Building roof water leakage detection method and system

Abstract

The invention discloses a building roof water leakage detection method and system, and relates to the technical field of monitoring analysis, and the method comprises the steps: guiding drilling equipment to carry out the punching operation based on a micropore path topological graph, collecting the real-time deformation parameters of the peripheral region of a hole site, carrying out the internal stress gradient analysis of a waterproof layer material, and generating a hole site stress diffusion vector diagram; performing spatial registration fusion on the waterproof layer three-dimensional density distribution model and the hole site stress diffusion vector diagram, calculating a potential leakage probability corresponding to the waterproof layer around each hole site, performing spatio-temporal evolution simulation on the potential leakage probability, and outputting a dynamic leakage risk thermodynamic diagram; and performing water leakage path reverse tracking modeling by combining the dynamic leakage risk thermodynamic diagram and the waterproof layer three-dimensional density distribution model, generating a roof water leakage detection optimization model, and transmitting the water leakage detection optimization model to a monitoring terminal to implement real-time water leakage early warning. The method has the effect of improving the water leakage detection efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of monitoring analysis, in particular to a building roof leakage detection method and system. BACKGROUND

[0002] As an important enclosure structure of buildings, the building roof is long-term exposed to the natural environment, and is prone to leakage problems due to aging of waterproof materials, construction defects, external loads and other factors, which not only affects the use function of the building, but also may cause chain hazards such as corrosion of structural steel bars and damage of indoor decoration. And in the process of installing new building roof equipment (such as photovoltaic panels and air conditioner outdoor units), the equipment needs to be fixed by punching holes in the waterproof layer, and the punching operation may damage the integrity of the original waterproof layer, aggravating the risk of leakage. With the development of building intelligence, there is an increasing demand for forward-looking detection, dynamic risk assessment and accurate early warning of roof leakage.

[0003] In related technologies, traditional roof leakage detection methods mainly include traditional visual inspection, infrared thermal imaging detection, humidity sensor array monitoring, etc., which lack forward-looking risk prevention and control during the construction phase (such as waterproof layer punching), leading to the expansion of leakage losses, and the hole planning is blind during the punching operation, without considering the structural characteristics of the waterproof layer, which may cause punching in stress concentration areas, further damaging the waterproof layer and resulting in low response efficiency, thereby reducing the efficiency of building roof leakage detection, which needs to be improved. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a building roof leakage detection method and system.

[0005] In a first aspect, the present application provides a building roof leakage detection method, comprising the following steps: Step S1: Before the waterproof layer punching operation in the equipment installation area corresponding to the building roof, the hole site pre-point planning is performed by a multispectral sensor array, a micro-hole path topology graph is generated, and a three-dimensional density distribution model of the waterproof layer is obtained by three-dimensional scanning reconstruction of the waterproof layer structure according to the micro-hole path topology graph; Step S2: Based on the micro-hole path topology graph, the drilling equipment is guided to perform the punching operation, and the real-time deformation parameters of the hole site surrounding area are synchronously collected, the internal stress gradient of the waterproof layer material is analyzed, and a hole site stress diffusion vector graph is generated; Step S3: The waterproof layer three-dimensional density distribution model and the hole site stress diffusion vector graph are spatially registered and fused, the potential leakage probability of the waterproof layer corresponding to each hole site is calculated, the potential leakage probability is simulated in space-time evolution, and a dynamic leakage risk thermal map is output; Step S4: Leak path reverse tracking modeling is performed by combining the dynamic leakage risk thermal map and the waterproof layer three-dimensional density distribution model, a roof leak detection optimization model is generated, and the leak detection optimization model is transmitted to a monitoring terminal for real-time leak warning.

[0006] Preferably, the multispectral sensor array comprises an infrared thermal imaging unit, a millimeter wave radar unit, and a laser ranging unit. The multispectral sensor array is arranged in a hexagonal honeycomb structure to form a redundant detection network. The waterproof layer three-dimensional density distribution model comprises a dielectric constant distribution matrix of the waterproof material and a thermal conductivity coefficient spatial gradient field.

[0007] Preferably, the step S1 specifically comprises the following steps: Extract the corresponding bearing capacity parameters, waterproof layer thickness parameters, and material aging parameters of the building roof from the cloud database, and construct a waterproof layer performance benchmark vector; Multi-modal scanning of the equipment installation area is performed by the multispectral sensor array, and temperature gradient data, dielectric constant distribution data, and micro-deformation vibration data are synchronously collected; A temperature conduction correlation model of the waterproof layer aging state and temperature conduction rate is established based on the temperature gradient data, and a dielectric characteristic spatial distribution matrix is constructed in combination with the dielectric constant distribution data; A waterproof layer structure fatigue damage degree surface model is constructed according to the micro-deformation vibration data, a density distribution feature tensor is generated by fusing the temperature conduction correlation model and the dielectric characteristic spatial distribution matrix; The spatial resolution parameters in the density distribution feature tensor are optimized using an adaptive genetic algorithm, and a micro-hole path topology graph is generated; The micro-hole path topology graph is calibrated in three-dimensional coordinates by the laser ranging unit, and a waterproof layer three-dimensional density distribution model is output based on the results of the three-dimensional coordinate calibration.

[0008] Preferably, the step S2 specifically comprises the following steps: According to the micro-hole path topology graph, the drilling equipment is driven to perform drilling operations, the real-time deformation parameters of the hole site peripheral area are synchronously collected by the pressure sensor and the micro-strain gauge, and the circumferential stress distribution data of the hole site are further confirmed; The water content rate change inside the waterproof layer during the drilling process is measured, a water content rate gradient surface is constructed, the circumferential stress distribution data is fitted with a stress gradient field, and a two-dimensional stress diffusion map is generated; The two-dimensional stress diffusion map is mapped to the surface of the waterproof layer three-dimensional density distribution model, and a hole site stress diffusion vector graph is constructed by fusing the water content rate gradient surface; An abnormal diffusion mode recognition is performed on the hole site stress diffusion vector graph, and a dangerous diffusion area exceeding a preset stress threshold is marked.

