plastic, creep and thermo-mechanical coupling effects. The boundary condition setting is based on the environmental parameters obtained in step S02 to simulate the environmental humidity, temperature cycle changes and wind pressure. The simulation calculation adopts a time domain-frequency domain hybrid analysis method. First, a transient thermal analysis is performed to obtain the temperature field distribution, and then the temperature field results are used as load input to perform structural mechanics analysis. The stress distribution state equation is applied to calculate the stress value of each point on the interface and identify the stress concentration area, where the critical stress value is 70% of the static strength of the material as the warning threshold. A sensitivity analysis is performed on the calculation results to determine the influence weight of each parameter on the stress distribution. The output results include a 3D stress distribution cloud map, a deformation displacement map, and a stress-time curve, providing verification targets and focus points for subsequent physical tests. The purpose of this step is to use computer simulation technology to predict the stress distribution state of the insulation system under different operating conditions, identify potential shedding risk points, and provide theoretical guidance for physical test design.

[0048] The specific implementation method of step S04 is to construct a 3×2m verification wall under standard laboratory conditions. The base layer adopts standard concrete wall panels, and the surface is treated according to the actual status of the project. The insulation system is installed in different zones according to different application schemes, and at least 3 parallel templates are set for each scheme. Strain gauges, displacement sensors and temperature and humidity monitoring probes are embedded in key positions of the template to form a multi-parameter synchronous monitoring network. Use environmental simulation equipment to create extreme working conditions, including rapid heating and cooling cycles (-20~+70℃, 8 hours / cycle), high humidity alternating hot and cold (5~40℃, relative humidity 90%), strong ultraviolet radiation (irradiation intensity 60W / m 2 ), wind pressure cycle loading (maximum 2kPa), each set of working conditions runs at least 50 cycles. The interface roughness is measured using a surface profiler, and the interface roughness calculation formula is used. Calculate the arithmetic mean deviation, where y i is the profile deviation value. At the same time, the changes in material aging indicators such as color change, mass loss and hardness change are recorded. The data acquisition frequency is set to once per hour to form a time series database. By comparing the relationship between interface stress changes and micro-displacements under different working conditions, an early warning indicator system is established. The key parameters obtained include the interface roughness coefficient (ideal range 1.5 to 3.0), ultraviolet radiation intensity (critical cumulative dose 120MJ / m 2), freeze-thaw cycle parameters (expected number of cycles within the design life is 300-500 times), material aging rate parameters (annual decay rate does not exceed 2%), and interface damage accumulation coefficient (critical value 0.65). The purpose of this step is to verify the accuracy of the theoretical model predictions through physical testing, obtain measured data on the impact of environmental factors on interface performance, and provide a reliable basis for subsequent anti-shedding structure design.

[0049] The specific implementation method of step S05 is to use machine learning methods to establish a mathematical model of the change of the insulation system application state over time based on the data obtained in steps S03 and S04. First, data preprocessing is performed, including outlier detection, data standardization and dimensionality reduction to ensure data quality. Then, the support vector regression (SVR) algorithm is used to construct the initial prediction model, and its kernel function selects the radial basis function (RBF), and the kernel parameters are optimized by cross-validation. The model input variables include material characteristic parameters, environmental condition parameters and time parameters, and the output variable is the change value of the interface bonding strength. In order to improve the model's ability to predict long-term performance, the time attenuation factor λ is introduced. t =e -αt , where α is the attenuation coefficient of the material and t is the time variable. At the same time, considering the cumulative effect of environmental factors, the environmental sensitivity coefficient is defined where w i is the weight coefficient, f i is the response function, e i The time attenuation factor and the environmental sensitivity coefficient are coupled to construct the patch shedding change function φ(t, e) = φ0·λ t β e , where φ0 is the initial application strength. The Markov Chain Monte Carlo (MCMC) method was used to simulate the application state at different time points, generating a time series curve. Key parameters were obtained, including the effectiveness parameter of the protective measure (efficiency coefficient 1.2 to 1.8). The purpose of this step was to establish a dynamic prediction model with a time dimension to evaluate the performance trend of the insulation system within its design service life and provide long-term stability assurance for the design of anti-shedding measures.

[0050] The specific implementation method of step S06 is to first construct an anti-shedding stability equation group, wherein the interface stress distribution equation is based on the principle of elastic mechanics, inputs the insulation layer thickness parameter, material elastic modulus parameter, temperature gradient parameter, interface roughness coefficient and base surface state parameter, and calculates the stress distribution of each point in space; the environmental response equation is based on the coupling effect of environmental factors on material performance, inputs the humidity cycle amplitude parameter, temperature change rate parameter, wind pressure effect coefficient, ultraviolet radiation intensity and freeze-thaw cycle number parameter, and calculates the environmental impact comprehensive coefficient; the time attenuation equation is based on the material aging mechanism, inputs the initial interface strength parameter, environmental impact comprehensive coefficient, material aging rate parameter, interface damage accumulation coefficient and protective measures effectiveness parameter, and calculates the time-related strength attenuation function. Substitute the solution of the above equation group into the shedding probability matrix calculation formula where Φ is the standard normal cumulative distribution function, σ ij is the stress value, φ ij (t) is the time-varying intensity value, V σ and V φ are the coefficients of variation of stress and strength, respectively. The calculation process utilizes Monte Carlo simulation, with 10,000 random sampling cycles to ensure statistical reliability. The output is a probability distribution matrix for discrete spatial points, with points with a probability threshold greater than 0.05 marked as high-risk areas. The purpose of this step is to quantitatively assess the risk of shedding across various parts of the insulation system, providing an accurate risk distribution map for subsequent anchor point placement and anti-shedding structure design.

[0051] The specific implementation of step S07 is to map the dropout probability matrix generated in step S06 into a grid node weight map, where the node weight is proportional to the dropout probability. The improved maximum covering set positioning algorithm is used to optimize the layout of anchor points. The core idea of the algorithm is to solve the anchor point position combination under a finite number of constraints to maximize the coverage of high-risk areas. The algorithm execution process includes initializing the candidate anchor point set, defining the coverage radius (generally 300-500mm), setting the minimum distance constraint between anchor points (usually not less than 200mm), considering the boundary effect factor (the weight of the edge area is increased by 20%) and the structural stress concentration area (such as the corner of the wall opening). The greedy strategy is used to iteratively select the optimal anchor point, and each time the candidate position that can cover the most uncovered high-risk points is selected. To avoid local optimal solutions, a simulated annealing mechanism is introduced, with an initial temperature of 100, a cooling rate of 0.95, and a termination temperature of 0.1. After multiple iterative optimizations, an anchor point distribution map is generated to ensure effective coverage of high-risk areas. The key parameters obtained thereby include the anchor point number density parameter (recommended value 4-8 / m 2), anchor depth parameters (minimum depth 50mm), anchor tensile strength parameters (no less than 1.2kN), and anchor distribution uniformity coefficient (coefficient of variation no more than 0.25). The purpose of this step is to optimize the anchor point layout plan, maximize material and construction cost savings while meeting safety requirements, and improve the reliability and economy of the overall system.

