Urban building house safety detection method, device and equipment and storage medium

Abstract

The invention relates to the technical field of house detection, in particular to an urban building house safety detection method and device, equipment and a storage medium. The method comprises the following steps: acquiring an image, temperature distribution data, humidity distribution data and structural deformation data of a to-be-detected urban building house; preprocessing the data, inputting a preset identification model to obtain a structural damage position of the urban building house, and determining a potential safety hazard area; generating a safety assessment report according to the structural damage position and the potential safety hazard area; according to the method, the safety evaluation report is returned to the detection equipment of the field staff, and the report content is displayed on the detection equipment, so that the real-time performance and accuracy of detection are improved, the detection operation efficiency of the field staff is improved by predicting potential safety hazards, and the problem of safety detection lag in the prior art is effectively solved.

Description

Technical Field

[0001] The present application relates to the technical field of house detection, and in particular to a method, device, equipment and storage medium for urban building house safety detection. Background Art

[0002] With the acceleration of urbanization, the number of urban buildings is increasing, and the issue of housing safety is becoming increasingly prominent. Housing safety is not only related to the safety of life and property of residents, but also affects the overall operation of the city and social stability. Therefore, it is of great significance to conduct regular safety inspections on urban buildings to timely discover and deal with potential safety hazards.

[0003] At present, a variety of technical methods have emerged in the field of urban building safety inspection. The sound wave inspection method determines the integrity and damage of the structure by measuring the propagation characteristics of sound waves in the structure of the house; the radiation inspection method uses the ability of radiation to penetrate materials to detect defects and damage inside the house. In addition, there are also inspection methods based on sensors and image recognition technology.

[0004] However, existing data processing and analysis methods are often based on simple threshold judgments or empirical formulas, lacking intelligent identification and analysis capabilities. They are unable to monitor the safety status of a house in real time and predict potential safety hazards, resulting in certain lags and limitations in house safety detection. This situation needs to be further improved. Summary of the invention

[0005] In order to solve the problem that the existing housing safety detection methods cannot monitor the safety status of the house in real time and predict potential safety hazards, the present application provides a method, device, equipment and storage medium for urban building housing safety detection, which adopts the following technical solutions: In a first aspect, the present application provides a method for detecting the safety of urban buildings, comprising the following steps: Acquire images, temperature distribution data, humidity distribution data and structural deformation data of urban buildings to be inspected; Preprocessing the image, temperature distribution data, humidity distribution data and structural deformation data; The pre-processed images, temperature distribution data, humidity distribution data and structural deformation data are input into a preset recognition model to obtain the structural damage location of urban buildings and determine the potential safety hazard areas; Generate a safety assessment report based on the structural damage location and the potential safety hazard area; The safety assessment report is returned to the detection equipment of the on-site staff, and the report content is displayed on the detection equipment.

[0006] By adopting the above technical solution, this application can accurately locate the location of structural damage and safety hazard areas and generate a detailed safety assessment report by acquiring multi-dimensional data and performing preprocessing and intelligent identification. At the same time, real-time feedback is provided to the detection equipment of on-site staff to facilitate timely response. This application improves the real-time and accuracy of detection, improves the efficiency of on-site staff's detection operations by predicting potential safety hazards, effectively solves the problem of lagging safety detection in existing technologies, and provides comprehensive and reliable technical support for urban building safety detection.

[0007] Optionally, the process of establishing the preset recognition model includes: Collect images of urban buildings with marked structural damage locations and potential safety hazards, temperature distribution data, humidity distribution data, and structural deformation data to form a training data set; Use deep learning algorithms to build recognition models; The training data set is input into the recognition model, and the model parameters are adjusted through the back propagation algorithm until the model reaches a preset accuracy rate on the verification data set.

[0008] By adopting the above technical solution, this application first collects images, temperature distribution, humidity distribution and structural deformation data of urban buildings and houses, and marks areas of structural damage and safety hazards to form a rich training data set; secondly, a deep learning algorithm is used to build a recognition model so that the model can automatically extract and learn features in the data; finally, the training data set is input into the model, and the parameters are adjusted through the back propagation algorithm until the model reaches the preset accuracy on the verification data set, thereby improving the recognition accuracy of the model.