[0009] Preferably, the step S3 specifically comprises the following steps: A spatial coordinate conversion matrix of the waterproof layer three-dimensional density distribution model and the hole site stress diffusion vector diagram is established, and then the waterproof layer three-dimensional density distribution model and the hole site stress diffusion vector diagram are spatially registered and fused based on the spatial coordinate conversion matrix; The corresponding waterproof material dielectric constant distribution matrix and thermal conductivity coefficient spatial gradient field in the waterproof layer three-dimensional density distribution model are extracted, and a multi-physical field coupling parameter space is constructed; The cosine similarity is calculated in the multi-physical field coupling parameter space, an initial leakage probability distribution matrix is generated, the initial leakage probability distribution matrix is subjected to feature importance analysis, key influencing factors are screened out, and a leakage risk prediction model is constructed; The historical leakage event data stored in the cloud database are input to train the leakage risk prediction model, the leakage probability is iteratively calculated by the sliding time window method, the results of the iterative calculation are corrected for stability, and a dynamic leakage risk thermal map is output.

[0010] Preferably, the step S4 specifically comprises the following steps: The high-risk leakage area is marked in the dynamic leakage risk thermal map, the density distribution feature tensor corresponding to the high-risk leakage area is extracted, and a three-dimensional seepage diffusion model is constructed; According to the three-dimensional seepage diffusion model, the water flow velocity gradient field of different leakage paths is calculated by the back propagation algorithm, and a candidate leakage path set is generated; The leakage development process of each candidate leakage path in the candidate leakage path set is simulated, and the leakage rate and influence range of each candidate leakage path are evaluated; A leakage propagation tree diagram is established based on the leakage rate and influence range of each candidate leakage path, a roof leakage detection optimization model is constructed according to the leakage propagation tree diagram, multi-level early warning threshold parameters are set, and the roof leakage detection optimization model is transmitted to a monitoring terminal to implement real-time leakage early warning.

[0011] In a second aspect, the present application provides a building roof leakage detection system, comprising: The dot planning module is used to perform hole site pre-dot planning through a multi-spectral sensor array before performing a waterproof layer drilling operation on the corresponding equipment installation area of the building roof, generate a micro-hole path topology map, and perform three-dimensional scanning reconstruction on the waterproof layer structure of the building roof according to the micro-hole path topology map to obtain a waterproof layer three-dimensional density distribution model; The processing module is used to guide the drilling equipment to perform the drilling operation based on the micro-hole path topology map, synchronously collect real-time deformation parameters of the hole site surrounding area, perform waterproof layer material internal stress gradient analysis on the real-time deformation parameters, and generate a hole site stress diffusion vector diagram; The analysis module is used for spatially registering and fusing the waterproof layer three-dimensional density distribution model and the hole site stress diffusion vector diagram, calculating the potential leakage probability of the waterproof layer corresponding to each hole site periphery, simulating the spatio-temporal evolution of the potential leakage probability, and outputting a dynamic leakage risk heat map. The optimization module is used for conducting reverse tracking modeling of the water leakage path in combination with the dynamic leakage risk heat map and the waterproof layer three-dimensional density distribution model, generating a roof water leakage detection optimization model, and transmitting the water leakage detection optimization model to the monitoring terminal to implement real-time water leakage early warning.

[0012] In a third aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, cause the computer to perform the building roof water leakage detection method of any one of the preceding aspects.

[0013] In summary, the present application has the following beneficial technical effects: The present application provides a building roof water leakage detection method, which performs hole site pre-point planning by a multi-spectral sensor array, generates a micro-hole path topology graph, reconstructs a waterproof layer structure of a building roof according to the micro-hole path topology graph to obtain a waterproof layer three-dimensional density distribution model, guides drilling equipment to perform drilling operations based on the micro-hole path topology graph, synchronously collects real-time deformation parameters of a hole site periphery and performs waterproof layer material internal stress gradient analysis to generate a hole site stress diffusion vector diagram, calculates the potential leakage probability of the waterproof layer corresponding to each hole site periphery, simulates the spatio-temporal evolution of the potential leakage probability, and outputs a dynamic leakage risk heat map. In combination with the dynamic leakage risk heat map and the waterproof layer three-dimensional density distribution model, reverse tracking modeling of the water leakage path is conducted to generate a roof water leakage detection optimization model, which is transmitted to the monitoring terminal to implement real-time water leakage early warning, thereby improving the forward-looking risk prevention and control during the construction phase and reducing the occurrence of low response efficiency due to blind hole site planning during drilling operations without considering the waterproof layer structure characteristics, thereby effectively improving the efficiency of building roof water leakage detection. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0015] Figure 1 is a method flowchart of the building roof water leakage detection of the embodiment of the present application.

[0016] Figure 2 is a system schematic diagram of the building roof water leakage detection of the embodiment of the present application. DETAILED DESCRIPTION

[0017] The following description will be made in conjunction with the accompanying drawings Figures 1-2 The application is further described in detail.

[0018] Embodiment 1 The embodiment of the application discloses a building roof leakage detection method.

[0019] Referring to Figure 1 A building roof leakage detection method comprises the following steps: Step S1: Before the waterproof layer punching operation in the equipment installation area corresponding to the building roof, the hole site pre-pointing planning is carried out through the multi-spectral sensor array, the micro-hole path topology graph is generated, and the waterproof layer structure of the building roof is reconstructed through three-dimensional scanning according to the micro-hole path topology graph, so as to obtain the waterproof layer three-dimensional density distribution model; Step S2: Based on the micro-hole path topology graph, the drilling equipment is guided to carry out the punching operation, the real-time deformation parameters of the hole site surrounding area are synchronously collected, the waterproof layer material internal stress gradient analysis is carried out on the real-time deformation parameters, and the hole site stress diffusion vector graph is generated; Step S3: The waterproof layer three-dimensional density distribution model and the hole site stress diffusion vector graph are fused through space registration, the potential leakage probability corresponding to the waterproof layer around each hole site is calculated, the potential leakage probability is simulated in time and space evolution, and the dynamic leakage risk heat map is output; Step S4: The dynamic leakage risk heat map and the waterproof layer three-dimensional density distribution model are combined to carry out the reverse tracking modeling of the leakage path, the roof leakage detection optimization model is generated, and the roof leakage detection optimization model is transmitted to the monitoring terminal to implement real-time leakage early warning.