[0052] The specific implementation method of step S08 is to design an interface reinforcement layer with flexible transition characteristics based on the results of interface stress analysis. First, the material formula is screened, and a polymer material with good deformation adaptability is selected as the matrix, and a nano-scale reinforcement phase is added to improve the interface bonding strength. Commonly used materials include modified acrylic emulsions, polyurethane elastomers and epoxy resins. The orthogonal experimental method is used to design a material formula screening experiment, and the factors examined include polymer type, reinforcement phase content, cross-linker dosage and additive combination. The material properties are evaluated by tensile tests, adhesion strength tests and thermal cycle stability tests, and the formula with the best comprehensive performance is selected. The structural design of the interface reinforcement layer adopts the principle of gradient change. The stiffness is higher on the side close to the base layer and lower on the side close to the insulation layer, forming a continuous transition to avoid stress concentration caused by sudden changes in stiffness. The thickness of the reinforcement layer is determined by finite element optimization and is generally controlled within the range of 1 to 3 mm. The flexible characteristics of the interface reinforcement layer are calculated using the formula Evaluate the transition effect of the enhancement layer, where E b 、E i 、E p are the elastic moduli of the base layer, reinforcement layer and insulation layer respectively, δ i , δ p are the thickness of the reinforcement layer and the insulation layer respectively. The optimization goal is to make C f The value is close to the range of 0.5 to 0.7 to achieve the best buffering effect. The purpose of this step is to design an interface reinforcement layer to improve the deformation coordination ability of the overall system, reduce the interface stress caused by ambient temperature changes and mechanical loads, and fundamentally improve the long-term stability of the insulation system.

[0053] The specific implementation method of step S09 is to integrate the results of the above steps and construct an optimal construction matrix. First, an evaluation index system is established, including safety index (fall-off risk rate is less than 0.1%), durability index (design service life is not less than 25 years), construction index (process complexity score does not exceed 3.5 points) and economic index (incremental cost rate does not exceed 15%). The hierarchical analysis method (AHP) is used to determine the weight of each indicator, construct a judgment matrix and calculate the eigenvector to obtain the standardized weight coefficient. Then, each scheme is comprehensively scored according to the index system, and the scheme with the highest score is selected as the optimal construction scheme. The optimal construction matrix formula is used. Calculate the final solution score, where w ij is the weight coefficient, x ijis a standardized scoring value. The optimal structural system includes material selection (determining the optimal insulation material type based on the application index matrix in step S01), interface treatment (determining the interface treatment solution based on the interface reinforcement layer design results in step S08), anchoring arrangement (determining the anchoring system solution based on the anchor point optimization arrangement results in step S07), and construction technology (developing a standardized construction process based on the anti-falling structural requirements). The final anti-falling insulation structural system comprehensively considers the additional stability coefficient provided by the anchoring system (recommended value 1.5 to 2.0), forms a complete technical solution, and formulates corresponding quality acceptance standards and long-term monitoring and maintenance procedures. The purpose of this step is to integrate the research results of each link, form a systematic anti-falling insulation structural system, achieve the long-term stability goal of the exterior wall insulation system, and provide comprehensive technical support for engineering applications.

[0054] The mathematical model or calculation process involved in the present invention is described in detail below.

[0055] The following is a detailed description of the formulas involved in the design method for preventing the exterior wall insulation structure from falling off:

[0056] The formula of the application index matrix is expressed as follows:

[0057] A=[A ij ] m×n ;

[0058] Where A is the application index matrix; A ij It represents the comprehensive application performance index of the i-th material on the j-th substrate; m is the number of material types; n is the number of substrate types.

[0059] The specific calculation formula is as follows:

[0060] A ij =w1·σ n，ij +w2·τ s，ij +w3·η c，ij ;

[0061] Where, σ n，ij is the normal bond strength, in MPa, ranging from 0.1 to 1.5 MPa; τ s，ij is the tangential shear strength, in MPa, ranging from 0.05 to 0.8 MPa; η c，ij is the interface contact area ratio, dimensionless, ranging from 0.6 to 0.95; w1, w2, and w3 are weight coefficients, with values of 0.4, 0.35, and 0.25, respectively, and satisfy w1+w2+w3=1.

[0062] The parameter acquisition method is: normal bond strength σ n，ijThe tensile bond strength was obtained by evenly distributing the test points on a 10×10 grid according to the standard specification, and each point was tested 5 times to obtain the average value; the tangential shear strength τ s，ij The interface contact area ratio η was obtained by shear strength test, using double shear components for testing, and the shear rate was controlled at 2 mm / min. c，ij The interface layer was sliced and observed under a scanning electron microscope under fluorescent staining, and the ratio of the actual contact area to the theoretical contact area was calculated using image analysis software.

[0063] The formula of the application stability matrix is expressed as follows:

[0064] M=[M ij ] 4×4 ;

[0065] Where, M is the application stability matrix; M ij It represents the stability index of the jth insulation system under the i-th environmental conditions.

[0066] The specific calculation formula is as follows:

[0067]

[0068] Where, α i is the characteristic factor of environmental conditions, dimensionless, ranging from 0.6 to 1.2; β j is the environmental performance factor of the insulation system, dimensionless, ranging from 0.5 to 1.5; γ ij is the environment-material interaction attenuation coefficient, dimensionless, ranging from 0.05 to 0.3; δ ij is the time decay rate in years -1 , the value range is 0.01~0.08 years -1 ; t is the time variable, the unit is year.

[0069] The calculation formula of environmental condition characteristic factor is:

[0070]

[0071] Where, T max 、T min are the maximum and minimum values of the ambient temperature, in °C; T ref The reference temperature difference is 100℃; RH max RH min Respectively, the maximum and minimum relative humidity, in %; RH ref The reference humidity difference is 75%; W max is the maximum wind pressure, in kPa; W ref As the reference wind pressure value, take 1.5kPa.

[0072] The calculation formula of the environment-material interaction attenuation coefficient is:

[0073]

[0074] Where, α T，j is the linear expansion coefficient of the jth material, in units of 10 -6 / ℃;ΔT i is the temperature change amplitude of the i-th environmental condition, in °C; k1 is the temperature effect correction coefficient, with a value of 300; β RH，j is the expansion coefficient of the jth material, in units of 10 -6 / (%RH); ΔRH i is the relative humidity variation range of the i-th environmental condition, in %; k2 is the humidity influence correction coefficient, with a value of 500; W i is the wind pressure value of the i-th environmental condition, in kPa; S j is the wind pressure sensitivity coefficient of the jth insulation system, in kPa -1 ; k3 is the wind pressure correction coefficient, and its value is 5.

[0075] The stress distribution state equation is expressed as follows:

[0076] σ(x, y, z, t) = σ b (x, y, z) + σ T (x, y, z, t) + σ W (x, y, z, t) + σ H (x, y, z, t) + ε σ ;

[0077] Where σ(x, y, z, t) is the stress value of the spatial point (x, y, z) at time t, in MPa; b (x, y, z) are the basic stress components, in MPa; σ T (x, y, z, t) is the temperature stress component, in MPa; σ W (x, y, z, t) is the wind pressure stress component, in MPa; σ H (x, y, z, t) is the humidity stress component, in MPa; ε σ It is the stress calculation error term, the unit is MPa, and the value range is -0.1~0.1MPa.

[0078] The calculation formula of foundation stress components is:

[0079] σ b (x, y, z) = ρ m ·g·h(z)·f θ (x, y);

[0080] Where, ρ m is the density of the insulation material, in kg / m 3 ; g is the acceleration due to gravity, which is 9.8m / s 2 ; h(z) is the height function, unit is m; f θ (x, y) is the installation angle correction function, dimensionless, and takes the value 1 when installed vertically.