[0009] Optionally, before obtaining the image, temperature distribution data, humidity distribution data and structural deformation data of the urban building to be inspected, the method further includes the following steps: Acquiring characteristic information of the urban building, wherein the characteristic information includes the structure type, building material and service life of the urban building; According to the feature information, selecting a recognition model that matches the urban building structure from a preset recognition model library; The selected recognition model is used as the preset recognition model to be used currently.

[0010] By adopting the above technical solution, due to the diverse structures and materials of houses, it is difficult to accurately identify the structural damage and safety hazards of different houses using a single recognition model. Before obtaining the image, temperature distribution data, humidity distribution data and structural deformation data of the urban building house to be inspected, the present application first obtains the house characteristic information such as structure type, material and age, and then selects a matching recognition model from the model library based on the characteristic information. By introducing the characteristic information matching recognition model, the pertinence and effectiveness of safety detection are improved, the possibility of false alarms and missed alarms is reduced, and the detection efficiency is improved.

[0011] Optionally, after the selected recognition model is used as the preset recognition model to be currently used, the method further includes the following steps: According to the feature information, matching and querying the detection strategy and detection data corresponding to the feature information in a preset detection type library; Automatically generate test recommendation information based on the test strategy and test data; The detection recommendation information is sent to the detection equipment of the on-site staff, so that the on-site staff can detect the urban building house according to the detection recommendation information.

[0012] By adopting the above-mentioned technical solution, the present application matches the detection strategy and detection data corresponding to the house characteristic information in the detection type library according to the house characteristic information, and then automatically generates detection recommendation information, and sends the detection recommendation information to the detection equipment of the on-site staff, so that the on-site staff can detect the urban building houses according to the detection recommendation information, thereby improving the pertinence and efficiency of the detection.

[0013] Optionally, after automatically generating the detection recommendation information according to the detection strategy and the detection data, the method further comprises the following steps: According to the temperature distribution data and the humidity distribution data, identifying the abnormal temperature and humidity area of ​​the urban building house, and determining the environmental risk index of the abnormal temperature and humidity area; According to the environmental risk index ranking of each of the abnormal temperature and humidity areas, the detection process in the detection recommendation information is adjusted.

[0014] By adopting the above-mentioned technical scheme, in the safety inspection of urban buildings and houses, the identification of abnormal temperature and humidity areas and the assessment of environmental risks can timely discover potential environmental risks. The present application identifies abnormal temperature and humidity areas through temperature distribution data and humidity distribution data, and calculates their environmental risk index. Then, the abnormal areas are sorted according to their risk index, the inspection process is adjusted, and high-risk areas are processed preferentially, thereby improving the detection efficiency and accuracy of abnormal temperature and humidity areas, and being able to timely discover and deal with potential environmental risks.

[0015] Optionally, after generating a safety assessment report according to the structural damage location and the potential safety hazard area, the method further comprises the following steps: Obtaining architectural geometry information of the urban buildings; Determining the safety level of each area of ​​the urban building according to the structural damage location and the potential safety hazard area; Establishing a three-dimensional model according to the building geometry information, the location of structural damage and the potential safety hazard area, and displaying corresponding safety level identification in each area of ​​the three-dimensional model according to the safety level of each area; The three-dimensional model with the safety level identification is added to the safety assessment report.

[0016] By adopting the above technical solution, traditional safety assessment reports often only contain text and two-dimensional images, lack intuitive three-dimensional display and detailed safety level division, and are difficult to fully reflect the safety status of the house. This application obtains the architectural geometry information of urban buildings and houses, determines the safety level of each area of ​​the building according to the structural damage location and the safety hazard area, and then establishes a three-dimensional model based on the architectural geometry information, structural damage location and safety hazard area, displays the corresponding safety level logo in each area of ​​the three-dimensional model according to the safety level of each area, and adds the three-dimensional model with the safety level logo to the safety assessment report, so that on-site staff can more intuitively understand and evaluate the safety status of the house.

