House building deformation monitoring method and device combined with artificial intelligence

Abstract

The invention discloses a house building deformation monitoring method and device combined with artificial intelligence, and relates to the related technical field of building deformation monitoring, and the method comprises the steps: carrying out the deformation sensitivity analysis after obtaining the information of a to-be-monitored house building through interaction, and building deformation sensitive attention; house building information is analyzed, feature salient point recognition is carried out, and candidate feature comparison points are established; establishing a first adaptation result according to the observation adaptation analysis result; correlation matching of the candidate feature comparison points is carried out, and a second adaptation result is established; and after feature comparison point confirmation is carried out by using the second adaptation result, house building deformation monitoring is carried out by using the distributed monitoring sensors and the feature comparison points. The technical problems of limited house building deformation monitoring precision, insufficient monitoring efficiency and poor adaptability to complex building structures caused by difficulty in comprehensively covering key parts and large environmental interference in the prior art are solved, and the technical effect of improving the house building deformation monitoring efficiency, accuracy and adaptability is achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to building deformation monitoring, and specifically to a method and device for monitoring building deformation in combination with artificial intelligence. Background Art

[0002] In the field of modern building construction, with the acceleration of urbanization and the continuous expansion of building scale, the demand for building safety monitoring is becoming increasingly urgent. However, traditional building deformation monitoring methods, such as conventional level measurement and total station monitoring, suffer from many drawbacks, such as low efficiency, limited accuracy, and difficulty in real-time and comprehensive monitoring. Level measurement monitors deformation by measuring the height difference between two points. However, its operation is cumbersome and requires manual point-by-point measurement, which is inefficient and significantly affected by the skill level of the observer, making it difficult to provide real-time and comprehensive monitoring of large-scale buildings. Total station monitoring uses angle and distance measurements to determine the position change of target points. Although relatively accurate, in complex environments, such as urban areas with tall buildings, the signal is easily blocked and interfered with, resulting in missing or inaccurate monitoring data. Sensor network-based monitoring technologies, such as strain gauges and displacement meters, can achieve a certain degree of automated monitoring, but the limited sensor deployment cannot cover all key parts of the building. They are also not well adapted to buildings with complex structures and diverse deformation modes, making it difficult to monitor subtle local deformations of buildings.

[0003] Therefore, the current relevant technologies have technical problems such as difficulty in fully covering key parts and being greatly affected by environmental interference, which in turn leads to limited accuracy in building deformation monitoring, insufficient monitoring efficiency and poor adaptability to complex building structures. Summary of the Invention

[0004] This application provides a building deformation monitoring method and device combined with artificial intelligence, which solves the technical problems in the existing technology that it is difficult to fully cover key parts and is greatly affected by environmental interference, resulting in limited accuracy of building deformation monitoring, insufficient monitoring efficiency and poor adaptability to complex building structures, thereby achieving the technical effect of improving the efficiency, accuracy and adaptability of building deformation monitoring.

[0005] The present application provides a building deformation monitoring method combined with artificial intelligence, the method comprising: after interactively obtaining building information to be monitored, using the building information to perform deformation sensitivity analysis and establish deformation sensitivity concerns, the deformation sensitivity analysis including structural weakness analysis, force concentration analysis, and environmental sensitivity analysis; parsing the building information, identifying feature prominent points based on the parsing results, and establishing candidate feature comparison points; obtaining observation adaptation analysis results of the candidate feature comparison points, and establishing a first adaptation result based on the observation adaptation analysis results; performing association matching of the candidate feature comparison points based on the deformation sensitivity concerns, and establishing a second adaptation result based on the association matching results and the first adaptation result; after confirming the feature comparison points using the second adaptation result, distributing monitoring sensors, and using the distributed monitoring sensors and feature comparison points to monitor building deformation.

[0006] In a possible implementation, the building deformation monitoring method combined with artificial intelligence also performs the following processing: extracting the BIM data, surveying and mapping data, and design drawing data of the building information to establish a digital twin model; performing finite element simulation based on the digital twin model to simulate the structural response under varying loads and establish a first deformation sensitive concern; performing force concentration analysis on the digital twin model to establish a second deformation sensitive concern; performing an impact analysis of the digital twin model based on the coupling effects of temperature, humidity, and vibration environment to establish a third deformation sensitive concern; and establishing the deformation sensitive concern using the first deformation sensitive concern, the second deformation sensitive concern, and the third deformation sensitive concern.

[0007] In a possible implementation, the building deformation monitoring method combined with artificial intelligence also performs the following processing: activating a physical simulation tool, and using the physical simulation tool to simulate the temperature field, humidity field, and vibration field; performing simulation fitting of the digital twin model through a mixed field composed of the temperature field, humidity field, and vibration field, performing coupling evaluation based on temperature sensitive indicators, humidity sensitive indicators, and vibration sensitive indicators, and establishing the third deformation sensitive concern.

[0008] In a possible implementation, the building deformation monitoring method combined with artificial intelligence also performs the following processing: using natural language processing and computer vision to extract structural geometric relationships, component relationships, material parameters and node position information from CAD drawings; using the structural geometric relationships and component relationships to extract geometric visual features and establish a first recognition result; using the component relationships and node position information to locate component intersections, load-bearing nodes, connection nodes, edge points, and corner points, and establishing a second recognition result based on the positioning results; using the material parameters to identify material change points and establish a third recognition result; and establishing candidate feature comparison points based on the first recognition result, the second recognition result, and the third recognition result.

[0009] In a possible implementation, the building deformation monitoring method combined with artificial intelligence also performs the following processing: generating deformation types and monitoring granularity based on the deformation sensitivity concerns; performing association matching of the candidate feature comparison points based on the deformation types and monitoring granularity to generate association matching results; using the association matching results and the first adaptation results to screen candidate feature points to establish screening results; using the monitoring granularity to perform coverage evaluation of the association matching results, and generating feature comparison point search instructions based on the coverage evaluation results; performing a new search for monitoring comparison points under the screening results based on the feature comparison point search instructions, and establishing a second adaptation result based on the new search results and the screening results.