[0081] The calculation formula for the temperature stress component is:

[0082] σ T (x, y, z, t) = E(z)·α T ·ΔT(x,y,z,t)·(1-v(z));

[0083] Where E(z) is the elastic modulus of the material, in MPa, which changes with depth z; α T is the linear expansion coefficient, in units of 10 -6 / ℃; ΔT(x, y, z, t) is the temperature change in °C; v(z) is the Poisson's ratio, which is dimensionless and generally takes a value of 0.25 to 0.4.

[0084] The calculation formula for wind pressure stress component is:

[0085]

[0086] Where, P w (t) is the reference wind pressure, in kPa; C p (x, y) is the wind pressure coefficient, dimensionless, determined according to the building shape; f H (z) is the height correction function, dimensionless, and increases with increasing height; S(x, y, z) is the force area, in m 2 ; d is the thickness of the insulation layer, in m.

[0087] The calculation formula of humidity stress component is:

[0088] σ H (x, y, z, t) = W(z)·β RH ΔRH(x, y, z, t) K H ;

[0089] Where, β RH is the material expansion coefficient, unit is 10 -6 / (%RH); ΔRH(x, y, z, t) is the humidity change, unit is %; K H It is the humidity stress conversion coefficient, dimensionless, ranging from 0.3 to 0.7.

[0090] The calculation formula of interface roughness is as follows:

[0091]

[0092] Where R a is the arithmetic mean roughness, in μm; y i is the contour deviation value of the i-th measuring point, in μm; n is the number of measuring points.

[0093] The calculation formula of interface roughness coefficient is as follows:

[0094]

[0095] Where K r is the interface roughness coefficient, dimensionless; R a is the measured arithmetic mean roughness, in μm; R a，ref is the reference roughness value, taking 10μm; K r，max is the maximum roughness coefficient, which is 4.5.

[0096] The calculation formula for the comprehensive environmental impact coefficient is as follows:

[0097] K e =w T ·K T +w RH ·K RH +w W ·K W +w UV ·K UV +w F ·K F ;

[0098] Where K e K is the comprehensive coefficient of environmental impact, dimensionless; T , K RH , K W , K UV , K F are the influence coefficients of temperature, humidity, wind pressure, ultraviolet radiation and freeze-thaw cycle, dimensionless; w T 、w RH 、w W 、w UV 、w F are the corresponding weight coefficients, satisfying w T +w RH +w W +w UV +w F =1.

[0099] The temperature influence coefficient calculation formula is:

[0100]

[0101] Where ΔT is the temperature change amplitude in °C; dT / dt is the temperature change rate in °C / h.

[0102] The calculation formula of humidity influence coefficient is:

[0103]

[0104] Where ΔRH is the humidity change, in %; RH max is the maximum relative humidity in %.

[0105] The calculation formula for wind pressure influence coefficient is:

[0106]

[0107] Where, P w is the wind pressure value, unit is kPa; P w，ref The reference wind pressure value is 1kPa; f gust is the gust coefficient, dimensionless, ranging from 2 to 3.5.

[0108] The calculation formula for the ultraviolet radiation impact coefficient is:

[0109]

[0110] Where, I UV is the intensity of ultraviolet radiation, in W / m 2 ;I UV，ref As the reference radiation intensity, take 30W / m 2 ;t exp is the cumulative exposure time, in h; t ref As the reference exposure time, 2000h is taken.

[0111] The calculation formula of freeze-thaw cycle influence coefficient is:

[0112]

[0113] Where N F is the number of freeze-thaw cycles, in times; N F，ref is the reference cycle number, which is 300 times; ΔT F It is the freeze-thaw temperature difference in ℃, usually 30℃.

[0114] The formula of the patch shedding change function is as follows:

[0115] φ(t, e)=φ0·λ t β e ·K p ;

[0116] Where, φ(t, e) is the application strength at time t and environmental condition e, in MPa; φ0 is the initial application strength, in MPa; λ t is the time decay factor, dimensionless; β e is the environmental sensitivity coefficient, dimensionless; K p It is the effectiveness parameter of protective measures, dimensionless, and ranges from 1.2 to 1.8.

[0117] The time decay factor is calculated as:

[0118]

[0119] Where α is the attenuation coefficient of the material, in years -n , the value range is 0.01~0.05 years -n ; t is the time variable, the unit is year; n is the attenuation exponent, dimensionless, ranging from 0.5 to 1.2.

[0120] The calculation formula of environmental sensitivity coefficient is:

[0121]

[0122] Where w i is the weight coefficient of the i-th environmental factor, satisfying f i (e i ) is the i-th environmental factor e i Response function, dimensionless; m is the number of environmental factors.

[0123] The response function takes the following form:

[0124]

[0125] Where k i is the influence strength coefficient, dimensionless, ranging from 0.6 to 1; e i is the environmental factor value; e i，opt is the optimal environmental value; e i，crit is the critical environmental value; p i It is the response curve index, dimensionless, and ranges from 1.2 to 2.5.

[0126] The calculation formula of the dropout probability matrix is as follows:

[0127]

[0128] Where, P ij is the probability of falling off at the spatial point (i, j), dimensionless; v is the standard normal cumulative distribution function; σ ij is the stress value, unit is MPa; φ ij(t) is the time-varying strength value, in MPa; V σ and V φ They are the coefficients of variation of stress and strength respectively, dimensionless, and generally range from 0.1 to 0.25.

[0129] The standard normal cumulative distribution function is defined as:

[0130]

[0131] In order to improve the calculation efficiency, the approximate formula can be used:

[0132] Applicable range: |x|<3.

[0133] The formula of the anchoring action equation is as follows:

[0134]

[0135] Where S a Additional stability factor provided for the anchoring system, dimensionless; N a is the number of anchor points per unit area, in units of / m 2 ; F a The tensile strength provided for a single anchor point, in kN; C d is the anchor depth correction coefficient, dimensionless; C s is the anchorage distribution uniformity coefficient, dimensionless; A p is the area of the insulation board, in m 2 .

[0136] The calculation formula of anchor depth correction coefficient is:

[0137]

[0138] Where, d a is the actual anchoring depth, in mm; d a，min The minimum anchoring depth is 50mm.

[0139] The calculation formula of anchorage distribution uniformity coefficient is:

[0140] C s =1-0.8·CV d ;

[0141] Where, CV d is the coefficient of variation of the anchor point spacing, dimensionless, and its value should not exceed 0.25.

[0142] The formula of the optimal construction matrix is expressed as follows:

[0143]

[0144] Where M opt is the optimal construction matrix; w ij is the weight coefficient of the jth indicator of the i-th category, satisfying x ij is the corresponding standardized score value, dimensionless, ranging from 0 to 1; n is the number of indicator categories; m is the number of items in each indicator category.

[0145] Standardized score calculation formula:

[0146] Positive indicators;

[0147] Negative indicators;

[0148] Where, v ij is the original index value; v ij，min and v ij，max are the minimum and maximum values of the indicator respectively.

[0149] The calculation formula of the flexibility characteristics of the interface enhancement layer is as follows:

[0150]

[0151] Where C f E is the flexibility coefficient of the interface reinforcement layer, dimensionless, with an ideal value range of 0.5 to 0.7; b 、E i 、E p are the elastic modulus of the base layer, reinforcement layer and insulation layer respectively, in MPa; δ i , δ p are the thickness of the reinforcement layer and the insulation layer respectively, in mm.

