A building structure detection and evaluation system and method based on multi-sensor fusion

Abstract

The present invention belongs to the technical field of building structure detection and assessment, and provides a multi-sensor fusion building structure detection and assessment system and method. The system includes a humidity detection module, an infrared thermal imaging module, an acoustic wave detection module, a data fusion module, and an assessment module. The humidity detection module is used to monitor changes in building humidity in real time and identify humidity anomalies caused by plant growth. The infrared thermal imaging module captures the temperature distribution in areas of abnormal humidity and further locates the plant growth area. The acoustic wave detection module emits acoustic waves in areas of abnormal humidity and temperature, analyzes the acoustic wave propagation characteristics, and confirms the growth activity of plant roots. The data fusion module integrates multimodal data to determine the plant growth location, status, and damage impact on the building structure. The assessment module uses this data to assess the safety and remaining lifespan of the building. The present invention has the advantage of being able to comprehensively and accurately detect the internal structure of a building, thereby comprehensively and effectively assessing the building's lifespan.

Description

Technical Field

[0001] The present invention relates to the field of building detection and evaluation, and in particular to a building structure detection and evaluation system and method using multi-sensor fusion. Background Art

[0002] Plants commonly grow in buildings on structural surfaces, walls, and internal locations such as wall cracks and pipes. Plants typically spread naturally through seeds or spores, and the pores and cracks in certain building materials, such as masonry and concrete, as well as the microclimate surrounding the building, often provide favorable conditions for plant growth.

[0003] However, plant growth within buildings may cause a degree of physical and chemical damage to the structures themselves. Specifically, plant root growth can undermine the integrity of building materials, and the added weight can cause deformation and collapse of walls or structures. Acidic substances secreted by plants can also corrode building materials, and microbial activity can synergize with plant roots to accelerate the deterioration of building materials.

[0004] Existing technologies primarily rely on manual inspection or a single technical approach to detect plant growth inside buildings. Common methods include visual inspections and regular structural inspections, but these methods have limitations. This is particularly true when plants are hidden within buildings, preventing them from being directly visible. Traditional techniques are unable to effectively detect hidden plants and cannot fully and effectively assess the damage caused by plants growing inside buildings. Summary of the Invention

[0005] The first object of the present invention is to provide a multi-sensor fusion building structure detection and evaluation system, which has the advantage of being able to comprehensively and accurately detect the internal structure of a building, thereby being able to comprehensively and effectively evaluate the life of the building.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions:

[0007] A multi-sensor fusion building structure detection and evaluation system, comprising:

[0008] Humidity detection modules, distributed at multiple locations within the building structure, are used to monitor humidity changes inside the building in real time and identify humidity anomalies caused by plant growth;

[0009] An infrared thermal imaging module, working in conjunction with the humidity detection module, is used to capture the temperature distribution on and inside the building, and further locate plant growth areas by analyzing heat changes in areas with abnormal humidity;

[0010] an acoustic wave detection module, which is linked to the humidity detection module and the infrared thermal imaging module, and is used to transmit acoustic waves in areas with abnormal humidity and abnormal heat, receive reflected signals, and analyze the propagation characteristics of the acoustic waves to confirm the growth activities of plant roots;

[0011] a data fusion module, configured to receive detection data from the humidity detection module, the infrared thermal imaging module, and the acoustic wave detection module, and comprehensively determine the growth position and status of the plants and their physical and chemical damage effects on the building structure through fusion analysis;

[0012] An evaluation module, connected to the data fusion module, is used to evaluate the remaining life of a building based on the physical and chemical damage effects of plant growth on the building structure.

[0013] Further configuration: the humidity detection module includes:

[0014] a humidity sensor network, comprising humidity sensors arranged at multiple locations of the building structure for collecting humidity data in real time;

[0015] A data acquisition unit is used to obtain the humidity change rate of each sensor and mark the area where the humidity change rate exceeds a threshold as an abnormal humidity area;

[0016] The regional analysis unit is used to calculate the humidity mean and standard deviation within the monitoring unit and analyze the humidity distribution characteristics;

[0017] The time series analysis unit is used to perform sliding window calculations on humidity data to determine the persistence of abnormal humidity areas;

[0018] The data transmission unit is used to upload the location information and humidity characteristics of the humidity abnormality area to the data fusion module.