[0017] In a second aspect, the present application provides an urban building safety detection device, comprising: A data acquisition module, used to acquire images, temperature distribution data, humidity distribution data and structural deformation data of urban buildings to be inspected; A preprocessing module, used for preprocessing the image, temperature distribution data, humidity distribution data and structural deformation data; The recognition module is used to input the pre-processed image, temperature distribution data, humidity distribution data and structural deformation data into a preset recognition model to obtain the structural damage location of urban buildings and determine the potential safety hazard area; A report generation module, used to generate a safety assessment report according to the structural damage location and the safety hazard area; The display module is used to return the safety assessment report to the detection equipment of the on-site staff and display the report content on the detection equipment.

[0018] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned urban building safety detection method when executing the computer program.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned urban building safety detection method.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: 1. This application can accurately locate the location of structural damage and potential safety hazards by acquiring multi-dimensional data, performing pre-processing and intelligent identification, and generate a detailed safety assessment report. At the same time, real-time feedback is provided to the detection equipment of on-site staff to facilitate timely response. This application improves the real-time and accuracy of detection, improves the efficiency of on-site staff detection operations by predicting potential safety hazards, effectively solves the problem of safety detection lag in existing technologies, and provides comprehensive and reliable technical support for urban building housing safety detection. 2. Due to the diversity of house structures and different materials, it is difficult to accurately identify the structural damage and safety hazards of different houses using a single recognition model. Before obtaining the image, temperature distribution data, humidity distribution data and structural deformation data of the urban building house to be detected, the present application first obtains the house feature information such as structure type, material and age, and then selects a matching recognition model from the model library based on the feature information. By introducing the feature information matching recognition model, the pertinence and effectiveness of safety detection are improved, the possibility of false alarms and missed alarms is reduced, and the detection efficiency is improved; 3. This application matches the detection strategies and detection data corresponding to the house feature information in the detection type library according to the house feature information, and then automatically generates detection recommendation information, and sends the detection recommendation information to the detection equipment of the on-site staff, so that the on-site staff can detect the urban building houses according to the detection recommendation information, thereby improving the pertinence and efficiency of the detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the urban building housing safety detection method according to the embodiment of the present application; Figure 2 It is a schematic diagram of the process before detection in the urban building safety detection method in one embodiment of the present application; Figure 3 It is a schematic diagram of adjusting the detection process in the urban building safety detection method according to an embodiment of the present application; Figure 4 It is a schematic diagram of generating a three-dimensional model in a method for detecting safety of urban buildings in an embodiment of the present application; Figure 5 This is a schematic diagram of a module of a device for detecting safety of urban buildings and houses according to an embodiment of the present application; Figure 6 It is a diagram of the internal structure of the electronic device according to the embodiment of the present application. DETAILED DESCRIPTION

[0022] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0023] The present application provides a method, device, equipment and storage medium for urban building and housing safety detection. By acquiring multi-dimensional data and performing preprocessing and intelligent identification, it can accurately locate the location of structural damage and safety hazard areas, and generate a detailed safety assessment report; at the same time, real-time feedback is provided to the detection equipment of on-site staff to facilitate timely response; the present application improves the real-time and accuracy of detection, improves the efficiency of on-site staff's detection operations by predicting potential safety hazards, effectively solves the problem of safety detection lag in the prior art, and provides comprehensive and reliable technical support for urban building and housing safety detection.

[0024] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0025] In the first aspect, the embodiment of the present application provides a method, which is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. In this embodiment, the terminal device is an urban building house safety detection device, but is not limited to this, and can also be a smart tablet, computer, etc. The terminal device and the server can be directly or indirectly connected through wired or wireless communication, and the embodiment of the present application does not limit this.

[0026] A method for detecting the safety of urban buildings and houses, referring to Figure 1 , including the following steps: S110, obtaining images, temperature distribution data, humidity distribution data and structural deformation data of urban buildings to be inspected.

[0027] In this embodiment, on-site staff obtain photos or video frames of the appearance and internal structure of urban buildings through cameras or other image acquisition equipment; measure the temperature distribution on the surface and inside of the building through infrared thermal imaging and other equipment; measure the humidity distribution inside the building through humidity sensors and other equipment, and obtain deformation and displacement data of the building structure through laser scanners and displacement sensors and other equipment.