[0152] Specifically, the principle of the present invention is: the core principle of the present invention to solve the problem of falling off of the exterior wall insulation system lies in constructing a complete multi-dimensional parameter space and dynamic evolution model, and regarding the insulation system as a complex system affected by multiple factors. Through the combination of mathematical modeling and simulation verification, accurate prediction and optimized control of system behavior are achieved.

[0153] From a microscopic perspective, this invention quantitatively describes the interface characteristics between the insulation material and the substrate using an application index matrix. This model integrates three key parameters—normal bond strength, tangential shear strength, and contact area—into a three-dimensional parameter space, enabling precise characterization of interfacial bonding performance. This matrix model, consistent with material interface mechanics theory, accurately reflects the interface state at different locations, providing fundamental data for subsequent shedding risk assessment.

[0154] From a macroscopic perspective, this paper establishes a stability matrix for the application, factoring in environmental factors such as ambient temperature, humidity, wind load, and material thermal expansion. This 4×4 matrix model allows for a comprehensive assessment of the stability of exterior wall insulation systems. This matrix model, based on thermodynamics and materials science principles, simulates the stress distribution changes in the insulation system caused by varying environmental conditions, reflecting the response characteristics of complex systems.

[0155] From a temporal perspective, the present invention designs a function for the change in adhesion and shedding, introducing a time-attenuation factor and an environmental sensitivity coefficient to establish a mathematical model for the time-dependent variation of interfacial bonding strength. This function, based on material aging theory and the principles of cumulative damage mechanics, accurately predicts the degradation trend of interfacial performance under long-term environmental conditions. Through coupled calculations of the anti-shedding stability equations, the shedding probability distribution of each node under different operating conditions is determined, providing a scientific basis for the optimal placement of anchor points.

[0156] The innovation of this invention lies in establishing a comprehensive technical approach, from characterizing material interface properties, assessing environmental impacts, predicting time evolution, to optimizing anchor design. Each step is logically linked and data-transmitted, forming a closed-loop design methodology. By optimizing anchor point placement through a facility site selection algorithm and developing an interface reinforcement layer design, the stability and reliability of the system are further improved, ultimately forming a comprehensive anti-sloughing and thermal insulation structural system.

[0157] A specific embodiment 1 of the present invention is provided below. The specific implementation of each step in this embodiment 1 is described in detail as follows.

[0158] The specific implementation method of step S01 is to first perform high-precision three-dimensional laser scanning on the surface of the exterior wall base to obtain the microscopic morphology data of the base surface, and then measure the normal bond strength, tangential shear strength and actual contact area between different insulation materials and the base under standard test conditions (temperature 23±2°C, relative humidity 50±5%). The measurement process uses material mechanics micro-area probe technology, uniformly selects 10×10 grid points on the sample surface, and performs 5 measurements at each point and takes the average value to ensure data reliability. Substitute the three sets of data obtained into the application index matrix formula to construct the application index matrix A=[A ij ] m×n , where A ij It represents the comprehensive application performance index of the i-th material on the j-th base layer, m is the number of material types, and n is the number of base types. The specific calculation adopts the weighted summation method, according to formula A ij =w1·σ n，ij +w2·τ s，ij +w3·η c，ij Calculate the comprehensive application index, where σ n，ijis the normal bond strength, ranging from 0.1 to 1.5 MPa; τ s，ij is the tangential shear strength, ranging from 0.05 to 0.8 MPa; η c，ij is the ratio of the interface contact area, with a value range of 0.6 to 0.95; w1, w2, and w3 are weight coefficients, with values of 0.4, 0.35, and 0.25, respectively, and satisfy w1+w2+w3=1. The key parameters obtained include the thickness parameter of the insulation layer (the recommended range is 50 to 200 mm), the elastic modulus parameter of the material (3 to 15 MPa for hard foam and 0.5 to 5 MPa for mineral wool), the surface state parameter of the base layer (roughness coefficient 0.5 to 4.5, the larger the rougher), and the initial interface strength parameter (minimum not less than 0.3 MPa). The purpose of this step is to establish a quantitative application performance evaluation system, objectively characterize the initial application state of different material combinations, and provide basic data support for the subsequent anti-shedding structure design.

[0159] The specific implementation method of step S02 is to continuously collect the ambient temperature, humidity, and wind speed data of the installation site for the past five years through a multi-site meteorological data acquisition system, and construct a regional microclimate model by combining the building height, orientation, and surrounding terrain factors. The principal component analysis (PCA) method is used to process multidimensional meteorological data, extract the main influencing factors, and use the extreme value statistical method to determine the design value of each environmental parameter. Then, based on the material thermophysical property test, the thermal expansion coefficient, hygroscopicity, and mechanical response parameters of each insulation material are obtained. The 4×4 order matrix M=[M ij ] 4×4 , where M ij It represents the stability index of the jth insulation system under the i-th environmental conditions. The matrix element calculation formula is Where α i is the environmental condition characteristic factor, ranging from 0.6 to 1.2; β j is the environmental performance factor of the insulation system, ranging from 0.5 to 1.5; γ ij is the environment-material interaction attenuation coefficient, ranging from 0.05 to 0.3; δ ij is the time decay rate, ranging from 0.01 to 0.08 years -1 ; t is the time variable, the unit is year. The calculation formula of environmental condition characteristic factor is: Where T max 、T min are the maximum and minimum values of the ambient temperature respectively; T ref The reference temperature difference is 100℃; RH max RH min Respectively, the maximum and minimum relative humidity; RH ref The reference humidity difference is 75%; W maxis the maximum wind pressure; W ref As the reference wind pressure value, take 1.5kPa. The calculation formula of environment-material interaction attenuation coefficient is: Where α T，j is the linear expansion coefficient of the jth material; ΔT i is the temperature variation range of the i-th environmental condition; k1 is the temperature effect correction coefficient, which is 300; β RH，j is the expansion coefficient of the jth material; ΔRH i is the relative humidity variation range of the i-th environmental condition; k2 is the humidity influence correction coefficient, which is 500; W i is the wind pressure value of the i-th environmental condition; S j is the wind pressure sensitivity coefficient of the jth insulation system; k3 is the wind pressure influence correction coefficient, which is 5. The matrix is constructed based on the principle of thermodynamic equilibrium and the stress-strain relationship of the material, taking into account the ambient temperature factor (design value -30 to +80°C), humidity influence factor (relative humidity cycle range 20% to 95%), wind load factor (maximum wind pressure 2.5kPa), material thermal expansion stress factor (linear expansion coefficient difference does not exceed 15×10 -6 Key parameters obtained from this analysis include the temperature gradient (recommended critical value 0.8°C / mm), humidity cycle amplitude (critical relative humidity change rate 15% / h), temperature change rate (critical value 8°C / h), and wind pressure coefficient (height correction factor 1.0 to 2.5). This step aims to construct an environmental-material coupled response model to assess the impact of external environmental changes on the stability of the insulation system and provide an environmental adaptability basis for anti-shedding design.

