House wall crack monitoring method and system

Abstract

The invention relates to the technical field of house wall monitoring, and particularly discloses a house wall crack monitoring method and system, and the method comprises the steps: S1, carrying out the continuous collection of wall surface image data through an image collection module; s2, analyzing the acquired wall surface image data through an analysis control module to obtain a time-varying curve of a wall danger characteristic value, and judging whether a wall danger early warning signal is generated or not; according to the invention, the analysis control module analyzes the collected wall surface image data, obtains a curve of wall danger characteristic values changing with time, judges whether a wall danger early warning signal is generated or not, and warns house wall monitoring personnel, so that the purpose of accurately and objectively monitoring house wall cracks can be achieved, and the safety of the house is improved. The subjective judgment in the manual observation process is avoided, the house wall crack can be monitored in real time, and the monitoring accuracy of the wall crack is improved.

Description

Technical Field

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

[0002] With the continuous development of the times, people have higher and higher requirements for housing quality. The safety and quality of housing are closely related to our lives. Due to geological disasters, foundation settlement, its own structure and disrepair, cracks may appear in the walls of houses. Cracked houses cannot be relocated immediately because a large number of people live in them. They need to be monitored while living in them, which seriously affects the lives and property safety of residents.

[0003] In the prior art, the monitoring technology for wall cracks mostly adopts manual observation and estimation. When there are many cracks on the wall, it is determined that the wall has the risk of collapse. This method of monitoring cracks in house walls has significant limitations. Manual observation relies on human experience and judgment, and is easily affected by the subjective factors of the observer. Different observers may have different judgments on the same crack. In addition, manual observation is difficult to track and record for a long time. The changes in cracks may be difficult to accurately track and analyze, which has certain safety hazards. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for monitoring cracks in a building wall to solve the following technical problems:

[0005] How to accurately and objectively monitor cracks in house walls.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for monitoring cracks in a house wall, the method comprising:

[0008] S1. Continuously collect wall surface image data through an image acquisition module;

[0009] S2. Analyze the collected wall surface image data through the analysis control module to obtain a curve of wall danger characteristic value changing over time, and determine whether to generate a warning signal of wall danger;

[0010] S3. Execute the early warning signal of wall danger through the early warning module.

[0011] Furthermore, in step S2, the process of obtaining the wall danger characteristic value versus time curve includes:

[0012] H s (t) = Q s (t)*τ1+W s (t)*τ2 (1)

[0013]

[0014] The time-varying curve H of the wall danger characteristic value is obtained by analyzing and calculating formulas (1)-(3): s (t);

[0015] Where N is the number of cracks on the wall, i∈[1,N], Z i (t) is the time-varying curve of the i-th crack length, B i (t) is the time variation curve of the i-th crack width, and is the first weight coefficient, Z i ′(t) is the time-varying curve of the length of the i-th crack, B i ′(t) is the time-varying curve of the i-th crack width change rate, ω1 and ω2 are the second weight coefficients, θ i is the type weight coefficient of the i-th crack, Q s (t) is the curve of the static risk factor of the wall changing with time, W s (t) is the curve of dynamic hazard coefficient of wall changing with time, τ1 and τ2 are the third weight coefficients.

[0016] Furthermore, in step S2, the process of determining whether to generate a warning signal of wall danger includes:

[0017] The wall danger characteristic value changes with time curve H s (t) and the preset wall danger warning characteristic value H risk Make a comparison;

[0018] When t=t0, H s (t0)≥H risk When a warning signal of wall danger is generated, step S3 is executed;

[0019] Instead, wall cracks are continuously monitored.

[0020] In one embodiment, the method further comprises:

[0021] S4. Further analyzing the wall cracks, judging whether it is necessary to install a stress sensor on the wall according to the curve of the wall danger characteristic value changing with time, and if it is necessary to install, generating a signal for fixing the stress sensor on the wall;

[0022] S5. Stress sensors are fixedly installed at each crack on the wall to collect stress change data at each crack, analyze and obtain a reference amount of wall stress change within a preset time period, and determine whether to generate an early warning signal of wall danger.

[0023] Furthermore, in step S4, the process of determining whether a stress sensor needs to be installed on the wall includes:

[0024] The wall danger characteristic value changes with time curve H s (t) and the preset critical characteristic value of wall danger H cr Make a comparison;

[0025] When t=t1, H s (t1)≥H cr When , a signal of a stress sensor fixedly installed on the wall is generated;

[0026] On the contrary, wall cracks are continuously monitored;

[0027] Among them, H cr <H risk .