[0020] By adopting the technical scheme, the whole-process closed-loop management from hole site planning to risk early warning is realized, the damage of blind punching to the waterproof layer is reduced through pre-pointing planning, the potential risks in the punching process can be found in time through real-time stress analysis, the dynamic risk heat map can intuitively present the time-space change of the leakage risk, the reverse tracking modeling improves the accuracy of the leakage detection and the timeliness of the early warning, compared with the traditional post-detection method, the loss caused by the leakage can be greatly reduced, and from data collection to model fusion and then to risk early warning, a complete technical chain is formed, so that the systematization and accuracy of the detection are ensured.

[0021] Further, the multi-spectral sensor array comprises an infrared thermal imaging unit, a millimeter wave radar unit and a laser ranging unit; The multi-spectral sensor array is arranged in a hexagonal honeycomb structure to form a redundant detection network; The waterproof layer three-dimensional density distribution model comprises a waterproof material dielectric constant distribution matrix and a thermal conductivity coefficient space gradient field.

[0022] Specifically, the multispectral sensor array is composed of an infrared thermal imaging unit, a millimeter wave radar unit and a laser ranging unit, and each unit in the multispectral sensor array is arranged in a hexagonal honeycomb structure to form a redundant detection network, wherein the infrared thermal imaging unit is used to collect temperature gradient data of the waterproof layer, the millimeter wave radar unit is used to obtain dielectric constant distribution data, and the laser ranging unit is used to realize three-dimensional coordinate calibration; the construction of the three-dimensional density distribution model of the waterproof layer is based on the data collected by the multispectral sensor array, the thermal conductivity coefficient spatial gradient field is obtained by processing the temperature data of the infrared thermal imaging unit, the dielectric constant distribution matrix of the waterproof material is constructed by using the detection results of the millimeter wave radar unit, and the three-dimensional coordinate data of the laser ranging unit are combined, the above parameters are integrated into the three-dimensional space to form the three-dimensional density distribution model of the waterproof layer containing the dielectric constant distribution matrix and the thermal conductivity coefficient spatial gradient field.

[0023] The multiple units of the multispectral sensor array in the above steps work cooperatively to obtain waterproof layer information from multiple dimensions such as temperature, dielectric properties and spatial position, and compared with a single sensor, the detection dimension is more comprehensive and the data is more abundant; the redundant detection network of the hexagonal honeycomb structure can reduce the detection blind area, improve the reliability and redundancy of data acquisition, and even if some units fail, the continuity of overall detection can still be ensured; the dielectric constant and the thermal conductivity coefficient in the three-dimensional density distribution model of the waterproof layer are key parameters reflecting the performance of the waterproof material, the dielectric constant can reflect the insulation and water content of the material, the thermal conductivity coefficient can reflect the heat conduction performance and aging degree of the material, and the combination of the two can more accurately depict the physical properties of the waterproof layer and provide reliable basic data for subsequent leakage risk assessment.

[0024] In the process of constructing the three-dimensional density distribution model of the waterproof layer, first, the size and arrangement density of the multispectral sensor array are determined according to the size and precision requirements of the equipment installation area, to ensure that the hexagonal honeycomb structure can achieve full coverage; then, each sensor unit is calibrated to ensure that the temperature measurement accuracy of the infrared thermal imaging unit, the dielectric constant detection accuracy of the millimeter wave radar unit, and the distance measurement error of the laser ranging unit are within the allowable range; next, each unit is started synchronously to collect data, the infrared thermal imaging unit generates a temperature gradient map by capturing the infrared radiation intensity of different areas, the millimeter wave radar unit calculates the spatial distribution of the dielectric constant by emitting millimeter waves and receiving reflected signals, and the laser ranging unit calculates the distance from the surface of the waterproof layer through the round-trip time of the laser pulse to obtain three-dimensional coordinate data; then, the collected temperature gradient data is processed, the spatial gradient field of the thermal conductivity coefficient is calculated by establishing a thermal conduction model, and the dielectric constant distribution matrix is constructed by interpolating and correcting the dielectric constant data; finally, the spatial gradient field of the thermal conductivity coefficient and the dielectric constant distribution matrix are spatially matched based on the three-dimensional coordinates obtained by the laser ranging, and integrated into a three-dimensional density distribution model of the waterproof layer. The three-dimensional density distribution model of the waterproof layer can comprehensively reflect the material performance and structural characteristics of the waterproof layer, providing accurate basic data support for subsequent hole planning and leakage detection.

[0025] It should be noted that the step S1 specifically comprises the following steps: Extract the bearing capacity parameters, waterproof layer thickness parameters and material aging parameters corresponding to the building roof from the cloud database to construct a waterproof layer performance benchmark vector; Multi-modal scanning of the equipment installation area is performed by a multispectral sensor array, and temperature gradient data, dielectric constant distribution data and micro-deformation vibration data are collected synchronously; A temperature conduction correlation model of the aging state of the waterproof layer and the temperature conduction rate is established based on the temperature gradient data, and a dielectric characteristic spatial distribution matrix is constructed in combination with the dielectric constant distribution data; A waterproof layer structure fatigue damage degree surface model is constructed according to the micro-deformation vibration data, a density distribution feature tensor is generated by fusing the temperature conduction correlation model and the dielectric characteristic spatial distribution matrix; The spatial resolution parameters in the density distribution feature tensor are optimized using an adaptive genetic algorithm to generate a micro-hole path topology graph; The micro-hole path topology graph is calibrated by a laser ranging unit in three-dimensional coordinates, and a three-dimensional density distribution model of the waterproof layer is output based on the results of the three-dimensional coordinate calibration.