[0160] The specific implementation method of step S03 is to first establish a three-dimensional digital model of the multi-layer composite structure of the insulation system, the geometric dimensions of which are consistent with the actual project, including the wall base layer, interface layer, insulation layer and finishing layer. Use finite element analysis software to discretize the geometric model into a high-density hexahedral unit grid, and divide the insulation layer into at least 10 layers of grid in the thickness direction to capture the temperature gradient effect. The material property setting adopts a nonlinear constitutive relationship, considering elastic-plastic, creep and thermo-mechanical coupling effects. The boundary condition setting is based on the environmental parameters obtained in step S02 to simulate the environmental humidity, temperature cycle changes and wind pressure. Apply the stress distribution state equation σ(x, y, z, t) = σ b (x, y, z) + σ T (x, y, z, t) + σ W (x, y, z, t) + σ H (x, y, z, t) + ε σ Calculate the stress value at each point on the interface, where σ(x, y, z, t) is the stress value of the spatial point (x, y, z) at time t; σ b(x, y, z) are the basic stress components; σ T (x, y, z, t) is the temperature stress component; σ W (x, y, z, t) is the wind pressure stress component; σ H (x, y, z, t) is the humidity stress component; ε σ is the stress calculation error term, ranging from -0.1 to 0.1 MPa. The basic stress component calculation formula is σ b (x, y, z) = ρ m ·g·h(z)·f θ (x, y), where ρ m is the density of the insulation material; g is the acceleration of gravity, which is 9.8m / s 2 ; h(z) is the height function; f θ (x, y) is the installation angle correction function, which takes the value 1 when installed vertically. The temperature stress component calculation formula is σ T (x, y, z, t) = E(z)·α T ΔT(x, y, z, t) (1-v(z)), where E(z) is the elastic modulus of the material, which varies with depth z; α T is the linear expansion coefficient; ΔT(x, y, z, t) is the temperature change; v(z) is the Poisson's ratio, which is generally 0.25 to 0.4. The wind pressure stress component calculation formula is Where P w (t) is the reference wind pressure; C p (x, y) is the wind pressure coefficient, which is determined according to the building shape; f H (z) is the height correction function, which increases with the height; S(x, y, z) is the stress area; d is the thickness of the insulation layer. The calculation formula for the humidity stress component is σ H (x, y, z, t) = E(z)·β RH ΔRH(x, y, z, t) K H , where β RH is the material expansion coefficient; ΔRH(x, y, z, t) is the humidity change; K H is the humidity stress conversion coefficient, with a value of 0.3 to 0.7. The simulation calculation adopts a time-domain-frequency domain hybrid analysis method. First, a transient thermal analysis is performed to obtain the temperature field distribution. Then, the temperature field results are used as load input to perform structural mechanics analysis to identify stress concentration areas. The critical stress value is 70% of the static strength of the material as the warning threshold. The output results include a three-dimensional stress distribution cloud map, a deformation displacement map, and a stress-time history curve, which provide verification objects and focus points for subsequent physical tests. The purpose of this step is to predict the stress distribution state of the insulation system under different working conditions through computer simulation technology, identify potential shedding risk points, and provide theoretical guidance for physical test design.

[0161] The specific implementation method of step S04 is to construct a 3×2m verification wall under standard laboratory conditions. The base layer adopts standard concrete wall panels, and the surface is treated according to the actual status of the project. The insulation system is installed in different zones according to different application schemes, and at least 3 parallel templates are set for each scheme. Strain gauges, displacement sensors and temperature and humidity monitoring probes are embedded in key positions of the template to form a multi-parameter synchronous monitoring network. Use environmental simulation equipment to create extreme working conditions, including rapid heating and cooling cycles (-20~+70℃, 8 hours / cycle), high humidity alternating hot and cold (5~40℃, relative humidity 90%), strong ultraviolet radiation (irradiation intensity 60W / m 2 ), wind pressure cycle loading (maximum 2kPa), each set of working conditions runs at least 50 cycles. The interface roughness is measured using a surface profiler, and the interface roughness calculation formula is used. Calculate the arithmetic mean deviation, where R a is the arithmetic mean roughness; y i is the profile deviation value of the i-th measuring point; n is the number of measuring points. The calculation formula of the interface roughness coefficient is Where K r is the interface roughness coefficient; R a is the measured arithmetic mean roughness; R a，ref is the reference roughness value, taking 10μm; K r，max is the maximum roughness coefficient, which takes a value of 4.5. At the same time, changes in material aging indicators such as color change, mass loss and hardness change are recorded. The data acquisition frequency is set to once per hour to form a time series database. By comparing the relationship between interface stress changes and micro-displacements under different working conditions, an early warning indicator system is established. The key parameters obtained include the interface roughness coefficient (ideal range 1.5 to 3.0), ultraviolet radiation intensity (critical cumulative dose 120MJ / m 2 ), freeze-thaw cycle parameters (expected number of cycles within the design life is 300-500 times), material aging rate parameters (annual decay rate does not exceed 2%), and interface damage accumulation coefficient (critical value 0.65). The purpose of this step is to verify the accuracy of the theoretical model predictions through physical testing, obtain measured data on the impact of environmental factors on interface performance, and provide a reliable basis for subsequent anti-shedding structure design.

[0162] The specific implementation method of step S05 is to use machine learning methods to establish a mathematical model of the change of the insulation system application state over time based on the data obtained in steps S03 and S04. First, data preprocessing is performed, including outlier detection, data standardization and dimensionality reduction to ensure data quality. Then, the support vector regression (SVR) algorithm is used to construct the initial prediction model, and its kernel function selects the radial basis function (RBF), and the kernel parameters are optimized by cross-validation. The model input variables include material characteristic parameters, environmental condition parameters and time parameters, and the output variable is the change value of the interface bonding strength. In order to improve the model's ability to predict long-term performance, a time attenuation factor is introduced. Where α is the attenuation coefficient of the material, ranging from 0.01 to 0.05 years -n ; t is the time variable; n is the attenuation exponent, ranging from 0.5 to 1.2. At the same time, considering the cumulative effect of environmental factors, the environmental sensitivity coefficient is defined Where w i is the weight coefficient of the i-th environmental factor, satisfying f i (e i ) is the i-th environmental factor e i The response function is: m is the number of environmental factors. Where k i is the influence intensity coefficient, ranging from 0.6 to 1; e i is the environmental factor value; e i，opt is the optimal environmental value; e i，crit is the critical environmental value; p i The response curve index is 1.2 to 2.5. The time attenuation factor and the environmental sensitivity coefficient are coupled to construct the patch shedding change function φ(t, e) = φ0·λ t β e ·K p , where φ(t, e) is the application strength at time t and environmental condition e; φ0 is the initial application strength; λ t is the time decay factor; β e is the environmental sensitivity coefficient; K p is the effectiveness parameter of the protective measure, ranging from 1.2 to 1.8. The Markov Chain Monte Carlo (MCMC) method was used to simulate the application status at different time points and generate a time series curve. This step aims to establish a dynamic prediction model with a time dimension to evaluate the performance trend of the insulation system over its design service life, thus ensuring the long-term stability of the anti-sloughing measure design.

