A method for preventing mold and moisture on interior walls of buildings in high-humidity coastal environments

Abstract

This invention provides a method for preventing mold and moisture damage to interior walls of buildings in high-humidity coastal environments, belonging to the field of building construction technology. The invention utilizes a humidity gradient optimization diffusion algorithm to establish a dynamic prediction model of humidity distribution within the wall and automatically matches appropriate moisture-permeable materials according to the moisture content range. It designs a gradient moisture-proof layer composite structure including a moisture-proof layer with a vapor permeability resistance greater than 200 MNs per gram-meter, a transition layer, and a humidity-regulating layer. A shortest path algorithm for salt migration is used to identify key areas susceptible to salt damage, and infrared thermal imaging is combined to determine the location of cold bridge risks. In high-risk areas, a locally reinforced insulation layer is constructed, and an electric heating film system is installed to achieve active temperature control. A life prediction model based on damage mechanics is established, and electric heating and local ventilation measures are automatically activated based on the duration of relative humidity and temperature difference. This invention solves the technical problem of mold or moisture damage to interior walls of coastal buildings.

Description

Technical Field

[0001] This invention belongs to the field of building construction technology, and specifically relates to a method for preventing mold and moisture on the interior walls of buildings in coastal high-humidity environments. Background Technology

[0002] Due to their long-term exposure to the high humidity and high salinity of the marine climate, the prevention of mold and moisture on interior walls has always been a key technical challenge in the construction engineering field. Traditional methods of interior wall moisture control mainly rely on passive protection measures such as applying waterproof coatings, pasting moisture-proof membranes, or installing moisture-proof boards. These methods form a physical barrier layer on the wall surface to prevent the penetration of external moisture and salt. These technologies are widely used in various coastal building projects, including residential communities, commercial complexes, hotels, and public facilities. However, traditional moisture control technologies have significant limitations. Their protective effect depends entirely on the moisture-blocking properties of the protective materials themselves and cannot be adjusted according to dynamic changes in environmental conditions. When faced with the complex and variable temperature and humidity conditions of the marine environment, they often fail to provide protection. At the same time, these methods lack an in-depth understanding of the migration patterns of moisture inside the walls and cannot accurately identify high-risk areas prone to mold and dampness, leading to improper deployment of protective measures and unreasonable allocation of resources. Due to the lack of effective environmental monitoring methods and early warning mechanisms, existing moisture-proof systems cannot detect abnormal changes in humidity and salt accumulation inside walls in a timely manner. Often, problems are only discovered when mold appears or the walls become severely damp, at which point irreversible damage to the building structure has already occurred. Simultaneously, the release of mold spores and harmful gases seriously affects indoor air quality and the health of residents. In other words, existing technologies present the technical problem of mold or dampness easily developing on the interior walls of coastal buildings, leading to structural damage and deterioration of the indoor environment. Summary of the Invention

[0003] In view of this, the present invention provides a method for preventing mold and moisture on the interior walls of buildings in coastal high-humidity environments, which can solve the technical problem in the prior art that the interior walls of coastal buildings are prone to mold or dampness, leading to damage to the building structure and deterioration of the indoor environment.

[0004] This invention is implemented as follows: It provides a method for preventing mold and moisture on the interior walls of buildings in high-humidity coastal environments. This includes deploying multiple relative humidity sensors and wall moisture content sensors on the surface of the interior walls, installing an infrared thermal imager to detect the temperature field distribution within the walls, establishing a real-time environmental parameter monitoring system, using a multi-factor coupled accelerated aging test device to test candidate moisture-proof materials, simulating the synergistic effects of temperature cycling, salt spray corrosion, and ultraviolet aging to obtain the material's moisture permeability coefficient and vapor permeation resistance parameters, and using a humidity gradient optimization diffusion algorithm to establish a dynamic prediction model of the humidity distribution inside the wall. The method also uses an implicit difference scheme to solve for the change in humidity at each node over time. Based on the wall's moisture content, appropriate permeable materials are selected, and a gradient moisture-proof layer composite structure is designed, including a moisture-blocking layer, a transition layer, and a moisture-regulating layer. The wall structure is abstracted into a graph theory model using a shortest path algorithm for salt migration. Dijkstra's algorithm is used to calculate the shortest path of salt from the outside to the inner surface. Combined with infrared thermal imaging detection results, the location of cold bridges is determined. Locally reinforced insulation layers are constructed at the identified high-risk locations of cold bridges, and an electric heating film system is installed. Salt barriers are increased based on salt concentration. A life prediction model based on damage mechanics is established. The electric heating film system and local ventilation are activated based on the duration of relative humidity and the difference between the wall surface temperature and the indoor air temperature.

[0005] The establishment steps of the real-time environmental parameter monitoring system include: deploying multiple relative humidity sensors and wall moisture content sensors on the surface of the building's interior walls, with a sensor spacing of 1.5m; and installing an infrared thermal imager to detect the temperature field distribution of the wall.

[0006] Specifically, the shortest path algorithm for salt migration involves abstracting the wall structure into a graph theory model and using Dijkstra's algorithm to calculate the shortest path for salt from the outside to the inner surface. Nodes represent material regions, and edge weights represent the resistance to salt transfer.

[0007] Specifically, the conditions for the multi-factor coupled accelerated aging test are a temperature cycling range of -20℃ to 80℃ and a salt spray concentration of [missing information]. The solution mass fraction is 5%, and the ultraviolet radiation intensity is 40 W / .

[0008] Specifically, the humidity gradient optimization diffusion algorithm is based on the numerical solution of partial differential equations to establish a dynamic prediction model of humidity distribution inside the wall. The humidity diffusion equation is as follows: , where u is the humidity field and D is the diffusion coefficient.

[0009] Specifically, the solution method for the dynamic prediction model is to solve the variation law of humidity of each node with time through implicit difference scheme, with grid spacing set to 0.01m and time step set to 3600s, and the solution is obtained by discretization through finite difference method.

[0010] Specifically, the life prediction model based on damage mechanics is established by setting up a material damage evolution equation. ,in Let σ be the damage variable and σ be the current stress. Let be the initial stress, α be the damage evolution parameter, and t be the time.

[0011] Wherein, the moisture permeability coefficient of the medium moisture permeability material is specifically, the moisture permeability coefficient ∈ [ , The moisture permeability coefficient was obtained by measuring g / (m·s·Pa) using the cup method.

[0012] Specifically, the gradient moisture-proof layer composite structure consists of a first layer that is a moisture-proof layer with a vapor permeability resistance greater than 200 MNs / g·m, a second layer that is a transition layer material, and a third layer that is a moisture-regulating layer with a moderate moisture permeability coefficient.

[0013] Specifically, the activation conditions for the control measures are as follows: when the relative humidity is ∈ (85%, 95%) and the duration is ∈ (72h, 168h), the electric heating film system is activated; when the temperature difference between the wall surface and the indoor air is ∈ (3℃, 6℃), local ventilation is added; and when the temperature difference is greater than 6℃, both the electric heating film system and local ventilation are activated simultaneously.

[0014] The selection criteria for the moisture-proof material are as follows: when the wall moisture content ∈ [0, 8%], a low-permeability material is selected; when the wall moisture content ∈ (8%, 15%), a medium-permeability material is selected. The permeability coefficient of the low-permeability material is less than [0, 8%]. g / (m·s·Pa). The difference in the linear expansion coefficients of each layer in the gradient moisture-proof composite structure is specifically controlled within... Within a certain temperature range, the linear expansion coefficient of each material is measured using a thermal expansion meter to ensure the coordination of thermal expansion and contraction between adjacent layers.