[0026] Specifically, first, the bearing capacity parameters, waterproof layer thickness parameters and material aging parameters of the building roof are extracted from the cloud database, and the above parameters are quantified to construct a waterproof layer performance benchmark vector as a reference benchmark for subsequent analysis; then the equipment installation area is scanned by a multi-spectral sensor array, and temperature gradient data, dielectric constant distribution data and micro-deformation vibration data are synchronously collected, wherein the temperature gradient data reflects the heat conduction difference of the waterproof layer, the dielectric constant distribution data reflects the insulation and water content characteristics of the material, and the micro-deformation vibration data reflects the stability of the structure; based on the temperature gradient data, the temperature conduction correlation model of the waterproof layer aging state and the temperature conduction rate is established by analyzing the correlation between the temperature conduction rate and the material aging degree in different regions, and the dielectric characteristic spatial distribution matrix is constructed by spatial interpolation and data fitting combined with the dielectric constant distribution data; according to the micro-deformation vibration data, the finite element analysis method is used to construct a waterproof layer structure fatigue damage degree surface model, which can reflect the fatigue degree of the structure at different positions; the temperature conduction correlation model, the dielectric characteristic spatial distribution matrix and the fatigue damage degree surface model are data fused to generate a density distribution feature tensor containing multi-dimensional features; the adaptive genetic algorithm is used to optimize the spatial resolution parameters in the density distribution feature tensor, and the optimal resolution is found through selection, crossover, mutation and other operations to generate a micro-hole path topology graph that can accurately reflect the hole distribution; finally, the three-dimensional coordinate calibration of the micro-hole path topology graph is performed by the laser ranging unit, and the waterproof layer three-dimensional density distribution model containing spatial position information is output based on the calibration result.

[0027] By combining the historical data with the real-time detection data through the above steps, the reference parameters extracted by the cloud database provide a historical reference for the detection, avoiding the deviation that may be caused by relying only on real-time data; the multi-dimensional data collected by multi-modal scanning can comprehensively reflect the performance of the waterproof layer, and the cooperative analysis of temperature, dielectric and deformation data can more accurately depict the state of the waterproof layer than single data; the fusion of the temperature conduction correlation model, the dielectric characteristic matrix and the fatigue damage surface model forms a more comprehensive density distribution feature tensor, providing multi-factor support for micro-hole path planning; the application of the adaptive genetic algorithm improves the efficiency and accuracy of spatial resolution optimization, making the micro-hole path topology graph more in line with actual needs and reducing unnecessary damage to the waterproof layer caused by drilling; the three-dimensional coordinate calibration of the laser ranging ensures the spatial accuracy of the three-dimensional density distribution model, providing a precise spatial reference for subsequent drilling operations and risk assessment.

[0028] In constructing the waterproof layer performance benchmark vector, the parameters in the cloud database need to be screened and standardized, abnormal values are removed, and parameters of different dimensions are converted to a uniform order of magnitude. For example, the bearing capacity parameter is converted to the bearing value per unit area, the thickness parameter is converted to the millimeter level value, and the aging parameter is quantified by aging period and performance decay rate. Then, the above parameters are integrated into the benchmark vector by vector synthesis method. In establishing the temperature conduction correlation model, the temperature gradient data are first smoothed to remove noise, and then by comparing the temperature conduction rates of different aging degree regions, the function relationship between aging state and conduction rate is fitted by regression analysis method to form the temperature conduction correlation model. In constructing the dielectric property spatial distribution matrix, the dielectric constant data collected by the millimeter wave radar are gridded, the missing data are supplemented by Kriging interpolation method, and then the data are organized into the dielectric property spatial distribution matrix by matrix operation. In constructing the waterproof layer structure fatigue damage degree surface model, the micro-deformation vibration data are converted to strain values, combined with the fatigue limit of the material, and simulated by finite element software to generate fatigue damage degrees at different positions, and then the fatigue damage degree surface model of the waterproof layer structure is formed by surface fitting. In fusing the above three models to generate the density distribution feature tensor, the data of each model need to be converted to a unified data format and spatial grid, and the data fusion is realized by tensor product operation to retain the feature information of each dimension. In optimizing the spatial resolution parameter, the fitness function of the adaptive genetic algorithm is set as the comprehensive index of resolution and data error, the crossover probability and mutation probability are dynamically adjusted to improve the convergence speed and optimization accuracy of the algorithm, and it is ensured that the generated micro-hole path topology map can accurately reflect the hole distribution and reduce data redundancy. Finally, in the three-dimensional coordinate calibration, the three-dimensional coordinate data collected by the laser ranging unit are matched with the two-dimensional coordinates of the micro-hole path topology map, the two-dimensional topology map is mapped to the three-dimensional space through the coordinate conversion matrix, and then the spatial parameters of the three-dimensional density distribution model of the waterproof layer are corrected to ensure that the spatial position of the model is consistent with the actual roof structure, and to provide accurate path guidance for subsequent drilling operations.

[0029] It should be noted that the step S2 specifically comprises the following steps: According to the micro-hole path topology map, a drilling device is driven to perform drilling operation, a pressure sensor and a micro-strain gauge are activated synchronously to collect real-time deformation parameters of the hole position periphery region, and then the hole position circumferential stress distribution data are confirmed; The water content rate change in the waterproof layer corresponding to the drilling process is measured, a water content rate gradient surface is constructed, the circumferential stress distribution data are fitted by stress gradient field, and a two-dimensional stress diffusion map is generated; The two-dimensional stress diffusion map is mapped to the surface of the three-dimensional density distribution model of the waterproof layer, and a hole position stress diffusion vector diagram is constructed by fusing the water content rate gradient surface; An abnormal diffusion mode of the hole site stress diffusion vector diagram is identified, and a dangerous diffusion area exceeding a preset stress threshold is marked.

[0030] Specifically, first, according to the hole site coordinates and path planning in the micro-hole path topological graph, the drilling equipment is driven to perform the drilling operation according to the preset trajectory. In the drilling process, the pressure sensor mounted on the drilling equipment and the micro-strain gauge around the hole site are activated synchronously, and the real-time deformation parameters of the surrounding area of the hole site are collected in real time, including displacement, strain value, etc. Through analysis and calculation of the above parameters, the hole site circumferential stress distribution data are confirmed, which reflect the stress change of the surrounding structure in the drilling process. At the same time, the corresponding water content change in the waterproof layer during drilling is measured, the humidity sensor is pre-buried near the drilling to collect the water content data at different depths, and the spatial interpolation method is used to construct the water content gradient surface, which reflects the distribution difference of the water content in space. Based on the hole site circumferential stress distribution data, a stress gradient field fitting is performed using a stress field fitting algorithm to generate a two-dimensional stress diffusion map that can reflect the stress diffusion direction and intensity. The two-dimensional stress diffusion map is mapped to the surface of the waterproof layer three-dimensional density distribution model through coordinate conversion, so that the stress data are associated with the three-dimensional spatial position, and then the information of the water content gradient surface is fused to construct a hole site stress diffusion vector diagram containing the stress diffusion direction, intensity and water content distribution through data superposition and vector synthesis. Finally, an abnormal diffusion mode of the hole site stress diffusion vector diagram is identified using an abnormal detection algorithm, a preset stress threshold is set, and areas exceeding the preset stress threshold are marked as dangerous diffusion areas, prompting possible structural damage risks.

