House deformation monitoring method and system

Abstract

The invention relates to the technical field of house monitoring, and discloses a house deformation monitoring method, which comprises the following steps: S1, acquiring a real-scene image of each wall surface of a house, importing the real-scene image into a trained image recognition model, and outputting a result to indicate whether a crack exists in the wall surface or not; s2, marking a wall body with a crack on the wall surface as a target wall body, and recording original characteristic parameters of the corresponding crack; s3, a crack measuring instrument is installed at the crack of the target wall, and crack width real-time change data is collected; s4, a plurality of first monitoring points are preset on the target wall body, and a plurality of second monitoring points are preset on a floor slab connected with the target wall body; and S5, acquiring position information data of the first monitoring point and the second monitoring point through monitoring, and comprehensively analyzing the position information data of the first monitoring point and the second monitoring point. According to the invention, the timeliness of capturing the house monitoring information is improved, and the potential safety hazard generated in the house deformation monitoring process is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of house monitoring, and in particular to a house deformation monitoring method and system. Background Art

[0002] With the aging of large urban infrastructure such as buildings and bridges in my country, building structural health monitoring is becoming more and more important, and there is an urgent need to develop an automatic structural health monitoring system based on new technologies. The existing technology for house deformation monitoring usually involves: staff regularly visit the house to be monitored, conduct field surveys using various measuring instruments, and then analyze the survey data to learn about the deformation status of the house.

[0003] Due to the emergence of various advanced measuring instruments and the continuous improvement of the professionalism of relevant staff, the accuracy of house deformation monitoring has been significantly improved. However, the problem with the existing house deformation monitoring methods is that regular measurement and analysis of houses still cannot achieve the effect of real-time monitoring. In addition, in special circumstances such as deformation monitoring of dangerous buildings, regular manual field surveys still have certain safety hazards. Summary of the invention

[0004] The purpose of the present invention is to provide a housing deformation monitoring method and system to solve the above technical problems:

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A housing deformation monitoring method, the method comprising:

[0007] S1, collecting real-life images of each wall of the house, and importing the real-life images into a trained image recognition model, and outputting results indicating whether there are cracks on the wall;

[0008] S2. Mark the wall with cracks on the wall surface as the target wall, and record the original characteristic parameters of the corresponding cracks;

[0009] S3. Install a crack measuring instrument at the crack of the target wall to collect real-time change data of the crack width;

[0010] S4, presetting a number of first monitoring points on the target wall, and presetting a number of second monitoring points on the floor slab connected to the target wall;

[0011] S5. Acquire location information data of the first monitoring point and the second monitoring point through monitoring, and perform comprehensive analysis on the location information data of the first monitoring point and the second monitoring point;

[0012] S6. Establish a housing deformation early warning strategy according to the results of the comprehensive analysis.

[0013] Through the above technical scheme, a method for monitoring deformation of a house is provided. When the deformation of a house is monitored, the target wall with cracks is first located through image acquisition and recognition, and then monitoring points are set on the target wall and the floor slab connected to the target wall. Combined with the monitoring of the change in crack width and the monitoring of the displacement change of each monitoring point, the monitoring result data is comprehensively analyzed to determine the deformation of the house. The monitoring process provided above only requires manual intervention when arranging measuring equipment (such as a crack meter), and then the house can be automatically monitored and analyzed in real time. Therefore, the above monitoring method not only improves the timeliness of capturing monitoring information data, but also reduces the safety hazards caused by the house deformation monitoring process.

[0014] As a further technical solution, before step S3, the method further includes:

[0015] S21, pre-analyzing original characteristic parameters of cracks on the target wall;

[0016] S22, performing a safety evaluation on the tested house according to the result of the pre-analysis, the result of the safety evaluation indicating whether the house meets the safety monitoring standard;

[0017] S23. If the tested house does not meet the safety monitoring standards, steps S3 to S6 are not executed, and a danger warning signal for the corresponding house is generated; otherwise, steps S3 to S6 are continued to be executed.