[0015] The multi-factor coupled accelerated aging test specifically simulates the long-term aging process of materials in a marine environment by simultaneously applying multiple environmental factors such as temperature cycling, salt spray environment and ultraviolet radiation. The test cycle is one-tenth of that of the traditional single-factor test.

[0016] Specifically, the method for determining the location of the cold bridge involves combining infrared thermal imaging detection results to determine the location of a medium-risk cold bridge when the temperature gradient is ∈ (5℃ / m, 10℃ / m), and to determine the location of a high-risk cold bridge when the temperature gradient is greater than 10℃ / m.

[0017] Specifically, the construction requirements for the locally reinforced insulation layer are that the thermal conductivity of the insulation layer is less than or equal to 0.035 W / (m·K), the thickness is determined based on the temperature gradient calculation results, and an electric heating film system is installed for active heating control.

[0018] Specifically, the criteria for setting up the salt barrier are as follows: when the salt concentration in the key area identified by the shortest path algorithm for salt migration is ∈ (500 mg / L, 1000 mg / L), a medium-strength salt barrier is added; when the salt concentration is greater than 1000 mg / L, a high-strength salt barrier is added.

[0019] This invention establishes a real-time monitoring network covering multiple environmental parameters such as temperature, humidity, and moisture content. Combined with a dynamic prediction model constructed using a humidity gradient optimization diffusion algorithm, it accurately identifies high-risk areas and implements timely preventative measures before mold and dampness occur, thus avoiding damage to the building structure and deterioration of the indoor environment. This invention optimizes the configuration of moisture-proof materials through multi-factor coupled accelerated aging tests, employs a gradient moisture-proof layer composite structure to provide multiple protective barriers, and uses a salt migration path algorithm to accurately locate key areas susceptible to salt damage, effectively preventing the continuous erosion of the interior wall structure by harmful external factors, protecting the integrity and durability of the building structure. Based on a damage mechanics-based life prediction model, and combined with an electric heating film system and active control measures such as local ventilation, this invention automatically activates corresponding environmental improvement measures when unfavorable environmental conditions are detected. It maintains the wall surface temperature above the dew point temperature, effectively preventing condensation and eliminating the temperature and humidity conditions conducive to mold growth, ensuring a healthy and comfortable indoor environment. In summary, this invention solves the technical problem mentioned in the background art: coastal buildings are prone to mold or dampness on their interior walls, leading to structural damage and deterioration of the indoor environment. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention.

[0021] Figure 2 This is a diagram showing the relationship between wall moisture content and permeability coefficient in the example.

[0022] Figure 3 This is a temperature gradient distribution diagram of the cold bridge risk location in the embodiment.

[0023] Figure 4 The figure shows the temperature change curves before and after the electric heating system is started, as shown in the example.

[0024] Figure 5 The graph shows the damage evolution process of the moisture-proof material in the example.

[0025] Figure 6 This is a diagram showing the relationship between system response time and control effect in the embodiment. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0027] like Figure 1 The diagram shows a flowchart of a method for preventing mold and moisture on the interior walls of buildings in high-humidity coastal environments, provided by this invention. The method includes the following steps:

[0028] S01. Install multiple relative humidity sensors and wall moisture content sensors on the surface of the building's interior walls, with a sensor spacing of 1.5m. At the same time, install an infrared thermal imager to detect the temperature field distribution of the wall and establish a real-time environmental parameter monitoring system.

[0029] S02. A multi-factor coupled accelerated aging test device was used to test the candidate moisture-proof materials, simulating the synergistic effect of temperature cycling, salt spray corrosion and ultraviolet aging, and obtaining the moisture permeability coefficient and vapor permeation resistance parameters of the materials.

[0030] S03. A dynamic prediction model of humidity distribution inside the wall is established by using a humidity gradient optimization diffusion algorithm. The change law of humidity at each node over time is solved by implicit difference scheme. When the wall moisture content ∈ [0, 8%], low moisture permeability material is selected, and when the wall moisture content ∈ (8%, 15%), medium moisture permeability material is selected.

[0031] S04. Design a gradient moisture-proof layer composite structure. The first layer is a moisture-proof layer with a vapor permeability resistance >200MNs / g·m. The second layer is a transition layer material. The third layer is a moisture-regulating layer with a moderate moisture permeability coefficient. The difference in the linear expansion coefficient of each layer is controlled within the specified range.

[0032] S05. The wall structure is abstracted into a graph theory model by adopting the shortest path algorithm for salt migration. The Dijkstra algorithm is used to calculate the shortest path of salt from the outside to the inner surface. Combined with the infrared thermal imaging detection results, the location of medium-risk cold bridge is determined when the temperature gradient ∈ (5℃ / m, 10℃ / m], and the location of high-risk cold bridge is determined when the temperature gradient > 10℃ / m.

[0033] S06. Construct locally reinforced insulation layers at identified high-risk locations of cold bridges. The thermal conductivity of the insulation layer should be ≤0.035W / (m·K), and the thickness should be determined based on the temperature gradient calculation results. Simultaneously, install an electric heating film system for active heating control. When the salt concentration in the key area identified by the shortest path algorithm for salt migration is ∈(500mg / L, 1000mg / L), add a medium-strength salt barrier. When the salt concentration is >1000mg / L, add a high-strength salt barrier.

[0034] S07. Establish a life prediction model based on damage mechanics. When the relative humidity is ∈ (85%, 95%) and the duration is ∈ (72h, 168h), start the electric heating film system. When the temperature difference between the wall surface and the indoor air is ∈ (3℃, 6℃), add local ventilation treatment. When the temperature difference is > 6℃, start the electric heating film system and local ventilation treatment at the same time.

[0035] The multi-factor coupled accelerated aging test device simulates the long-term aging process of materials in a marine environment by simultaneously applying multiple environmental factors such as temperature cycling, salt spray environment, and ultraviolet radiation. The test cycle is 1 / 10 of that of traditional single-factor tests, the temperature cycling range is -20℃ to 80℃, and the salt spray concentration is [missing information]. The solution mass fraction is 5%, and the ultraviolet radiation intensity is 40 W / The durability of materials is evaluated by the rate of change in mass and the rate of performance degradation.

[0036] Among them, the moisture permeability coefficient of low moisture permeability materials is < g / (m·s·Pa), the moisture permeability coefficient of medium-permeability materials ∈ [ The concentration was 1 g / (m·s·Pa), which was determined by the cup method.

[0037] Among them, the difference in the linear expansion coefficients of each layer in the gradient moisture-proof composite structure is controlled within Within a certain temperature range, the linear expansion coefficient of each material is measured using a thermal expansion meter to ensure the coordination of thermal expansion and contraction between adjacent layers.

[0038] The moisture permeability coefficient refers to the mass of water vapor passing through a unit area per unit area per unit time under a unit vapor pressure difference of a unit thickness material. It is determined by the cup method, in which the material sample is sealed above a test cup containing a desiccant and placed in an environment with a temperature of 23±2℃ and a relative humidity of 50±5% to measure the change in mass.

[0039] The moisture content of the wall was determined by a resistance moisture meter. The test probe was inserted 5cm into the wall and the displayed value was read. The measurement accuracy was ±0.5%. Each measuring point was measured three times and the average value was taken.

[0040] Among them, the humidity gradient optimized diffusion algorithm is based on the numerical solution method of partial differential equations to establish a dynamic prediction model of humidity distribution inside the wall. The humidity diffusion equation is as follows: , where u is the humidity field and D is the diffusion coefficient, is solved by discretization using the finite difference method, with a grid spacing of 0.01m and a time step of 3600s.

