House health intelligent physical examination method based on large model

Abstract

The invention discloses a house health intelligent physical examination method based on a large model, and the method comprises the following specific steps: (1) carrying out the fusion analysis of multi-source monitoring data, and extracting the features of the safety hidden troubles of a house; (2) formulating house health examination standards; (3) constructing a house health intelligent physical examination large model, and establishing an intelligent evaluation algorithm; (4) developing a mobile terminal APP; according to the method, fusion analysis is carried out on monitoring data, space features of potential safety hazards of the houses are extracted, mass house feature data collected by non-professionals in an area are subjected to batch, efficient, low-cost and intelligent analysis through a house health AI intelligent evaluation algorithm, and the house health state is judged; according to the invention, the health conditions of massive houses in the area can be evaluated, a unified house evaluation standard is provided during evaluation, and the convenience and efficiency of monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of building inspection technology, and in particular to a smart health check method for buildings based on a large model. Background Technology

[0002] Building inspection and assessment refers to the process of evaluating the structural safety of a building using scientific methods and technical means. Its main purpose is to identify potential safety hazards in a building and provide corresponding solutions. Building inspection is not only related to the safety of residents' lives and property but also to the maintenance of the city's image. Over time, some houses may develop problems such as cracks and settlement, which require professional inspection to determine the severity of the problems and take necessary remedial measures.

[0003] In the process of building safety assessment, many regions still use traditional testing methods, and the standards and specifications for building safety assessment vary from region to region, which brings difficulties to the assessment work. The lack of unified standards may lead to inconsistent results for the same building when assessed in different regions, affecting residents' trust. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to address the shortcomings of existing technologies and provide a smart health check method for houses based on a large model.

[0005] Technical solution: This invention provides a smart health check method for houses based on a large model, comprising the following steps: (1) Multi-source monitoring data fusion analysis to extract characteristics of building safety hazards; (2) Establish health checkup standards for housing; (3) Construct a large-scale intelligent health checkup model for houses and establish an intelligent assessment algorithm; (4) Develop a mobile app; (5) Identify unhealthy buildings and formulate urban renewal decisions.

[0006] A further improvement of the present invention is that, in step (1), through the fusion analysis of multi-source monitoring data from text, images and on-site distributed sensors, spatial characteristics of building safety hazards are extracted, and the correlation between building spatial characteristics and building health is clarified. The correlation includes building usage time, structural type, and structural displacement or deformation, structural component cracks and defects caused by internal and external forces.

[0007] A further improvement of the present invention is that the structural types include reinforced concrete structures, masonry structures, and steel structures, and the internal and external forces include uneven settlement of the foundation, earthquakes, explosive forces, impact forces, typhoons, building demolition and alteration, and material deterioration.

[0008] A further improvement of the present invention is that, in step (2), by summarizing the results of multi-source monitoring data and the extraction of characteristics of building safety hazards, the feasibility of using collected images of internal and external building hazards and simple spatial morphology data to assess building health is explored, a building health checkup program that supports the participation of non-professionals is studied, and building health checkup standards are formulated.

[0009] A further improvement of the present invention is that, in step (3), a large-scale intelligent health checkup model for houses is constructed using standards, specifications, on-site hazard pictures, house appraisal reports and data, and distributed real-time monitoring data; at the same time, for a specific area, a typical building intelligent monitoring system is established in the area according to standards and specifications to collect house health data in real time, which serves as a verification and optimization of the large-scale intelligent health checkup model for houses, and a regional house health system and a house health AI intelligent assessment algorithm are developed. The algorithm performs batch, efficient, inexpensive and intelligent analysis on massive house feature data collected by non-professionals in the area to determine the health status of houses.

[0010] A further improvement of the present invention is that, in step (4), a mobile APP suitable for non-professionals is developed based on the housing health examination standards. Non-professionals include homeowners and area managers. Non-professionals use the mobile APP to collect housing characteristic data on-site. The mobile APP summarizes the collected data into the regional housing health database. The housing health supervision system calls the regional housing health AI intelligent assessment algorithm to assess the health status of a large number of houses in the region.

[0011] A further improvement of the present invention is that, in step (5), if the regional intelligent assessment algorithm determines that a house is unhealthy, the house owner decides whether to apply for a house safety assessment. If a house is assessed as unhealthy or the owner abandons the assessment but the physical examination shows that the house is unhealthy, the urban renewal command decides the demolition time node.

[0012] A further improvement of this invention is that each individual component in the component set U = {slab, beam, column} is quantitatively described for building safety status data analysis. The safety of each component is evaluated using language and data descriptions, and is divided into three categories: A, B, and C. The safety rating of building components is divided into three levels: Au, Bu, and Cu. Based on the different levels of the component set, its qualitative description is provided to support future building safety inspections. The component set safety assessment is as follows, where w(X) is the number of components rated at level X. Au grade: and ; Bu level: ; Cu grade: .