[0019] By adopting the above technical solutions, the humidity detection module can achieve rapid, dynamic and accurate positioning of hidden plant growth areas inside buildings through real-time data collection, abnormal area identification, time series analysis and distribution characteristic calculation;

[0020] By monitoring the humidity data inside the building structure in real time through a humidity sensor network and calculating the humidity change rate, areas with abnormal humidity can be quickly and accurately identified, laying the foundation for subsequent precise detection by infrared thermal imaging and acoustic wave detection modules.

[0021] By using time series analysis, we can detect the persistence and changing trends of humidity anomalies, realize dynamic monitoring of humidity detection, capture long-term humidity changes caused by plant growth, and avoid misjudgment caused by short-term fluctuations;

[0022] By calculating the mean and standard deviation of humidity within the monitoring unit, the spatial distribution pattern of humidity can be intuitively reflected, the spatial characteristics of the plant growth area (distribution range and concentration) can be provided, and the errors that may be caused by local data anomalies can be reduced, thereby improving the accuracy of the overall analysis.

[0023] Further configuration: the infrared thermal imaging module includes:

[0024] A heat map capture unit is used to scan the abnormal area marked by the humidity detection module and generate a heat map of the temperature distribution in the area;

[0025] The temperature difference analysis unit is used to calculate the temperature difference of each point in the thermal map and mark the temperature abnormal points according to the set temperature difference threshold;

[0026] Regional clustering unit, used to cluster adjacent temperature anomaly points to form potential plant growth areas;

[0027] The heat-humidity joint analysis unit is used to cross-validate the temperature anomaly area and the humidity anomaly area, and extract the overlapping part as the plant growth area.

[0028] By adopting the above technical solution and collaborating with the humidity detection module, the detection area can be accurately locked, redundant scanning can be reduced, and the efficiency of thermal imaging can be improved; the accuracy of positioning the plant growth area can be enhanced by combining humidity and heat characteristics; infrared imaging technology can be used to achieve rapid area positioning, and the plant growth range can be refined through clustering algorithms.

[0029] Further configuration: the acoustic wave detection module includes:

[0030] an acoustic wave transmitting unit, used for transmitting acoustic waves in an area with abnormal humidity and temperature;

[0031] an acoustic wave receiving unit, for receiving the reflected signal and recording the propagation time and amplitude of the signal;

[0032] A reflection analysis unit, used to calculate the spatial position and material properties of the reflection point based on the propagation time formula and the attenuation formula;

[0033] Feature extraction unit, used to extract the time delay, amplitude change and waveform distortion of the reflected signal to identify the growth activity of plant roots;

[0034] The joint analysis unit is used to cross-validate the reflection point with the humidity anomaly area and the temperature anomaly area to determine the actual location of the plant root system.

[0035] By adopting the above technical solutions, the growth activities of plant roots can be accurately confirmed through sound wave propagation path, attenuation and reflection analysis; combined with humidity and thermal imaging data, the detection range can be greatly reduced and positioning accuracy can be improved; material property analysis: the acoustic characteristics of plant roots are extracted based on the reflected signal to provide a basis for subsequent evaluation modules.

[0036] It is further configured that the joint analysis unit is configured with a plant root distribution model generation strategy, and the plant root distribution model generation strategy includes:

[0037] Data fusion step: receiving data from humidity detection, infrared thermal imaging, and acoustic wave detection to form a joint point set;

[0038] 3D point cloud generation steps: converting the joint point set into a 3D point cloud model of the plant root system through an interpolation algorithm;

[0039] 3D surface fitting steps: spatial fitting of point cloud data based on radial basis function;

[0040] Voxelization processing step: discretize the three-dimensional surface into a voxel grid to show the spatial distribution of plant roots.

[0041] By adopting the above technical solutions, the spatial distribution and density of plant roots can be displayed through a three-dimensional model, providing an intuitive basis for evaluating the impact of building structures; by combining multimodal data, a more accurate three-dimensional model can be generated to avoid misjudgment of a single mode; and the weights can be dynamically adjusted according to the accuracy of the detection module to ensure the scientific nature and reliability of the model generation.

[0042] Further configuration: the data fusion module includes:

[0043] A data pre-processing unit for receiving and normalizing data from humidity detection, infrared thermal imaging, and acoustic wave detection;

[0044] A position determination unit, configured to calculate the center position of the plant growth area and generate a three-dimensional boundary using a weighted distance algorithm;

[0045] Growth status analysis unit, used to calculate the growth activity and root density of plants;

[0046] Impact Assessment Unit for evaluating the physical and chemical damage of plants to building structures based on the added weight of the root system and the degree of chemical attack.