[0031] Through the above steps, real-time monitoring of the drilling process is realized. Through real-time data collection of the pressure sensor and the micro-strain gauge, the influence of drilling on the surrounding structure can be grasped in time, and structural damage caused by blind drilling can be avoided. The measurement of water content change and the construction of the gradient surface combine stress analysis with the water content state of the material. Because excessive water content may exacerbate the damage of stress to the waterproof layer, the fusion of the two makes the risk assessment more comprehensive. The conversion of the two-dimensional stress diffusion map to the three-dimensional vector diagram realizes the spatialization of the stress data, which is more in line with the three-dimensional characteristics of the actual structure. The abnormal diffusion mode identification can quickly locate the dangerous area, provide timely warning for the operator, facilitate the adjustment of drilling parameters or the suspension of work, and reduce the potential leakage risk.

[0032] In the process of collecting real-time deformation parameters, the pressure sensor and the micro-strain gauge need to be calibrated to ensure the accuracy of the measurement data. The pressure sensor collects the pressure changes during drilling, and the micro-strain gauge collects the strain response of the surrounding material. Through the data acquisition system, the above parameters are transmitted to the processing terminal in real time. After filtering and amplification, the circumferential stress distribution data of the hole site is calculated. When constructing the moisture content gradient surface, the moisture content data collected by the humidity sensor need to be temperature compensated to eliminate the influence of temperature on the measurement results. Then, the inverse distance weighted interpolation method is used to perform spatial interpolation on the discrete moisture content data to generate a continuous moisture content gradient surface. The slope of the surface reflects the rate of change of moisture content. When fitting the stress gradient field, based on the circumferential stress distribution data of the hole site, the least squares method is used to fit the stress distribution function in the radial and circumferential directions to generate a two-dimensional stress diffusion map. The color or grayscale in the map represents the stress magnitude, and the arrow represents the diffusion direction. When constructing the hole site stress diffusion vector map, first, establish the coordinate correspondence between the two-dimensional stress diffusion map and the three-dimensional density distribution model of the waterproof layer. Through affine transformation, the two-dimensional map is mapped to the surface of the three-dimensional model. Then, the information of the moisture content gradient surface is used as a weight factor to correct the stress vectors in the three-dimensional space, so that the size and direction of the stress vectors reflect the influence of stress and moisture content at the same time, forming a vector map containing multiple factors. When identifying abnormal diffusion patterns, the stress threshold is determined according to the compressive strength and safety factor of the waterproof layer material. The clustering algorithm is used to divide the data in the stress diffusion vector map into normal and abnormal regions. The dangerous diffusion area exceeding the threshold is highlighted to provide a clear basis for subsequent drilling adjustment and ensure the safety of the waterproof layer structure during drilling.

[0033] It should be noted that the step S3 specifically includes the following steps: A spatial coordinate conversion matrix is established between the three-dimensional density distribution model of the waterproof layer and the hole site stress diffusion vector map, and then the three-dimensional density distribution model of the waterproof layer and the hole site stress diffusion vector map are spatially registered and fused based on the spatial coordinate conversion matrix; The corresponding dielectric constant distribution matrix and thermal conductivity coefficient spatial gradient field of the waterproof material in the three-dimensional density distribution model of the waterproof layer are extracted, and a multi-physical field coupling parameter space is constructed; The cosine similarity is calculated in the multi-physical field coupling parameter space to generate an initial leakage probability distribution matrix. The initial leakage probability distribution matrix is analyzed for feature importance, and the key influencing factors are selected to construct a leakage risk prediction model; The historical leakage event data stored in the cloud database is input to train the leakage risk prediction model. The sliding time window method is used for spatio-temporal evolution iterative calculation of the leakage probability. The stability of the iterative calculation result is corrected, and a dynamic leakage risk thermodynamic map is output.

[0034] Specifically, first, a spatial coordinate conversion matrix of the waterproof layer three-dimensional density distribution model and the hole site stress diffusion vector diagram is established. By collecting the coordinates of multiple corresponding feature points in the waterproof layer three-dimensional density distribution model and the hole site stress diffusion vector diagram, the conversion parameters are calculated by using the least square method. Then, the waterproof layer three-dimensional density distribution model and the hole site stress diffusion vector diagram are spatially registered and fused based on the matrix, so that the data of the two are aligned in the same spatial coordinate system. The dielectric constant distribution matrix and the thermal conductivity coefficient spatial gradient field of the waterproof layer three-dimensional density distribution model are extracted from the fused model, and the above parameters are taken as the core variables of the multi-physical field to construct a multi-physical field coupling parameter space. The multi-physical field coupling parameter space includes the electrical and thermal characteristics of the material. In the multi-physical field coupling parameter space, the cosine similarity of the dielectric constant and the thermal conductivity coefficient at different positions and the historical leakage point parameters is calculated, and the initial leakage probability distribution matrix is generated by the similarity size. Each element in the initial leakage probability distribution matrix represents the initial leakage probability of the corresponding position. The feature importance analysis is performed on the initial leakage probability distribution matrix, and the feature importance score of each parameter is calculated by using the random forest algorithm. The key influencing factors that have a greater impact on the leakage probability, such as high water content area and low dielectric constant area, are screened out, and a leakage risk prediction model is constructed based on the above factors. The historical leakage event data, including the leakage position, occurrence time, and environmental conditions, are extracted from the cloud database, and the above data are input into the leakage risk prediction model as training samples. The model parameters are adjusted for training to improve the prediction accuracy of the model. The sliding time window method is used for spatio-temporal evolution iterative calculation of the leakage probability. A fixed time window size is set, and the real-time monitoring data is updated in each window to recalculate the leakage probability. Through multiple iterations, the probability distribution is more consistent with the actual changes. The stability correction is performed on the results of the iterative calculation to remove abnormal fluctuation values. The exponential smoothing method is used for smoothing the probability data, and finally the dynamic leakage risk thermal map that can dynamically reflect the change of the leakage risk is output.