[0013] A further improvement of the present invention is that the safety level of each representative floor of the building is rated as min{slab set rating, beam set rating, column set rating}; The safety rating of the superstructure's load-bearing capacity is determined by the proportion of each representative layer's safety level. The proportions of each representative layer's safety level under different ratings are as follows: Au grade: and ; Bu level: ; Cu grade: .

[0014] A further improvement of the present invention is that the safety level of the upper load-bearing structure is determined according to the following steps based on the assessment results: S11. Based on the rating results of the superstructure's load-bearing capacity and the structure's lateral displacement or tilt, the lower of the two levels shall be taken as the safety level of the superstructure's load-bearing capacity. S12. When the superstructure is rated as Bu according to the preceding clause, but if any of the c-grade components contained in each major component set are found to be in one of the following situations, the rating shall be downgraded to Cu: (1) Node connections where Class C components intersect; (2) More than one Class C incident exists in densely populated areas or other locations with severe consequences of damage; That is, the safety level of the superstructure is min{superstructure load-bearing capacity rating, superstructure lateral displacement rating}; A building rating of Au indicates that the building's safety meets the requirements of Au level in this standard, does not affect the overall load-bearing capacity, and may require measures to be taken for individual general components. The building is judged to be in good condition. A building rating of Bu indicates that the building's safety is slightly lower than the standard's requirement for Au level, but it does not significantly affect the overall load-bearing capacity. There may be a very small number of components that require measures, and the building is judged to be in a sub-healthy state. A building rated as Cu indicates that its safety does not meet the requirements of the Au grade in this standard, seriously affecting the overall load-bearing capacity. Immediate measures must be taken, and the building is judged to be unhealthy.

[0015] Compared with existing technologies, the intelligent health check method for houses based on a large model provided by this invention achieves at least the following beneficial effects: This invention integrates and analyzes multi-source monitoring data to extract characteristics of building safety hazards; supports building health check-up programs involving non-professionals and establishes building health check-up standards; utilizes standards, specifications, on-site hazard images, building appraisal reports and data, and distributed real-time monitoring data to construct a large-scale intelligent building health check-up model; for specific areas, it establishes intelligent monitoring systems for typical buildings within the area based on standards and specifications, collects building health data in real time, and uses this data to verify and optimize the large-scale intelligent building health check-up model; it develops a regional building health system and a building health AI intelligent assessment algorithm. This algorithm performs batch, efficient, inexpensive, and intelligent analysis of massive amounts of building characteristic data collected by non-professionals within the area to determine the building health status and assess the health status of massive numbers of buildings within the area. The assessment has unified building evaluation standards and is convenient and reliable. Attached Figure Description

[0016] Figure 1 This is a flowchart of the process of the present invention. Detailed Implementation

[0017] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0018] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

[0019] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0020] Implementation, for example Figure 1 As shown, a smart health check method for houses based on a large model includes the following steps: (1) Multi-source monitoring data fusion analysis to extract characteristics of building safety hazards; (2) Establish health checkup standards for housing; (3) Construct a large-scale intelligent health checkup model for houses and establish an intelligent assessment algorithm; (4) Develop a mobile app; (5) Identify unhealthy buildings and formulate urban renewal decisions.

[0021] To further explain this embodiment, it should be noted that in step (1), through the fusion analysis of multi-source monitoring data from text, images and on-site distributed sensors, spatial characteristics of building safety hazards are extracted, and the correlation between building spatial characteristics and building health is clarified. The correlation includes building usage time, structural type, and structural displacement or deformation, structural component cracks and defects caused by internal and external forces.

[0022] To further explain this embodiment, it should be noted that the structural types include reinforced concrete structures, masonry structures, and steel structures, and the internal and external forces include uneven settlement of the foundation, earthquakes, explosive forces, impact forces, typhoons, building demolition and alteration, and material deterioration.

[0023] To further explain this embodiment, it should be noted that in step (2), by summarizing the results of multi-source monitoring data and the extraction of characteristics of building safety hazards, the feasibility of using collected images of internal and external building hazards and simple spatial morphology data to assess building health is explored, a building health checkup program that supports the participation of non-professionals is studied, and building health checkup standards are formulated.