[0047] By adopting the above technical solutions, the three-dimensional spatial distribution of plant roots can be located through multimodal data fusion; combined with humidity, temperature and acoustic wave data, the plant growth activity and distribution density can be quantified; thus, the physical and chemical damage caused by plants to building structures can be accurately assessed.

[0048] Further configuration: the evaluation module is configured with an impact evaluation model and a life evaluation model, and the algorithm formula of the impact evaluation model is as follows:

[0049] I=α1Ax+α2D+α3W r +α4E c

[0050] in,

[0051] I represents the comprehensive impact index (dimensionless);

[0052] α1, α2, α3, and α4 represent the weights of each factor;

[0053] Ax represents plant growth activity;

[0054] D represents the density of the root system;

[0055] W r Indicates the additional weight of the root system;

[0056] E c Indicates a risk of chemical attack;

[0057] The remaining life assessment model of the building structure is adjusted based on the comprehensive impact index. The algorithm formula of the life assessment model is as follows:

[0058] L=L0-γ·I

[0059] in,

[0060] L represents the remaining life of the building;

[0061] L0 represents the building design life;

[0062] Y represents the life loss coefficient.

[0063] By adopting the above technical solutions, introducing additional weight and chemical corrosion risks, and combining activity and density, a multi-dimensional building impact analysis is provided; through the comprehensive impact index, the building life prediction model is dynamically adjusted to enhance scientificity and applicability.

[0064] Another object of the present invention is to provide a multi-sensor fusion building structure detection and evaluation method, comprising the following steps:

[0065] S1: Use the humidity detection module to monitor humidity changes inside the building and identify humidity anomalies caused by plant growth;

[0066] S2: Use infrared thermal imaging modules to detect temperature changes on the surface and inside of buildings and locate plant growth areas;

[0067] S3: The acoustic wave detection module transmits acoustic waves and receives reflected signals, analyzes the acoustic wave propagation characteristics, and identifies the growth activities of plant roots;

[0068] S4: Input the humidity, temperature and acoustic wave detection data into the data fusion module for comprehensive analysis to identify the plant growth status and its location;

[0069] S5: Evaluate the potential physical and chemical damage to the building caused by plant growth based on the analysis results and assess the remaining life of the building.

[0070] In summary, the present invention has the following beneficial effects:

[0071] The present invention provides a multi-dimensional detection method that can accurately identify the growth status of plants in buildings. By simulating the growth status of plants, possible damage to buildings can be predicted in advance to avoid potential safety hazards of building structures. Combined with the specific conditions of the building, it can output an assessment of the damage caused by plant growth to the building, which can help formulate scientific building repair and maintenance plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a system architecture block diagram of the present invention;

[0073] Figure 2 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0074] The present invention will be further described in detail below with reference to the accompanying drawings.

[0075] Example:

[0076] A multi-sensor fusion building structure detection and evaluation system, such as Figure 1 Shown, including:

[0077] Humidity detection modules, distributed at multiple locations within the building structure, are used to monitor humidity changes inside the building in real time and identify humidity anomalies caused by plant growth;

[0078] An infrared thermal imaging module, working in conjunction with the humidity detection module, is used to capture the temperature distribution on and inside the building, and further locate plant growth areas by analyzing heat changes in areas with abnormal humidity;

[0079] an acoustic wave detection module, which is linked to the humidity detection module and the infrared thermal imaging module, and is used to transmit acoustic waves in areas with abnormal humidity and abnormal heat, receive reflected signals, and analyze the propagation characteristics of the acoustic waves to confirm the growth activities of plant roots;

[0080] a data fusion module, configured to receive detection data from the humidity detection module, the infrared thermal imaging module, and the acoustic wave detection module, and comprehensively determine the growth position and status of the plants and their impact on the building structure through fusion analysis;

[0081] An evaluation module is connected to the data fusion module and is used to evaluate the physical and chemical damage of plant growth to the building structure based on the fused data and predict the remaining life of the building.