[0018] As a further technical solution, the original characteristic parameters of the cracks include: initial length and initial width of the cracks and the total number of cracks on the same target wall.

[0019] As a further technical solution, the process of pre-analyzing the original characteristic parameters of the cracks on the target wall includes:

[0020] By formula Calculate the risk assessment coefficient R of the house;

[0021] Among them, k i is the preset correlation coefficient, and k i >0; N is the number of cracks on the target wall; l i and are the length and average width of the i-th crack on the target wall respectively; S is the total area of ​​the target wall;

[0022] The risk assessment coefficient R is compared with the preset safety standard value R st To compare:

[0023] If R>R st , then it is judged that the house does not meet the safety monitoring standards;

[0024] If R≤R st, then the house is judged to meet the safety monitoring standards.

[0025] As a further technical solution, the process of presetting the first monitoring point and the second monitoring point includes:

[0026] A plurality of first monitoring points are evenly distributed in rows and columns around the cracks of the target wall;

[0027] Install a laser rangefinder at a preset position on the floor slab connected to the target wall;

[0028] Taking the position of the laser rangefinder as the reference point, select several measuring points at the intersection of the floor slab and the target wall, and install a static level. Both the measuring points and the reference points are marked as the second monitoring points.

[0029] The distance from each monitoring point on the target wall to the reference point is obtained by a laser rangefinder, and the vertical displacement data of each measuring point is obtained by a static level.

[0030] As a further technical solution, the process of comprehensively analyzing the location information data of the first monitoring point and the second monitoring point includes:

[0031] By formula Calculate and obtain the settlement variation coefficient D1 of the corresponding floor;

[0032] Among them, ε1 and ε2 are the preset first weight coefficients; ω i is the preset second weight coefficient, Δh i is the height difference between each measuring point and the reference point; is the average height difference; n is the number of measuring points;

[0033] The settlement variation coefficient D1 of the corresponding floor is compared with the first preset threshold D θ，1 Conduct comparative analysis:

[0034] If D1>D θ，1 , then a corresponding danger warning signal about excessive deformation of the house floor is generated;

[0035] If D1≤D θ，1 , no danger warning signal for floor deformation is generated.

[0036] As a further technical solution, the process of comprehensively analyzing the location information data of the first monitoring point and the second monitoring point also includes:

[0037]

[0038] The wall deformation coefficient D2 of the target wall is calculated by formula (3) to (4);

[0039] Wherein, dj is the measured distance between the jth first monitoring point and the reference point; h j is the initial height from the jth first monitoring point to the intersection of the target wall and the floor; αj is the laser elevation angle when the laser measures the position of the jth monitoring point; Δw is the maximum change value of the crack width on the target wall; w0 is the preset standard proportional coefficient; m is the number of the first monitoring points; μj is the preset third weight coefficient;

[0040] The wall deformation coefficient D2 and the second preset threshold D θ，2 Conduct comparative analysis:

[0041] If D2>D θ，2 , then a corresponding danger warning signal about excessive deformation of the house wall is generated;

[0042] If D2≤D θ，2 , no danger warning signal for wall deformation is generated.

[0043] A housing deformation monitoring method and system, the system comprising:

[0044] An image acquisition module, used to acquire image information of the house wall;

[0045] Image recognition module, which identifies cracks in wall images based on convolutional neural networks and records the original characteristic parameters of the cracks;

[0046] The pre-analysis module is used to pre-analyze the original characteristic parameters and conduct safety evaluation on the tested house based on the results of the pre-analysis;

[0047] A data acquisition module, used to collect location information data of a first monitoring point and a second monitoring point on a wall and a floor;

[0048] A data processing module, used for comprehensively analyzing the location information data of the first monitoring point and the second monitoring point, and establishing a house deformation early warning strategy based on the comprehensive analysis;

[0049] Human-computer interaction module, used to implement house deformation early warning strategy.

