Automatic detection device for waterproofing of building main body

Abstract

The invention relates to the technical field of waterproof detection, in particular to a building main body waterproof automatic detection device, and solves the problem that multi-aspect waterproof performance of a building main body cannot be comprehensively detected. According to the technical scheme, the system comprises a carrying mechanism, a sensing detection module, a data interaction module, a communication transmission module and an operation and maintenance decision module. The sensing detection module is arranged on the carrying mechanism and used for collecting data of a building main body, and the data interaction module is arranged on the carrying mechanism and used for uploading the data collected by the sensing detection module to the cloud BIM collaborative management platform, building a BIM model and sending the data collected by the sensing detection module to the rear end; the communication transmission module is arranged on the carrying mechanism, and the communication transmission module synchronously transmits data acquired by the sensing detection module to the rear end; the operation and maintenance decision-making module is arranged at the rear end, and the operation and maintenance decision-making module generates a defect diagnosis report according to the data collected by the sensing detection module and intelligently matches an operation and maintenance strategy library according to the quantitative risk rating.

Description

Technical Field

[0001] The present invention relates to the technical field of waterproof detection, in particular to an automatic detection device for waterproofing of a building main body. Background Art

[0002] In the construction industry, waterproofing is a key factor in ensuring structural stability, maintaining a healthy indoor environment, and extending a building's service life. Traditional building waterproofing testing methods have long suffered from numerous limitations, making them unable to meet the complex and high-standard waterproofing quality control requirements of modern architecture.

[0003] On the one hand, conventional manual visual inspection is the most basic and traditional method. Inspectors rely on the naked eye to observe the building's roof, exterior walls, basement, and other areas for obvious signs of leakage, such as water stains, cracks, and blisters. However, this method relies heavily on the inspector's experience and subjective judgment, making it nearly incapable of addressing hidden issues such as minute cracks, hidden internal leakage pathways, and initial changes in moisture content. For example, subtle delamination beneath the roofing membrane is impossible to detect through visual inspection alone until visible bulging or damage appears on the membrane surface. Furthermore, tiny holes and cracks in the basement concrete wall, caused by inadequate vibration, are often concealed by the exterior finish or covering soil. Manual visual inspection is like looking through a fog, making them easily missed. This allows potential leaks to quietly grow and expand, eventually developing into serious problems, impacting the building's normal use and increasing maintenance costs.

[0004] With technological advancements in the construction industry, nondestructive testing (NDT) technology has gradually gained traction. However, the application of a single NDT technology often compromises its overall approach. Ultrasonic testing focuses on internal structural defects and is sensitive to minute cracks, but it has shortcomings in detecting moisture distribution over large areas and correlating surface temperature anomalies with leak analysis. Infrared thermal imaging can keenly detect surface temperature differences to identify leaks, but it lacks the ability to accurately determine the density and moisture content of deeper structures. Microwave testing, while highly penetrating and capable of detecting internal moisture content, struggles to pinpoint tiny structural defects or measure their severity when used alone. Summary of the Invention

[0005] This solves the problem that existing detection technology is unable to conduct comprehensive detection of multiple aspects of the waterproof performance of the building body.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] An automatic detection device for waterproofing of a building main body, comprising:

[0008] Carrying mechanism;

[0009] A sensor detection module is provided on the carrying mechanism, and collects data from the building body.

[0010] A data interaction module is provided on the carrying mechanism, and uploads the data collected by the sensor detection module to the cloud BIM collaborative management platform, builds a BIM model, and sends the data collected by the sensor detection module to the back end;

[0011] A communication transmission module, which is provided on the carrying mechanism and synchronously transmits the data collected by the sensing detection module to the back end;

[0012] An operation and maintenance decision module is provided at the back end. The operation and maintenance decision module generates a defect diagnosis report based on the data collected by the sensor detection module, and intelligently matches the operation and maintenance strategy library based on the quantitative risk rating.

[0013] Preferably, the carrying mechanism includes a drone, a water tank is carried on the top of the drone, a water pump is installed on one side of the outer wall of the water tank, the input end of the water pump is connected to the water tank, the output end of the water pump is connected to a water pipe (4), and a sensor detection module is installed on the outer wall of the drone.

[0014] Preferably, the sensing detection module includes:

[0015] Microwave detection unit, which transmits microwaves into the building detection layer;

[0016] The infrared thermal imaging unit, relying on the Stefan-Boltzmann law and a two-color infrared temperature measurement algorithm, quickly scans the building surface to capture temperature gradients caused by abnormal heat conduction due to waterproofing failure;

[0017] Ultrasonic flaw detection unit, which transmits longitudinal waves, shear waves and surface waves to the building surface through the probe;

[0018] The humidity detection unit uses an array of distributed humidity sensors to cover key waterproof parts of the building.