[0163] The specific implementation of step S06 is to first construct an anti-shedding stability equation group, wherein the interface stress distribution equation is based on the principle of elastic mechanics, and the insulation layer thickness parameter, material elastic modulus parameter, temperature gradient parameter, interface roughness coefficient and base surface state parameter are input to calculate the stress distribution of each point in space; the environmental response equation is based on the coupling effect of environmental factors on material properties, and the humidity cycle amplitude parameter, temperature change rate parameter, wind pressure effect coefficient, ultraviolet radiation intensity and freeze-thaw cycle number parameter are input to calculate the comprehensive environmental impact coefficient K e =w T ·K T +w RH ·K RH +w W ·K W +w UV ·K UV +w F ·K F , where K e is the comprehensive coefficient of environmental impact; K T , K RH , K W , K UV , K F are the influence coefficients of temperature, humidity, wind pressure, ultraviolet radiation and freeze-thaw cycle respectively; w T 、w RH 、w W 、w UV 、w F are the corresponding weight coefficients, satisfying w T +w RH +w W +w UV +w F =1. The temperature influence coefficient calculation formula is: Where ΔT is the temperature change amplitude; dT / dt is the temperature change rate. The calculation formula for the humidity influence coefficient is: Where ΔRH is the humidity change amplitude; RH max is the maximum relative humidity. The calculation formula for wind pressure influence coefficient is: Where P w is the wind pressure value; P w，ref The reference wind pressure value is 1kPa; f gust is the gust coefficient, ranging from 2 to 3.5. The calculation formula for the ultraviolet radiation influence coefficient is: Where I UV is the intensity of ultraviolet radiation; I UV，ref As the reference radiation intensity, take 30W / m 2 ;t exp is the cumulative exposure time; t refAs the reference exposure time, 2000h is taken. The calculation formula of freeze-thaw cycle influence coefficient is: Where N F is the number of freeze-thaw cycles; N F，ref is the reference cycle number, which is 300 times; ΔT F The time decay equation is based on the material aging mechanism. It inputs the initial interface strength parameter, environmental impact comprehensive coefficient, material aging rate parameter, interface damage accumulation coefficient and protective measures effectiveness parameter to calculate the time-related strength decay function. Substitute the solution of the above equations into the calculation formula of the shedding probability matrix Where P ij is the probability of falling off at the spatial point (i, j); Φ is the standard normal cumulative distribution function; σ ij is the stress value; φ ij (t) is the time-varying intensity value; V σ and V φ are the coefficients of variation of stress and strength, respectively, and are generally between 0.1 and 0.25. The standard normal cumulative distribution function can be approximated by the formula Applicable range: |x| < 3. The calculation process uses Monte Carlo simulation and 10,000 random samplings to ensure the statistical reliability of the results. The output is a shedding probability distribution matrix for discrete points in space. Points with a probability threshold greater than 0.05 are marked as high-risk areas. The purpose of this step is to quantitatively assess the shedding risk of various parts of the insulation system and provide an accurate risk distribution map for subsequent anchor point placement and anti-shedding structure design.

[0164] The specific implementation method of step S07 is to map the shedding probability matrix generated in step S06 into a grid node weight map, and the node weight is proportional to the shedding probability. The improved maximum covering set positioning algorithm is used to optimize the layout of anchor points. The core idea of the algorithm is to solve the anchor point position combination under a finite number of constraints to maximize the coverage of high-risk areas. The algorithm execution process includes initializing the candidate anchor point set, defining the coverage radius (generally 300 to 500 mm), setting the minimum distance constraint between anchor points (usually not less than 200 mm), considering the boundary effect factor (the weight of the edge area is increased by 20%) and the structural stress concentration area (such as the corner of the wall opening). The greedy strategy is used to iteratively select the optimal anchor point, and each time the candidate position that can cover the most uncovered high-risk points is selected. In order to avoid local optimal solutions, a simulated annealing mechanism is introduced, and the initial temperature is set to 100, the cooling rate is 0.95, and the termination temperature is 0.1. After multiple iterative optimizations, the anchor point distribution map is generated to ensure effective coverage of high-risk areas. The formula of the anchoring action equation is expressed as follows: Where S a Additional stability factor provided for the anchoring system; N a is the number of anchor points per unit area; Fa The tensile strength provided for a single anchor point; C d is the anchor depth correction coefficient; C s is the anchorage distribution uniformity coefficient; A p is the area of the insulation board. The calculation formula for the anchor depth correction coefficient is: Where d a is the actual anchoring depth; d a，min The minimum anchoring depth is 50mm. The anchoring distribution uniformity coefficient is calculated as C s =1-0.8·CV d , where CV d The coefficient of variation of the spacing between anchor points should not exceed 0.25. The key parameters obtained from this include the density of anchor points (recommended value 4 to 8 / m 2 ), anchor depth parameters (minimum depth 50mm), anchor tensile strength parameters (no less than 1.2kN), and anchor distribution uniformity coefficient (coefficient of variation no more than 0.25). The purpose of this step is to optimize the anchor point layout plan, maximize material and construction cost savings while meeting safety requirements, and improve the reliability and economy of the overall system.

[0165] The specific implementation method of step S08 is to design an interface reinforcement layer with flexible transition characteristics based on the results of interface stress analysis. First, the material formula is screened, and a polymer material with good deformation adaptability is selected as the matrix, and a nano-scale reinforcement phase is added to improve the interface bonding strength. Commonly used materials include modified acrylic emulsions, polyurethane elastomers and epoxy resins. The orthogonal experimental method is used to design a material formula screening experiment, and the factors examined include polymer type, reinforcement phase content, cross-linker dosage and additive combination. The material properties are evaluated by tensile tests, adhesion strength tests and thermal cycle stability tests, and the formula with the best comprehensive performance is selected. The structural design of the interface reinforcement layer adopts the principle of gradient change. The stiffness is higher on the side close to the base layer and lower on the side close to the insulation layer, forming a continuous transition to avoid stress concentration caused by sudden changes in stiffness. The thickness of the reinforcement layer is determined by finite element optimization and is generally controlled within the range of 1 to 3 mm. The flexible characteristics of the interface reinforcement layer are calculated using the formula Evaluate the transition effect of the enhancement layer, where C f E is the flexibility coefficient of the interface reinforcement layer, with an ideal value range of 0.5 to 0.7; b 、E i 、E p are the elastic moduli of the base layer, reinforcement layer and insulation layer respectively; δ i , δ p are the thickness of the reinforcement layer and the insulation layer respectively. The optimization goal is to make C fThe value is close to the range of 0.5 to 0.7 to achieve the best buffering effect. The purpose of this step is to design the interface reinforcement layer, improve the deformation coordination ability of the overall system, reduce the interface stress caused by ambient temperature changes and mechanical loads, and fundamentally improve the long-term stability of the insulation system.

[0166] The specific implementation method of step S09 is to integrate the results of the above steps and construct an optimal construction matrix. First, an evaluation index system is established, including safety index (fall-off risk rate is less than 0.1%), durability index (design service life is not less than 25 years), construction index (process complexity score does not exceed 3.5 points) and economic index (incremental cost rate does not exceed 15%). The hierarchical analysis method (AHP) is used to determine the weight of each indicator, construct a judgment matrix and calculate the eigenvector to obtain the standardized weight coefficient. Then, each scheme is comprehensively scored according to the index system, and the scheme with the highest score is selected as the optimal construction scheme. The optimal construction matrix formula is used. Calculate the final solution score, where M opt is the optimal construction matrix; w ij is the weight coefficient of the jth indicator of the i-th category, satisfying x ij is the corresponding standardized score value, ranging from 0 to 1; n is the number of indicator categories; m is the number of items in each indicator category. The formula for calculating the standardized score value is a positive indicator. Negative indicators Where v ij is the original index value; v ij，min and v ij，max are the minimum and maximum values of the index respectively. The optimal structural system includes material selection (determining the optimal insulation material type based on the application index matrix in step S01), interface treatment (determining the interface treatment solution based on the design results of the interface reinforcement layer in step S08), anchoring arrangement (determining the anchoring system solution based on the results of the anchor point optimization arrangement in step S07), and construction technology (developing a standardized construction process based on the anti-falling structural requirements). The final anti-falling insulation structural system comprehensively considers the additional stability coefficient provided by the anchoring system (recommended value 1.5 to 2.0), forms a complete technical solution, and formulates corresponding quality acceptance standards and long-term monitoring and maintenance procedures. The purpose of this step is to integrate the research results of each link, form a systematic anti-falling insulation structural system, achieve the long-term stability goal of the exterior wall insulation system, and provide comprehensive technical support for engineering applications.