[0028] Specifically, the image can intuitively display the appearance and structural characteristics of the building and identify possible damaged areas; the temperature distribution data can reflect the thermal performance of the building and promptly discover potential energy waste and structural safety hazards; the humidity distribution data can understand the humidity conditions inside the building and prevent problems such as corrosion and mold growth caused by excessive humidity; and the structural deformation data can accurately measure the deformation of the building, providing an important basis for assessing the health of the structure and predicting potential risks. When conducting safety inspections, the inspection team first uses a drone equipped with a high-definition camera to shoot the appearance of the building, and then uses the inspection team to shoot the internal structure of the building to obtain image data, and then uses an infrared thermal imager to scan the exterior walls, roof and interior of the building to obtain temperature distribution data. At the same time, humidity sensors installed inside the building are used to collect humidity distribution data on different floors. Finally, a laser scanner is used to scan the key structural parts of the building to obtain structural deformation data.

[0029] S120, pre-processing the image, temperature distribution data, humidity distribution data and structural deformation data.

[0030] In this embodiment, preprocessing refers to cleaning, denoising, standardization, etc. of the acquired images, temperature distribution data, humidity distribution data, and structural deformation data to improve the quality and consistency of the data.

[0031] Specifically, image processing software is used to remove noise and enhance contrast in order to better identify structural damage. Data analysis software is used to smooth temperature and humidity distribution data to remove outliers and noise. Coordinate transformation and standardization are performed on structural deformation data, and then the formats and units between various data sources are unified.

[0032] S130, inputting the preprocessed image, temperature distribution data, humidity distribution data and structural deformation data into a preset recognition model to obtain the structural damage location of the urban building and determine the potential safety hazard area.

[0033] In this embodiment, the preprocessed data is input into a pre-trained deep learning model. The recognition model is trained based on a large amount of labeled structural damage and safety hazard area data. By extracting and classifying the input data, the structural damage location of the building and the potential safety hazard area are finally output.

[0034] Specifically, the process of establishing the preset recognition model includes: S131. Collect images, temperature distribution data, humidity distribution data, and structural deformation data of urban buildings with marked structural damage locations and potential safety hazards to form a training data set.

[0035] Specifically, a large number of images of urban buildings with labeled structural damage locations and potential safety hazards, temperature distribution data, humidity distribution data, and structural deformation data are collected. These data need to be labeled in detail by professionals to ensure the accuracy of the location, scope, and severity of the damage and potential safety hazards. These labeled data will be used as labels for the training data set to guide the model training process.

[0036] S132. Use deep learning algorithms to build a recognition model.

[0037] S133, input the training data set into the recognition model, and adjust the model parameters through the back propagation algorithm until the model reaches a preset accuracy on the verification data set.

[0038] The validation dataset is a part of data that is independent of the training dataset and is used to test the performance of the model on unseen data. When the model reaches the preset accuracy on the validation dataset, the model is considered to have been trained and can be used in actual urban building safety inspections.

[0039] S140. Generate a safety assessment report based on the location of structural damage and safety hazard areas.

[0040] In this embodiment, based on preset safety assessment standards or rules, the system classifies the risk level of each damaged location and potential hazard area, and gives corresponding repair or maintenance suggestions, and then integrates these analysis results and suggestions into a detailed safety assessment report.

[0041] S150: Return the safety assessment report to the detection equipment of the on-site staff, and display the report content on the detection equipment.

[0042] Specifically, after the safety assessment report is generated, it is sent to the testing equipment of the on-site staff. The testing equipment can be a smart phone, tablet computer or dedicated testing instrument, etc., as long as it has the function of receiving and displaying the report content. When the report is transmitted to the testing equipment, the staff can directly view the report content on the device to understand the structural damage and safety hazards of the building, as well as the corresponding treatment suggestions.

[0043] In one embodiment, referring to Figure 2 Before step S10, the method further includes the following steps: S210: Acquire characteristic information of urban buildings, where the characteristic information includes structural type, building materials and service life of urban buildings.

[0044] In this embodiment, different buildings have different damage patterns and safety hazards due to different structural types, building materials and service life. Therefore, directly using a general recognition model may not accurately identify safety issues of a specific type of building.