[0019] Preferably, the data interaction module includes:

[0020] The data transmission unit runs a statistically-based data verification algorithm when the sensor detection module collects data and transmits it to the drone's microprocessor. It sets a data credibility interval to filter out erroneous and suspicious data. Qualified data is then uploaded to the cloud-based BIM collaborative management platform in real time via the 5G network.

[0021] Parameter entry unit, which enters the collected parameters into a unified system;

[0022] The conversion unit encapsulates the collected data into a unified data packet, adds a timestamp, device number, and data check code, and then sends it to the backend.

[0023] Preferably, the communication transmission module includes:

[0024] Wireless communication unit, using WIFI dual-band to transmit data over short distances;

[0025] Wired communication unit, which uses high-speed Ethernet cable combined with optical fiber for data transmission;

[0026] The dynamic update unit enables the back-end to be deeply integrated with the BIM model based on coordinate matching and data type identification rules. When data is detected to trigger humidity exceeding the threshold, temperature anomalies, structural defects, etc., the corresponding components of the BIM model will change color and a pop-up window will display detailed detection values, waveform graphs and preliminary defect judgment results.

[0027] Preferably, the operation and maintenance decision module includes:

[0028] After the inspection is completed, the risk rating unit calls the intelligent analysis module based on the fuzzy comprehensive evaluation method to build a risk rating system covering the degree of moisture content anomaly, temperature gradient anomaly, and the severity of structural defects;

[0029] The strategy generation unit intelligently matches the operation and maintenance strategy library based on quantitative risk ratings and detailed defect diagnosis reports.

[0030] Preferably, when the data transmission unit runs the data verification algorithm, based on statistical principles, the microwave detection moisture content value is defined as a credible interval according to the standard deviation and mean of multiple measurement samples.

[0031] Preferably, the dynamic update unit assigns colors according to risk levels when automatically changing the colors of components corresponding to the BIM model.

[0032] Preferably, the carrying mechanism further comprises a positioning calibration unit. When the positioning calibration unit utilizes RTK-GPS technology, its base station can be connected to a local continuously operating reference station system.

[0033] Preferably, the carrying mechanism further includes a posture adjustment component, and the posture adjustment component includes a gyroscope and an accelerometer, and the gyroscope and accelerometer are electrically connected to the drone respectively.

[0034] The beneficial effects of the present invention are:

[0035] By integrating advanced nondestructive testing technologies such as microwave testing, infrared thermal imaging, and ultrasonic testing, and relying on high-precision sensors and sophisticated algorithms, we achieve a multi-dimensional, in-depth analysis of a building's waterproofing status. Microwave testing, with its adjustable emission parameters and unique detection depth formula, can penetrate waterproof layers of varying materials and accurately detect internal moisture anomalies.

[0036] By planning the optimal inspection path in advance based on the building information model and combining it with a mobile platform, including multi-rotor drones, intelligent crawling robots, automatic cruise functions and positioning navigation systems, the inspection process is orderly and efficient. When inspecting roofs and high-rise exterior walls, drones can quickly traverse large areas with their high-speed flight and wide-angle scanning capabilities, while intelligent crawling robots can navigate complex environments such as basements and pipeline exteriors.

[0037] By applying fuzzy comprehensive evaluation methods to in-depth analysis of multi-source inspection data, a rigorous factor set covering key indicators such as moisture content, temperature, and structural defects is constructed. The weights of these factors are then weighed using the analytic hierarchy process (AHP) method, and a complex calculation process is used to output a quantitative risk rating. This method breaks the ambiguity of traditional qualitative assessments, using scientific and quantitative means to clearly define the risk level of each building waterproofing area, accurately grading it from low to high.

[0038] The platform design is optimized for different inspection scenarios. The multi-rotor drone has a lightweight and high-strength body, and the obstacle avoidance sensors work together to ensure flight safety. It can flexibly shuttle through complex high-rise facades. The intelligent crawling robot's tracks or magnetic suction structure are adapted to vertical walls and narrow pipes. The attitude stability control and obstacle crossing function overcome obstacles in complex terrain to ensure the smooth progress of inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a diagram of the architecture of the automatic detection device of the present invention;

[0040] Figure 2 Schematic diagram of the carrying mechanism of the present invention.