[0010] In a possible implementation, the building deformation monitoring method combined with artificial intelligence also performs the following processing: reading the monitoring results of the monitoring sensor, performing deformation identification through the monitoring results and the feature comparison points, and generating a first deformation identification result; obtaining historical monitoring data, using the historical monitoring data to perform monitoring result deviation analysis based on the feature comparison points, and establishing a second deformation identification result; and reporting a deformation warning through the first deformation identification result and the second deformation identification result.

[0011] In a possible implementation, the building deformation monitoring method combined with artificial intelligence also performs the following processing: configuring an early warning response database; using the early warning response database to perform early warning trigger analysis of the first deformation identification result and the second deformation identification result; calling the early warning plan to execute deformation early warning output according to the early warning trigger analysis results.

[0012] The present application also provides a building deformation monitoring device combined with artificial intelligence, the device including: a deformation sensitivity analysis module, which is used to interactively obtain building information to be monitored, and then use the building information to perform deformation sensitivity analysis and establish deformation sensitivity concerns, wherein the deformation sensitivity analysis includes structural weakness analysis, force concentration analysis, and environmental sensitivity analysis; a feature highlight point identification module, which is used to parse the building information, identify feature highlight points based on the analysis results, and establish candidate feature comparison points; a first adaptation result establishment module, which is used to obtain the observed adaptation analysis results of the candidate feature comparison points, and establish a first adaptation result based on the observed adaptation analysis results; a second adaptation result establishment module, which is used to perform association matching of the candidate feature comparison points based on the deformation sensitivity concerns, and establish a second adaptation result based on the association matching results and the first adaptation result; a building deformation monitoring module, which is used to use the second adaptation result to confirm the feature comparison points, and then distribute monitoring sensors to monitor building deformation using the distributed monitoring sensors and feature comparison points.

[0013] The proposed method and device for monitoring deformation of buildings combined with artificial intelligence in this application is intended to interactively obtain information about the building to be monitored, then perform deformation sensitivity analysis and establish deformation sensitivity concerns; analyze the building information, identify prominent feature points, and establish candidate feature comparison points; establish a first adaptation result based on the observation adaptation analysis results; perform correlation matching of the candidate feature comparison points to establish a second adaptation result; after confirming the feature comparison points using the second adaptation result, distribute monitoring sensors and use the distributed monitoring sensors and feature comparison points to monitor building deformation. This solves the technical problems in the prior art of difficulty in fully covering key areas and being subject to significant environmental interference, which in turn leads to limited accuracy, insufficient monitoring efficiency, and poor adaptability to complex building structures in monitoring deformation of buildings, thereby achieving the technical effect of improving the efficiency, accuracy, and adaptability of monitoring deformation of buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A schematic flow chart of a building deformation monitoring method combined with artificial intelligence provided in an embodiment of the present application.

[0016] Figure 2 Schematic diagram of the structure of a building deformation monitoring device combined with artificial intelligence provided in an embodiment of the present application.

[0017] Explanation of the accompanying drawings: deformation sensitivity analysis module 10, feature salient point recognition module 20, first adaptation result establishment module 30, second adaptation result establishment module 40, building deformation monitoring module 50. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms “first\second” involved are merely to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0021] The present application provides a building deformation monitoring method combined with artificial intelligence, such as Figure 1 As shown, the method includes: Step S100, after interactively obtaining the building information to be monitored, use the building information to perform deformation sensitivity analysis and establish deformation sensitivity focus, wherein the deformation sensitivity analysis includes structural weakness analysis, force concentration analysis, and environmental sensitivity analysis.

[0022] Preferably, interactively acquiring building information to be monitored may include obtaining basic design information such as the building's structural layout, dimensions, and materials from architectural drawings; obtaining actual construction information, such as construction techniques and quality control, from construction records; obtaining geological conditions at the building site, such as soil type, bearing capacity, and groundwater level, from geological exploration reports; and collecting environmental information surrounding the building, such as temperature, humidity, and wind speed, through sensor data. Deformation sensitivity analysis is then performed based on this building information, identifying key locations and factors prone to deformation. This deformation sensitivity analysis includes structural weakness analysis, force concentration analysis, and environmental sensitivity analysis.

[0023] Preferably, structural weakness analysis includes structural component analysis, structural connection analysis and structural system analysis. Among them, structural component analysis refers to an in-depth assessment of major structural components such as beams, columns, and plates based on architectural design drawings and construction records. Components with smaller sizes or lower material strength have relatively weaker bearing capacity and are more likely to deform when subjected to stress. For example, beams with too small a cross-sectional area may bend when subjected to large loads, and columns with insufficient concrete strength may be compressed under pressure. Connection node analysis refers to analyzing the connection method (such as welding, bolt connection), structural details, and construction quality of the nodes to determine whether there are loose connections, stress concentration, etc. Problems: parts with loose connections or unreasonable structures are prone to loosening and cracking when subjected to stress, which can cause structural deformation. For example, poor welding quality of steel structure nodes may cause node damage under repeated loads, which in turn affects the stability of the entire structure; structural system analysis refers to the evaluation of the overall structural system of a building, such as frame structure, shear wall structure, cylindrical structure, etc. Different structural systems have different stress performance. Some structural systems may be more prone to deformation in specific directions or under loads. For example, the lateral stiffness of a frame structure is relatively small, and under the action of horizontal loads (such as wind loads and seismic loads), it may produce large lateral displacements.