[0041] Among them, the gradient moisture-proof layer composite structure is designed based on the principle of stress relief. It achieves a gradual transition of the linear expansion coefficient between adjacent materials through the transition layer material, avoiding interface cracking and delamination caused by the difference in thermal expansion and contraction.

[0042] The transition layer material is a modified polymer-based composite material with a linear expansion coefficient between that of the moisture barrier layer and the moisture regulation layer. Its thickness is 1-3 mm. It achieves stable bonding with the adjacent layers through chemical bonding and physical interlocking, and has a tensile strength ≥2.5 MPa.

[0043] Among them, the shortest path algorithm for salt migration abstracts the wall structure into a graph theory model, where nodes represent material regions and edge weights represent the resistance to salt transmission. The Dijkstra algorithm is used to calculate the shortest path for salt from the outside to the inner surface, identify key areas prone to salt damage, and guide the optimized layout of the salt barrier.

[0044] Among them, infrared thermal imaging detection uses an infrared thermal imager to capture images of the temperature distribution on the wall surface, with a detection accuracy of ±0.1℃ and a detection distance of 0.5~2m. It can visually display areas of abnormal temperature and calculate the temperature gradient using the formula... Determine the location and strength of the cold bridge.

[0045] The electric heating film system uses a carbon fiber heating film with a power density of 150–300 W / m³. The heating power is automatically adjusted by the thermostat to maintain the wall surface temperature 2-3°C above the dew point temperature, thus preventing condensation.

[0046] The salt barrier is constructed using low-permeability materials, while the permeability of a medium-strength salt barrier is ∈ [ , ] High-strength salt barrier with permeability < m 2 By preventing Ion penetration protects the interior wall structure.

[0047] Among them, the life prediction model based on damage mechanics establishes a material damage evolution equation. ,in Let σ be the damage variable and σ be the current stress. Let α be the initial stress, t be the damage evolution parameter, and t be the time. The service life of the moisture-proof system is predicted by combining environmental factors and material performance parameters.

[0048] The salt concentration was determined by the conductivity method. The conductivity meter probe was immersed in the seepage liquid on the wall surface, the conductivity value was read, and the salt concentration was calculated by converting it through a standard curve. The measurement accuracy was ±2%.

[0049] The relative humidity is obtained in real time through a digital temperature and humidity sensor with a measurement accuracy of ±3% and a response time of <30s. The sensor is installed at a height of 1.5m from the center of the wall.

[0050] The dew point temperature is calculated from the relative humidity and air temperature using the following formula: ,in Here, RH is the dew point temperature, T is the relative humidity, and T is the air temperature.

[0051] The specific implementation methods of the above steps are described in detail below.

[0052] The specific implementation of step S01 involves establishing a multi-parameter environmental monitoring network to achieve real-time sensing of wall humidity status. First, digital relative humidity sensors are arranged at uniform intervals of 1.5m on the interior wall surface. These sensors use capacitive humidity-sensitive elements, achieving a measurement accuracy of ±3% and a response time of less than 30s. The installation height is uniformly set at the center of the wall, 1.5m above the ground. Simultaneously, resistive wall moisture content sensors are installed, with the test probe inserted 5cm into the wall. The moisture content is reflected by measuring changes in the wall material's resistance, with a measurement accuracy of ±0.5%. Three repeated measurements are taken at each measuring point, and the average value is used to ensure data reliability. An infrared thermal imager is installed for non-contact temperature field detection. The device has a detection accuracy of ±0.1℃, a working distance controlled within the range of 0.5–2m, and can acquire real-time images of the wall surface temperature distribution and identify areas of abnormal temperature. A data acquisition and transmission system based on Internet of Things (IoT) technology is established. All sensors transmit monitoring data to the central processing unit in real-time via wireless communication modules, achieving continuous monitoring and data storage of environmental parameters. The purpose of this step is to provide accurate basic data support for subsequent humidity prediction models and moisture-proof strategies.

[0053] The specific implementation of step S02 involves conducting a comprehensive performance evaluation of candidate moisture-proof materials using a multi-factor coupled accelerated aging test device. The test device integrates a temperature cycling system, a salt spray corrosion environment, and an ultraviolet radiation module, simultaneously applying multiple environmental stress factors to simulate the complex mechanisms of the marine environment. The temperature cycling system is set to operate within a range of -20℃ to 80℃, and a programmable controller implements periodic temperature changes to simulate the effects of diurnal temperature variations and seasonal temperature fluctuations on material performance. The salt spray environment uses a 5% (by mass) salt spray solution. The solution, atomized by an ultrasonic atomizer, generates uniform salt spray particles, creating a stable corrosive environment within the test chamber to accelerate the chemical degradation process of the material. The ultraviolet radiation module uses a xenon lamp light source, with the radiation intensity set to 40W / m². This study simulates the destructive effects of ultraviolet radiation from sunlight on the molecular structure of materials. During the experiment, the rate of mass change and the degree of degradation of key performance parameters of the material samples were measured periodically. The correlation between the acceleration factor and the actual service life was established using the Arrhenius equation. The moisture permeability coefficient of the material was determined using the cup method. The material sample was sealed above a test cup containing a desiccant and placed in a standard environment at 23±2℃ and 50±5% relative humidity. The water vapor transfer capacity per unit thickness of the material under a unit vapor pressure difference was calculated by measuring the mass change. Compared with traditional single-factor experiments, this experimental method can shorten the testing cycle to one-tenth and more realistically reflect the actual performance of materials in complex marine environments.

[0054] The specific implementation of step S03 is to establish a dynamic prediction model of humidity distribution inside the wall based on a humidity gradient optimization diffusion algorithm. The core of the algorithm uses a numerical solution method for partial differential equations to transform the complex moisture diffusion process into a computable mathematical model. The humidity field u changes with time t according to the diffusion equation, and the diffusion coefficient D reflects the moisture transfer characteristics of the material. The continuous moisture diffusion equation is discretized using the finite difference method, dividing the wall structure into uniform grid cells with a grid spacing of 0.01m to ensure calculation accuracy. The time step is set to 3600s, corresponding to a 1-hour time interval. An implicit difference scheme is used for numerical solution, which has good numerical stability and can handle the computational requirements of large time steps. The algorithm obtains the humidity values ​​of each grid node at different times through iterative calculation, forming the spatiotemporal distribution law of humidity inside the wall. Based on the calculation results of the prediction model, material selection criteria are established. When the wall moisture content is in the range of 0-8%, a material with a permeability coefficient less than [value missing] is selected. For low moisture permeability materials with a moisture content of g / (m·s·Pa), when the moisture content is in the range of 8% to 15%, a moisture permeability coefficient of [value missing] should be selected. ~ Medium-permeability materials within the range of g / (m·s·Pa). This algorithm can predict the humidity evolution trend inside the wall under different environmental conditions, providing a scientific basis for the rational selection and placement of moisture-proof materials.

[0055] The specific implementation of step S04 involves designing a multi-layer composite moisture-proof structure with gradient transition characteristics. The structural design is based on the principle of stress relief, addressing the interface stress concentration problem caused by differences in thermal expansion and contraction properties between different materials through a layer-by-layer transition approach. The first moisture-barrier layer uses a high moisture-barrier material with a vapor permeability resistance greater than 200 MNs / g·m, forming the main barrier against water vapor transmission and preventing external moisture from penetrating into the wall. The second transition layer uses a modified polymer-based composite material with a thickness controlled within the range of 1–3 mm. Its linear expansion coefficient is precisely designed to be an intermediate value between the moisture-barrier layer and the moisture-regulating layer, achieving stable bonding with adjacent layers through chemical bonding and physical interlocking mechanisms. The material's tensile strength is not less than 2.5 MPa to ensure structural integrity. The third moisture-regulating layer uses a material with a moderate moisture permeability coefficient, capable of moderately regulating humidity according to changes in ambient humidity, maintaining relative stability of the humidity inside the wall. The linear expansion coefficient of each layer is accurately measured using a thermal expansion meter to ensure that the difference in linear expansion coefficients between adjacent layers is controlled within a certain range. Within a temperature range of / ℃, it avoids interface cracking and delamination caused by excessive differences in thermal expansion and contraction. The gradient design principle effectively ensures the strain coordination between layers when the temperature changes, significantly improving the durability and reliability of the composite structure.