[0041] In the picture, 1. Drone; 2. Water tank; 3. Water pump; 4. Water pipe; 5. Sensor. DETAILED DESCRIPTION

[0042] Please see the attached Figure 1 - Attachment Figure 2 , an embodiment of the present invention provides an automatic detection device for waterproofing of a building main body, comprising: a carrying mechanism for carrying the automatic detection device to move around various parts of the building main body to be detected;

[0043] The carrying mechanism includes a drone 1, a water tank 2 is carried on the top of the drone 1, a water pump 3 is installed on one side of the outer wall of the water tank 2, the input end of the water pump 3 is connected to the water tank 2, and the output end of the water pump 3 is connected to a water pipe 4. A sensor 5 is installed on the outer wall of the drone 1;

[0044] Specifically, drone 1, as the main payload, possesses excellent flight maneuverability and stability, adapting to operational requirements in diverse building environments. Its top water tank 2 is constructed from corrosion-resistant, well-sealed materials, with a capacity designed to range from 20 to 50 liters. This ensures the required water reserves for testing scenarios like simulated precipitation without excessively increasing the drone's payload, potentially impacting flight endurance and operational safety. A water pump 3, mounted on the outer wall of tank 2, connects to tank 2 and water pipe 4 via high-pressure piping. This pump precisely regulates water output, providing water at varying pressures and flow rates on demand, assisting in waterproofing performance testing operations such as simulating rainwater flushing a roof.

[0045] 1. Sensing detection module

[0046] In this embodiment, sensors are used to collect data on the building's main structure, including humidity and thickness, as follows: 1.1、

[0048] In this embodiment, a non-destructive testing device that integrates multiple functions such as microwave, infrared thermal imaging, and ultrasonic flaw detection is carried out by a mounting mechanism, or an independent base station mode is adopted. RTK-GPS technology (real-time dynamic carrier phase difference technology, through differential processing of satellite signals between the base station and the mobile station, like dual-lens calibration and photography, to achieve high-precision positioning) is used. In combination with the coordinates of known control points around the building (provided by a professional surveying and mapping department, and verified with high precision such as closed-line measurement to ensure a solid foundation for coordinate accuracy), the initial positioning and calibration of the testing equipment is performed to ensure that the equipment position coordinates (X0, Y0, Z0) are accurately consistent with the BIM model coordinate system, and the error is controlled within the centimeter level.

[0049] According to the GPS navigation instructions, first go to the starting detection point on the roof or basement, use the laser rangefinder to measure the distance between the equipment and the surrounding building feature points (such as wall corners, beam and column edges), and compare and verify with the theoretical distance of the BIM model. If the deviation exceeds the allowable value, fine-tune the equipment position to ensure that the starting position of the on-site detection is closely aligned with the model planning. Simultaneously start the equipment self-test program, calibrate the microwave transmission power, use the power meter to monitor the feedback, and adjust the internal power amplifier circuit parameters to the preset P m The ultrasonic frequency is calibrated to the set value f according to the crystal oscillator and frequency synthesis technology. c , according to the on-site ambient temperature T a , humidity RH real-time correction infrared thermal imager thermal compensation coefficient k t :

[0050] k t =1+α(T a -T0)+β(RH-RH0)

[0051] Among them, T0 and RH0 are standard ambient temperature and humidity, and α and β are empirical coefficients, which are verified and determined by the carrying organization to ensure that thermal imaging temperature measurement accurately reflects the actual thermal state of the building surface. 1.2、

[0053] In this embodiment, all-round data collection is performed on each part of the building body, as follows:

[0054] Roof waterproofing detection process: the antenna array is scanned at a step length S m The preset range is 5-20 mm, and the fine step size ensures that a large area of ​​the roof is detected without omission and with guaranteed accuracy. The roof is scanned line by line to receive the reflected signal, and the spectrum characteristics are analyzed by the Fast Fourier Transform (FFT) algorithm. The integration algorithm is used:

[0055]

[0056] Assess the area of ​​abnormal water content S a , where ΔS i It is a tiny unit area (divided by the scanning step length, such as a step length of 10 mm, then the unit area is 10 square millimeters, and the roof condition is finely analyzed with small units). The actual water content in the unit (calculated by signal attenuation and phase change, according to the calibration curve and electromagnetic theory, just like deciphering a code to obtain water content information), The normal moisture content is the reference value (set according to material standards and experience, such as the normal moisture content range of asphalt membrane is 3% to 5%). The infrared thermal imager is activated synchronously to collect roof thermal radiation images at a frame rate of 1-5 frames per second. According to the Stefan-Boltzmann law:

[0057] M=∈σT 4

[0058] Where M is the radiant emittance, ∈ is the emissivity, and σ is the Stefan-Boltzmann constant 5.67×10 -8 W / (m 2 ·K 4 ), where T is the absolute temperature of the object, and combined with the two-color infrared temperature measurement algorithm (based on Planck's blackbody radiation law, the temperature is calculated by the radiation energy ratio of two different bands to weaken environmental interference), the abnormal temperature gradient area is identified (the temperature difference exceeds the set threshold, such as 2-3 degrees Celsius, and is judged as a suspected leakage point based on experience), and the abnormal point is located in the imaging plane coordinate (x img ,y img ) is converted to building space coordinates (x i ,y i ,z i) is associated with the BIM model annotation. For suspected leakage points or complex structural areas, switch to the ultrasonic flaw detector, which uses a high-frequency probe with a center frequency of 50-100kHz. Apply coupling agent, such as water-based or oil-based coupling agent, to improve sound energy transmission, reduce interface reflection, and ensure good sound energy transmission. Ultrasonic pulses are emitted. According to the Huygens-Fresnel principle, each point on the wavefront can be regarded as a new wave source. The emitted sub-waves are superimposed to form a subsequent wavefront. The sound wave propagation path and the reflection and refraction laws are analyzed. The reflected echo is received and the ultrasonic time difference method is used to calculate the propagation distance based on the echo time t and the sound speed v. The defect depth and length are solved by combining trigonometric functions. The crack width is estimated by the diffraction time difference method formula:

[0059]

[0060] Among them, k2 is an empirical constant, Δt is the time difference of the diffraction wave, and θ is the diffraction angle. It analyzes the integrity of the internal structure, collects data in all directions and transmits it wirelessly to the on-site mobile data processing terminal.

[0061] Basement waterproofing inspection process: The basement waterproofing inspection process is similar, based on the surrounding groundwater level H ω , which is obtained from the long-term water level monitoring well data, soil permeability coefficient k s , obtained through on-site pumping test or geological survey report, to estimate the leakage risk area, focus on waterproof concrete wall, post-casting belt, construction joint and other parts, first use ultrasonic longitudinal wave to detect the internal density of concrete, compare the sound velocity v s The deviation rate of sound speed from the standard value v0 is:

[0062]

[0063] When the warning exceeds 10%, microwave detection is used to assess abnormal soil moisture content outside the wall to assist in determining potential areas of waterproofing failure. All detection data is instantly linked to the corresponding component positions in the BIM model to build a dynamic detection scenario.

[0064] 3. Data interaction module

[0065] In this embodiment, a BIM model of the building body is constructed using the data detected by the sensor, as follows:

[0066] Focusing on the detailed modeling of the waterproof structural layer, taking roof waterproofing as an example, the team leveraged Revit's "family" function to create custom waterproof membrane layer families. For common SBS modified asphalt membranes, the team thoroughly edited family parameters, clearly defining the material properties of each layer. These included the wear-resistant and weather-resistant material parameters of the surface protective layer (quantified indicators of hardness and UV resistance), the fiber material and structural parameters of the base layer (fiber type and weaving density affect the membrane's flexibility and strength), and the precise setting of the modified asphalt coating thickness (each layer controlled with millimeter-level precision). Furthermore, each layer was assigned corresponding mechanical and physical properties, such as the elastic modulus E and Poisson's ratio v.

[0067] The insulation layer modeling is closely related to the material properties. After setting the extruded polystyrene foam board material, the thermal conductivity coefficient λ (set according to product quality standards and laboratory measured data, the unit is watts per meter Kelvin, this parameter directly reflects the thermal insulation efficiency of the insulation layer, is related to the overall thermal performance of the roof, affects water vapor condensation and the durability of the waterproof layer), volume density ρ (kilograms per cubic meter, related to the influence of the material's own weight on the roof structure load and the stability of the insulation effect) and other parameters are accurately entered. The fine stone concrete protective layer has clear strength grade (such as C25, corresponding to the standard compressive strength design value), thickness (set in the range of 40-60 mm in combination with design specifications and actual project needs), and reinforcement information (the diameter, spacing, and level of the steel bars are determined based on the structural force calculation and construction requirements). For key nodes such as downspouts, gutters, and partition joints, parametric design is used to give them real dimensions consistent with the actual product (the downspout diameter is designed to be accurate to the millimeter based on the drainage flow rate, the gutter cross-sectional dimensions include detailed settings for width and depth, and the partition joint width meets the requirements of construction specifications), material properties (cast iron downspouts are marked with cast iron grade and wall thickness, and galvanized steel gutters are marked with galvanized layer thickness and steel material specifications), and the flow coefficient C is set according to the principles of hydraulics. d (The flow coefficient of the water outlet is determined by professional experiments based on the shape and roughness, and is used to calculate the drainage flow formula:

[0068]

[0069] Where Q is the flow rate, A is the water flow area, g is the acceleration of gravity, and H is the water head height. This is used to accurately assess drainage smoothness and provide early warning of water accumulation and leakage risks.