[0082] The humidity detection module includes:

[0083] The humidity sensor network includes humidity sensors arranged at multiple locations of the building structure to collect humidity data in real time. i (t);

[0084] Data acquisition unit, used to obtain the humidity change rate ΔH of each sensor i , and mark the area where the humidity change rate exceeds the threshold θ as the humidity abnormal area;

[0085] The regional analysis unit is used to calculate the average humidity H within the monitoring unit. avg and standard deviation σ H , analyze humidity distribution characteristics;

[0086] Time series analysis unit, used to perform sliding window calculation W on humidity data t , determine the persistence of the humidity anomaly area;

[0087] The data transmission unit is used to upload the location information and humidity characteristics of the humidity abnormality area to the data fusion module.

[0088] The strategy, steps and algorithms for the humidity detection module to obtain humidity data are as follows:

[0089] Acquisition strategies include:

[0090] Sensor placement: Arrange a humidity sensor network based on the material properties of the building structure, spatial distribution, and possible plant growth paths (such as wall gaps, near pipes, etc.).

[0091] Key monitoring areas: cracks on building surfaces, basements and humid areas to avoid blind spots.

[0092] Real-time monitoring by area: The building structure is divided into several monitoring units, each unit is covered by multiple humidity sensors, and the humidity data is monitored in real time and uploaded to the data fusion module.

[0093] The specific steps include:

[0094] Data collection steps:

[0095] Each humidity sensor collects environmental humidity data H in real time i (t), where i represents the sensor number and t represents time; the data sampling frequency is adjusted according to the characteristics of the building environment, such as a higher sampling frequency in high humidity areas.

[0096] Abnormal identification steps:

[0097] By calculating the humidity change rate of each sensor, the specific algorithm formula is as follows:

[0098]

[0099] If the humidity change rate ΔH i If the threshold value θ is exceeded, the area is marked as abnormal.

[0100] Parameter explanation:

[0101] ΔH i : Humidity change rate of the i-th sensor;

[0102] H i (t): humidity value of the i-th sensor at time t;

[0103] H i (t-1): humidity value of the i-th sensor at the previous moment;

[0104] Δt: the time interval between two consecutive samplings;

[0105] θ: Humidity change rate threshold, used to determine whether it is abnormal.

[0106] Steps for analyzing spatial humidity distribution:

[0107] Calculate the average value H of the sensor data in each monitoring unit avg and standard deviation σ H , the specific algorithm formula is as follows:

[0108]

[0109] According to H avg and σ H Analyze humidity distribution characteristics and locate areas with abnormal humidity.

[0110] Parameter explanation:

[0111] H avg : The average humidity in the current monitoring unit;

[0112] σ H : The standard deviation of humidity within the current monitoring unit, used to reflect the unevenness of humidity distribution.

[0113] H i: Humidity data of the i-th sensor;

[0114] n: The number of humidity sensors in the monitoring unit.

[0115] Time series anomaly detection steps:

[0116] The sliding window algorithm is used to perform time series analysis on humidity data to determine whether anomalies persist in the area. The specific algorithm formula is as follows:

[0117]

[0118] If W t >T threshold , then mark the exception.

[0119] Parameter explanation:

[0120] W t : The average humidity value within the time window, used to detect trend anomalies;

[0121] k: The size of the sliding window, indicating the time span of the analysis;

[0122] H i : Humidity data within the sliding window;

[0123] T threshold : Humidity threshold for abnormal judgment.

[0124] Data upload and fusion steps:

[0125] The location information and data features of the humidity anomaly area are passed to the data fusion module and combined with other detection modules for further analysis.

[0126] The infrared thermal imaging module includes:

[0127] A heat map capture unit is used to scan the abnormal area marked by the humidity detection module and generate a temperature distribution heat map T(x, y) in the area;

[0128] The temperature difference analysis unit is used to calculate the temperature difference ΔT(x, y) of each point in the heat map and calculate the temperature difference according to the set temperature difference threshold θ T Mark temperature anomalies;

[0129] Regional clustering unit, used to cluster adjacent temperature anomaly points to form potential plant growth areas;

[0130] The heat-humidity joint analysis unit is used to cross-validate the temperature anomaly area and the humidity anomaly area, and extract the overlapping part as the plant growth area.

[0131] The positioning strategies of the infrared thermal imaging module include:

[0132] Initial positioning coordination: Based on the humidity abnormality areas identified by the humidity detection module, the infrared thermal imaging module limits its detection range to avoid full area scanning and improve efficiency;

[0133] Thermal anomaly detection: Use thermal imaging equipment to capture heat maps of areas with abnormal humidity, and further locate possible plant growth areas by analyzing temperature distribution and temperature differences;

[0134] Multi-point temperature comparison: In an area with abnormal humidity, compare and analyze the temperatures at different points to find areas with abnormal temperature rise or fall (plant growth usually causes local temperature fluctuations).