[0056] The specific implementation of step S05 involves using a graph theory model and a shortest path algorithm to identify critical paths for salt migration and cold bridge risk areas in the wall structure. First, the complex wall structure is abstracted into a graph theory model, where nodes represent different material regions or structural parts, and edge weights represent the resistance encountered by salt during transfer between adjacent regions. The weight values ​​are related to physical parameters such as material permeability and thickness. The Dijkstra algorithm is used to calculate the shortest path for salt migration from the outer surface to the inner surface of the wall. The algorithm uses a greedy strategy to progressively determine the shortest distance for each node, ultimately obtaining the main channels for salt penetration and critical areas prone to salt damage. The location of cold bridges is quantitatively assessed using infrared thermal imaging detection results. The location and intensity level of cold bridges are determined by calculating temperature gradients. Cold bridges with a temperature gradient between 5℃ / m and 10℃ / m are classified as medium-risk, while those exceeding 10℃ / m are classified as high-risk. The advantage of this algorithm is that it transforms the complex three-dimensional mass transfer problem into a graph theory optimization problem, significantly reducing computational complexity while providing accurate risk area identification results. Quantitative analysis provides precise location guidance and intensity level recommendations for subsequent local reinforcement measures.

[0057] The specific implementation of step S06 involves implementing targeted local reinforcement and active control measures based on risk identification. For identified high-risk cold bridge locations, a locally reinforced insulation layer is constructed. The insulation material selected is a low thermal conductivity material with a thermal conductivity not exceeding 0.035 W / (m·K). The material thickness is quantitatively determined based on temperature gradient calculations to ensure that the thermal resistance at the cold bridge location meets anti-condensation requirements. A carbon fiber electric heating film system is installed to achieve active control of the wall surface temperature, with the film power density set between 150 and 300 W / m·K. Within the specified range, an intelligent thermostat automatically adjusts the heating power based on ambient temperature and humidity conditions, maintaining the wall surface temperature 2-3°C above the dew point temperature to effectively prevent condensation. Based on the salt migration path algorithm, tiered salt protection measures are implemented. When the salt concentration in critical areas is between 500 mg / L and 1000 mg / L, a medium-strength salt barrier is added, with the barrier material permeability controlled within a specified range. ~ m 2 Within the specified range. When the salt concentration exceeds 1000 mg / L, a high-strength salt barrier is added, with a material permeability of less than [value missing]. m 2 This system protects the interior wall structure from salt damage by preventing the penetration and diffusion of chloride ions. Salt concentration is measured on-site using a conductivity method. The conductivity meter probe is immersed in the permeate from the wall surface to measure the conductivity value, and the accurate salt concentration value is calculated using a pre-established standard curve, achieving a measurement accuracy of ±2%. This step represents a shift from passive protection to active control, significantly improving the reliability and adaptability of the moisture-proof and salt-proof system.

[0058] The specific implementation of step S07 involves establishing an intelligent response control system and a life prediction model based on damage mechanics theory. The damage mechanics model establishes material damage variables... The mathematical relationship between stress state and time factor, where damage variable The current stress σ reflects the degree of degradation of material properties compared to the initial stress. The ratio represents the stress level, the damage evolution parameter α reflects the damage development rate of the material, and time t reflects the cumulative damage effect. The model can comprehensively consider the impact of environmental factors and material performance parameters on the service life of the moisture-proof system, providing a quantitative basis for the formulation of maintenance strategies. The intelligent control system automatically activates corresponding protective measures based on real-time monitored environmental parameters. When the relative humidity is in the range of 85% to 95% and the duration is within the range of 72h to 168h, the electric heating film system is automatically activated to prevent mold growth by raising the temperature and lowering the humidity. When the temperature difference between the wall surface and the indoor air is in the range of 3℃ to 6℃, local ventilation is increased to accelerate moisture diffusion and air flow. When the temperature difference exceeds 6℃, the electric heating film system and local ventilation are activated simultaneously, forming a synergistic mechanism of raising the temperature and dehumidifying and forced ventilation. The dew point temperature is calculated in real time using the mathematical relationship between relative humidity RH and air temperature T, providing an accurate judgment benchmark for condensation risk warning and heating control. This control system realizes the upgrade from single-parameter control to multi-parameter collaborative control, which can dynamically adjust the protection intensity according to environmental changes to ensure the continuous effectiveness of the moisture-proof effect.

[0059] It should be noted that this invention has four key technical ideas, which show significant technical advantages compared with traditional moisture-proof methods.

[0060] The first key technological approach is multi-factor coupled accelerated aging testing. Traditional material performance evaluation typically employs separate testing methods for single environmental factors, which cannot accurately reflect the synergistic effects of multiple factors such as temperature cycling, salt spray corrosion, and ultraviolet radiation in the marine environment. This invention, by integrating the simultaneous application of multiple environmental stress factors, can more accurately simulate the actual degradation process of materials in complex marine environments, significantly shortening the testing cycle while improving the reliability of performance predictions. The multi-factor coupling effect can reveal material failure modes that cannot be detected by single-factor testing, providing more comprehensive and accurate performance data support for the optimal selection of moisture-proof materials.

[0061] The second key technological approach is the humidity gradient optimization diffusion algorithm. Traditional humidity control methods are mainly based on empirical judgment and static design, lacking a deep understanding of the dynamic evolution of humidity within the wall. This invention establishes a humidity prediction model based on the numerical solution of partial differential equations, which can accurately calculate the spatiotemporal distribution of humidity at various points within the wall, achieving a leap from qualitative analysis to quantitative prediction. The algorithm uses numerical discretization processing via the finite difference method to transform the continuous moisture diffusion process into a computable mathematical model, providing theoretical guidance for the scientific selection of moisture-proof materials under different moisture contents, and avoiding the moisture-proof failure problem caused by improper material configuration in traditional methods.

[0062] The third key technological approach is the analysis of salt migration using graph theory models and shortest path algorithms. Traditional salt protection designs typically rely on uniform distribution or empirical judgment, failing to accurately identify critical paths and high-risk areas for salt penetration. This invention abstracts the complex three-dimensional mass transfer problem into a graph theory optimization problem, using Dijkstra's algorithm to precisely calculate the shortest migration path of salt from the external surface to the internal surface. This allows for the quantitative identification of critical nodes prone to salt damage and weak points with minimal transmission resistance. This method represents a shift from empirical protection to precise protection, significantly improving salt protection efficiency through targeted local reinforcement measures while avoiding material waste and increased costs caused by over-protection.

[0063] The fourth key technological approach is an intelligent response control system based on damage mechanics. Traditional moisture-proof systems often employ passive protection strategies, unable to dynamically adjust to environmental changes, leading to insufficient protection or excessive energy consumption. The damage mechanics model established in this invention can quantitatively predict the service life and performance degradation patterns of the moisture-proof system, providing a scientific basis for maintenance decisions. The intelligent control system automatically activates corresponding protective measures based on real-time monitoring of multi-parameter environmental information, achieving a technological upgrade from passive protection to active control. It can dynamically adjust the protection intensity according to the risk level, ensuring the continuous effectiveness of moisture protection while optimizing energy consumption.