[0024] Preferably, the force concentration analysis specifically includes load type analysis, load distribution analysis and load combination analysis. Among them, load type analysis refers to clarifying the various loads acting on the building, including dead loads (such as the weight of the structure and the weight of decoration), live loads (such as the weight of personnel, equipment, and furniture), wind loads, snow loads, earthquake loads, etc. Different types of loads have different ways of acting on the structure and the degree of their influence. For example, wind loads mainly produce horizontal thrusts on the facade and high-rise parts of the building, which may cause lateral deformation of the building. Earthquake loads will cause the building to produce complex vibration responses, which may affect various parts of the structure. Load distribution analysis refers to Analyze the distribution of loads on the building structure and identify areas of concentrated loads. For example, in large shopping malls, the floor slabs around the atrium may bear a large crowd load. In industrial plants, the beams and columns in the equipment placement area may bear a large concentrated load. If the load-concentrated areas are not fully considered during the design, deformation problems are likely to occur. Load combination analysis refers to the evaluation of the situation where multiple loads act at the same time. Different load combinations may cause certain parts of the structure to bear greater internal forces, thereby increasing the risk of deformation. For example, under the action of an earthquake, considering the combination of wind loads and live loads at the same time may make the stress situation of the structure more complicated.

[0025] Preferably, the environmental sensitivity analysis specifically includes temperature impact analysis, humidity impact analysis and geological condition analysis, among which temperature changes will cause thermal expansion and contraction of building materials, thereby leading to structural deformation. For buildings that are more sensitive to temperature changes, such as large-span structures and ultra-long structures, it is necessary to focus on evaluating the impact of temperature stress. For example, in high temperatures in summer, concrete structures may crack due to expansion, and in low temperatures in winter, steel structures may loosen at the joints due to contraction; changes in humidity will affect the performance of building materials, especially materials such as wood and masonry. When the humidity is high, wood may rot and deform, and masonry may become damp and weathered. When the humidity is low, the materials may crack due to drying; geological conditions affect the foundation stability of housing buildings. If the building is located in areas with poor geological conditions such as soft soil foundations and earthquake-prone areas, the structure may be deformed due to foundation settlement, earthquakes, etc. For example, uneven settlement of soft soil foundations may cause the building to tilt, wall cracks, and earthquakes may cause the foundation of the building to shift, thereby affecting the safety of the superstructure. After completing the structural weakness analysis, force concentration analysis and environmental sensitivity analysis, conduct a comprehensive evaluation of the analysis results. Based on the degree of influence of different factors on structural deformation, comprehensively determine the key parts and areas in the building that are prone to deformation, and establish deformation sensitivity focus, so as to conduct more targeted deformation monitoring of the building and timely discover potential safety hazards.

[0026] Furthermore, step S100 also includes step S110, extracting the BIM data, surveying and mapping data, and design drawing data of the building information to establish a digital twin model; step S120, performing finite element simulation based on the digital twin model, simulating the structural response under varying loads, and establishing a first deformation sensitive concern; step S130, performing force concentration analysis on the digital twin model to establish a second deformation sensitive concern; step S140, performing an impact analysis of the digital twin model based on the coupling effects of temperature, humidity, and vibration environment to establish a third deformation sensitive concern; step S150, establishing the deformation sensitive concern using the first deformation sensitive concern, the second deformation sensitive concern, and the third deformation sensitive concern.

[0027] Preferably, BIM data, surveying and mapping data, design drawing data, etc. are extracted from the building construction information, wherein BIM data includes detailed data such as the three-dimensional model of the building, component information, material properties, etc.; surveying and mapping data provides information such as the actual geographical location, topography, and actual size of the building; design drawing data includes detailed drawings of the building design, such as plan views, sections, elevations, etc.; the extracted data is used to establish a digital twin model, which is a digital mapping of the real building and can accurately reflect the building's geometric shape, structural characteristics, material properties and other information. Then, finite element simulation analysis is performed based on the digital twin model. Finite element simulation analysis is a numerical calculation method that discretizes a continuous object into a finite number of units for analysis. Specifically, by dividing the digital twin model into a large number of finite element units and assigning corresponding material properties and boundary conditions to each unit, the response of the building structure under various changing loads is simulated. During the simulation process, the stress, strain distribution and displacement changes of the structure are observed to find out the parts that are prone to large deformation, which are the first deformation-sensitive areas of concern. For example, when simulating seismic loads, it may be found that certain weak layers or key nodes of the building have obvious stress concentration and large displacement changes.

[0028] Preferably, a force concentration analysis is performed on the digital twin model. By analyzing the load size and distribution of various parts in the model, areas of concentrated force are identified. For example, in a building, the intersections of columns and beams and the support points of large equipment may bear large concentrated loads, which are prone to large deformations. A second deformation sensitivity focus is then established. The digital twin model is impact analyzed by considering the coupled effects of temperature, humidity, and vibration environment. Specifically, temperature changes cause thermal expansion and contraction of materials, humidity changes affect material properties and structural stability, and vibration environments may cause fatigue damage to structures. The interaction of these three factors has a complex impact on the deformation of the building structure. By analyzing the response of the structure under different environmental conditions, the key factors and sensitive areas with the greatest impact on structural deformation are identified, and a third deformation sensitivity focus is established. For example, in some industrial plants, the vibration of production equipment and changes in ambient temperature may cause certain connections to loosen and deform. Finally, the first, second, and third deformation sensitivity focuses are comprehensively analyzed to form a deformation sensitivity focus, which facilitates the key monitoring and maintenance of deformation-sensitive areas to ensure the safety and stability of the building.

[0029] Furthermore, step S140 also includes step S141, activating the physical simulation tool, and using the physical simulation tool to simulate the temperature field, humidity field, and vibration field; step S142, performing simulation fitting of the digital twin model through a mixed field composed of the temperature field, humidity field, and vibration field, performing coupling evaluation based on temperature sensitive indicators, humidity sensitive indicators, and vibration sensitive indicators, and establishing the third deformation sensitive concern.