[0135] The specific positioning steps include:

[0136] Heatmap capture steps:

[0137] Use infrared thermal imaging equipment to scan the humidity abnormality area and generate a temperature distribution heat map T(x, y), where (x, y) is the coordinate point in the heat map.

[0138] Temperature difference calculation steps:

[0139] Compute the temperature difference at each point in the heat map:

[0140] ΔT(x, y) = T(x, y) - T avg

[0141] If |ΔT(x, y)|>θ T (temperature difference threshold), it is marked as a temperature anomaly point.

[0142] Parameter Description:

[0143] T(x, y): temperature of point (x, y) in the heat map;

[0144] T avg : average temperature in the humidity anomaly area;

[0145] θ T : Temperature difference threshold, used to determine temperature anomalies.

[0146] Regional cluster analysis steps:

[0147] Based on the temperature difference results, the abnormal points are clustered to form the potential plant growth region RRR;

[0148] A clustering algorithm is used to classify adjacent temperature anomaly points into the same area. The regional clustering unit is based on the K-Means algorithm to classify adjacent temperature anomaly points into plant growth areas:

[0149]

[0150] Parameter Description:

[0151] k: the number of clustered regions;

[0152] C i : the i-th cluster area;

[0153] μ i |: The center of the i-th region.

[0154] Combined heat and moisture analysis steps:

[0155] The temperature anomaly areas and humidity anomaly areas were cross-validated, and the overlapping parts were extracted as possible plant growth areas.

[0156] Steps to output positioning results:

[0157] Output the final plant growth area heat map, marking the center coordinates and range of the potential plant growth area.

[0158] The acoustic wave detection module includes:

[0159] an acoustic wave transmitting unit, used for transmitting acoustic waves in an area with abnormal humidity and temperature;

[0160] The sound wave transmitting unit determines the sound wave propagation path based on the optimized path integral method, specifically as follows:

[0161]

[0162] Parameter Description:

[0163] L min : optimal propagation path length;

[0164] P1, P2: coordinates of the sound wave emission point and receiving point;

[0165] z: Path height.

[0166] An acoustic wave receiving unit, configured to receive the reflected signal and record the signal's propagation time t and amplitude A;

[0167] A reflection analysis unit, used to calculate the spatial position and material properties of the reflection point based on the propagation time formula and the attenuation formula;

[0168] The reflection analysis unit determines the acoustic impedance characteristics of the plant root area through the reflection coefficient, which is as follows:

[0169]

[0170] Parameter Description:

[0171] R: reflection coefficient;

[0172] Z1: acoustic impedance of sound waves entering the medium;

[0173] Z2: Acoustic impedance of sound waves entering the plant root system.

[0174] Feature extraction unit, used to extract the time delay, amplitude change and waveform distortion of the reflected signal to identify the growth activity of plant roots;

[0175] The joint analysis unit is used to cross-validate the reflection point with the humidity anomaly area and the temperature anomaly area to determine the actual location of the plant root system.

[0176] The steps for locating the acoustic wave detection module include:

[0177] (1) Sound wave transmission and reception

[0178] Arrange multiple acoustic wave transmitting and receiving points (array sensors) in the detection area;

[0179] The emission frequency f and the sound wave wavelength λ are designed according to the acoustic characteristics of building materials and plant roots;

[0180] (2) Signal propagation and reflection analysis

[0181] When sound waves propagate through building materials, their path length L can be described by the following formula:

[0182]

[0183] in,

[0184] (x1, y1, z1) represents the coordinates of the sound wave emission point;

[0185] (x2, y2, z2) represents the coordinates of the reflection point.

[0186] (3) Calculation of propagation time

[0187] According to the path length L and the speed of sound v, the propagation time formula corresponding to the propagation time t can be expressed as:

[0188]

[0189] Where v is the speed of sound, which depends on the acoustic properties of the material.

[0190] (4) Analysis of acoustic wave attenuation characteristics

[0191] The energy of sound waves will attenuate during propagation. The attenuation formula is:

[0192] A=A0e -αL

[0193] Parameter Description:

[0194] A: the amplitude of the received sound wave;

[0195] A0: the amplitude of the sound wave during emission;

[0196] α: Attenuation coefficient, which is related to material and frequency.