[0064] The synergistic effect of four key technological approaches has formed a complete intelligent moisture-proof and salt-proof technology system. Multi-factor coupling experiments provide a reliable performance data foundation for material selection; humidity prediction algorithms guide the scientific configuration and layout optimization of materials; salt migration analysis enables accurate identification of risk areas and targeted protection; and the intelligent control system ensures the dynamic adaptation and continuous effectiveness of protective measures. Compared to traditional empirical moisture-proofing methods, this technology system achieves a comprehensive technological upgrade from qualitative to quantitative, from passive to active, and from extensive to precise. It can significantly improve the reliability and economy of moisture-proof and salt-proofing of interior walls in coastal high-humidity environments, providing strong technical support for the long-term safe operation of marine engineering structures.

[0065] Furthermore, traditional material testing often employs performance evaluation methods under single environmental conditions, failing to accurately reflect the combined effects of multiple adverse factors in coastal environments, such as temperature cycling, salt spray corrosion, and UV aging. This leads to significant discrepancies between the actual durability performance of materials in engineering applications and laboratory test results, impacting the reliability design of moisture-proof systems. This invention utilizes a multi-factor coupled accelerated aging test device. By simultaneously applying a temperature cycling environment ranging from -20°C to 80°C, a 5% sodium chloride salt spray environment, and a UV radiation intensity of 40 watts per square meter, it realistically simulates the combined effects of marine environmental conditions. The test cycle is only one-tenth that of traditional methods, significantly improving the accuracy and timeliness of material evaluation. By monitoring the material's mass change rate and performance degradation rate, more reliable parameters such as moisture permeability and vapor permeation resistance can be obtained, providing a solid data foundation for the scientific selection and optimized configuration of moisture-proof materials. Traditional composite moisture-proof layer designs often overlook the differences in thermal expansion and contraction properties between different materials. When the ambient temperature changes, the uneven deformation of the materials in each layer can easily generate shear and tensile stresses at the interface, leading to adhesion failure, cracking, or delamination, severely affecting the overall performance and service life of the moisture-proof system. The gradient moisture-proof layer composite structure designed in this invention is based on the principle of stress relief. By setting a transition layer material, it achieves a gradual transition in the linear expansion coefficient between adjacent layers, strictly controlling the difference in the linear expansion coefficients of each layer within a certain range. Within a certain temperature range (within a few degrees Celsius), it effectively eliminates interfacial stress concentration caused by differences in thermal expansion and contraction. The transition layer uses a modified polymer-based composite material, whose coefficient of linear expansion is precisely controlled between the moisture-barrier layer and the moisture-regulating layer. Through a dual mechanism of chemical bonding and physical interlocking, it forms a stable interfacial bond with adjacent layers, ensuring the long-term stability and reliability of the composite structure under temperature cycling.

[0066] Specifically, the principle of this invention is as follows: This invention solves the technical problem of mold or dampness easily forming on the interior walls of coastal buildings, leading to structural damage and indoor environmental deterioration. The key lies in establishing an intelligent protection system based on predictive maintenance principles. The essence of mold and dampness problems is that the water activity inside the wall exceeds a critical threshold, providing suitable environmental conditions for mold growth, while salt accumulation accelerates the corrosion and damage of building materials. This invention, through a monitoring network composed of multi-point deployed relative humidity sensors, wall moisture content sensors, and infrared thermal imagers, can monitor the moisture status and temperature distribution of the walls in real time, providing accurate data support for preventative control. The humidity gradient optimization diffusion algorithm establishes a mathematical description model of moisture migration inside the wall by solving the partial differential equation of moisture diffusion. Using an implicit difference scheme for numerical calculation, it can predict humidity change trends at different times and spatial locations. When the prediction results show that the moisture content of a certain area will exceed the critical value for mold growth, the system can activate protective measures in advance to prevent mold problems from occurring. Simultaneously, based on real-time monitoring data of the wall's moisture content, the system automatically selects moisture-proof materials with appropriate permeability coefficients, achieving dynamic optimization of material configuration and ensuring that the protective effect is always at its best. The gradient moisture-proof layer composite structure, through the coordinated operation of the moisture-blocking layer, transition layer, and humidity-regulating layer, forms multiple protective barriers. It effectively blocks the penetration of external moisture and salt while regulating the internal moisture balance of the wall, preventing excessively high local humidity. The shortest path algorithm for salt migration transforms the wall structure into a graph theory network model. By calculating the shortest path for salt transfer, it accurately identifies weak points prone to salt damage, guiding the rational arrangement of the salt-proof barriers and effectively curbing the corrosive damage of salt to the building structure. Infrared thermal imaging technology can visually display abnormal temperature distribution on the wall surface, identify the location and intensity of cold bridges, and provide a basis for implementing localized enhanced insulation measures. The electric heating film system and the local ventilation system constitute the core of active environmental control. By maintaining the wall surface temperature above the dew point temperature, condensation conditions are eliminated, the moisture supply chain for mold growth is cut off, and mold problems are prevented from the source. At the same time, indoor air quality is improved, ensuring a healthy and comfortable living environment.

[0067] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0068] In this embodiment, the specific implementation of step S01 is the same as described above, and will not be repeated in detail here.

[0069] The specific implementation of step S02 involves determining the moisture permeability coefficient of the material using a multi-factor coupled accelerated aging test device. The formula for calculating the moisture permeability coefficient is as follows:

[0070] ;

[0071] In the formula, The moisture permeability coefficient is expressed in g / (m·s·Pa). The mass of water vapor passing through the material is expressed in grams. The thickness of the material is expressed in meters (m). The effective area of ​​the material is expressed in units of... ; The test time is in seconds. This represents the steam pressure difference, measured in Pa.

[0072] The specific implementation of step S03 is to establish a dynamic prediction model of humidity distribution inside the wall based on the humidity gradient optimization diffusion algorithm. The specific expression of its humidity diffusion equation is as follows:

[0073] ;

[0074] In the formula, Let be the humidity field function, representing the humidity distribution at various points inside the wall; The variable is time, and the unit is seconds (s). The diffusion coefficient represents the moisture transfer characteristics of a material, and its unit is 1000 ppm. ; Let be the Laplace operator, representing the second-order spatial derivative operation. Discretization is performed using the finite difference method; the discretization scheme for the spatial derivative is as follows:

[0075] ;

[0076] In the formula, For the second dimensional grid Line number Humidity values ​​at column nodes, in g / ; and The spatial grid spacing is set to 0.01m. For nodes The humidity value of the adjacent node on the right, in g / ; For nodes Humidity value of the left adjacent node, in g / ; For nodes The humidity value of the adjacent node above, in g / ; For nodes The humidity value of the adjacent node below, in g / The time derivative is expressed using an implicit difference scheme:

[0077] ;

[0078] In the formula, For the first Time step space nodes The humidity value, in g / ; For the first Time step space nodes The humidity value, in g / ; The time step number is dimensionless. The time step is set to 3600s.

[0079] The specific implementation of step S04 is the same as described above, and will not be repeated in detail here.

[0080] The specific implementation of step S05 is to use Dijkstra's shortest path algorithm to calculate the salt migration path. The distance update formula of the algorithm is:

[0081] ;

[0082] In the formula, From the starting node to the target node The current shortest distance, expressed in dimensionless resistance value; From the starting node to the intermediate node The determined shortest distance is expressed as a dimensionless resistance value. and These represent intermediate nodes and target nodes in a graph theory model, respectively. For the node To the node The edge weight represents the resistance to salt transfer between adjacent material regions, expressed as a dimensionless resistance value, and is proportional to the reciprocal of the material's permeability. The formula for calculating the temperature gradient is:

[0083] ;

[0084] In the formula, This represents the temperature gradient, expressed in °C / m. The temperature difference is obtained through infrared thermal imaging detection. The distance interval is in meters (m).