[0167] To better understand and implement the present invention, Example 2 of a specific application scenario is provided below: In a residential community renovation project in a northern city, researchers employed the present invention's design method for preventing exterior wall insulation from frequently falling off due to the local severe cold weather. The community, built in 2000, had been exposed to severe weather conditions with long-term temperature cycles of -25°C to 32°C, humidity fluctuations of 35% to 90%, and maximum wind speeds of 25m / s. The exterior wall insulation system had cracked and fallen off in multiple locations, seriously impacting building safety and insulation effectiveness.

[0168] The researchers first conducted on-site surveys of typical residential buildings and selected three different types of base walls (concrete shear walls, aerated concrete block walls, and fired brick walls) and four commonly used insulation materials (polystyrene boards, extruded boards, rock wool boards, and phenolic boards) for bonding performance testing. According to step S01, the normal bond strength, tangential shear strength, and contact area of 12 combinations were measured under standard conditions (temperature 23±2°C, relative humidity 50±5%). The resulting bonding index matrix is shown in Table 1:

[0169] Table 1 Application index matrix of different material and base combinations

[0170] Insulation material / base type Concrete shear wall Aerated concrete block wall Fired brick wall polystyrene board 0.658 0.527 0.612 extruded board 0.704 0.586 0.645 rock wool board 0.732 0.683 0.725 Phenolic board 0.763 0.692 0.736

[0171] According to the calculation results of the bonding index matrix, the combination of phenolic board and concrete shear wall achieved the highest bonding performance index. The researchers then collected meteorological data for the region over the past five years according to step S02, determined the environmental condition parameters, and constructed a bonding stability matrix, as shown in Table 2:

[0172] Table 2 Stability matrix of the combination of phenolic board and concrete shear wall

[0173]

[0174] Next, the researchers performed finite element simulation according to step S03 and established a three-dimensional digital model including the concrete base layer, interface layer, phenolic insulation board, and anti-cracking mortar layer. The material parameters are shown in Table 3:

[0175] Table 3 Material parameters of each layer

[0176]

[0177]

[0178] Through finite element analysis, the researchers obtained the stress distribution under the most unfavorable working conditions and identified high-risk areas, which are mainly concentrated in the corners of wall openings, floor joints, and insulation board splicing positions. Based on these results, the researchers built a 3×2m test wall in the laboratory and conducted actual tests on typical working condition combinations. The test results show that after 50 freeze-thaw cycles (-20~+40℃), the interface roughness coefficient increased from the initial 1.85 to 2.37, an increase of 28.1%; the material aging rate was an annual attenuation rate of 1.42%, and the interface damage accumulation coefficient was 0.58. Based on these data, the researchers established a patch peeling change function and predicted the changes in interface bonding strength at different time points, as shown in Table 4:

[0179] Table 4 Prediction of interface bonding strength over time

[0180]

[0181] Based on the shedding probability matrix, researchers calculated the shedding risk distribution of each node within a 25-year service life and used an improved maximum covering set positioning algorithm to optimize the anchor point layout. After multiple iterative calculations, the anchor point layout was finally determined, with a density of 6.2 anchor points / m 2 The anchoring depth is 65 mm, the anchor tensile strength is 1.5 kN, and the anchor distribution uniformity coefficient is 0.15. The additional stability coefficient provided by the anchoring system is 1.72, which significantly reduces the risk of falling off. The results of the anchor point distribution optimization are shown in Table 5:

[0182] Table 5 Anchor point optimization distribution results

[0183]

[0184] For the interface layer design, researchers used orthogonal experimental design to identify the optimal interface reinforcement layer formulation. The formulation primarily consists of a modified acrylic emulsion (65%), nanosilica (12%), a polyurethane elastomer (18%), and an additive (5%). This formulation achieves a flexibility coefficient of 0.63, within the ideal range of 0.5 to 0.7, effectively mitigating deformation disharmony between the base layer and the insulation layer. The performance parameters of the interface reinforcement layer are shown in Table 6.

[0185] Table 6 Performance parameters of interface enhancement layer

[0186]

[0187]

[0188] The specific insulation system structure is as follows: Figure 2As shown, the researchers ultimately synthesized the optimization results of each step to generate an optimal construction matrix and construct a complete design for an anti-sloughing insulation system. This design utilizes phenolic insulation board as the primary insulation material, adds a customized interface reinforcement layer, optimizes the anchor point layout, and improves the construction process. This design has been verified to have the highest overall score, with an expected service life of over 25 years and a sloughing risk rate below 0.05%.

[0189] Traditional exterior wall insulation anti-slip design relies primarily on empirical judgment to increase the number of anchor bolts or increase their diameter, lacking scientific quantitative indicators and systematic considerations. This approach often results in either too many or too few anchor bolts, failing to ensure system safety while also wasting materials and reducing construction efficiency. Furthermore, traditional methods fail to differentiate designs based on the risk of slippage in different areas, nor can they predict long-term system performance changes. In contrast, the proposed method establishes a comprehensive mathematical model and calculation system. It quantitatively characterizes the material-base bond performance using an application index matrix, assesses the impact of environmental factors using an application stability matrix, predicts long-term performance changes using an application slippage change function, and precisely identifies high-risk areas using a slippage probability matrix, thereby enabling precise and differentiated anti-slip design. Compared to traditional methods, the proposed system achieves a 35%-50% improvement in durability and a 20%-30% increase in material efficiency, providing strong assurance for the long-term safety and stability of exterior wall insulation systems.

[0190] The following is a specific example 3: During the construction of a large residential project, researchers used the anti-sloughing exterior wall insulation system of the present invention in the exterior wall insulation construction of an 11-story high-rise residential building. Located in a cold region with winter temperatures reaching -28°C and summer temperatures reaching 35°C, the project faced significant annual temperature fluctuations and high wind pressure loads, placing extremely high demands on the stability and durability of the exterior wall insulation system.