[0035] The spatial registration and fusion ensure the consistency of different model data, avoiding analysis errors caused by coordinate differences. The multi-physical field coupling parameter space integrates the key physical characteristics of the material, which can more comprehensively reflect the potential conditions of leakage occurrence compared with single physical field analysis. The cosine similarity calculation and feature importance analysis improve the accuracy of the initial leakage probability calculation, making the model more focused on key influencing factors. The historical data training enhances the generalization ability of the leakage risk prediction model, which can better adapt to different roof environments. The sliding time window method realizes the dynamic update of the leakage probability, making the risk assessment time-effective. The stability correction ensures the reliability of the dynamic leakage risk thermal map, providing an intuitive and accurate risk reference for subsequent water leakage detection and early warning.

[0036] In establishing the spatial coordinate conversion matrix, first, at least three non-collinear feature points, such as the hole center, the waterproof layer edge point, etc., are selected in the waterproof layer three-dimensional density distribution model and the hole site stress diffusion vector diagram, the coordinate values of the above-mentioned points in the waterproof layer three-dimensional density distribution model and the hole site stress diffusion vector diagram are obtained, then the least square method is used to solve the parameters of the conversion matrix, and the deviation of the converted model data in the spatial position is ensured to be less than a preset threshold; when constructing the multi-physical field coupling parameter space, the dielectric constant distribution matrix and the thermal conductivity coefficient space gradient field are converted into three-dimensional grid data, each grid cell contains two parameters of dielectric constant and thermal conductivity coefficient, the two parameters are integrated into a coupling parameter by defining the weight coefficient of the parameter, and the multi-physical field coupling parameter space is formed; when calculating the initial leakage probability distribution matrix, first, the dielectric constant and thermal conductivity coefficient data of the historical leakage points are collected as standard samples, then the cosine similarity of each grid cell in the multi-physical field coupling parameter space and the standard sample is calculated, the higher the similarity, the greater the initial leakage probability, and the similarity value is normalized to form an initial probability matrix; when constructing the leakage risk prediction model, the initial leakage probability is taken as the output variable, the dielectric constant, the thermal conductivity coefficient, the water content rate, etc. are taken as the input variables, the neural network model is taken as the basic framework, the input layer receives the characteristic factors, the hidden layer performs nonlinear mapping, and the output layer outputs the predicted leakage probability; when training the model, the historical leakage event data is divided into a training set and a test set according to a ratio of 7:3, the weights and biases of the model are adjusted through the training set, the prediction accuracy of the model is verified using the test set, and the cross-validation method is used to avoid overfitting; in the sliding time window method, the size of the time window is determined according to the leakage development speed of the waterproof layer, and is generally set to 1-2 hours, in each window, the newly collected temperature, humidity, stress and other data are input into the model, the leakage probability is recalculated, and dynamic updating is realized; when correcting the stability, the 3σ criterion is used to identify and eliminate abnormal values, then the exponential smoothing method is used to process the probability data, the smoothing coefficient is determined according to the volatility of the data, so that the heat map can reflect the risk change trend and avoid the interference brought by short-term fluctuations, and finally the generated dynamic leakage risk heat map can clearly show the leakage risk level at different times and different positions, thereby providing accurate decision support for roof leakage detection.

[0037] It should be noted that the step S4 specifically comprises the following steps: In the dynamic leakage risk heat map, the high-risk leakage area is marked, the density distribution feature tensor corresponding to the high-risk leakage area is extracted, and a three-dimensional seepage diffusion model is constructed; According to the three-dimensional seepage diffusion model, the water flow velocity gradient field of different leakage paths is calculated through a back propagation algorithm to generate a candidate water leakage path set; The leakage development process corresponding to each candidate water leakage path in the candidate water leakage path set is simulated, and the leakage rate and influence range of each candidate water leakage path are evaluated; A water leakage propagation tree diagram is established based on the leakage rate and influence range of each candidate water leakage path, a roof water leakage detection optimization model is constructed according to the water leakage propagation tree diagram, multi-level early warning threshold parameters are set, and the roof water leakage detection optimization model is transmitted to a monitoring terminal to implement real-time water leakage early warning.

[0038] Specifically, first, in the dynamic leakage risk thermal map, the high-risk leakage area is marked according to the preset high-risk threshold, the high-risk leakage area is the key object of potential water leakage, and the density distribution feature tensor corresponding to the area is extracted, including dielectric constant, thermal conductivity coefficient, structure density and other parameters. Based on the above parameters and combined with the principle of fluid mechanics, a three-dimensional seepage diffusion model is constructed, which can simulate the penetration process of water flow in the waterproof layer; according to the three-dimensional seepage diffusion model, the possible water leakage source is deduced from the high-risk leakage area by using the back propagation algorithm, the water flow velocity gradient field on different leakage paths is calculated, the size of the velocity gradient reflects the flow speed of water flow on different paths, and a candidate water leakage path set containing multiple possible paths is generated based on this; the seepage development process of each candidate water leakage path in the candidate water leakage path set is simulated, different initial conditions (such as initial water quantity, water pressure, etc.) are set, and the finite element simulation software is used to calculate the leakage state of each path at different time points, and the leakage rate (water leakage quantity per unit time) and influence range (area size affected by leakage) of each candidate water leakage path are evaluated; based on the leakage rate and influence range of each candidate water leakage path, a tree structure modeling method is used to establish a water leakage propagation tree diagram, the root of the tree is the possible water leakage source, the branches are different propagation paths, and the leaves are the influence range. The propagation relationship of water leakage is clearly displayed through the tree diagram; according to the water leakage propagation tree diagram, combined with the evaluation results of the leakage rate and the influence range, a roof water leakage detection optimization model is constructed, and multi-level early warning threshold parameters are set in the roof water leakage detection optimization model, such as a first-level warning corresponding to a lower leakage rate and a smaller influence range, a second-level warning corresponding to a medium risk, and a third-level warning corresponding to a high risk; the water leakage detection optimization model is transmitted to the monitoring terminal, the terminal receives the risk evaluation results output by the model in real time, and when the risk of a certain area reaches the corresponding early warning threshold, the corresponding early warning mechanism is triggered immediately, such as sound and light alarm, short message notification, etc., to implement real-time water leakage early warning.