[0085] The specific implementation method of step S06 is to implement graded salt prevention measures based on the identification results. The conversion formula between salt concentration and conductivity is as follows:

[0086] ;

[0087] In the formula, Salt concentration, in mg / L; The value is a measurement of electrical conductivity, in μS / cm; The conversion factor is obtained through standard curve fitting, with a typical value of 0.64. Here is the intercept constant, expressed in mg / L. The formula for calculating the moisture content of the wall is:

[0088] ;

[0089] In the formula, The moisture content of the wall is expressed in %; The resistance value of the material in its dry state, in Ω; The resistance value of the material under measured conditions, in Ω; This is the measurement error term, with a range of ±0.5%.

[0090] The specific implementation of step S07 is to establish a lifetime prediction model based on damage mechanics, and the damage evolution equation is specifically expressed as follows:

[0091] ;

[0092] In the formula, The damage variable takes values ​​from 0 to 1. The material was not damaged at the time. The material completely failed at that time; The current stress state is expressed in Pa. This represents the initial stress state, expressed in Pa. Damage evolution parameter, reflecting the damage development rate of the material, unit: ; The time variable is used, and the unit is seconds (s). The formula for calculating dew point temperature is:

[0093] ;

[0094] In the formula, This is the dew point temperature, expressed in °C. Relative humidity, in %; Air temperature, in °C; The constants 243.04 and 17.625 are fitting parameters based on the empirical formula for water vapor saturation pressure. 243.04 corresponds to the critical temperature correction coefficient, and 17.625 corresponds to the saturation pressure slope coefficient.

[0095] The parameter is obtained as follows: diffusion coefficient The method used was experimental, including step 1: preparing a standard sample with a thickness of 10 mm and an area of ​​[missing information]. Step 2: Conduct a moisture permeability test in an environment with a temperature of 23±2℃ and a relative humidity of 50±5%; Step 3: Record the change in sample mass over 24 hours, calculate the moisture permeability coefficient, and divide by the material density to obtain the diffusion coefficient. (Water vapor mass) Measured using an electronic balance, with an accuracy of 0.1 mg. Vapor pressure difference. Calculated using saturated vapor pressure ,in It is a saturated vapor pressure function. and These represent the temperatures on both sides of the test chamber. Damage evolution parameters. The parameters were obtained using accelerated aging tests, including: Step 1: conducting material performance degradation tests in a multi-factor coupled aging apparatus; Step 2: periodically measuring material strength changes; and Step 3: using the least squares method to fit the damage evolution curve to obtain parameter values. Initial stress The initial stress value is obtained through materials mechanics testing, including: Step 1: preparing a standard tensile specimen; Step 2: conducting a tensile test on a universal testing machine; Step 3: recording the yield strength as the initial stress value. Conversion factor. Obtained using the standard curve method, including step 1: preparing different concentrations of... Standard solution; Step 2: Measure the conductivity of each solution; Step 3: Establish a linear regression equation between concentration and conductivity to obtain the conversion factor. Intercept constant. The intercept term of the standard curve is obtained through linear regression, with typical values ​​ranging from 5 to 15 mg / L. Resistivity in dry state. The resistance was obtained by a drying method, including step 1: drying the wall sample in an oven at 105℃ for 24 hours; step 2: immediately measuring the resistance value after cooling to room temperature; and step 3: repeating the measurement three times and taking the average value as the reference value. The measured resistance value was then used. The real-time measured values ​​are obtained using a resistance moisture meter.

[0096] It needs to be explained that the formula for calculating the moisture permeability coefficient... Based on the fundamental principle of mass transfer, this formula describes the mass of water vapor passing through a unit area per unit area per unit time under a unit vapor pressure difference in a material of unit thickness. The numerator of the formula... The product of mass and thickness representing water vapor transfer, denominator part This represents the product of the transmission area, time, and driving force. Compared to traditional qualitative material classification methods, this quantitative calculation formula establishes a numerical evaluation system for the moisture permeability of materials through precise measurement. It provides a quantitative basis for the scientific selection of moisture-proof materials under different moisture contents, avoids the problem of material performance mismatch caused by empirical selection, and realizes the technical transformation of moisture-proof design from qualitative to quantitative.

[0097] Salt concentration conversion formula Based on the linear relationship between conductivity and solution ion concentration, a standard curve is established to achieve rapid on-site detection. The linear term in the formula... The constant term reflects the contribution of the main salts to the conductivity. Correcting the influence of background ions;

[0098] ;

[0099] Compared to traditional chemical titration analysis methods, this conversion relationship enables rapid on-site detection of salt concentration, reducing the detection time from several hours to several minutes. It provides technical support for real-time monitoring and dynamic protection against salt migration risks, and significantly improves the response speed and implementation accuracy of salt prevention measures.

[0100] Formula for calculating the moisture content of walls Based on the negative correlation between material resistance and moisture content, the resistance change reflects the humidity state of the wall. The resistance ratio term in the formula... The effect of moisture content on the electrical properties of materials was quantified, and the error term was calculated. Measurement uncertainties were taken into account;

[0101] ;

[0102] Compared with the traditional gravimetric method for moisture content determination, this calculation method achieves non-destructive and rapid detection, avoids damage to the wall structure caused by sampling, provides continuous and reliable input data for humidity prediction models, and improves the intelligent monitoring level and response accuracy of the moisture-proof system.

[0103] Finite difference discretization scheme The continuous Laplace operator is transformed into discrete algebraic operations, and the numerical solution of partial differential equations is achieved through a second-order precision approximation using Taylor expansion. The central difference scheme of the equations is used. This ensures the stability and accuracy of numerical calculations;

[0104] ;

[0105] Compared with traditional analytical solution methods, this discretization method can handle practical engineering problems with complex geometric boundaries and non-uniform material distribution. It obtains detailed humidity distribution information through computer numerical iteration, providing an effective calculation tool for humidity control of complex wall structures and realizing the organic combination of theoretical models and engineering applications.

[0106] Dijkstra's algorithm distance update formula Based on the concept of dynamic programming, a greedy strategy is used to gradually determine the shortest distance to each node. The minimum value function in the formula... Ensure that the optimal path is selected for each update;

[0107] ;

[0108] The time complexity of the algorithm is ,in For the number of nodes, The number of edges is given. Compared to traditional salt diffusion analysis methods, this algorithm abstracts the three-dimensional mass transfer problem into a graph theory optimization problem. Through mathematical modeling, it accurately identifies the critical paths and high-risk areas of salt penetration, achieving a technological upgrade from experience-based protection to precision protection. This avoids material waste caused by blind protection and improves the targeting and effectiveness of salt prevention measures.

[0109] Damage evolution equation Based on the theory of continuous damage mechanics, the exponential decay term in the equation It describes the degradation process of material strength over time, stress ratio This reflects the impact of the current load level on damage development;

[0110] ;

[0111] Damage variables The mathematical model quantifies the degree of material performance degradation. Compared with traditional qualitative life assessment methods, this mathematical model establishes a quantitative relationship between damage variables and time and stress state, enabling scientific prediction of the service life of the moisture-proof system. This provides theoretical support for the formulation of maintenance strategies, avoids the risk of premature replacement or unexpected failure, and significantly improves the reliability and economy of system operation.