[0028] Furthermore, in step S5, the process of obtaining the reference amount of wall stress change within the preset time period includes:

[0029]

[0030] The reference value C of wall stress change within the preset time period is obtained by analyzing and calculating formula (4): s ;

[0031] Where M is the number of cracks where stress sensors are installed, j∈[1,M], [t a ,t b ] is the preset time period, F j (t) is the stress variation curve monitored at the jth crack, θ j is the type weight coefficient of the j-th crack.

[0032] Furthermore, in step S5, the process of determining whether to generate a warning signal of wall danger includes:

[0033] The wall stress change reference value C within the preset time period s Compared with the preset wall stress change danger reference C risk Make a comparison;

[0034] If C s ≥C risk , then a warning signal of wall danger is generated, and step S3 is executed;

[0035] Instead, wall cracks are continuously monitored.

[0036] A house wall crack monitoring system, the system is used in a house wall crack monitoring method, the system comprises:

[0037] An image acquisition module is used to continuously monitor the wall surface and collect image data of the wall surface;

[0038] The stress sensor is fixedly installed on the wall and is used to collect stress change data at each crack on the wall;

[0039] The analysis control module analyzes the wall image data continuously collected by the image acquisition module, generates a curve of the wall danger characteristic value changing over time, and determines whether to generate a warning signal of the wall danger; according to the curve of the wall danger characteristic value changing over time, determines whether it is necessary to install a stress sensor on the wall; analyzes the stress change data at each crack collected by the stress sensor, and determines whether to generate a warning signal of the wall danger;

[0040] The early warning module is used to execute the early warning signal of wall danger.

[0041] Beneficial effects of the present invention:

[0042] (1) The present invention analyzes the collected wall surface image data through the analysis control module to obtain the crack data of the wall surface, and calculates and obtains the wall danger characteristic value change curve over time according to formulas (1)-(3), and then determines whether to generate a wall danger warning signal. When the wall danger warning signal is generated, the warning signal is executed by the warning module to warn the house wall monitoring personnel, indicating that there is a safety hazard of wall collapse. In this way, the purpose of accurately and objectively monitoring the cracks in the house wall can be achieved, avoiding the subjective judgment in the manual observation process, and the house wall cracks can be monitored in real time, thereby improving the accuracy of wall crack monitoring.

[0043] (2) The present invention further analyzes the time-varying curve of the wall danger characteristic value to determine whether it is necessary to install a stress sensor on the wall and monitor the stress at each crack, thereby achieving the purpose of further monitoring the wall cracks and avoiding the problem of increased costs caused by installing stress sensors when the wall danger characteristic value is small.

[0044] (3) In the process of monitoring the cracks in the wall of a house by the image acquisition module of the present invention, when it is determined that there is a small probability of the wall collapsing, the wall cracks are further monitored by the stress sensor to further determine the degree of danger of the wall, thereby further improving the accuracy of the process of monitoring the cracks in the wall of the house and greatly reducing the potential safety hazards that may exist during the living process of the house. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further described below in conjunction with the accompanying drawings.

[0046] Figure 1It is a flow chart of steps of a method for monitoring cracks in a house wall proposed by the present invention;

[0047] Figure 2 The present invention provides a schematic block diagram of a house wall crack monitoring system. DETAILED DESCRIPTION

[0048] 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.

[0049] See also Figure 1 As shown, in one embodiment, a method for monitoring cracks in a house wall is provided, the method comprising:

[0050] S1. Continuously collect wall surface image data through an image acquisition module;

[0051] S2. Analyze the collected wall surface image data through the analysis control module to obtain a curve of wall danger characteristic value changing over time, and determine whether to generate a warning signal of wall danger;

[0052] S3. Execute the early warning signal of wall danger through the early warning module.

[0053] Through the above technical method, this embodiment provides a method for monitoring cracks in a house wall. First, an image acquisition module is installed near the house wall to be monitored, and the wall surface is continuously photographed and recorded to collect wall surface image data. The image acquisition module can be a high-definition camera. Then, the analysis control module analyzes the collected wall surface image data through an edge detection algorithm or a convolutional neural network technology to obtain crack data on the wall surface. The crack data on the wall surface includes the length, width and type of the cracks. Then, the crack data on the wall surface and its change process are analyzed to obtain a curve of the wall danger characteristic value changing with time, and then it is determined whether to generate a warning signal for the wall danger. When the warning signal for the wall danger is generated, the warning signal is executed by the warning module to warn the house wall monitoring personnel, indicating that there is a safety hazard of wall collapse. In this way, the purpose of accurately and objectively monitoring the cracks in the house wall can be achieved, and the subjective judgment in the manual observation process can be avoided. In addition, the cracks in the house wall can be monitored in real time, and the accuracy of wall crack monitoring is improved.