[0050] Beneficial effects of the present invention:

[0051] When the present invention performs deformation monitoring on a house, the target wall with cracks is first located through image acquisition and recognition, and then monitoring points are set on the target wall and the floor slab connected to the target wall. Combined with the monitoring of crack width changes and displacement changes of each monitoring point, the monitoring result data is comprehensively analyzed to judge the deformation of the house. The above monitoring process only requires manual intervention when arranging the measuring equipment, and then the house can be automatically monitored and analyzed in real time. Therefore, the timeliness of capturing house monitoring information is improved, and the safety hazards caused by the house deformation monitoring process are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described below in conjunction with the accompanying drawings.

[0053] Figure 1 It is a flow chart of the housing deformation monitoring method in the present invention;

[0054] Figure 2 This is a block diagram of the contents of the house deformation monitoring system in the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 As shown, a housing deformation monitoring method comprises:

[0057] S1. Collect real-life images of each wall of the house. The real-life images can be taken by staff in the house to be tested, or other collection methods can be selected according to the specific environment of the house. For example, for dangerous buildings that have been uninhabited for a long time, drones can be used to collect images of scenes inside and outside the house. Then the real-life images are imported into the trained image recognition model, and the output results indicate whether there are cracks on the wall, such as using a convolutional neural network recognition model to identify wall cracks in the image.

[0058] S2. Mark the wall with cracks as the target wall, and record the original characteristic parameters of the corresponding cracks. The original characteristic parameters are specifically the initial length and initial width of the cracks and the total number of cracks on the same target wall.

[0059] S3. Install a crack meter at the crack of the target wall to collect real-time crack width change data. This change data represents the development trend of the wall crack and is also the driving factor of wall deformation. The crack width change trend can reflect whether the stability and safety of the wall structure are gradually deteriorating. If the crack is expanding, it may mean that the structural problem is getting worse.

[0060] S4, presetting a number of first monitoring points on the target wall, and presetting a number of second monitoring points on the floor slab connected to the target wall;

[0061] S5. Acquire the position information data of the first monitoring point and the second monitoring point through monitoring. In the specific monitoring process, obtain the distance change data from the first monitoring point to the specific reference point through a laser rangefinder, obtain the settlement change data of the second monitoring point through a static level, and perform a comprehensive analysis on the position information data of the first monitoring point and the second monitoring point;

[0062] S6. Establish a housing deformation early warning strategy according to the results of the comprehensive analysis.

[0063] Through the above technical scheme, this embodiment provides a method for monitoring deformation of a house. When monitoring the deformation of a house, the target wall with cracks is first located through image acquisition and recognition, and then monitoring points are set on the target wall and the floor slab connected to the target wall. Combined with the monitoring of the change in crack width and the monitoring of the displacement change of each monitoring point, the monitoring result data is comprehensively analyzed to determine the deformation of the house. The above monitoring process only requires manual intervention when arranging the measuring equipment, and then the house can be automatically monitored and analyzed in real time. Therefore, the timeliness of capturing monitoring information is improved, and the safety hazards caused by the house deformation monitoring process are reduced.

[0064] Before step S3, the method further includes:

[0065] S21, pre-analyzing original characteristic parameters of cracks on the target wall;

[0066] S22, performing a safety evaluation on the tested house according to the result of the pre-analysis, the result of the safety evaluation indicating whether the house meets the safety monitoring standard;

[0067] S23. If the tested house does not meet the safety monitoring standards, steps S3 to S6 are not executed, and a danger warning signal for the corresponding house is generated; otherwise, steps S3 to S6 are continued to be executed.

[0068] Through the above technical scheme, this embodiment provides the preparatory work content before step S3. Specifically, the original characteristic parameters of the cracks on the target wall are first pre-analyzed. The purpose of the pre-analysis is to determine whether there are major safety hazards in the house to be tested. If so, step S3 and other subsequent steps cannot be executed. At the same time, a corresponding danger warning signal needs to be generated, which can be sent to people near the house.

[0069] The original characteristic parameters of the cracks include: initial length and initial width of the cracks and the total number of cracks on the same target wall.

[0070] Through the above technical solution, this embodiment provides the specific content of the original characteristic parameters of the cracks. The above-mentioned parameters can reflect the danger of the house, especially for the deformation monitoring of dangerous buildings.