[0112] Dew point temperature calculation formula Based on the simplified form of the Antoine equation, the critical temperature for condensation is calculated using relative humidity and temperature parameters. The numerator term in the formula... Sum of denominator terms The constant coefficients are derived from the empirical relationship between water vapor saturation pressure and temperature;

[0113] ;

[0114] Compared to traditional lookup table methods or simplified estimations, this precise calculation formula can obtain accurate dew point temperature values ​​in real time, providing a scientific basis for condensation risk warning and active heating control. By maintaining the wall surface temperature above the dew point temperature, it effectively prevents condensation, significantly improving the intelligence level and response accuracy of the moisture-proof system. (Moisture permeability coefficient calculation formula) Based on the fundamental principle of mass transfer, this formula describes the mass of water vapor passing through a unit area per unit area per unit time under a unit vapor pressure difference in a material of unit thickness. The numerator of the formula... The product of mass and thickness representing water vapor transfer, denominator part This represents the product of the transmission area, time, and driving force. Compared to traditional qualitative material classification methods, this quantitative calculation formula establishes a numerical evaluation system for the moisture permeability of materials through precise measurement. It provides a quantitative basis for the scientific selection of moisture-proof materials under different moisture contents, avoids the problem of material performance mismatch caused by empirical selection, and realizes the technical transformation of moisture-proof design from qualitative to quantitative.

[0115] Wet diffusion equation Based on Fick's second diffusion law, the process of humidity transfer in porous media is described. The left side of the equation... The right side represents the rate of change of humidity over time. This represents the diffusion flux generated by the spatial humidity gradient;

[0116] ;

[0117] This equation can accurately predict the spatiotemporal evolution of humidity inside the wall. Compared with traditional empirical estimation methods, this mathematical model realizes the transformation of humidity prediction from qualitative judgment to quantitative calculation through rigorous numerical solution of partial differential equations. It provides a theoretical basis for the scientific selection of moisture-proof materials, avoids moisture-proof failure caused by improper material configuration, and significantly improves the reliability and economy of moisture-proof design.

[0118] To better understand and implement this invention, the following is a specific application scenario of this invention, Example 2:

[0119] A research building located in a coastal port city has suffered from mold growth, dampness, yellowing, and partial peeling on the interior walls of its 3rd to 8th floors due to years of sea breeze erosion and high humidity. This has seriously affected the indoor environmental quality and the building's structural safety.

[0120] The technical team first deployed 36 relative humidity sensors and 24 wall moisture content sensors at 1.5m intervals in typical damaged areas on the 3rd, 5th, and 7th floors of the building. The relative humidity sensors were installed at the center of the wall at a height of 1.5m above the ground, with a measurement accuracy of ±3% and a response time of 25s. The wall moisture content sensors had their probes inserted 5cm into the wall, with a measurement accuracy of ±0.5%, and each measurement point was measured three times and the average value was taken. Simultaneously, two infrared thermal imagers were installed on each floor, with a detection accuracy of ±0.1℃ and a detection distance of 1.2m, to detect the temperature field distribution of the walls. The real-time monitoring system showed that the indoor relative humidity fluctuated within the range of 75% to 92%, and the wall moisture content in different areas was 6.2%, 11.8%, and 14.3%, respectively. The relationship between wall moisture content and permeability coefficient is as follows: Figure 2 As shown.

[0121] The technical team systematically evaluated four different types of candidate moisture-proof materials collected from the market. Material A is a modified bitumen-based moisture-barrier membrane, possessing excellent moisture-barrier performance and chemical stability, suitable for long-term protection in low-moisture-content environments. Material B is a polymer-modified cement-based coating, combining moisture permeability regulation and mechanical strength, suitable for dynamic humidity regulation in moderate humidity environments. Material C is a porous ceramic matrix composite material, with good moisture permeability and slow-release moisture absorption, suitable for humidity balance regulation in high-moisture-content environments. Material D is an organosilicon-modified acrylic coating, possessing some moisture permeability but relatively poor durability.

[0122] To select suitable moisture-proof materials, the technical team used a multi-factor coupled accelerated aging test device to test four candidate materials. The test conditions were set to a temperature cycling range of -20℃ to 80℃. A salt spray environment with a solution mass fraction of 5% and an ultraviolet radiation intensity of 40 W / m². The test cycle is 1 / 10 of the traditional single-factor test, meaning that 15 days completes the aging effect equivalent to 150 days. The moisture permeability coefficient of the selected material A was determined using the cup method. g / (m·s·Pa), the moisture permeability coefficient of material B is g / (m·s·Pa), the moisture permeability coefficient of material C is g / (m·s·Pa). The evaluation results of mass change rate and performance degradation rate are shown in Table 1:

[0123] Table 1 Aging test results of candidate moisture-proof materials

[0124]

[0125] The technical team established a dynamic prediction model for humidity distribution inside the wall using a humidity gradient optimized diffusion algorithm. The humidity diffusion equation was discretized and solved using the finite difference method, with a grid spacing of 0.01m and a time step of 3600s. The variation of humidity at each node over time was calculated using an implicit difference scheme. The prediction results show that material A is selected for areas with a moisture content of 6.2%, while materials B and C are selected for areas with moisture contents of 11.8% and 14.3%, respectively.

[0126] Based on the material selection results, the technical team designed a gradient moisture-proof layer composite structure. The first moisture-barrier layer uses a modified bitumen-based material with a vapor permeability resistance of 235 MNs / g·m and a thickness of 2 mm. The second transition layer uses a modified polymer-based composite material with a thickness of 2.5 mm and a tensile strength of 2.8 MPa. The third moisture-regulating layer uses a porous ceramic-based material with a thickness of 3 mm. The linear expansion coefficients of each material were measured using a thermal expansion meter. / ℃、 / ℃ and / ℃, the difference in the coefficient of linear expansion between adjacent layers is controlled within Within a certain temperature range, ensure the coordination of thermal expansion and contraction.

[0127] To identify locations of cold bridge risks, the technical team used a shortest path algorithm for salt migration to abstract the wall structure into a graph theory model. The wall was divided into 127 nodes, with edge weights determined based on the material's salt transport resistance. Using Dijkstra's algorithm, three shortest paths were calculated from the outside to the inner surface, with total lengths of 2.85m, 3.12m, and 3.67m, respectively. Combined with infrared thermal imaging results, five medium-risk cold bridge locations with a temperature gradient of 7.3℃ / m and three high-risk cold bridge locations with temperature gradients of 12.8℃ / m and 15.4℃ / m were identified. Figure 3 As shown, cold bridges are mainly concentrated at building corners, the junction of exterior and interior walls, and where pipelines pass through walls.

[0128] For the identified high-risk locations of cold bridging, the technical team constructed locally reinforced insulation layers. The insulation material used was rigid polyurethane foam with a thermal conductivity of 0.032 W / (m·K), with thicknesses set to 80 mm, 95 mm, and 110 mm according to temperature gradients. A carbon fiber electric heating film system was also installed, with a power density set to 180 W / (m·K). and 245W / Two levels of protection were provided. Salt concentration measurements using the conductivity method revealed concentrations of 680 mg / L, 950 mg / L, and 1250 mg / L in key areas. Correspondingly, a medium-strength salt barrier was added, with a permeability of [missing information]. m 2 And a high-strength salt barrier with a permeability of m 2 .

[0129] A life prediction model based on damage mechanics was established, and the service life of the moisture-proof system was calculated by establishing a material damage evolution equation. The damage evolution parameter α was taken as 0.0125 / initial stress The pressure is 1.8 MPa. Predictive models show the moisture-proof system has a design lifespan of up to 25 years. The technical team has developed an intelligent control strategy: the electric heating film system is activated when the relative humidity reaches 88% for 96 hours. Local ventilation is added when the temperature difference between the wall surface and the indoor air is 4.5℃, with the airflow set to 150. / h. When the temperature difference reaches 8.2℃, the electric heating film system and local ventilation are activated simultaneously.