[0070] The basement modeling follows a similar logic. The waterproof concrete exterior walls are set to an impermeability rating of P (strictly in accordance with design requirements, such as P6 and P8, corresponding to different impermeability pressure values, based on concrete permeability test specifications and measured by professional laboratories to effectively reflect the ability to resist groundwater penetration). The permeability coefficient k (meters per day, a measure of the rate of groundwater penetration through concrete, a key quantitative indicator for leakage analysis) is imported into the laboratory. The waterproofing membrane is meticulously characterized by polymer membrane material parameters similar to those of the roof. The width of the post-cast strip is defined in detail (800-1000 mm is set based on comprehensive considerations of structural deformation and waterproofing requirements), reinforcement measures are taken (increasing the number of steel bars and improving the steel bar specifications to ensure structural integrity and waterproofing reliability), and the waterstop steel plate specifications are 3-5 mm thick and 300-400 mm wide, based on the theoretical formula for seepage through thin plate gaps:

[0071]

[0072] Where q is the seepage rate, k ω is the water permeability coefficient in the gap of the water-stop steel plate, H is the head difference, L is the seepage path length, b is the width of the water-stop steel plate, and t is the thickness. To evaluate the water-stop effect, each component is assembled into a complete basement BIM model according to the actual spatial position. At the same time, the technical parameters of the non-destructive testing equipment planned to be used are systematically entered into the BIM model attribute library. Taking the microwave testing equipment as an example, the transmission power P is recorded in detail. m (The value range is set according to the device model and detection scenario requirements, such as 1-10 watts. Different powers affect the microwave emission intensity and detection depth, just like the brightness adjustment of a flashlight affects the illumination distance and clarity), frequency f m (Adjustable range 5-20GHz, different frequencies have different penetration and reflection characteristics on building materials, similar to different band radios receiving different channel signals, adapting to various building materials to obtain the best detection effect), antenna gain G, its empirical formula for detection depth in different building media is:

[0073]

[0074] Here D m is the detection depth, k1 is a comprehensive constant (related to the electromagnetic characteristics of the equipment antenna, dielectric and permeability of the medium, and calibrated by a large number of professional experiments, such as about 0.1-0.2 in concrete medium), μ r is the relative magnetic permeability (the relative magnetic permeability of concrete is about 1, masonry is 0.9-1.1, and coiled material is 0.8-1), ∈ r is the relative dielectric constant (4-6 for concrete, 3-5 for masonry, 2-4 for coiled material); the ultrasonic testing equipment records the center frequency f c , probe diameter d and resolution formula:

[0075]

[0076] Where λ is the ultrasonic wave length (given by the medium sound velocity v and frequency f c Relationship determination The speed of sound varies with different media (concrete is about 3000-4000 m / s, steel is 5000-6000 m / s), v is the speed of sound of the medium, f c The center frequency is related to the ability to distinguish tiny cracks and holes, laying the foundation for accurate detection;

[0077] In addition, the building site's terrain elevation data is imported to visually present the site's undulations in the form of a digital elevation model (DEM). This correlates the groundwater runoff direction with the building's natural drainage slope, providing a basis for considering water accumulation factors in waterproofing design and testing. Historical groundwater level data (years of continuous water level monitoring records collected through the cloud to clearly define the highest, lowest, and average water levels, with the fluctuation range used to assess the long-term pressure-bearing status of basement waterproofing) is also integrated with years of statistical meteorological data from surrounding meteorological stations, covering annual precipitation and precipitation intensity distribution probability functions. By statistically fitting functions to the frequency of precipitation at various intensities, the frequency and volume of extreme weather events such as rainstorms are estimated, guiding the early deployment of defense measures during key inspection periods and locations. This is linked to the BIM model's environmental attributes and utilizing a simulation analysis plug-in. In this embodiment, a plug-in based on the EnergyPlus core is used to simulate heat and moisture transfer, much like a weather forecast simulates atmospheric heat and moisture changes. This accurately predicts the impact of the building's internal heat and moisture environment on waterproofing, simulates the impact of different climate and hydrological conditions on building waterproofing, enriches the model's analysis dimensions, assists in formulating scientific inspection plans, and provides early insight into potential waterproofing risk points.

[0078] 4. Communication transmission module

[0079] In this embodiment, the main function of the communication transmission unit is to transmit real-time data to realize the dynamic update of the BIM module, as follows:

[0080] 4.1 Data Transmission Unit

[0081] In this embodiment, the communication transmission module transmits the collected data, specifically as follows: the mobile data processing terminal receives the data from each device and runs the built-in data verification algorithm at the same time. Based on statistical principles, the data credibility interval is set (such as the value of microwave detection water content, based on multiple measurements of sample standard deviation σ and mean μ, μ±3σ is defined as a credible interval, similar to defining the range of data), and the data outside the interval is marked for review; using spatial correlation analysis, the temperature value of the infrared thermal imaging of adjacent detection points should comply with the law of heat conduction continuity, temperature gradient mutation point verification, and multi-technology cross-validation logic, the ultrasonic detection of crack depth and the change of water content at the corresponding position of microwave detection should be mutually verified under the framework of the leakage physical model. If the depth and water content do not match the positive correlation trend, an abnormality is prompted to check for errors or suspicious data. The qualified data is uploaded to the cloud BIM collaborative management platform in real time via the 5G network. The platform is deeply integrated with the BIM model based on coordinate matching and data type recognition rules to trigger the intelligent update mechanism. When the humidity exceeds the threshold, such as the first-level standard for roof waterproofing, the relative humidity inside the membrane exceeds 70%, the temperature is abnormal (the temperature difference between adjacent areas exceeds 3 degrees Celsius), and there are structural defects (the width of the crack detected by ultrasonic testing exceeds 0.3 mm), the corresponding components of the BIM model will automatically change color (graded according to the degree of risk, red for high risk, yellow for warning, green for normal, and color warning lights for eye-catching prompts), and a pop-up window will display detailed detection values, waveform graphs and preliminary defect judgment results. Based on deep learning image recognition and data analysis models, after training and optimization with massive waterproofing defect samples, the input multi-source data is classified and judged, and outputs diagnostic conclusions such as "the membrane around the roof downspout is likely to delaminate and leak", forming a visual and traceable detection file, which is convenient for tracing back historical data at any time and comparing and analyzing the evolution of waterproofing performance, laying a solid data foundation for subsequent operation and maintenance decisions.