[0045] Specifically, the present application obtains characteristic information of a building, and subsequently selects a matching recognition model based on the characteristic information of the building, which can ensure that the model has better recognition capabilities for damage and hidden dangers of specific building types, thereby improving the accuracy of detection.

[0046] S220: Selecting a recognition model that matches the urban building structure from a preset recognition model library according to the feature information.

[0047] In this embodiment, the recognition model library contains models for different types of buildings. By screening the matching recognition models, the most suitable recognition model for the current building can be quickly located, reducing unnecessary calculation and comparison processes. At the same time, using a model that matches the building characteristics can reduce false positives and false negatives caused by model mismatch, ensure the reliability of the detection results, and more accurately identify the structural damage and safety hazards of the building.

[0048] Specifically, different building structure types have different appearances, stress characteristics, and damage modes. The recognition model library establishes special recognition models for different building structure types, such as frame structures, brick-concrete structures, reinforced concrete structures, etc., according to the structural type, to accurately capture their unique characteristics and identify the corresponding damage. Then, considering the different physical properties and damage manifestations of different building materials, such as steel, concrete, and masonry, sub-models of different building materials are established in different building structure types. Then, according to the types and degrees of damage that may occur in buildings with different service years, corresponding damage factors are set for buildings with different service years. The types and degrees of damage that may occur in different building structure types and building materials are different, so the corresponding damage factors are different.

[0049] S230: Use the selected recognition model as the preset recognition model to be currently used.

[0050] Specifically, the identification model suitable for the current detection task was determined and used in the subsequent data analysis and damage identification process to ensure the accuracy and reliability of the detection results.

[0051] S240: According to the feature information, a detection strategy and detection data corresponding to the feature information are matched and queried in a preset detection type library.

[0052] In this embodiment, the system also presets a detection type library, which includes recommended detection strategies and detection data for different types of buildings, different materials and different service life, to guide on-site staff to conduct efficient and accurate building safety detection.

[0053] Specifically, by comparing the feature information with the records in the detection type library, the detection strategy and detection data that best match the current building are found. The detection strategy includes the recommended detection instruments, detection methods, detection points, and detection frequency, etc., among which the detection data includes preset thresholds, reference standards, and historical data to guide subsequent data analysis and damage identification.

[0054] S250: Automatically generate detection recommendation information based on the detection strategy and detection data.

[0055] Specifically, after matching the detection strategy and detection data, the system will automatically generate detection recommendation information based on this information. Personalized suggestions are made for the current building to be inspected, thereby guiding on-site staff to better conduct safety inspections. Recommended information includes specific detection steps, operating instructions, precautions, etc. It may also include recommended detection areas or points, as well as expected detection results or risk levels.

[0056] S260: Send the detection recommendation information to the detection equipment of the on-site staff, so that the on-site staff can detect the urban buildings according to the detection recommendation information.

[0057] Specifically, the system sends the generated test recommendation information to the test equipment of the on-site staff. These devices can be smart phones, tablet computers, special test instruments, etc., as long as they can receive and display the recommended information. After receiving the test recommendation information, the on-site staff can conduct targeted safety inspections on urban buildings and houses according to the guidance and suggestions therein.

[0058] In one embodiment, referring to Figure 3 After step S250, the method further includes the following steps: S310. Identify abnormal temperature and humidity areas of urban buildings based on the temperature distribution data and the humidity distribution data, and determine the environmental risk index of the abnormal temperature and humidity areas.

[0059] Specifically, the system uses the collected temperature distribution data and humidity distribution data to analyze the temperature and humidity conditions of urban buildings. By comparing the actual temperature and humidity data with the preset threshold or standard range, the abnormal temperature and humidity areas that exceed the normal range are identified. These abnormal areas are used to indicate potential environmental problems, such as water leakage, insulation failure, or poor ventilation. Then, an environmental risk assessment is performed on each abnormal temperature and humidity area, and the environmental risk index is calculated based on the area, duration, rate of change, and correlation with other damage indicators of the abnormal area.

[0060] S320. According to the environmental risk index ranking of each temperature and humidity abnormality area, adjust the detection process in the detection recommendation information.