[0130] The key parameter monitoring data during the project implementation process are shown in Table 2:

[0131] Table 2 Monitoring data of key parameters during implementation

[0132]

[0133] After six months of continuous monitoring, the renovated walls demonstrated excellent mold and moisture resistance. Relative humidity was stably controlled within the range of 65%–72%, and the wall moisture content decreased to 3.8%–4.6%, with no further mold growth observed. The dew point temperature, calculated using the dew point temperature formula, was 12.8℃, and the wall surface temperature was maintained between 15.2℃ and 16.7℃, ensuring no condensation occurred. Salt concentration testing showed that the salt barrier effectively prevented... With the penetration of ions, the salt concentration on the interior wall surface decreased from 1150 mg / L before the renovation to 285 mg / L.

[0134] System stability monitoring shows that the electric heating film system starts an average of 18 times per month, with each run lasting 4.5 hours, and energy consumption is kept within a reasonable range. The local ventilation system works in conjunction with the electric heating system to quickly regulate the wall's microenvironment under high humidity conditions. Figure 4 As shown, the temperature distribution is more uniform, eliminating the original temperature anomaly areas. The material interfaces are firmly bonded, and no cracking or delamination was found, verifying the effectiveness of the gradient moisture-proof layer composite structure design.

[0135] To verify the long-term effectiveness, the technical team established a damage accumulation monitoring system. By periodically monitoring changes in material performance parameters, the damage status of the moisture-proof system was assessed. Monitoring results showed that after six months of operation, the material damage variables... The value is 0.083, far below the critical damage threshold of 0.3, indicating that the system is expected to operate normally for more than 22 years. Figure 5 As shown, the damage evolution curve exhibits a slow upward trend, which meets the expected design life requirements.

[0136] In system response tests under extreme weather conditions, when the outdoor relative humidity reached 96% and the indoor relative humidity rose to 91%, the system was able to reduce the indoor humidity to 73% within 2.5 hours. When the temperature difference between the wall surface and the indoor temperature exceeded 6°C, the electric heating system and the ventilation system started simultaneously, controlling the temperature difference to within 3°C within 30 minutes. Figure 6 As shown, the system's response speed and control accuracy both meet design requirements, enabling rapid adaptation to complex and changing environmental conditions. Analysis of environmental parameter distribution characteristics reveals a more uniform humidity distribution inside the modified wall, avoiding the risk of mold growth caused by localized humidity concentrations. Through multi-point sensor data fusion, a three-dimensional humidity field distribution map of the wall was established, providing a scientific basis for subsequent maintenance.

[0137] Compared to traditional moisture-proofing methods, this invention achieves a shift from passive protection to active control through multi-factor coupling analysis and an intelligent response mechanism. Traditional methods, relying solely on a single moisture-proofing material, cannot cope with the complex and ever-changing marine environment, while this technology, through a gradient composite structure design, achieves precise matching under different humidity conditions. The salt migration path optimization algorithm accurately identifies weak points, avoiding the blind protection of traditional methods. The damage mechanics-based life prediction model provides scientific maintenance guidance, while traditional methods lack effective life assessment tools. The intelligent control system enables real-time response to environmental parameters, offering greater adaptability and reliability compared to traditional fixed protection methods.

[0138] It should be noted that the variables involved in this invention are explained in detail in Table 3.

[0139] Table 3. Variable Explanation Table

[0140]

[0141] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for preventing mold and moisture on the interior walls of buildings in high-humidity coastal environments, characterized in that, This involves deploying multiple relative humidity sensors and wall moisture content sensors on the interior wall surface, and installing an infrared thermal imager to detect the wall temperature field distribution, establishing a real-time environmental parameter monitoring system. A multi-factor coupled accelerated aging test device is used to test candidate moisture-proof materials, simulating the synergistic effects of temperature cycling, salt spray corrosion, and UV aging to obtain the material's moisture permeability coefficient and vapor permeation resistance parameters. A humidity gradient optimization diffusion algorithm is used to establish a dynamic prediction model of the humidity distribution inside the wall. The implicit difference scheme is used to solve for the change in humidity at each node over time. Appropriate moisture-permeable materials are selected based on the wall moisture content. The gradient moisture-proof composite structure includes a moisture-blocking layer, a transition layer, and a humidity-regulating layer. A shortest path algorithm for salt migration is used to abstract the wall structure into a graph theory model. Dijkstra's algorithm is used to calculate the shortest path for salt from the outside to the inner surface. Combined with infrared thermal imaging detection results, cold bridge locations are determined. Locally reinforced insulation layers are constructed at identified high-risk cold bridge locations, and an electric heating film system is installed. Salt barriers are increased based on salt concentration. A life prediction model based on damage mechanics is established. The electric heating film system and local ventilation are activated based on the duration of relative humidity and the temperature difference between the wall surface and the indoor air.

2. The method for preventing mold and moisture on the interior walls of buildings in coastal high-humidity environments according to claim 1, characterized in that, The establishment steps of the real-time environmental parameter monitoring system are as follows: Multiple relative humidity sensors and wall moisture content sensors are deployed on the surface of the building's interior walls, with a sensor spacing of 1.5m. At the same time, an infrared thermal imager is installed to detect the temperature field distribution of the wall.

3. The method for preventing mold and moisture on the interior walls of buildings in coastal high-humidity environments according to claim 2, characterized in that, The shortest path algorithm for salt migration specifically abstracts the wall structure into a graph theory model and uses Dijkstra's algorithm to calculate the shortest path for salt from the outside to the inner surface. Nodes represent material regions, and edge weights represent the resistance to salt transfer.

4. The method for preventing mold and moisture on the interior walls of buildings in coastal high-humidity environments according to claim 3, characterized in that, The conditions for the multi-factor coupled accelerated aging test are specifically a temperature cycling range of -20℃ to 80℃ and a salt spray concentration of [missing information]. The solution mass fraction is 5%, and the ultraviolet radiation intensity is 40 W / .

5. The method for preventing mold and moisture on the interior walls of buildings in coastal high-humidity environments according to claim 4, characterized in that, The humidity gradient optimization diffusion algorithm is specifically based on the numerical solution of partial differential equations to establish a dynamic prediction model of humidity distribution inside the wall. The humidity diffusion equation is as follows: , where u is the humidity field and D is the diffusion coefficient.

6. The method for preventing mold and moisture on the interior walls of buildings in coastal high-humidity environments according to claim 5, characterized in that, The solution method for the dynamic prediction model is specifically to solve the variation law of humidity of each node with time through implicit difference scheme, with grid spacing set to 0.01m and time step set to 3600s, and the solution is discretized by finite difference method.

7. The method for preventing mold and moisture on the interior walls of buildings in coastal high-humidity environments according to claim 6, characterized in that, The damage mechanics-based lifetime prediction model specifically establishes a material damage evolution equation. ,in Let σ be the damage variable and σ be the current stress. Let be the initial stress, α be the damage evolution parameter, and t be the time.

8. The method for preventing mold and moisture on the interior walls of buildings in coastal high-humidity environments according to claim 7, characterized in that, The gradient moisture-proof composite structure specifically consists of a first layer that is a moisture-proof layer with a vapor permeability resistance greater than 200 MNs / g·m, a second layer that is a transition layer material, and a third layer that is a moisture-regulating layer with a moderate moisture permeability coefficient.

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