[0054] In one embodiment, in step S2, the process of obtaining the wall danger characteristic value versus time curve includes:

[0055] H s (t) = Q s (t)*τ1+W s (t)*τ2 (1)

[0056]

[0057] The time-varying curve H of the wall danger characteristic value is obtained by analyzing and calculating formulas (1)-(3): s (t);

[0058] Where N is the number of cracks on the wall, which can be obtained by analyzing the collected wall surface image data through edge detection algorithm or convolutional neural network technology, i∈[1,N], Z i (t) is the time-varying curve of the length of the i-th crack, which is obtained by monitoring the change of the length of the i-th crack. i (t) is the time-varying curve of the width of the i-th crack, which is obtained by monitoring the change of the width of the i-th crack. and is the first weight coefficient, which is obtained by experience. Specifically, it can be obtained by experimental analysis of the influence of wall crack length and width on the wall. i ′(t) is the time-varying curve of the length of the i-th crack, which is obtained by taking the derivative of the time-varying curve of the length of the i-th crack. i ′(t) is the time-varying curve of the i-th crack width change rate, which is obtained by taking the derivative of the time-varying curve of the i-th crack width. ω1 and ω2 are the second weight coefficients, which are obtained by experience. Specifically, they can be obtained by experimental analysis of the influence of the length and width change rates of wall cracks on the wall. θ i is the weight coefficient of the type of the i-th crack, which is obtained by preset experience. Specifically, it can be obtained by experimental analysis of the influence of different types of cracks on the wall. The types of wall cracks include but are not limited to horizontal cracks, vertical cracks, oblique cracks, radial cracks, mesh cracks, curved cracks and staggered cracks, etc. Q s (t) is the curve of the static risk factor of the wall changing with time, W s (t) is the curve of the dynamic hazard coefficient of the wall changing with time, τ1 and τ2 are the third weight coefficients, which are obtained based on experience and can be obtained based on the experimental analysis of the influence of the static hazard coefficient and dynamic hazard coefficient of the wall on the wall.

[0059] In step S2, the process of determining whether to generate a warning signal of wall danger includes:

[0060] The wall danger characteristic value changes with time curve H s (t) and the preset wall danger warning characteristic value Hrisk The preset wall danger warning characteristic value H risk It can be obtained by experimental selection based on the relationship between the wall danger characteristic value and the wall collapse;

[0061] When t=t0, H s (t0)≥H risk When a warning signal of wall danger is generated, step S3 is executed;

[0062] Instead, wall cracks are continuously monitored.

[0063] Through the above technical solution, this embodiment provides a specific method for judging whether a wall is dangerous by analyzing the wall surface image data. The analysis control module analyzes the collected wall surface image data through edge detection algorithm or convolutional neural network technology to obtain relevant data such as the length, width and type of each crack on the wall, and models and analyzes the length and width data of each crack to obtain the time-varying curve of each crack length and the time-varying curve of the crack width. Then, according to formulas (1)-(3), the time-varying curve H of the wall danger characteristic value is obtained by analysis and calculation. s (t), and compare this curve with Y=H risk When the first intersection point appears or the wall hazard characteristic value obtained by analysis and calculation changes with time, H s (t) is located at Y = H risk When it is above the straight line, that is, there is t=t0, H s (t0)≥H risk When the wall danger characteristic value reaches or exceeds the preset wall danger warning characteristic value, it means that there is a high probability of the wall collapsing. Then, a wall danger warning signal is generated, and step S3 is executed to issue a wall danger warning signal. Otherwise, the wall cracks are continuously monitored or further analyzed through the image acquisition module and the analysis control module.

[0064] In one embodiment, the method further comprises:

[0065] S4. Further analyzing the wall cracks, judging whether it is necessary to install a stress sensor on the wall according to the curve of the wall danger characteristic value changing with time, and if it is necessary to install, generating a signal for fixing the stress sensor on the wall;

[0066] S5. Stress sensors are fixedly installed at each crack on the wall to collect stress change data at each crack, analyze and obtain a reference amount of wall stress change within a preset time period, and determine whether to generate an early warning signal of wall danger.

[0067] In step S4, the process of determining whether a stress sensor needs to be installed on the wall includes:

[0068] The wall danger characteristic value changes with time curve H s (t) and the preset critical characteristic value of wall danger H cr Make a comparison;

[0069] When t=t1, G s (t1)≥H cr When the wall is cracked, it indicates that there is a small probability of collapse risk, and further monitoring of the cracks on the wall is required, and a signal of a stress sensor fixed on the wall is generated;

[0070] On the contrary, wall cracks are continuously monitored;

[0071] Among them, the preset wall danger critical characteristic value H cr It can be obtained by experimental selection based on the relationship between the wall danger characteristic value and the wall collapse, H cr <H risk , preset wall danger warning characteristic value H risk is the wall danger characteristic value corresponding to the high probability of wall collapse, and the preset wall danger critical characteristic value H cr It is the wall danger characteristic value corresponding to the small probability of wall collapse.