[0071] The process of pre-analyzing the original characteristic parameters of the cracks on the target wall includes:

[0072] By formula Calculate the risk assessment coefficient R of the house;

[0073] Among them, k i is the preset correlation coefficient, which is related to the shape and direction of the crack, and k i >0; N is the number of cracks on the target wall; l i and are the length and average width of the i-th crack on the target wall respectively; S is the total area of ​​the target wall;

[0074] The risk assessment coefficient R is compared with the preset safety standard value R st To compare:

[0075] If R>R st , then it is judged that the house does not meet the safety monitoring standards;

[0076] If R≤R st , then the house is judged to meet the safety monitoring standards.

[0077] Through the above technical solution, this embodiment provides a process for pre-analyzing the original characteristic parameters of the cracks on the target wall. Specifically, firstly, the formula Calculate the risk assessment coefficient R of the house. The larger the risk assessment coefficient, the more dangerous the house is, and it is not suitable for the layout of monitoring equipment.

[0078] The process of presetting the first monitoring point and the second monitoring point includes:

[0079] Several first monitoring points are evenly distributed in rows and columns around the cracks of the target wall, and a laser rangefinder is installed at a preset position on the floor connected to the target wall. The first monitoring points are marked with highly reflective materials to improve the measurement accuracy of the laser rangefinder. The laser rangefinder is installed on the floor through an automatically adjustable multi-axis bracket (such as a robotic arm) to facilitate the collection of position information of multiple first monitoring points.

[0080] Taking the position of the laser rangefinder as the reference point, select several measuring points at the intersection of the floor slab and the target wall, and install a static level. Both the measuring points and the reference points are marked as the second monitoring points.

[0081] The distance from each monitoring point on the target wall to the reference point is obtained by a laser rangefinder, and the vertical displacement data of each measuring point is obtained by a static level.

[0082] Through the above technical scheme, this embodiment provides a process of presetting the first monitoring point and the second monitoring point. When presetting the first monitoring point, since the wall surface around the crack is prone to obvious deformation, a number of first monitoring points are evenly distributed in rows and columns around the crack of the target wall. When presetting the second monitoring point, the position of the laser rangefinder is used as the reference point, and a number of measuring points are selected at the intersection of the floor and the target wall, and a static level is installed. The measuring points and the reference points are marked as second monitoring points.

[0083] The process of comprehensively analyzing the location information data of the first monitoring point and the second monitoring point includes:

[0084] By formula Calculate and obtain the settlement variation coefficient D1 of the corresponding floor;

[0085] Among them, ε1 and ε2 are the preset first weight coefficients; ω i is the preset second weight coefficient, Δh i is the height difference between each measuring point and the reference point; is the average height difference; n is the number of measuring points;

[0086] The settlement variation coefficient D1 of the corresponding floor is compared with the first preset threshold D θ，1 Conduct comparative analysis:

[0087] If D1>D θ，1 , then a corresponding danger warning signal about excessive deformation of the house floor is generated;

[0088] If D1≤D θ，1 , no danger warning signal for floor deformation is generated.

[0089] Through the above technical solution, this embodiment provides a process for comprehensively analyzing the location information data of the first monitoring point and the second monitoring point. Specifically, firstly, the formula Calculate and obtain the settlement variation coefficient D1 of the corresponding floor, and then compare the settlement variation coefficient D1 of the corresponding floor with the first preset threshold D θ，1 For comparative analysis, if D1>D θ，1 , indicating that a significant settlement change has occurred at the intersection of the floor slab and the target wall, so a corresponding danger warning signal about excessive deformation of the house floor slab is generated. If D1≤D θ，1 , no danger warning signal for floor deformation is generated. It should be noted that ε1 and ε2 are the preset first weight coefficients, ω i is a preset second weight coefficient, which can be obtained by fitting empirical data or experimental data, and will not be described in detail here.