[0191] Based on the building's characteristics and climatic conditions, the researchers selected 100mm thick graphite polystyrene boards as the insulation core material and used an anti-shedding exterior wall insulation system for construction. The system's main components and performance parameters are shown in Table 7:

[0192] Table 7 Main components and parameters of anti-shedding exterior wall insulation system

[0193]

[0194] During the construction process, the researchers first prefabricated a composite insulation formwork with interface grooves in the factory according to the design requirements, and embedded the three-dimensional shear connectors in the insulation formwork (i.e., anchor points) in a plum blossom pattern with a horizontal spacing of 380 mm and a vertical spacing of 550 mm. The anchoring depth was designed to be 40 mm. Figure 3The connection parts are made of Q345B grade steel metal screws with a diameter of not less than 14mm. The arrangement of the connection parts is shown in Table 8:

[0195] Table 8 Layout parameters of three-dimensional shear connectors

[0196]

[0197]

[0198] During on-site construction, the prefabricated composite insulation formwork is directly used as a non-dismantling formwork for concrete pouring. After being fixed in place by the formwork support system, reinforcement binding and concrete pouring are carried out. The specific dimensions are as follows: Figure 4 As shown in the figure, vibrating technology was used during the pouring process to ensure that the concrete fully fills the interface grooves and tightly wraps the connectors, forming a strong mechanical interlocking structure. This mechanical interlocking structure has a tensile strength of 1.85MPa, far higher than the 0.6MPa of traditional external insulation systems, significantly improving the insulation system's resistance to shedding.

[0199] Actual engineering applications and long-term laboratory performance testing have shown that after 25 years of simulated environmental aging, the insulation layer retains 86% of its bond strength with the main structure, compared to only 51% for traditional anchoring systems. Furthermore, the system can effectively withstand temperature fluctuations of -30°C to +80°C and wind pressure loads of up to 2.8 kPa without breaking, significantly improving the long-term safety and reliability of the exterior wall insulation system.

[0200] It should be noted that the variables involved in the present invention are explained in detail as shown in Tables 9 and 10 below.

[0201] Table 9 Variable Explanation Table (Part 1)

[0202]

[0203]

[0204] Table 10 Variable Explanation Table (Part 2)

[0205]

[0206] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A design method for an anti-falling exterior wall insulation structure, characterized in that: include: Establish an application index matrix for the exterior wall insulation system; Construct a patch stability matrix; perform simulation experiments, using finite element analysis software to simulate the stress distribution of different material combinations under various environmental conditions; conduct physical test verification; design a patch shedding change function; use the anti-shedding stability equations to calculate the shedding probability matrix; and apply a facility site selection algorithm to optimize the layout of anchor points. Develop interface enhancement layer design; Generate an optimal structural matrix and integrate all parameter optimization results to form a comprehensive anti-falling insulation structural system including material selection, interface treatment, anchoring arrangement and construction technology to achieve long-term stability of the exterior wall insulation system.

2. The design method of a fall-proof exterior wall insulation structure according to claim 1, characterized in that: The establishment of the external wall insulation system application index matrix includes: constructing a three-dimensional parameter space by measuring the bonding strength, shear strength and interface contact area between different materials and the base layer, and quantifying the bonding performance between the application material and the base layer; using the application index matrix formula to calculate the normal bonding strength, tangential shear strength and interface contact area ratio at different positions, and obtaining the insulation layer thickness parameters, material elastic modulus parameters, base layer surface state parameters and initial interface strength parameters.

3. The design method of the anti-falling exterior wall insulation structure according to claim 2, characterized in that: The construction of the application stability matrix includes: collecting data on ambient temperature, humidity, wind load and substrate expansion coefficient, and establishing a 4×4 matrix to characterize the stability performance of the exterior wall insulation system under different environmental conditions; using the application stability matrix formula to construct a matrix including ambient temperature factors, humidity influence factors, wind load factors and material thermal expansion stress factors, and obtaining temperature gradient parameters, humidity cycle amplitude parameters, temperature change rate parameters and wind pressure action coefficients.

4. The design method of the anti-falling exterior wall insulation structure according to claim 3 is characterized in that: The simulation experiment includes: using finite element analysis software to simulate the stress distribution state of different material combinations under various environmental conditions, calculating the position and value of stress concentration points, and obtaining preliminary application stability data; using the stress distribution state equation to calculate the stress distribution at each point in space.

5. The design method of the anti-falling exterior wall insulation structure according to claim 4, characterized in that: The physical small-scale test verification includes: installing the insulation system on the standard test wall according to different application schemes, simulating extreme temperature cycles, moisture and heat cycles and wind pressure changes, monitoring interface stress changes and small displacements; using the interface roughness calculation formula to obtain the interface roughness coefficient, ultraviolet radiation intensity, freeze-thaw cycle number parameters, material aging rate parameters and interface damage accumulation coefficient.

6. The design method of the anti-falling exterior wall insulation structure according to claim 5, characterized in that: The design of the application and shedding change function includes: based on simulation and small-scale test data, establishing a mathematical model of the change of the application state of the insulation material over time, introducing a time attenuation factor and an environmental sensitivity coefficient, and constructing a dynamic prediction model; using the application and shedding change function equation to calculate the application state parameters at different time points and obtain the effectiveness parameters of the protective measures; the application and shedding change function is a mathematical function that describes the change law of the application state of the insulation material over time. By introducing the time attenuation factor and the environmental sensitivity coefficient, a prediction model is established for the change of the interface bonding strength with changes in environmental conditions and the passage of time.

7. The design method of the anti-falling exterior wall insulation structure according to claim 6, characterized in that: The method of calculating the shedding probability matrix using the anti-shedding stability equation group includes: coupling the application stability matrix with the application shedding change function to obtain the shedding probability distribution of each node under different working conditions and identify high-risk areas; using the shedding probability matrix calculation formula, combined with the interface stress distribution function and the time-related strength attenuation function, to calculate the shedding probability of each node; the shedding probability matrix is a probability distribution matrix that characterizes the possibility of shedding of each node under different working conditions, obtained by coupling the application stability matrix with the application shedding change function, and is used to identify high-risk areas and guide the design of anti-shedding measures.

8. The design method of the anti-falling exterior wall insulation structure according to claim 7, characterized in that: The application of the facility site selection algorithm to optimize the layout of anchor points includes: mapping the detachment probability matrix into a node weight graph, determining the optimal position of the anchor points by solving the minimum weight covering problem, and considering the minimum distance constraint between the anchor points, the boundary effect influencing factor and the structural stress concentration area to generate an anchor optimization layout diagram; using the minimum weight covering problem objective function and constraint conditions to obtain the anchor point number density parameter, anchor depth parameter, anchor tensile strength parameter and anchor distribution uniformity coefficient.

9. The design method of the anti-falling exterior wall insulation structure according to claim 8, characterized in that: The development of the interface reinforcement layer design includes: adding an interface reinforcement layer with flexible transition characteristics between the thermal insulation material and the base layer, adjusting the thickness and material composition of the interface reinforcement layer to improve the strain adaptability of the overall system; and using a calculation formula for the flexible characteristics of the interface reinforcement layer to optimize the comprehensive strain capacity of the interface reinforcement layer.

10. The design method of the anti-falling exterior wall insulation structure according to claim 9, characterized in that: The application index matrix is a mathematical expression that describes the bonding performance between the thermal insulation material and the base layer. By measuring the normal bond strength, tangential shear strength, and interface contact area ratio at different positions, a three-dimensional parameter space is constructed to quantitatively characterize the degree of material interface bonding. The application stability matrix is a mathematical model that characterizes the structural stability of the exterior wall insulation system under different environmental conditions. The application stability matrix includes environmental temperature factors, humidity influence factors, wind load factors, and material thermal expansion stress factors, forming a 4×4 matrix structure. The optimal construction matrix is an anti-falling insulation system design scheme formed by comprehensively optimizing all parameter results. The optimal construction matrix includes the best material combination, interface treatment method, anchoring arrangement scheme, and construction process requirements, forming a complete exterior wall insulation anti-falling structural system.

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