[0197] (5) Reflection signal feature extraction

[0198] The following key features are extracted from the received reflected signal:

[0199] Time delay: The reflection point position is calculated using the propagation time t.

[0200] Amplitude change: Estimate the material properties of the reflecting area through the attenuation formula.

[0201] Waveform distortion: Identify the presence of plant roots by analyzing changes in signal shape.

[0202] (6) Joint analysis

[0203] Compare the reflection points with infrared thermal imaging and areas of humidity anomalies to confirm the actual location and extent of plant roots.

[0204] Furthermore,

[0205] The joint analysis unit is configured with a plant root distribution model generation strategy, and the plant root distribution model generation strategy includes:

[0206] Data fusion and interpolation processing:

[0207] The coordinate data of humidity, temperature and acoustic wave detection points are fused to define the joint point set P = {(xi, yi, zi, vi)}, where vi represents the multimodal joint weight:

[0208] v i =w h H i +w t T i +w s S i

[0209] H i : Humidity characteristic value, normalized representation;

[0210] T i : Temperature characteristic value, normalized representation;

[0211] S i : Acoustic wave reflection characteristic value, normalized representation;

[0212] w h 、w t 、w s : The weight parameters of humidity, temperature and acoustic wave features are dynamically adjusted according to the detection accuracy.

[0213] 3D point cloud generation:

[0214] The detection point set P is converted into a continuous 3D point cloud model through data interpolation algorithms (such as cubic spline interpolation or Delaunay triangulation):

[0215] PointCloud={(x,y,z,v)}

[0216] Spatial distribution fitting:

[0217] The radial basis function (RBF) is used to perform spatial fitting on the point cloud data to generate the three-dimensional distribution surface S (x, y, z) of the plant root system:

[0218] Voxelization:

[0219] Discretize the 3D surface S(x, y, z) into a voxel grid for visualization and subsequent analysis.

[0220] The data fusion module includes:

[0221] A data pre-processing unit for receiving and normalizing data from humidity detection, infrared thermal imaging, and acoustic wave detection;

[0222] A position determination unit, configured to calculate the center position of the plant growth area and generate a three-dimensional boundary using a weighted distance algorithm;

[0223] Growth status analysis unit, used to calculate the growth activity and root density of plants;

[0224] Impact Assessment Unit for evaluating the physical and chemical damage of plants to building structures based on the added weight of the root system and the degree of chemical attack.

[0225] Specific steps:

[0226] 1. Data preprocessing:

[0227] Input data:

[0228] Humidity abnormality points output by the humidity detection module;

[0229] Abnormal temperature points output by the infrared thermal imaging module;

[0230] The reflection point and acoustic impedance characteristics output by the acoustic wave detection module;

[0231] Data Normalization:

[0232] Normalize the data of different modalities:

[0233]

[0234] V i : characteristic value of a point (humidity, temperature, acoustic impedance);

[0235] v min , v max : corresponds to the minimum and maximum values ​​of the mode.

[0236] 2. Data fusion and location determination

[0237] Distance-weighted algorithm:

[0238] The weighted distance method is used for the abnormal points detected by different modes to calculate the center point of the plant growth area (x c ,y c , z c ):

[0239]

[0240] wi: weight, dynamically adjusted according to the reliability of humidity, temperature, and acoustic wave detection modules;

[0241] (X i ,y i , z i ): The coordinates of the i-th outlier point.

[0242] Three-dimensional region fitting:

[0243] The radial basis function (RBF) is used to perform spatial fitting on the outliers to generate the three-dimensional boundary of the plant root growth area.

[0244] 3. Growth status analysis

[0245] Activity analysis:

[0246] Combined with temperature and humidity data, calculate the plant growth activity Ax:

[0247] Ax=αH+βT

[0248] H: humidity value;

[0249] T: temperature value;

[0250] α, β: weights of humidity and temperature.

[0251] Root density:

[0252] Based on the reflection point density of the acoustic wave detection, the distribution of roots in the detection area is evaluated D:

[0253]

[0254] N: the number of detected outliers;

[0255] V: Region volume.