[0039] The precise labeling of high-risk areas by the above steps makes the water leakage detection more targeted, avoiding the inefficiency of comprehensive investigation; the application of the three-dimensional seepage diffusion model and the back propagation algorithm improves the accuracy of water leakage path tracing, which can quickly locate the potential water leakage source; the simulation and evaluation of the leakage development process provide a quantitative basis for risk level classification, making the early warning more scientific; the water leakage propagation tree diagram intuitively displays the propagation relationship of water leakage, which is convenient for operators to understand the development trend of water leakage; the setting of multi-level early warning thresholds realizes differentiated early warning, which can take corresponding measures according to the risk level, improving the practicality and response efficiency of early warning; the real-time early warning of the monitoring terminal can timely discover problems in the early stage of water leakage, reducing property loss and structural damage caused by water leakage.

[0040] In constructing the three-dimensional seepage diffusion model, first, the boundary conditions (such as the upper and lower surfaces of the waterproof layer) and initial conditions (such as the initial water content) of the model are determined according to the density distribution tensor of the high-risk leakage area, Darcy's law is used as the basic equation of seepage motion, combined with the permeability parameters of the waterproof layer material, the three-dimensional area is divided into grids through finite element discretization, and the numerical model of the seepage control equation is established; in calculating the water flow velocity gradient field, the back propagation algorithm takes the high-risk area as the target output, adjusts the permeability coefficients and other parameters of each path, so that the water flow velocity distribution output by the model matches the actual monitoring speed data, and then the water flow velocity gradient field of each path is obtained; in generating the candidate water leakage path set, according to the direction and size of the water flow velocity gradient field, the paths with larger water flow velocity gradient are selected as candidate paths, and a clustering algorithm is used to remove repeated or similar paths, ensuring the diversity and representativeness of the set; in simulating the leakage development process, the computational fluid dynamics software is used to numerically simulate each candidate water leakage path, the time step is set, and the change of water leakage amount and the expansion of the affected area are calculated in each time step, and the leakage state at different times is obtained through iterative calculation; in establishing the water leakage propagation tree diagram, the potential water leakage source is taken as the root node, the branches and trunks are constructed according to the connection relationship and propagation order of the paths, each branch and trunk is labeled with the corresponding leakage rate and influence range parameters, and the propagation priority of water leakage is reflected through the hierarchical structure of the tree; in constructing the roof water leakage detection optimization model, the parameters of the water leakage propagation tree diagram are integrated into the input variables of the model, a fuzzy control algorithm is used to set multi-level early warning thresholds, the determination of the thresholds is based on the leakage rate and influence range corresponding to different risk levels in the historical data, and the risk assessment results are mapped to the corresponding early warning levels through fuzzy rules; after the roof water leakage detection optimization model is transmitted to the monitoring terminal, the terminal receives the data of the field sensors in real time through the interface, inputs the roof water leakage detection optimization model for calculation, and when the result exceeds a certain level of early warning threshold, the preset early warning program is started immediately to ensure that relevant personnel can take maintenance measures in time and minimize water leakage loss.

[0041] Example 2 The embodiment of the application further discloses a building roof leakage detection system.

[0042] Referring to Figure 2 The application discloses a building roof leakage detection system. The dot planning module is used for planning the hole positions by using the multispectral sensor array before the waterproof layer punching operation is performed on the equipment installation area of the building roof, generating a micropore path topology graph, and performing three-dimensional scanning reconstruction on the waterproof layer structure of the building roof according to the micropore path topology graph, so as to obtain a waterproof layer three-dimensional density distribution model. The processing module is used for guiding the drilling equipment to perform the punching operation based on the micropore path topology graph, synchronously collecting real-time deformation parameters of the hole position surrounding area, performing waterproof layer material internal stress gradient analysis on the real-time deformation parameters, and generating a hole position stress diffusion vector graph. The analysis module is used for performing space registration and fusion on the waterproof layer three-dimensional density distribution model and the hole position stress diffusion vector graph, calculating potential leakage probabilities of the waterproof layer surrounding each hole position, performing time-space evolution simulation on the potential leakage probabilities, and outputting a dynamic leakage risk heat map. The optimization module is used for performing leakage path reverse tracking modeling in combination with the dynamic leakage risk heat map and the waterproof layer three-dimensional density distribution model, generating a roof leakage detection optimization model, and transmitting the roof leakage detection optimization model to a monitoring terminal to implement real-time leakage early warning.

[0043] The above content is merely an example and description of the concept of the application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as the concept of the application is not deviated, which should belong to the protection scope of the application.

[0044] In the description of the present specification, the description of the terms "one embodiment", "example", "specific example" and the like means that the specific features, structures, materials or characteristics described in combination with the embodiment or example are contained in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0045] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present specification. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application.

Claims

1. A method for detecting roof leaks in buildings, characterized in that, Includes the following steps: Step S1: Before drilling holes in the waterproof layer in the equipment installation area on the building roof, a multispectral sensor array is used to plan the pre-placement of holes, generate a micropore path topology map, and then the structure of the waterproof layer on the building roof is reconstructed by three-dimensional scanning based on the micropore path topology map to obtain a three-dimensional density distribution model of the waterproof layer. Step S2: Guide the drilling equipment to perform drilling operations based on the micropore path topology map, and simultaneously collect real-time deformation parameters of the area around the hole. Analyze the internal stress gradient of the waterproof layer material based on the real-time deformation parameters to generate a stress diffusion vector map of the hole location. Step S3: Spatial registration and fusion of the three-dimensional density distribution model of the waterproof layer and the stress diffusion vector map of the pore locations are performed to calculate the potential leakage probability of the waterproof layer around each pore location, and the spatiotemporal evolution simulation of the potential leakage probability is performed to output a dynamic leakage risk heat map. Step S4: Combine the dynamic leakage risk heat map and the three-dimensional density distribution model of the waterproof layer to perform reverse tracing modeling of the leakage path, generate the roof leakage detection optimization model, and transmit the leakage detection optimization model to the monitoring terminal to implement real-time leakage early warning.