[0082] 5. Operation and maintenance decision module

[0083] In this embodiment, the main function of the operation and maintenance decision module is to conduct a comprehensive assessment of the building wall and make subsequent decisions, as follows:

[0084] 5.1 Risk Rating Unit

[0085] After the inspection is completed, the cloud platform summarizes the non-destructive inspection data of the waterproof parts of the entire building, calls the intelligent analysis module based on the fuzzy comprehensive evaluation method (FCE), and constructs the factor set U = {u1, u2, u3}, which covers the key indicators of water content abnormality u1, temperature gradient abnormality u2, and structural defect severity u3. Each indicator is normalized according to the inspection data, such as water content abnormality

[0086]

[0087] Among them, S totalIn order to detect the total area, the data is “standardized” to facilitate unified evaluation, and the evaluation set V = {v1, v2, ..., v m}(corresponding to different risk levels, v1 is low risk, v2 is relatively low risk, v3 is medium risk, v4 is high risk, and v5 is extremely high risk, divided into "risk level ladder"), the factor weight distribution matrix W is determined by the hierarchical analysis method:

[0088]

[0089] Establish a fuzzy relationship matrix R (determined by the membership function of each factor to the evaluation set, and fit the membership according to the indicator value distribution through the Gaussian membership function to depict the relationship between the indicator and the risk). After the fuzzy synthesis operation B = W·R (the evaluation result is calculated by the maximum-minimum synthesis method or the weighted average method), the comprehensive evaluation vector B = {b1, b2, ..., b m}, determine the risk level R of each waterproof part, such as roof partition and basement wall panel, according to the principle of maximum membership g Combined with the building service life t, functional importance coefficient α (depending on the building use, 1.2-1.5 for hospitals and data centers, 1.0 for ordinary residences) and environmental erosion intensity E i (Taking into account precipitation erosion, groundwater infiltration, and solar aging, quantified as 1 to 10 points), using the risk correction model:

[0090]

[0091] Among them, k3 is the empirical correction coefficient, which further quantifies and adjusts the risk rating, displays the risk heat map globally in the BIM model, and renders colors according to the risk level to intuitively present the building waterproofing situation, accurately reveal the priority ranking of weak links, and guide the allocation of operation and maintenance resources.

[0092] 5.2 Strategy Generation Unit

[0093] In this embodiment, based on the quantitative risk rating and detailed defect diagnosis report generated by the risk rating unit, the cloud platform intelligently matches the operation and maintenance strategy library and recommends a high-pressure grouting repair process for high-risk roof cracks and leaks. The grouting material dosage Q (liters) is estimated based on the crack length L (meter), width w (mm), depth h (mm) and material filling coefficient λ (valued based on material properties and construction process experience, such as 0.8-1.2 for epoxy resin) according to the formula Q = L × w × h × λ × 10 -6In the case of leakage in the basement waterproof concrete structure, if the concrete is not vibrated densely and forms honeycomb holes, a plan for partial chiseling and re-pouring will be formulated. The volume V (cubic meters) of the re-pouring concrete will be accurately calculated according to the size of the defect range. Combined with waterproofing enhancement measures (such as adding water-expanding waterstops, with specifications, dimensions, and usage adapted to the circumference and depth of the holes), a complete construction blueprint will be formed. The operation and maintenance team uses the BIM model's three-dimensional visualization and construction simulation functions (using the Navisworks software animation production module to simulate construction procedures, material stacking, and personnel and equipment access routes, and virtual rehearsals to ensure smooth construction) to accurately locate construction sites and control key nodes of the repair process. The BIM model records construction progress and quality acceptance information throughout the construction process, and links photos, videos, and inspection reports to the corresponding component timeline. After completion review, the model's waterproofing performance status is updated. Closed-loop management ensures the long-term stability of building waterproofing, extends the waterproofing life expectancy by 1-2 years, and reduces leakage repair costs by 20% to 30%.