[0256] 4. Impact assessment on building structures

[0257] Risk of physical damage:

[0258] Calculate the additional root weight W based on the root distribution and density r :

[0259] W r =ρ r ·V r

[0260] ρ r : density of plant roots;

[0261] Vr: Volume of the plant root system.

[0262] Chemical attack risk:

[0263] Based on the humidity and sound wave test results, analyze whether the plant roots may secrete acidic substances (such as oxalic acid) and the concentration of acidic substances C a and distribution range V a :

[0264] E c =C a ·V a β m

[0265] E c : Chemical attack risk;

[0266] C a : Acidic substance concentration;

[0267] V a : Volume of the area affected by acidic substances;

[0268] β m : Tolerance coefficient of building materials (related to material properties).

[0269] The evaluation module is configured with an impact evaluation model and a life evaluation model. The algorithm formula of the impact evaluation model is as follows:

[0270] I=α1Ax+α2D+α3W r +α4E c

[0271] in,

[0272] I represents the comprehensive impact index (dimensionless);

[0273] α1, α2, α3, and α4 represent the weights of each factor, which are adjusted according to the building type and inspection requirements;

[0274] Ax represents plant growth activity;

[0275] D represents the density of the root system;

[0276] W r Indicates the additional weight of the root system;

[0277] E c Indicates a risk of chemical attack;

[0278] The remaining life assessment model of the building structure is adjusted based on the comprehensive impact index. The algorithm formula of the life assessment model is as follows:

[0279] L=L0-γ·I

[0280] in,

[0281] L represents the remaining life of the building;

[0282] L0 represents the building design life;

[0283] γ represents the life loss coefficient (adjusted according to the characteristics of the building materials and environmental conditions).

[0284] This system is equipped with corresponding detection and evaluation methods, such as Figure 2 As shown, the following steps are included:

[0285] S1: Use the humidity detection module to monitor humidity changes inside the building and identify humidity anomalies caused by plant growth;

[0286] S2: Use infrared thermal imaging modules to detect temperature changes on the surface and inside of buildings and locate plant growth areas;

[0287] S3: The acoustic wave detection module transmits acoustic waves and receives reflected signals, analyzes the acoustic wave propagation characteristics, and identifies the growth activities of plant roots;

[0288] S4: Input the humidity, temperature and acoustic wave detection data into the data fusion module for comprehensive analysis to identify the plant growth status and its location;

[0289] S5: Evaluate the potential physical and chemical damage to the building caused by plant growth based on the analysis results and assess the remaining life of the building.

[0290] This solution has the following advantages:

[0291] Precise positioning: Combining humidity, thermal imaging, and acoustic wave detection modules, through multimodal data fusion, the specific location of plant growth can be accurately identified.

[0292] Comprehensive status assessment: Comprehensively analyze the growth status of plants through activity, density, root weight and chemical erosion risk.

[0293] Scientific Life Prediction: The assessment module scientifically predicts the remaining life of building structures by quantifying the physical and chemical impacts of plants on buildings.

[0294] Efficient module linkage: All modules in the system work together to optimize detection efficiency and result accuracy.

[0295] The above-described embodiments do not constitute a limitation on the scope of protection of this technical solution. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the above-described embodiments shall be included in the scope of protection of this technical solution.