2. The method for detecting roof leaks according to claim 1, characterized in that, The multispectral sensor array includes an infrared thermal imaging unit, a millimeter-wave radar unit, and a laser ranging unit. The multispectral sensor array is arranged in a hexagonal honeycomb structure to form a redundant detection network; The three-dimensional density distribution model of the waterproof layer includes the dielectric constant distribution matrix of the waterproof material and the spatial gradient field of the thermal conductivity coefficient.

3. The method for detecting roof leaks according to claim 2, characterized in that, Step S1 specifically includes the following steps: Extract the load-bearing capacity parameters, waterproof layer thickness parameters, and material aging parameters corresponding to the building roof from the cloud database, and construct a benchmark vector for waterproof layer performance. Multimodal scanning of the equipment installation area is performed using a multispectral sensor array, and temperature gradient data, dielectric constant distribution data, and micro-deformation vibration data are collected simultaneously. A temperature conduction correlation model between the aging state of the waterproof layer and the temperature conduction rate was established based on temperature gradient data, and a spatial distribution matrix of dielectric properties was constructed by combining dielectric constant distribution data. A fatigue damage surface model of the waterproof layer structure is constructed based on micro-deformation vibration data, and a density distribution feature tensor is generated by integrating the temperature conduction correlation model and the dielectric property spatial distribution matrix. An adaptive genetic algorithm is used to optimize the spatial resolution parameter in the density distribution feature tensor to generate a micropore path topology map. The micropore path topology map is calibrated in three dimensions using a laser ranging unit, and a three-dimensional density distribution model of the waterproof layer is output based on the results of the three-dimensional coordinate calibration.

4. The method for detecting roof leaks according to claim 2, characterized in that, Step S2 specifically includes the following steps: The drilling equipment is driven to perform drilling operations based on the micro-hole path topology map, and pressure sensors and micro-strain gauges are activated simultaneously to collect real-time deformation parameters of the area around the hole, thereby confirming the circumferential stress distribution data of the hole. The moisture content change inside the waterproof layer during drilling was measured, a moisture content gradient surface was constructed, and the stress gradient field was fitted to the circumferential stress distribution data to generate a two-dimensional stress diffusion map. The two-dimensional stress diffusion map is mapped onto the surface of the three-dimensional density distribution model of the waterproof layer, and the stress diffusion vector map of the pore location is constructed by integrating the moisture content gradient surface. Abnormal diffusion patterns are identified in the stress diffusion vector map at the pore location, and dangerous diffusion areas exceeding the preset stress threshold are marked.

5. The method for detecting roof leaks according to claim 2, characterized in that, Step S3 specifically includes the following steps: A spatial coordinate transformation matrix is ​​established between the three-dimensional density distribution model of the waterproof layer and the stress diffusion vector map of the pores. Then, based on the spatial coordinate transformation matrix, the three-dimensional density distribution model of the waterproof layer and the stress diffusion vector map of the pores are spatially registered and fused. Extract the dielectric constant distribution matrix and thermal conductivity spatial gradient field of the waterproof material from the three-dimensional density distribution model of the waterproof layer, and construct a multi-physics coupling parameter space. Cosine similarity is calculated in the multiphysics coupling parameter space to generate an initial leakage probability distribution matrix. Feature importance analysis is performed on the initial leakage probability distribution matrix to screen out key influencing factors and construct a leakage risk prediction model. The leakage risk prediction model is trained by inputting historical leakage event data stored in the cloud database. The spatiotemporal evolution of leakage probability is iteratively calculated using the sliding time window method. The stability of the iterative calculation results is corrected, and a dynamic leakage risk heat map is output.

6. The method for detecting roof leaks according to claim 2, characterized in that, Step S4 specifically includes the following steps: High-risk leakage areas are marked in the dynamic leakage risk heat map, the density distribution feature tensor corresponding to the high-risk leakage areas is extracted, and a three-dimensional seepage diffusion model is constructed. Based on the three-dimensional seepage diffusion model, the water flow velocity gradient field of different seepage paths is calculated using the backpropagation algorithm to generate a set of candidate seepage paths. Simulate the leakage development process corresponding to each candidate leakage path within the candidate leakage path set, and evaluate the leakage rate and impact range of each candidate leakage path; A leakage propagation tree diagram is established based on the leakage rate and impact range of each candidate leakage path. A roof leakage detection optimization model is constructed based on the leakage propagation tree diagram, multi-level early warning threshold parameters are set, and the leakage detection optimization model is transmitted to the monitoring terminal to implement real-time leakage early warning.

7. A building roof leakage detection system, applied to the building roof leakage detection method according to any one of claims 1-6, characterized in that, include: The point planning module is used to plan the pre-placement of holes in the waterproof layer in the equipment installation area on the building roof before drilling the holes. It uses a multispectral sensor array to generate a micro-hole path topology map and performs a three-dimensional scan and reconstruction of the waterproof layer structure on the building roof based on the micro-hole path topology map to obtain a three-dimensional density distribution model of the waterproof layer. The processing module is used to guide the drilling equipment to perform drilling operations based on the micropore path topology map, simultaneously collect real-time deformation parameters of the area around the hole, perform internal stress gradient analysis of the waterproof layer material on the real-time deformation parameters, and generate a stress diffusion vector map of the hole. The analysis module is used to spatially register and fuse the three-dimensional density distribution model of the waterproof layer with the stress diffusion vector map of the pore location, calculate the potential leakage probability of the waterproof layer around each pore location, simulate the spatiotemporal evolution of the potential leakage probability, and output a dynamic leakage risk heat map. The optimization module is used to combine the dynamic leakage risk heat map and the three-dimensional density distribution model of the waterproof layer to perform reverse tracing modeling of leakage paths, generate an optimized model for roof leakage detection, and transmit the optimized model to the monitoring terminal to implement real-time leakage early warning.

8. A computer-readable storage medium, characterized in that: The device stores instructions that, when executed on a computer, cause the computer to perform a method for detecting leaks in a building roof as described in any one of claims 1 to 6.

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