[0072] In step S5, the process of obtaining the reference amount of wall stress change within the preset time period includes:

[0073]

[0074] The reference value C of wall stress change within the preset time period is obtained by analyzing and calculating formula (4): s ;

[0075] Wherein, M is the number of cracks where stress sensors are installed. Not all cracks on the wall are installed with stress sensors. They can be installed manually based on the length, width and type of the cracks. Alternatively, the data parameters of each crack can be input into formulas (1)-(3) for analysis. When the wall hazard characteristic value corresponding to a single crack reaches a preset wall hazard monitoring characteristic value, the stress sensor is selected to be installed on the crack. The preset wall hazard monitoring characteristic value is preset based on experience, j∈[1,M], [t a ,t b ] is the preset time period, which is selected based on experience. Generally, a time period of one hour is selected. j (t) is the stress variation curve monitored at the j-th crack over time, which can be obtained by monitoring the stress variation collected at the j-th crack. j is the type weight coefficient of the j-th crack, which is preset based on experience and can be specifically obtained based on experimental analysis of the effects of different types of cracks on the wall.

[0076] In step S5, the process of determining whether to generate a warning signal of wall danger includes:

[0077] The wall stress change reference value C within the preset time period s Compared with the preset wall stress change danger reference C risk For comparison, the preset wall stress change dangerous reference value C risk It can be obtained by experimental selection based on the relationship between wall stress change and wall collapse within a preset time period;

[0078] If C s ≥C risk , then a warning signal of wall danger is generated, and step S3 is executed;

[0079] Instead, wall cracks are continuously monitored.

[0080] Through the above technical solution, this embodiment provides a method for further accurately monitoring cracks in house walls. First, the curve of the wall danger characteristic value changing over time is further analyzed to determine whether it is necessary to install a stress sensor on the wall. The specific process is to convert the wall danger characteristic value changing over time curve H s (t) and Y=H cr When the first intersection point appears or the wall hazard characteristic value obtained by analysis and calculation changes with time, H s (t) is located at Y = H cr When it is above the straight line, there exists t=t1, H s (t1)≥H cr When the wall danger characteristic value reaches or exceeds the preset wall danger critical characteristic value, it means that there is a small probability of the wall collapsing. It is necessary to install stress sensors on the wall to monitor the stress at each crack to achieve the purpose of further monitoring the wall cracks and avoid the problem of cost increase caused by installing stress sensors when the wall danger characteristic value is small. On the contrary, the wall cracks are continuously monitored through the image acquisition module and the analysis and control module.

[0081] After the corresponding stress sensors are fixedly installed at each crack on the wall, each stress sensor collects stress change data at each crack. The analysis control module models and analyzes the collected stress change data at each crack to obtain the stress change curve at each crack over time. Then, the reference value C of the wall stress change within the preset time period is obtained by analysis and calculation using formula (4): s , the wall stress change reference value C within the preset time period s Compared with the preset wall stress change danger reference C risk Compare, if C s ≥C risk, indicating that there is a high probability of the wall collapsing, a warning signal of the wall danger is generated, and step S3 is executed to issue a warning signal of the wall danger. Conversely, the wall cracks are continuously monitored through the image acquisition module, the stress sensor and the analysis and control module. In this way, when the image acquisition module is used to monitor the cracks in the house wall, when it is judged that there is a small probability of the wall collapsing, the wall cracks are further monitored through the stress sensor, and the degree of danger of the wall is further judged, which can further improve the accuracy of the monitoring process of the house wall cracks and greatly reduce the potential safety hazards in the house living process.

[0082] See also Figure 2 In one embodiment, a house wall crack monitoring system is provided, the system is used in a house wall crack monitoring method, the system comprises:

[0083] An image acquisition module is used to continuously monitor the wall surface and collect image data of the wall surface;

[0084] The stress sensor is fixedly installed on the wall and is used to collect stress change data at each crack on the wall;

[0085] The analysis control module analyzes the wall image data continuously collected by the image acquisition module to obtain the data of each crack on the wall, and further generates a curve of wall danger characteristic value variation over time based on the crack data on the wall surface and its variation process, so as to determine whether to generate a warning signal of wall danger; based on the curve of wall danger characteristic value variation over time, determine whether there is a risk of small probability of wall collapse, and install stress sensors on the wall as needed to further accurately monitor the wall cracks; analyze the stress change data at each crack collected by the stress sensor to determine whether to generate a warning signal of wall danger;

[0086] The early warning module is used to execute the early warning signal of wall danger.