Claims

1. A multi-sensor fusion building structure detection and evaluation system, characterized by: include: Humidity detection modules, distributed at multiple locations within the building structure, are used to monitor humidity changes inside the building in real time and identify humidity anomalies caused by plant growth; An infrared thermal imaging module, working in conjunction with the humidity detection module, is used to capture the temperature distribution on and inside the building, and further locate plant growth areas by analyzing heat changes in areas with abnormal humidity; an acoustic wave detection module, which is linked to the humidity detection module and the infrared thermal imaging module, and is used to transmit acoustic waves in areas with abnormal humidity and abnormal heat, receive reflected signals, and analyze the propagation characteristics of the acoustic waves to confirm the growth activities of plant roots; a data fusion module, configured to receive detection data from the humidity detection module, the infrared thermal imaging module, and the acoustic wave detection module, and comprehensively determine the growth position and status of the plants and their physical and chemical damage effects on the building structure through fusion analysis; an assessment module, connected to the data fusion module, for assessing the remaining life of a building based on the physical and chemical damage effects of plant growth on the building structure; The humidity detection module includes: a humidity sensor network, comprising humidity sensors arranged at multiple locations of the building structure for collecting humidity data in real time; A data acquisition unit is used to obtain the humidity change rate of each sensor and mark the area where the humidity change rate exceeds a threshold as an abnormal humidity area; The regional analysis unit is used to calculate the humidity mean and standard deviation within the monitoring unit and analyze the humidity distribution characteristics; The time series analysis unit is used to perform sliding window calculations on humidity data to determine the persistence of abnormal humidity areas; A data transmission unit, used to upload the location information and humidity characteristics of the humidity abnormality area to the data fusion module; The infrared thermal imaging module includes: A heat map capture unit is used to scan the abnormal area marked by the humidity detection module and generate a heat map of the temperature distribution in the area; The temperature difference analysis unit is used to calculate the temperature difference of each point in the thermal map and mark the temperature abnormal points according to the set temperature difference threshold; Regional clustering unit, used to cluster adjacent temperature anomaly points to form potential plant growth areas; The heat-humidity joint analysis unit is used to cross-validate the temperature anomaly area and the humidity anomaly area, and extract the overlapping part as the plant growth area; The acoustic wave detection module includes: an acoustic wave transmitting unit, used for transmitting acoustic waves in an area with abnormal humidity and temperature; an acoustic wave receiving unit, for receiving the reflected signal and recording the propagation time and amplitude of the signal; A reflection analysis unit, used to calculate the spatial position and material properties of the reflection point based on the propagation time formula and the attenuation formula; Feature extraction unit, used to extract the time delay, amplitude change and waveform distortion of the reflected signal to identify the growth activity of plant roots; The joint analysis unit is used to cross-validate the reflection point with the humidity anomaly area and the temperature anomaly area to determine the actual location of the plant root system.

2. The multi-sensor fusion building structure detection and evaluation system according to claim 1 is characterized in that: The joint analysis unit is configured with a plant root distribution model generation strategy, and the plant root distribution model generation strategy includes: Data fusion step: receiving data from humidity detection, infrared thermal imaging, and acoustic wave detection to form a joint point set; 3D point cloud generation steps: converting the joint point set into a 3D point cloud model of the plant root system through an interpolation algorithm; 3D surface fitting steps: spatial fitting of point cloud data based on radial basis function; Voxelization processing step: discretize the three-dimensional surface into a voxel grid to show the spatial distribution of plant roots.

3. The multi-sensor fusion building structure detection and evaluation system according to claim 1 is characterized in that: The data fusion module includes: A data pre-processing unit for receiving and normalizing data from humidity detection, infrared thermal imaging, and acoustic wave detection; A position determination unit, configured to calculate the center position of the plant growth area and generate a three-dimensional boundary using a weighted distance algorithm; Growth status analysis unit, used to calculate the growth activity and root density of plants; Impact Assessment Unit for evaluating the physical and chemical damage to building structures caused by plant growth based on the added weight of the root system and the degree of chemical attack.

4. The multi-sensor fusion building structure detection and evaluation system according to claim 3 is characterized in that: The evaluation module is configured with an impact evaluation model and a life evaluation model. The algorithm formula of the impact evaluation model is as follows: I=α1Ax+α2D+α3W r +α4E c in, I represents the comprehensive impact index; α1, α2, α3, and α4 represent the weights of each factor; Ax represents plant growth activity; D represents the density of the root system; W r Indicates the additional weight of the root system; E c Indicates a risk of chemical attack; The remaining life assessment model of the building structure is adjusted based on the comprehensive impact index. The algorithm formula of the life assessment model is as follows: L=L0-γ-I in, L represents the remaining life of the building; L0 represents the building design life; γ represents the life loss coefficient.

5. A multi-sensor fusion building structure detection and evaluation method, applied to the multi-sensor fusion building structure detection and evaluation system according to any one of claims 1 to 4, characterized in that: The following steps are involved: S1: Use the humidity detection module to monitor humidity changes inside the building and identify humidity anomalies caused by plant growth; S2: Use infrared thermal imaging modules to detect temperature changes on the surface and inside of buildings and locate plant growth areas; S3: The acoustic wave detection module transmits acoustic waves and receives reflected signals, analyzes the acoustic wave propagation characteristics, and identifies the growth activities of plant roots; S4: Input the humidity, temperature and acoustic wave detection data into the data fusion module and perform comprehensive analysis to identify the plant growth status and location; S5: Evaluate the potential physical and chemical damage of plant growth to the building based on the analysis results and assess the remaining life of the building.

Images