[0024] To further explain this embodiment, it should be noted that in step (3), a large-scale intelligent health checkup model for houses is constructed using standards, specifications, on-site hazard pictures, house appraisal reports and data, and distributed real-time monitoring data. At the same time, for a specific area, a typical building intelligent monitoring system is established in the area according to standards and specifications to collect house health data in real time. This data is used to verify and optimize the large-scale intelligent health checkup model for houses. A regional house health system and a house health AI intelligent assessment algorithm are developed. The algorithm performs batch, efficient, inexpensive, and intelligent analysis on massive house feature data collected by non-professionals in the area to determine the health status of houses.

[0025] To further explain this embodiment, it should be noted that in step (4), a mobile APP suitable for non-professionals is developed based on the housing health examination standards. Non-professionals include homeowners and area managers. Non-professionals use the mobile APP to collect housing feature data on-site. The mobile APP summarizes the collected data into the regional housing health database. The housing health supervision system calls the regional housing health AI intelligent assessment algorithm to assess the health status of a large number of houses in the region.

[0026] To further explain this embodiment, it should be noted that in step (5), if the regional intelligent assessment algorithm determines that a house is unhealthy, the house owner shall decide whether to apply for a house safety assessment. If a house is assessed as unhealthy or the owner waives the assessment but the physical examination shows that the house is unhealthy, the urban renewal command shall decide on the demolition time.

[0027] To further explain this embodiment, it should be noted that each individual component in the component set U={slab, beam, column} is quantitatively described for building safety status data analysis. The safety of each component is evaluated using language and data descriptions, and is divided into three standards: A, B, and C. The safety rating of building components is divided into three levels: Au, Bu, and Cu. Based on the different levels of the component set, its qualitative description is provided to support future building safety inspections. The component set safety assessment is as follows, where w(X) is the number of components rated at level X. Au grade: and ; Bu level: ; Cu grade: .

[0028] To further explain this embodiment, it should be noted that the safety level of each representative floor of the building is rated as min{slab set rating, beam set rating, column set rating}. The safety rating of the superstructure's load-bearing capacity is determined by the proportion of each representative layer's safety level. The proportions of each representative layer's safety level under different ratings are as follows: Au grade: and ; Bu level: ; Cu grade: .

[0029] To further explain this embodiment, it should be noted that the safety level of the upper load-bearing structure is determined according to the following steps based on the assessment results: S11. Based on the rating results of the superstructure's load-bearing capacity and the structure's lateral displacement or tilt, the lower of the two levels shall be taken as the safety level of the superstructure's load-bearing capacity. S12. When the superstructure is rated as Bu according to the preceding clause, but if any of the c-grade components contained in each major component set are found to be in one of the following situations, the rating shall be downgraded to Cu: (1) Node connections where Class C components intersect; (2) More than one Class C incident exists in densely populated areas or other locations with severe consequences of damage; That is, the safety level of the superstructure is min{superstructure load-bearing capacity rating, superstructure lateral displacement rating}; A building rating of Au indicates that the building's safety meets the requirements of Au level in this standard, does not affect the overall load-bearing capacity, and may require measures to be taken for individual general components. The building is judged to be in good condition. A building rating of Bu indicates that the building's safety is slightly lower than the standard's requirement for Au level, but it does not significantly affect the overall load-bearing capacity. There may be a very small number of components that require measures, and the building is judged to be in a sub-healthy state. A building rated as Cu indicates that its safety does not meet the requirements of the Au grade in this standard, seriously affecting the overall load-bearing capacity. Immediate measures must be taken, and the building is judged to be unhealthy.

[0030] Based on the above embodiments, this invention integrates and analyzes multi-source monitoring data to extract characteristics of building safety hazards; supports building health check-up programs involving non-professionals and formulates building health check-up standards; utilizes standards, specifications, on-site hazard images, building appraisal reports and data, and distributed real-time monitoring data to construct a large-scale intelligent building health check-up model; for specific areas, it establishes a typical building intelligent monitoring system based on standards and specifications, capable of collecting building health data in real time, which serves as verification and optimization of the large-scale intelligent building health check-up model; it develops a regional building health system and a building health AI intelligent assessment algorithm; the building health AI intelligent assessment algorithm performs batch, efficient, inexpensive, and intelligent analysis of massive building characteristic data collected by non-professionals within the region to determine the building health status and assess the health status of massive buildings within the region, with unified building assessment standards, making it convenient and reliable.

[0031] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

Claims

1. A method for intelligent health check of houses based on a large model, characterized in that, Includes the following steps: (1) Multi-source monitoring data fusion analysis to extract characteristics of building safety hazards; (2) Establish health checkup standards for housing; (3) Construct a large-scale intelligent health checkup model for houses and establish an intelligent assessment algorithm; (4) Develop a mobile app; (5) Identify unhealthy buildings and formulate urban renewal decisions.