[0030] Preferably, the physical simulation tool is activated to perform temperature field simulation on the digital twin model of the building, including simulating the ambient temperature, sunshine conditions, thermal conductivity characteristics of building materials, and internal heat sources (such as electrical equipment, personnel heat dissipation, etc.) of the building, and simulating the temperature distribution of the building at different times and in different parts. For example, through simulation, it can be determined how much the wall temperature under direct sunlight will increase in summer, and the temperature differences in different rooms inside the building due to different thermal insulation performance; then, humidity field simulation is performed, including simulating the ambient humidity, the building's waterproof and moisture-proof measures, the hygroscopicity of the materials, and the ventilation conditions, and simulating the humidity changes at different locations inside and outside the building. For example, the basement may have a higher humidity due to poor ventilation, while the humidity in rooms near windows and with good ventilation is relatively low; finally, vibration field simulation is performed, including simulating external vibration sources to the building, such as vehicles driving on nearby roads, the operation of industrial equipment, seismic activities, etc., as well as the vibration characteristics of the building's own structure. Through simulation, the vibration response of each part of the building under the action of different vibration sources is analyzed, including parameters such as vibration amplitude and frequency.

[0031] Preferably, a mixed field consisting of temperature field, humidity field and vibration field is applied to the digital twin model for simulation fitting, that is, a coupled evaluation is performed based on temperature sensitive indicators, humidity sensitive indicators and vibration sensitive indicators. Specifically, temperature sensitive indicators include thermal stress and thermal deformation (calculating the stress and displacement caused by temperature changes in various parts of the building, and identifying high temperature difference areas) and temperature gradient sensitivity (establishing an evaluation index for the impact of temperature gradient on local structure based on the thermal expansion coefficient of different components); humidity sensitive indicators include hygroscopic expansion / contraction effect (evaluating the dimensional change and stiffness change of materials caused by moisture absorption) and corrosion / hydration risk (using material aging model and combining humidity data to predict possible corrosion or hydration areas); vibration sensitive indicators include natural frequency change (monitoring the offset of the natural frequency of the building structure under different environmental conditions) and dynamic response amplitude (recording the maximum acceleration and displacement of the vibration response, and evaluating local or overall vibration anomalies). By comprehensively combining coupling sensitivity indicators, a coupling model among temperature, humidity and vibration is constructed to obtain the coupling effect coefficient. Then, combined with time series data, the long-term cumulative effect is calculated to obtain the cumulative damage index, such as the fatigue damage degree of the material under temperature and humidity interaction and the fatigue crack growth under vibration excitation. Finally, the deformation sensitivity prediction result is obtained, and the third deformation sensitivity focus is established.

[0032] Step S200: parse the building information, identify prominent feature points based on the parsing results, and establish candidate feature comparison points.

[0033] Preferably, the building information (building design drawings, BIM data, surveying and mapping data, etc.) is parsed, for example, information such as the building's structural type, dimensional parameters, and component layout is extracted from the design drawings; the building's three-dimensional model information, component attribute information, and their relationships are obtained from the BIM data; and information such as the building's actual geographical location, topography, actual size, and spatial position are obtained from the surveying and mapping data. Feature highlights are then identified, that is, points with significant features that can represent certain key attributes of the building or distinguish it from other buildings are found. These points can be key nodes of the building structure, such as beam-column connection points, wall corners, etc., or unique features of the building's appearance, such as the building's entrances and exits, window corners, special roof shaping points, etc., or the location of the elevator shaft, the starting and ending points of the stairs, etc.; candidate feature comparison points are then formed and used as reference points in subsequent comparison and recognition tasks. For example, when monitoring building deformation, the candidate feature comparison points can be used as monitoring points to judge the building's deformation by comparing position changes at different times.

[0034] Furthermore, step S200 also includes step S210, using natural language processing and computer vision to extract structural geometric relationships, component relationships, material parameters and node position information from CAD drawings; step S220, using the structural geometric relationships and component relationships to extract geometric visual features, and establish a first recognition result; step S230, using the component relationships and node position information to locate component intersections, load-bearing nodes, connection nodes, edge points, and corner points, and establish a second recognition result based on the positioning results; step S240, using the material parameters to identify material change points, and establish a third recognition result; step S250, establishing candidate feature comparison points based on the first recognition result, the second recognition result, and the third recognition result.

[0035] Preferably, natural language processing is used to identify text content in CAD drawings, and through text parsing analysis, the name, specification, material, dimension marking and other information of each component of the building are extracted, such as beam number and size information such as "Q235 steel", "C30 concrete" or "KL1 (2A) 250×500"; computer vision is used to identify lines, graphics and other elements of CAD drawings to analyze the geometric shape and spatial relationship of the structure. For example, by identifying graphics such as straight lines, curves, and polygons, the outline and boundary of the component are determined, and then the geometric parameters such as the length, width, height, and angle of the component and the relative position relationship between different components are calculated; the connection mode and mutual relationship between different components in the CAD drawings, as well as the node position information, can also be identified. For example, the position of the beam-column node can be identified, and then the structural geometric relationship, component relationship, material parameters and node position information can be obtained.

[0036] Preferably, geometric visual features are extracted from structural geometric relationships and component relationships, including information such as the length, angle, area, and shape of the components, as well as relational features such as the connection method and relative position between the components, thereby establishing a first recognition result of the building geometry, which is used to describe the overall geometric form of the building structure and the spatial relationship between the components; the building component relationship and node position information are used to accurately locate key locations such as component intersections, load-bearing nodes, connection nodes, edge points, and corner points. By accurately identifying the locations of key nodes, a second recognition result is established to analyze the mechanical properties and structural stability of the building structure; material change points are identified based on material parameters, that is, the boundaries and change areas of different materials in the CAD drawings are found to understand the distribution of different materials in the building and the possible structural performance changes at the intersections of different materials. By establishing a third recognition result, the material distribution and changes of the building are understood. Finally, the first, second, and third recognition results are combined, the various extracted features and identified key locations are integrated and screened, and candidate feature comparison points are determined for subsequent building structure analysis and deformation monitoring.