[0090] The process of comprehensively analyzing the location information data of the first monitoring point and the second monitoring point also includes:

[0091]

[0092] The wall deformation coefficient D2 of the target wall is calculated by formula (3) to (4);

[0093] Wherein, dj is the measured distance between the jth first monitoring point and the reference point; h j is the initial height from the jth first monitoring point to the intersection of the target wall and floor; α j is the laser elevation angle when the laser measures the position of the jth monitoring point; Δw is the maximum change value of the crack width on the target wall; w0 is the preset standard proportional coefficient; m is the number of the first monitoring point; μ is the preset third weight coefficient;

[0094] The wall deformation coefficient D2 and the second preset threshold D θ，2 Conduct comparative analysis:

[0095] If D2>D θ，2 , then a corresponding danger warning signal about excessive deformation of the house wall is generated;

[0096] If D2≤D θ，2 , no danger warning signal for wall deformation is generated.

[0097] Through the above technical solution, this embodiment first calculates the wall deformation coefficient D2 of the target wall through formulas (3) to (4), where is the distance from the reference point to the first monitoring point under ideal conditions (the wall surface is flat and the wall is perpendicular to the floor slab), and the wall deformation coefficient D2 is compared with the second preset threshold D θ，2 Comparative analysis: If D2>Dθ，2 , indicating that the wall has a large deformation, so a corresponding danger warning signal about excessive deformation of the house wall is generated. If D2≤D θ，2 , no danger warning signal for wall deformation is generated. w0 is the preset standard proportional coefficient, μ j It is a preset third weight coefficient, and this type of preset coefficient is obtained specifically based on empirical data.

[0098] See also Figure 2 As shown, a housing deformation monitoring method and system, the system comprising:

[0099] The image acquisition module is used to collect image information of the wall of the house, and specifically includes a camera and a drone with a camera function.

[0100] Image recognition module, which identifies cracks in wall images based on convolutional neural networks and records the original characteristic parameters of the cracks;

[0101] The pre-analysis module is used to pre-analyze the original characteristic parameters and conduct safety evaluation on the tested house based on the results of the pre-analysis;

[0102] A data acquisition module, including a laser rangefinder and a static level, for collecting position information data of a first monitoring point and a second monitoring point on a wall and a floor;

[0103] A data processing module, used for comprehensively analyzing the location information data of the first monitoring point and the second monitoring point, and establishing a house deformation early warning strategy based on the comprehensive analysis;

[0104] The human-computer interaction module specifically includes a handheld terminal or other display device used by staff to implement the house deformation warning strategy.

[0105] Through the above technical solution, this embodiment provides a house deformation monitoring system. When the system monitors the deformation of the house, it first locates the target wall with cracks through the cooperation of the image acquisition module and the image recognition module, collects the displacement change data of each monitoring point through the data acquisition module, and the data processing module performs a comprehensive analysis of the monitoring result data and determines the deformation of the house. The monitoring system can automatically monitor and analyze the house in real time, which improves the monitoring efficiency and makes the monitoring process safer.

[0106] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A housing deformation monitoring method, characterized in that: The method comprises: S1, collecting real-life images of each wall of the house, and importing the real-life images into a trained image recognition model, and outputting results indicating whether there are cracks on the wall; S2. Mark the wall with cracks on the wall surface as the target wall, and record the original characteristic parameters of the corresponding cracks; S3. Install a crack measuring instrument at the crack of the target wall to collect real-time change data of the crack width; S4, presetting a number of first monitoring points on the target wall, and presetting a number of second monitoring points on the floor slab connected to the target wall; S5. Acquire location information data of the first monitoring point and the second monitoring point through monitoring, and perform comprehensive analysis on the location information data of the first monitoring point and the second monitoring point; S6. Establish a housing deformation early warning strategy according to the results of the comprehensive analysis.

2. A housing deformation monitoring method according to claim 1, characterized in that: Before step S3, the method further includes: S21, pre-analyzing original characteristic parameters of cracks on the target wall; S22, performing a safety evaluation on the tested house according to the result of the pre-analysis, the result of the safety evaluation indicating whether the house meets the safety monitoring standard; S23. If the tested house does not meet the safety monitoring standards, steps S3 to S6 are not executed, and a danger warning signal for the corresponding house is generated; otherwise, steps S3 to S6 are continued to be executed.