[0094] Working Principle: Drone 1, leveraging its exceptional maneuverability and stability, carries key inspection components and shuttles between various locations on the building awaiting inspection. A water tank 2 stores a fixed amount of water. Its corrosion-resistant, airtight material ensures safe water storage, and its optimal capacity prevents excessive loads from impacting the drone's flight endurance and flight safety. Pump 3 draws water from the tank as needed and distributes it through pipe 4, simulating rainfall and other scenarios to verify the effectiveness of roof waterproofing and drainage. Simultaneously, sensors 5 onboard the drone activate their data collection "antennae," providing raw information for subsequent inspection processes. Leveraging the drone's mobility, the drone, carrying integrated multi-faceted inspection technology, travels to the site. Utilizing RTK-GPS technology, it collaborates with highly accurate coordinates of building perimeter control points provided by professional surveying and mapping departments. Similar to the "dual-mirror, seven-head calibration" method based on satellite signals, differential processing achieves initial high-precision positioning of the device, precisely aligning the device coordinates with the BIM model coordinate system, minimizing errors to the centimeter level. After arriving at the starting detection point, the laser rangefinder compares the actual and theoretical distances between the equipment and the building's feature points, and makes fine adjustments if the deviation exceeds the limit to ensure compliance with the plan; the equipment self-inspects synchronously, the power meter calibrates the microwave transmission power, the crystal oscillator calibrates the ultrasonic frequency, and the infrared thermal imaging thermal compensation coefficient is corrected according to the ambient temperature and humidity to ensure that all detection methods are accurate and ready.

[0095] The antenna array scans the roof at a precise preset step size (5-20 mm), captures the reflected signal, and "analyzes" the spectrum through fast Fourier transform. Based on electromagnetic theory and calibration curves, combined with integration algorithms, the water content of small units (divided by step size) is inferred from signal attenuation and phase changes, and the abnormal area is locked against the benchmark value; the infrared thermal imager simultaneously captures thermal radiation images at a specific frame rate (1-5 frames / second), and relies on the thermal radiation law and two-color temperature measurement algorithm to identify areas with temperature gradients exceeding the threshold (exceeding 2-3 degrees Celsius) as suspected leakage points, and then "locates" them to the BIM model through image and coordinate conversion algorithms; in case of complex and suspicious areas, the high-frequency ultrasonic flaw detector is switched, coupling agent is applied to optimize sound energy transmission, and the internal defect parameters of the structure are solved according to acoustic principles (Huygens-Fresnel principle, time difference method, etc.), and all-round data is wirelessly transmitted to On mobile terminals, data from long-term water level monitoring and pumping tests are used to predict leakage risk areas. Ultrasonic longitudinal waves are used first to check the density of concrete, and compared with the standard sound velocity. If the deviation rate exceeds 10%, an early warning will be issued. Microwave detection of soil moisture content outside the wall is used to assist in judgment. The obtained data is associated with the corresponding components of the BIM model in real time, dynamically displaying the waterproofing status of the basement. Based on the BIM model, for the waterproof structural layer, such as the roof SBS modified asphalt membrane, Revit "family" is used to customize it, and the material parameters of each layer (wear resistance, fiber structure, coating thickness, etc. and corresponding mechanical and physical properties) are carefully entered. The thermal conductivity and density parameters of the insulation layer are set according to the material properties, and the strength and thickness of the concrete protective layer are clearly specified. Key nodes (downspouts, etc.) are assigned actual dimensions, materials and hydraulic flow coefficients. The same is true for the basement, and the anti-seepage and permeability coefficients are set to construct the model.The parameters of non-destructive testing equipment are also entered. Microwave equipment detects based on power, frequency, gain and other factors, and ultrasonic equipment assists in accurate detection based on the correlation resolution such as center frequency. Topography, water level and meteorological data are also imported, and simulation plug-ins are used to analyze the impact of climate and hydrology on waterproofing to assist in scientific planning and detection. When the mobile terminal receives data, the credibility interval is set based on statistics to "screen" the data and mark suspicious ones. The infrared temperature is verified by using the law of spatial heat conduction, and the correlation between ultrasonic cracks and microwave moisture is cross-validated with multiple technologies to ensure data reliability. After verification, it is sent to the cloud platform via 5G. The platform matches and integrates the data into the BIM model according to coordinates and types. In case of humidity, temperature and structural abnormalities, the model components change color to warn (red danger, yellow warning, green normal), and pop-up windows are displayed. Display numerical values, graphs and intelligent diagnosis, build visual archives, help retrospective analysis, after detection, the cloud aggregates data to adjust the fuzzy comprehensive evaluation module, normalizes moisture content, temperature, and structural defect indicators to build a factor set, determines the weight matrix by layer, and fits the membership to build a fuzzy relationship matrix. The fuzzy synthesis operation obtains the quantitative risk level, and the rating is adjusted with the correction model based on the building age, functional coefficient, and environmental erosion. BIM displays the heat map to guide the allocation of operation and maintenance resources. Based on the rating and diagnosis, the platform matches the operation and maintenance strategy. For example, the grouting of roof cracks is calculated according to the size and coefficient, and the basement leakage is removed and re-poured according to the defect volume and water stop strips are installed. With the help of BIM visualization, the construction is simulated and controlled throughout the process. Closed-loop management consolidates waterproofing, extends life, and reduces costs.