[0037] Step S300: Obtain observation adaptation analysis results of candidate feature comparison points, and establish a first adaptation result based on the observation adaptation analysis results.

[0038] Preferably, observation refers to actual measurement and monitoring of the relevant objects (such as buildings or building structures represented by CAD drawings) containing candidate feature comparison points. For example, using a measuring instrument to measure the actual size and position of the building, or obtaining digital coordinates, dimensions, and other information from CAD drawings, then performing an adaptation analysis of the candidate feature comparison points, including calculating the difference between the observed and theoretical values, evaluating the consistency and accuracy of the data, and analyzing the impact of these differences on the overall structure. For example, comparing the actual measured component length with the length of the corresponding component in the CAD drawing, analyzing whether the error between the two is within the allowable range, and the potential impact of the error on the stability of the building structure, thereby determining whether the candidate feature comparison points can accurately reflect the characteristics and status of the building structure. Then, considering the various information obtained from the observation adaptation analysis, including difference data, consistency assessment results, etc., a comprehensive evaluation and judgment of the candidate feature comparison points is performed, and a first adaptation result is established based on the candidate feature comparison points that meet the requirements. This is used to detect whether the building has deformed, the extent of the deformation, and the location, thereby more accurately understanding and describing the characteristics and performance of the building structure.

[0039] Step S400 : performing association matching of the candidate feature comparison points based on the deformation-sensitive attention, and establishing a second adaptation result according to the association matching result and the first adaptation result.

[0040] Preferably, for each candidate feature comparison point, its change under the influence of different deformation sensitive factors is analyzed, and the association between the candidate feature comparison point and various deformation sensitive factors is established. Then, each candidate feature comparison point is matched with the corresponding deformation sensitive factor and the deformation type that may be generated. For example, for candidate feature comparison points at node positions that are susceptible to shear deformation caused by earthquake forces, they are associated with the shear deformation caused by earthquake forces to clarify the importance and observability of the point under such deformation conditions. The associated matching results and the first adaptation results are comprehensively evaluated to more comprehensively understand the characteristics and value of each candidate feature comparison point. Based on the results of the comprehensive evaluation, the candidate feature comparison points are screened and optimized. Those candidate feature comparison points that are closely associated with important deformation sensitive factors in the associated matching and also perform well in the first adaptation results are retained. Those candidate feature comparison points that are not highly associated with deformation sensitive factors or have obvious deficiencies in the first adaptation results are eliminated. After screening and optimization, the remaining candidate feature comparison points are sorted and integrated to form a second adaptation result, which can more accurately reflect the state changes of the structure when it is affected by deformation sensitive factors.

[0041] Furthermore, step S400 also includes step S410, generating a deformation type and a monitoring granularity based on the deformation sensitivity; step S420, performing association matching of the candidate feature comparison points based on the deformation type and the monitoring granularity, and generating an association matching result; step S430, using the association matching result and the first adaptation result to screen the candidate feature points, and establish a screening result; step S440, using the monitoring granularity to perform coverage evaluation of the association matching result, and generating a feature comparison point search instruction based on the coverage evaluation result; step S450, performing a new search for the monitoring comparison points under the screening result according to the feature comparison point search instruction, and establishing a second adaptation result based on the new search result and the screening result.

[0042] Preferably, the possible deformation types of the building structure are determined based on the factors of deformation sensitivity, such as tension, compression, bending, torsion, shear deformation, etc. For example, if the impact of temperature changes on the building is of concern, the tensile or compressive deformation of the components caused by thermal expansion and contraction may be considered. If the earthquake effect is of concern, the complex deformations such as bending and shearing of the structure may be included, thereby clarifying the detail level and accuracy requirements for deformation monitoring. The monitoring granularity is determined according to the specific engineering needs and structural characteristics. For example, for key parts or areas that are more sensitive to deformation, a higher monitoring granularity is required, that is, the smaller deformation can be accurately measured; the candidate feature comparison points are compared with different deformation types and monitoring Specifically, the sensitivity and correlation of each candidate feature comparison point under different deformation conditions are analyzed to determine whether it can effectively reflect a specific type of deformation and whether it meets the requirements of the monitoring granularity. For example, for the candidate feature comparison point located at the beam-column node, its displacement changes during bending deformation and shear deformation are analyzed to evaluate whether it can accurately reflect these two deformations, and at the same time, its accuracy is checked to meet the monitoring granularity; then the association matching results are obtained, that is, a list of matching relationships between each candidate feature comparison point and the deformation type and monitoring granularity, which records the degree of association between each candidate feature comparison point and different deformation types and whether it meets the monitoring granularity requirements.

[0043] Preferably, the candidate feature comparison points are further screened in combination with the association matching results and the first adaptation results to remove candidate feature comparison points that have low correlation with the deformation type or do not meet the monitoring granularity requirements and perform poorly in the first adaptation results, and establish a screening result, which includes candidate feature comparison points with high potential and reliability in deformation monitoring; then, according to the requirements of the monitoring granularity, the association matching results are evaluated to check whether the distribution of candidate feature comparison points under different deformation types can fully cover the areas and deformation conditions that need to be monitored. For example, if the monitoring granularity requires detailed monitoring of the deformation of a certain area of the building structure, but the association matching results show that there are few candidate feature comparison points in the area and cannot meet the coverage requirements of the monitoring granularity, based on the coverage evaluation results, determine whether new candidate feature comparison points need to be added and where to search, generate feature comparison point search instructions, and clarify the search scope, direction and target to obtain more candidate feature comparison points that meet the requirements. Finally, according to the feature comparison point search instruction, further search is carried out on relevant data such as building structures or CAD drawings based on the screening results to find new monitoring comparison points that meet the requirements and add them to the screening results to form a second adaptation result, which can more accurately and comprehensively reflect the deformation of the building structure under complex working conditions, thereby improving the comprehensiveness and accuracy of building structure deformation monitoring.