3. A housing deformation monitoring method according to claim 2, characterized in that: The original characteristic parameters of the cracks include: initial length and initial width of the cracks and the total number of cracks on the same target wall.

4. A housing deformation monitoring method according to claim 3, characterized in that: The process of pre-analyzing the original characteristic parameters of the cracks on the target wall includes: By formula Calculate the risk assessment coefficient R of the house; Among them, k i is the preset correlation coefficient, and k i >0; N is the number of cracks on the target wall; l i and are the length and average width of the i-th crack on the target wall respectively; S is the total area of ​​the target wall; The risk assessment coefficient R is compared with the preset safety standard value R st To compare: If R>R st , then it is judged that the house does not meet the safety monitoring standards; If R≤R st , then the house is judged to meet the safety monitoring standards.

5. A housing deformation monitoring method according to claim 4, characterized in that: The process of presetting the first monitoring point and the second monitoring point includes: A plurality of first monitoring points are evenly distributed in rows and columns around the cracks of the target wall; Install a laser rangefinder at a preset position on the floor slab connected to the target wall; Taking the position of the laser rangefinder as the reference point, select several measuring points at the intersection of the floor slab and the target wall, and install a static level, and both the measuring points and the reference points are marked as the second monitoring points; The distance from each monitoring point on the target wall to the reference point is obtained by a laser rangefinder, and the vertical displacement data of each measuring point is obtained by a static level.

6. A housing deformation monitoring method according to claim 5, characterized in that: The process of comprehensively analyzing the location information data of the first monitoring point and the second monitoring point includes: By formula Calculate and obtain the settlement variation coefficient D1 of the corresponding floor; Among them, ε1 and ε2 are the preset first weight coefficients; ω i is the preset second weight coefficient, Δh i is the height difference between each measuring point and the reference point; is the average height difference; n is the number of measuring points; The settlement variation coefficient D1 of the corresponding floor is compared with the first preset threshold D θ,1 Conduct comparative analysis: If D1>D θ,1 , then a corresponding danger warning signal about excessive deformation of the house floor is generated; If D1≤D θ,1 , no danger warning signal for floor deformation is generated.

7. A housing deformation monitoring method according to claim 6, characterized in that: The process of comprehensively analyzing the location information data of the first monitoring point and the second monitoring point also includes: The wall deformation coefficient D2 of the target wall is calculated by formula (3) to (4); Among them, d j h is the measured distance between the jth first monitoring point and the reference point; j is the initial height from the jth first monitoring point to the intersection of the target wall and floor; α j is the laser elevation angle when the laser measures the position of the jth monitoring point; Δw is the maximum change value of the crack width on the target wall; w0 is the preset standard proportional coefficient; m is the number of the first monitoring point; μ j is a preset third weight coefficient; The wall deformation coefficient D2 and the second preset threshold D θ,2 Conduct comparative analysis: If D2>D θ,2 , then a corresponding danger warning signal about excessive deformation of the house wall is generated; If D2≤D θ,2 , no danger warning signal for wall deformation is generated.

8. A housing deformation monitoring method and system, characterized in that: The system is used to execute the housing deformation monitoring method and method according to any one of claims 1 to 7, and the system includes: An image acquisition module, used to acquire image information of the house wall; Image recognition module, which identifies cracks in wall images based on convolutional neural networks and records the original characteristic parameters of the cracks; The pre-analysis module is used to pre-analyze the original characteristic parameters and conduct safety evaluation on the tested house based on the results of the pre-analysis; A data acquisition module, used to collect location information data of a first monitoring point and a second monitoring point on a wall and a floor; A data processing module, used for comprehensively analyzing the location information data of the first monitoring point and the second monitoring point, and establishing a house deformation early warning strategy based on the comprehensive analysis; Human-computer interaction module, used to implement house deformation early warning strategy.

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