[0096] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An automatic detection device for waterproofing of a building main body, characterized in that: include: Carrying mechanism; A sensor detection module is provided on the carrying mechanism, and collects data from the building body. A data interaction module is provided on the carrying mechanism, and uploads the data collected by the sensor detection module to the cloud BIM collaborative management platform, builds a BIM model, and sends the data collected by the sensor detection module to the back end; A communication transmission module, which is provided on the carrying mechanism and synchronously transmits the data collected by the sensing detection module to the back end; An operation and maintenance decision module is provided at the back end. The operation and maintenance decision module generates a defect diagnosis report based on the data collected by the sensor detection module, and intelligently matches the operation and maintenance strategy library based on the quantitative risk rating.

2. The automatic detection device for waterproofing of a building main body according to claim 1, characterized in that: The carrying mechanism comprises a drone (1), a water tank (2) is carried on the top of the drone (1), a water pump (3) is installed on one side of the outer wall of the water tank (2), an input end of the water pump (3) is connected to the water tank (2), and an output end of the water pump (3) is connected to a water pipe (4), and a sensor detection module (5) is installed on the outer wall of the drone (1).

3. The automatic detection device for waterproofing of a building main body according to claim 1, characterized in that: The sensing detection module includes: Microwave detection unit, which transmits microwaves into the building detection layer; The infrared thermal imaging unit, relying on the Stefan-Boltzmann law and a two-color infrared temperature measurement algorithm, quickly scans the building surface to capture temperature gradients caused by abnormal heat conduction due to waterproofing failure; Ultrasonic flaw detection unit, which transmits longitudinal waves, shear waves and surface waves to the building surface through the probe; The humidity detection unit uses an array of distributed humidity sensors to cover key waterproof parts of the building.

4. The automatic detection device for waterproofing of a building main body according to claim 1, characterized in that: The data interaction module includes: The data transmission unit runs a statistically-based data verification algorithm when the sensor detection module collects data and transmits it to the drone's microprocessor. It sets a data credibility interval to filter out erroneous and suspicious data. Qualified data is then uploaded to the cloud-based BIM collaborative management platform in real time via the 5G network. Parameter entry unit, which enters the collected parameters into a unified system; The conversion unit encapsulates the collected data into a unified data packet, adds a timestamp, device number, and data check code, and then sends it to the backend.

5. The automatic detection device for waterproofing of a building main body according to claim 1, characterized in that: The communication transmission module includes: Wireless communication unit, using WIFI dual-band to transmit data over short distances; Wired communication unit, which uses high-speed Ethernet cable combined with optical fiber for data transmission; The dynamic update unit enables the back-end to be deeply integrated with the BIM model based on coordinate matching and data type identification rules. When data is detected to trigger humidity exceeding the threshold, temperature anomalies, structural defects, etc., the corresponding components of the BIM model will change color and a pop-up window will display detailed detection values, waveform graphs and preliminary defect judgment results.

6. The automatic detection device for waterproofing of a building main body according to claim 1, characterized in that: The operation and maintenance decision module includes: After the inspection is completed, the risk rating unit calls the intelligent analysis module based on the fuzzy comprehensive evaluation method to build a risk rating system covering the degree of moisture content anomaly, temperature gradient anomaly, and the severity of structural defects; The strategy generation unit intelligently matches the operation and maintenance strategy library based on quantitative risk ratings and detailed defect diagnosis reports.

7. The automatic detection device for waterproofing of a building main body according to claim 4, characterized in that: When the data transmission unit runs the data verification algorithm, based on statistical principles, the microwave-detected moisture content value is defined as a credible interval according to the standard deviation and mean of multiple measurement samples.

8. The automatic detection device for waterproofing of a building main body according to claim 5, characterized in that: When the dynamic update unit automatically changes the color of the corresponding component of the BIM model, the color is assigned according to the risk level.

9. The automatic detection device for waterproofing of a building main body according to claim 2, characterized in that: The carrying mechanism also includes a positioning and calibration unit. When the positioning and calibration unit uses RTK-GPS technology, its base station can be connected to a local continuously operating reference station system.

10. The automatic detection device for waterproofing of a building main body according to claim 9, characterized in that: The carrying mechanism further comprises a posture adjustment component, wherein the posture adjustment component comprises a gyroscope and an accelerometer, and the gyroscope and the accelerometer are electrically connected to the drone (1) respectively.

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