[0044] Step S500: After confirming the characteristic comparison points using the second adaptation result, the monitoring sensors are distributed, and the deformation of the building is monitored using the distributed monitoring sensors and the characteristic comparison points.

[0045] Preferably, the second adaptation result is used to confirm the characteristic comparison points, that is, the points that can most accurately reflect the deformation characteristics of the building structure are selected from the second adaptation result, which are usually located in the key parts of the structure, such as wall corners, column tops, beam mid-spans, foundation connections, etc.; then, according to the characteristics of the characteristic comparison points and the specific requirements of building deformation monitoring, the appropriate type of monitoring sensor is selected, for example, fiber grating strain sensors, resistance strain gauges, etc. are selected to monitor linear deformation, for measuring displacement, laser displacement sensors, inclination sensors, etc. can be selected, for measuring vibration, acceleration sensors, etc. can be selected; then the selected monitoring sensors are distributed and installed on the building to ensure that each characteristic comparison point can be effectively monitored. When arranging, the accuracy, range, accessibility of the installation location, impact on the structure, etc. of the sensor should be considered. For example, when installing a strain sensor on a column, the appropriate height and position should be selected to avoid installation in places with decorative layers or places that are easily affected by external interference; when installing a displacement sensor on the roof, it should be ensured that it can accurately measure the displacement changes of the roof under different working conditions.

[0046] Preferably, distributed detection sensors are used to collect various physical quantity data at the feature comparison points in real time, such as strain, displacement, acceleration, etc., to reflect the state changes of the feature comparison points during the use of the building. For example, when the house is subjected to wind load, the strain sensor installed on the wall will collect the change data of the wall strain, and the displacement sensor installed on the roof will collect the change data of the horizontal displacement and vertical displacement of the roof. Then, by analyzing the sensor data, the deformation of the feature comparison points, such as deformation amount, deformation rate, deformation trend, etc., is obtained; and then, based on the analyzed and processed data, the deformation of the building is monitored and evaluated. For example, the deformation of the feature comparison point is compared with a preset threshold. If the deformation exceeds the threshold, it means that the building may have a safety hazard, and the cause is analyzed and corresponding measures are taken to deal with it.

[0047] Furthermore, step S500 also includes step S510, reading the monitoring results of the monitoring sensor, performing deformation identification through the monitoring results and the feature comparison points, and generating a first deformation identification result; step S520, obtaining historical monitoring data, using the historical monitoring data to perform deviation analysis of the monitoring results based on the feature comparison points, and establishing a second deformation identification result; step S530, reporting a deformation warning through the first deformation identification result and the second deformation identification result.

[0048] Preferably, the real-time monitoring data of each monitoring sensor on the building is read, and then combined with the characteristic comparison point to perform deformation identification, that is, whether the characteristic comparison point has been deformed and the degree and direction of the deformation are judged according to the changes in the monitoring data. For example, for a strain sensor installed on a beam, if the monitored strain value exceeds the normal range, and combined with the stress condition of the beam and the position of the characteristic comparison point, it can be judged that the beam has undergone a certain degree of bending deformation at this point, and then a first deformation identification result is formed, which may include the deformation amount of each characteristic comparison point, the deformation type (such as tensile deformation, compression deformation, shear deformation, etc.), the time when the deformation occurred, and other information; the historical monitoring data of the target building is obtained from the data record, including the characteristic comparison under different time periods and different working conditions. The monitoring information of the point is collected, and then the current monitoring results are compared and analyzed with the historical monitoring data, mainly to evaluate the deviation of the monitoring results of the feature comparison points relative to the historical monitoring data. Specifically, by calculating indicators such as deviation value and change rate, it is evaluated whether the current monitoring results exceed the normal change range reflected by the historical data. For example, if it is found that the displacement monitoring value of a feature comparison point suddenly increases in the near future, and there is a deviation compared with the historical data of the same season and the same usage conditions in the past, a second deformation identification result is established based on the deviation analysis result, which not only includes the degree and nature of the deviation between the current monitoring results and the historical data, but also may make a preliminary judgment on the cause of the deviation, such as whether it is caused by an increase in external load, accumulation of structural damage or changes in environmental factors.

[0049] Preferably, the first deformation recognition result and the second deformation recognition result are comprehensively evaluated, and the current deformation of the feature comparison point and the deviation from the historical data are fully considered. Then, based on the result of the comprehensive evaluation, it is decided whether to issue a deformation warning based on the preset warning threshold. Specifically, if the first deformation recognition result shows that the deformation of certain feature comparison points is large and has approached or exceeded the safety limit, or the second deformation recognition result shows that the degree of deviation of the monitoring result from the historical data is abnormal and there is a potential safety risk, a deformation warning is triggered and a detailed warning report is generated, which clearly points out the location of the feature comparison point where the deformation warning occurs, the type and degree of deformation, the deviation from the historical data, the possible cause analysis, and the recommended measures.

[0050] Furthermore, step S530 also includes step S531, configuring an early warning response database; step S532, using the early warning response database to perform early warning trigger analysis of the first deformation recognition result and the second deformation recognition result; step S533, calling the early warning plan to execute deformation early warning according to the early warning trigger analysis result.