2. The intelligent health check method for houses based on a large model according to claim 1, characterized in that, In step (1), the spatial characteristics of building safety hazards are extracted by fusion analysis of multi-source monitoring data from text, images and on-site distributed sensors, and the correlation between building spatial characteristics and building health is clarified. The correlation includes building usage time, structural type and structural displacement or deformation, structural component cracks and defects caused by internal and external forces.

3. The intelligent health check method for houses based on a large model according to claim 2, characterized in that... , The structural types include reinforced concrete structures, masonry structures, and steel structures. The internal and external forces include uneven settlement of the foundation, earthquakes, explosive forces, impact forces, typhoons, building demolition and alteration, and material deterioration.

4. The intelligent health check method for houses based on a large model according to claim 1, characterized in that... , In step (2), the feasibility of using collected images of internal and external hazards and simple spatial morphology data to assess the health of a house is explored by summarizing the results of multi-source monitoring data and the extraction of characteristics of house safety hazards. The study also explores house health check-up programs that support the participation of non-professionals and formulates house health check-up standards.

5. The intelligent health check method for houses based on a large model according to claim 1, characterized in that, In step (3), a large-scale intelligent health checkup model for houses is constructed using standards, specifications, on-site hazard images, house appraisal reports and data, and distributed real-time monitoring data. At the same time, for a specific area, a typical building intelligent monitoring system is established in the area according to standards and specifications to collect house health data in real time. This data is used to verify and optimize the large-scale intelligent health checkup model for houses. A regional house health system and a house health AI intelligent assessment algorithm are developed. The algorithm performs batch, efficient, inexpensive, and intelligent analysis on massive house feature data collected by non-professionals in the area to determine the health status of houses.

6. The intelligent health check method for houses based on a large model according to claim 1, characterized in that, In step (4), a mobile APP suitable for non-professionals is developed based on the housing health examination standards. Non-professionals include homeowners and area managers. Non-professionals use the mobile APP to collect housing characteristic data on-site. The mobile APP summarizes the collected data into the regional housing health database. The housing health supervision system calls the regional housing health AI intelligent assessment algorithm to assess the health status of a large number of houses in the region.

7. The intelligent health check method for houses based on a large model according to claim 1, characterized in that, In step (5), if a house is determined to be unhealthy by the regional intelligent assessment algorithm, the house owner shall decide whether to apply for a house safety assessment. If a house is determined to be unhealthy or the owner waives the assessment but the physical examination shows that the house is unhealthy, the urban renewal command shall decide on the demolition time.

8. The intelligent health check method for houses based on a large model according to claim 6, characterized in that, Quantitative descriptions are provided for each individual component in the component set U={slab, beam, column} for building safety status data analysis. The safety of each component is evaluated using language and data descriptions, and is divided into three standards: A, B, and C. The safety rating of building components is divided into three levels: Au, Bu, and Cu. Based on the different levels of the component set, its qualitative description is provided to support future building safety inspections. The component set safety assessment is as follows, where w(X) is the number of components rated at level X. Au grade: and ; Bu level: ; With level: 。 9. The intelligent health check method for houses based on a large model according to claim 6, characterized in that, The safety level of each representative floor of the building is rated as min{slab set rating, beam set rating, column set rating}; The safety rating of the superstructure's load-bearing capacity is determined by the proportion of each representative layer's safety level. The proportions of each representative layer's safety level under different ratings are as follows: Au grade: and ; Bu level: ; With level: 。 10. The intelligent health check method for houses based on a large model according to claim 9, characterized in that, The safety level of the superstructure is determined according to the following steps based on the assessment results: S11. Based on the rating results of the superstructure's load-bearing capacity and the structure's lateral displacement or tilt, the lower of the two levels shall be taken as the safety level of the superstructure's load-bearing capacity. S12. When the superstructure is rated as Bu according to the preceding paragraph, but if any of the c-grade components contained in each major component set are found to be in one of the following situations, the rating shall be downgraded to Cu: (1) Node connections where Class C components intersect; (2) More than one Class C incident exists in densely populated areas or other locations with severe consequences of damage; That is, the safety level of the superstructure is min{superstructure load-bearing capacity rating, superstructure lateral displacement rating}; A building rating of Au indicates that the building's safety meets the requirements of Au level in this standard, does not affect the overall load-bearing capacity, and may require measures to be taken for individual general components. The building is judged to be in good condition. A building rating of Bu indicates that the building's safety is slightly lower than the standard's requirement for Au level, but it does not significantly affect the overall load-bearing capacity. There may be a very small number of components that require measures, and the building is judged to be in a sub-healthy state. A building rated as Cu indicates that its safety does not meet the requirements of the Au grade in this standard, seriously affecting the overall load-bearing capacity. Immediate measures must be taken, and the building is judged to be unhealthy.

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