[0087] 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 method for monitoring cracks in a house wall, characterized in that: The method comprises: S1. Continuously collect wall surface image data through an image acquisition module; S2, analyzing the collected wall surface image data through the analysis control module, obtaining a curve of wall danger characteristic value changing over time, and determining whether to generate a warning signal of wall danger; S3. Execute the early warning signal of wall danger through the early warning module.

2. A method for monitoring cracks in a house wall according to claim 1, characterized in that: In step S2, the process of obtaining the wall danger characteristic value over time curve includes: H s (t)=Q s (t)*τ1+W s (t)*τ2 (1) The time-varying curve H of the wall danger characteristic value is obtained by analyzing and calculating formulas (1)-(3): s (t); Where N is the number of cracks on the wall, i∈[1,N], Z i (t) is the time-varying curve of the i-th crack length, B i (t) is the time variation curve of the i-th crack width, and is the first weight coefficient, Z i ′(t) is the time-varying curve of the length of the i-th crack, B i ′(t) is the time-varying curve of the i-th crack width change rate, ω1 and ω2 are the second weight coefficients, is the type weight coefficient of the i-th crack, Q s (t) is the curve of the static risk factor of the wall changing with time, W s (t) is the curve of dynamic hazard coefficient of wall changing with time, τ1 and τ2 are the third weight coefficients.

3. A method for monitoring cracks in a house wall according to claim 2, characterized in that: In step S2, the process of determining whether to generate a warning signal of wall danger includes: The wall danger characteristic value changes with time curve H s (t) and the preset wall danger warning characteristic value H risk Make a comparison; When t=t0, H s (t0)≥H risk When a warning signal of wall danger is generated, step S3 is executed; Instead, wall cracks are continuously monitored.

4. A method for monitoring cracks in a house wall according to claim 2, characterized in that: The method further comprises: S4. Further analyzing the wall cracks, judging whether it is necessary to install a stress sensor on the wall according to the curve of the wall danger characteristic value changing with time, and if it is necessary to install, generating a signal for fixing the stress sensor on the wall; S5. Stress sensors are fixedly installed at each crack on the wall to collect stress change data at each crack, analyze and obtain a reference amount of wall stress change within a preset time period, and determine whether to generate an early warning signal of wall danger.

5. A method for monitoring cracks in a house wall according to claim 4, characterized in that: In step S4, the process of determining whether a stress sensor needs to be installed on the wall includes: The wall danger characteristic value changes with time curve H s (t) and the preset critical characteristic value of wall danger H cr Make a comparison; When t=t1, H s (t1)≥H cr When , a signal of a stress sensor fixedly installed on the wall is generated; On the contrary, wall cracks are continuously monitored; Among them, H cr <H risk .

6. A method for monitoring cracks in a house wall according to claim 5, characterized in that: In step S5, the process of obtaining the reference amount of wall stress change within the preset time period includes: The reference value C of wall stress change within the preset time period is obtained by analyzing and calculating formula (4): s ; Where M is the number of cracks where stress sensors are installed, j∈[1,M], [t a ,t b ] is the preset time period, F j (t) is the stress variation curve monitored at the jth crack over time, is the type weight coefficient of the j-th crack.

7. A method for monitoring cracks in a house wall according to claim 5, characterized in that: In step S5, the process of determining whether to generate a warning signal of wall danger includes: The wall stress change reference value C within the preset time period s Compared with the preset wall stress change danger reference C risk Make a comparison; If C s ≥C risk , then a warning signal of wall danger is generated, and step S3 is executed; Instead, wall cracks are continuously monitored.

8. A house wall crack monitoring system, characterized in that: The system is used for a method for monitoring cracks in a house wall according to any one of claims 4 to 7, and the system comprises: An image acquisition module is used to continuously monitor the wall surface and collect image data of the wall surface; The stress sensor is fixedly installed on the wall and is used to collect stress change data at each crack on the wall; The analysis control module analyzes the wall image data continuously collected by the image acquisition module, generates a curve of the wall danger characteristic value changing over time, and determines whether to generate a warning signal of the wall danger; according to the curve of the wall danger characteristic value changing over time, determines whether it is necessary to install a stress sensor on the wall; analyzes the stress change data at each crack collected by the stress sensor, and determines whether to generate a warning signal of the wall danger; The early warning module is used to execute early warning signals of wall danger.

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