[0051] Preferably, an early warning response database is configured based on deformation safety thresholds at different locations of different types of building structures, historical early warning records (including past early warning situations, corresponding deformation data, response measures taken, and final processing results), various parameters related to early warnings (such as sensor calibration parameters, relevant parameters of building structures, etc.), and detailed content of early warning plans (such as response processes corresponding to different levels of early warnings). Then, various data in the first deformation recognition results and the second deformation recognition results are compared and analyzed with relevant information such as thresholds in the early warning response database. For example, the deformation amount of the feature comparison point in the first deformation recognition result is compared with the corresponding safety threshold in the database to see whether it exceeds the allowable range. At the same time, the degree to which the monitoring result in the second deformation recognition result deviates from the historical data is analyzed to see whether it meets the early warning standard, thereby determining whether the early warning trigger condition is met, that is, obtaining an early warning trigger analysis result. If the early warning condition is met, the corresponding deformation early warning operation is performed according to the early warning plan stored in the early warning response database. For example, different notification methods may be used for deformation warnings of different severities, and the response measures that should be taken are clearly specified, such as building repair, to ensure the safety of the building.

[0052] In the above, refer to Figure 1 The deformation monitoring method of building structure combined with artificial intelligence according to the embodiment of the present invention is described in detail. Figure 2 A building deformation monitoring device combined with artificial intelligence according to an embodiment of the present invention is described.

[0053] The building deformation monitoring device combined with artificial intelligence according to the embodiment of the present invention is used to solve the technical problems existing in the prior art, such as difficulty in fully covering key parts, being greatly affected by environmental interference, and thus resulting in limited building deformation monitoring accuracy, insufficient monitoring efficiency, and poor adaptability to complex building structures. It achieves the technical effect of improving the efficiency, accuracy, and adaptability of building deformation monitoring. Figure 2 As shown, the building deformation monitoring device combined with artificial intelligence includes: a deformation sensitivity analysis module 10, a feature highlight point recognition module 20, a first adaptation result establishment module 30, a second adaptation result establishment module 40, and a building deformation monitoring module 50.

[0054] The deformation sensitivity analysis module 10 is used to interactively obtain the building information to be monitored, and then use the building information to perform deformation sensitivity analysis and establish deformation sensitivity concerns. The deformation sensitivity analysis includes structural weakness analysis, force concentration analysis, and environmental sensitivity analysis; the feature highlight point identification module 20 is used to parse the building information, identify feature highlight points based on the analysis results, and establish candidate feature comparison points; the first adaptation result establishment module 30 is used to obtain the observation adaptation analysis results of the candidate feature comparison points, and establish a first adaptation result based on the observation adaptation analysis results; the second adaptation result establishment module 40 is used to perform association matching of the candidate feature comparison points based on the deformation sensitivity concerns, and establish a second adaptation result based on the association matching results and the first adaptation result; the building deformation monitoring module 50 is used to use the second adaptation result to confirm the feature comparison points, and then distribute monitoring sensors to monitor the deformation of the building using the distributed monitoring sensors and feature comparison points.

[0055] The specific configuration of the deformation sensitivity analysis module 10 will be described in detail below. The deformation sensitivity analysis module 10 further includes: extracting the BIM data, surveying and mapping data, and design drawing data of the building information to establish a digital twin model; performing finite element simulation based on the digital twin model to simulate the structural response under varying loads and establish a first deformation sensitivity concern; performing force concentration analysis on the digital twin model to establish a second deformation sensitivity concern; performing an impact analysis of the digital twin model based on the coupling effects of temperature, humidity, and vibration environment to establish a third deformation sensitivity concern; and establishing the deformation sensitivity concern using the first deformation sensitivity concern, the second deformation sensitivity concern, and the third deformation sensitivity concern.

[0056] The specific configuration of the deformation sensitivity analysis module 10 will be described in detail below. The deformation sensitivity analysis module 10 further includes: activating a physical simulation tool and utilizing the tool to simulate the temperature, humidity, and vibration fields; performing simulation fitting of the digital twin model using a hybrid field composed of the temperature, humidity, and vibration fields; performing coupled evaluation based on the temperature, humidity, and vibration sensitivity indicators to establish the third deformation sensitivity focus.

[0057] The specific configuration of the feature salient point recognition module 20 will be described in detail below. The feature salient point recognition module 20 further includes: extracting structural geometric relationships, component relationships, material parameters, and node location information from CAD drawings using natural language processing and computer vision; extracting geometric visual features using the structural geometric relationships and component relationships to establish a first recognition result; locating component intersections, load-bearing nodes, connection nodes, edge points, and corner points using the component relationships and node location information, and establishing a second recognition result based on the positioning results; identifying material change points using the material parameters to establish a third recognition result; and establishing candidate feature comparison points based on the first, second, and third recognition results.

[0058] The specific configuration of the second adaptation result establishment module 40 will be described in detail below. The second adaptation result establishment module 40 further includes: generating a deformation type and monitoring granularity based on the deformation sensitivity concern; performing association matching on the candidate feature comparison points based on the deformation type and monitoring granularity to generate an association matching result; using the association matching result and the first adaptation result to screen candidate feature points and establish a screening result; using the monitoring granularity to perform a coverage evaluation on the association matching result, and generating a feature comparison point search instruction based on the coverage evaluation result; performing a new search for monitoring comparison points under the screening result based on the feature comparison point search instruction, and establishing a second adaptation result based on the new search result and the screening result.

[0059] The specific configuration of the building deformation monitoring module 50 will be described in detail below. The building deformation monitoring module 50 further includes: reading monitoring sensor monitoring results, performing deformation identification based on the monitoring results and the feature comparison points to generate a first deformation identification result; acquiring historical monitoring data, using the historical monitoring data to perform a deviation analysis of the monitoring results based on the feature comparison points to generate a second deformation identification result; and issuing a deformation warning based on the first and second deformation identification results.

[0060] The specific configuration of the building deformation monitoring module 50 will be described in detail below. The building deformation monitoring module 50 further includes: configuring an early warning response database; utilizing the early warning response database to perform early warning trigger analysis based on the first and second deformation identification results; and invoking an early warning solution to execute deformation early warning based on the early warning trigger analysis results.

[0061] The building deformation monitoring device combined with artificial intelligence provided in an embodiment of the present invention can execute the building deformation monitoring method combined with artificial intelligence provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0062] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0063] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A building deformation monitoring method combined with artificial intelligence is characterized by: The method comprises: After interactively obtaining the building information to be monitored, deformation sensitivity analysis is performed using the building information to establish deformation sensitivity concerns. The deformation sensitivity analysis includes structural weakness analysis, force concentration analysis, and environmental sensitivity analysis. Analyze the building information, identify prominent feature points based on the analysis results, and establish candidate feature comparison points; Obtaining observation adaptation analysis results of candidate feature comparison points, and establishing a first adaptation result based on the observation adaptation analysis results; performing association matching of the candidate feature comparison points based on the deformation-sensitive attention, and establishing a second adaptation result according to the association matching result and the first adaptation result; After confirming the characteristic comparison points using the second adaptation result, the monitoring sensors are distributed, and the deformation of the building is monitored using the distributed monitoring sensors and the characteristic comparison points.

2. The building deformation monitoring method combined with artificial intelligence according to claim 1, characterized in that: The method of performing deformation sensitivity analysis using the building construction information and establishing deformation sensitivity concerns includes: Extracting BIM data, surveying and mapping data, and design drawing data of the building construction information to establish a digital twin model; Perform finite element simulation based on the digital twin model to simulate the structural response under varying loads and establish a first deformation sensitivity concern; Performing a force concentration analysis on the digital twin model to establish a second deformation sensitivity focus; Conduct an impact analysis of the digital twin model based on the coupling effects of temperature, humidity, and vibration environment, and establish a third deformation sensitivity concern; The deformation-sensitive attention is established using the first deformation-sensitive attention, the second deformation-sensitive attention, and the third deformation-sensitive attention.

3. The building deformation monitoring method combined with artificial intelligence according to claim 2, characterized in that: The impact analysis of the digital twin model based on the coupling effect of temperature, humidity and vibration environment is carried out to establish the third deformation sensitivity concern, including: Activating a physical simulation tool, and using the physical simulation tool to simulate temperature field, humidity field, and vibration field; The digital twin model is simulated and fitted through a mixed field consisting of temperature field, humidity field and vibration field, and a coupling evaluation is performed based on temperature sensitivity index, humidity sensitivity index and vibration sensitivity index to establish the third deformation sensitivity concern.

4. The building deformation monitoring method combined with artificial intelligence according to claim 1, characterized in that: The step of analyzing the building information, identifying prominent feature points based on the analysis results, and establishing candidate feature comparison points includes: Use natural language processing and computer vision to extract structural geometry, component relationships, material parameters, and node location information from CAD drawings; Extracting geometric visual features using the structural geometric relationship and component relationship to establish a first recognition result; Locating component intersections, load-bearing nodes, connection nodes, edge points, and corner points using the component relationships and node position information, and establishing a second recognition result based on the positioning results; Identifying material change points using the material parameters to establish a third identification result; Candidate feature comparison points are established based on the first recognition result, the second recognition result, and the third recognition result.

5. The building deformation monitoring method combined with artificial intelligence according to claim 1, characterized in that: Performing association matching on the candidate feature comparison points based on the deformation-sensitive attention, and establishing a second adaptation result according to the association matching result and the first adaptation result, including: generating deformation types and monitoring granularity according to the deformation sensitivity concerns; Performing association matching on the candidate feature comparison points based on the deformation type and monitoring granularity to generate an association matching result; Screening candidate feature points using the association matching result and the first adaptation result to establish a screening result; Performing coverage evaluation of the associated matching results using the monitoring granularity, and generating a feature comparison point search instruction based on the coverage evaluation results; A new search for monitoring comparison points under the screening results is performed according to the feature comparison point search instruction, and a second adaptation result is established based on the new search results and the screening results.

6. The building deformation monitoring method combined with artificial intelligence according to claim 1, characterized in that: The method of using distributed monitoring sensors and feature comparison points to monitor building deformation also includes: Reading a monitoring result of a monitoring sensor, performing deformation recognition based on the monitoring result and the characteristic comparison point, and generating a first deformation recognition result; Acquiring historical monitoring data, performing a monitoring result deviation analysis based on feature comparison points using the historical monitoring data, and establishing a second deformation recognition result; A deformation warning is issued based on the first deformation recognition result and the second deformation recognition result.

7. The building deformation monitoring method combined with artificial intelligence according to claim 6, characterized in that: The issuing of a deformation warning based on the first deformation recognition result and the second deformation recognition result includes: Configure the early warning response database; Using the early warning response database to perform early warning trigger analysis on the first deformation recognition result and the second deformation recognition result; According to the warning trigger analysis results, the warning plan is called to execute the deformation warning.

8. The building deformation monitoring device combined with artificial intelligence is characterized by: The device is used to implement the building deformation monitoring method combined with artificial intelligence according to any one of claims 1 to 7, and the device comprises: A deformation sensitivity analysis module is used to interactively obtain building information to be monitored, and then use the building information to perform deformation sensitivity analysis and establish deformation sensitivity concerns. The deformation sensitivity analysis includes structural weakness analysis, force concentration analysis, and environmental sensitivity analysis. A feature salient point recognition module is used to analyze the building construction information, identify feature salient points based on the analysis results, and establish candidate feature comparison points; A first adaptation result establishment module is used to obtain the observed adaptation analysis results of the candidate feature comparison points and establish the first adaptation result according to the observed adaptation analysis results; A second adaptation result establishing module, configured to perform association matching of the candidate feature comparison points based on the deformation-sensitive attention, and establish a second adaptation result according to the association matching result and the first adaptation result; The housing construction deformation monitoring module is used to distribute monitoring sensors after confirming the characteristic comparison points using the second adaptation result, and monitor the deformation of the housing construction using the distributed monitoring sensors and characteristic comparison points.

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