[0061] Specifically, after determining the abnormal temperature and humidity areas and their environmental risk indexes, the detection process in the detection recommendation information is dynamically adjusted according to the environmental risk index. Specifically, the system will prioritize abnormal areas with higher environmental risk indexes into the detection process to ensure that potential risks are promptly addressed and handled. At the same time, for areas with lower risk indexes, the system may recommend simpler or less frequent detection methods to improve detection efficiency.

[0062] In one embodiment, referring to Figure 4 After step S150, the method further comprises the following steps: S410: Obtain architectural geometry information of urban buildings.

[0063] In this embodiment, the building geometry information refers to information such as the three-dimensional size and spatial layout of the building. The building geometry information is extracted by acquiring the design drawings or CAD files of the building.

[0064] S420. Determine the safety level of each area of ​​urban buildings based on the location of structural damage and potential safety hazards.

[0065] Specifically, after obtaining the location of structural damage and the potential safety hazard area, the system conducts a safety level assessment on different areas of the building based on the location of structural damage and the potential safety hazard area. The safety level assessment is based on the severity of the damage, the potential risk of the hazard, and the functional importance of the area. Through quantitative analysis, a corresponding safety level is assigned to each area, such as "high risk", "medium risk" or "low risk".

[0066] S430. Establish a three-dimensional model based on the building geometry information, the location of structural damage, and the potential safety hazard area, and display corresponding safety level labels in each area of ​​the three-dimensional model according to the safety level of each area.

[0067] Specifically, in this embodiment, a three-dimensional model is established based on the building geometry information, the location of structural damage and the potential safety hazard areas, and then according to the safety level of each area evaluated in step S320, a safety level identification is added to the corresponding area of ​​the three-dimensional model. The safety level identification can be in the form of color, label or icon, which can clearly display the safety status of each area.

[0068] S440. Add the three-dimensional model with the safety level identification to the safety assessment report.

[0069] Finally, the system integrates the 3D model with the safety level logo into the safety assessment report. In this way, when viewing the report, on-site staff can not only read the text description and analysis, but also intuitively understand the safety status of the building through the 3D model. The combination of text and pictures makes the report more vivid and intuitive, which helps staff better understand and evaluate the safety risks of the building.

[0070] The implementation principle of a method for urban building safety detection in an embodiment of the present application is as follows: the present application acquires multi-dimensional data and performs preprocessing and intelligent identification to accurately locate structural damage locations and safety hazard areas and generate a detailed safety assessment report; at the same time, real-time feedback is provided to the detection equipment of on-site staff to facilitate timely response; the present application improves the real-time and accuracy of detection, improves the efficiency of on-site staff detection operations by predicting potential safety hazards, effectively solves the problem of safety detection lag in the prior art, and provides comprehensive and reliable technical support for urban building safety detection.

[0071] In the second aspect, the present application provides a device for detecting the safety of urban buildings and houses. The device for detecting the safety of urban buildings and houses of the present application is described below in combination with the above-mentioned method for detecting the safety of urban buildings and houses. Figure 5 , Figure 5 It is a module schematic diagram of the urban building safety detection device according to the embodiment of the present application.

[0072] A safety detection device for urban buildings and houses, comprising: The data acquisition module 510 is used to acquire the image, temperature distribution data, humidity distribution data and structural deformation data of the urban building to be inspected; A preprocessing module 520, for preprocessing images, temperature distribution data, humidity distribution data and structural deformation data; The recognition module 530 is used to input the pre-processed image, temperature distribution data, humidity distribution data and structural deformation data into a preset recognition model to obtain the structural damage location of the urban building and determine the potential safety hazard area; A report generation module 540, for generating a safety assessment report based on the structural damage location and the safety hazard area; The display module 550 is used to return the safety assessment report to the detection equipment of the on-site staff and display the report content on the detection equipment.

[0073] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown. The electronic device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for detecting the safety of urban buildings and houses is implemented.

[0074] Those skilled in the art will understand that Figure 6 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0075] In one embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0076] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0077] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for detecting the safety of urban buildings, characterized in that: The steps include: Acquire images, temperature distribution data, humidity distribution data and structural deformation data of urban buildings to be inspected; Preprocessing the image, temperature distribution data, humidity distribution data and structural deformation data; The pre-processed images, temperature distribution data, humidity distribution data and structural deformation data are input into a preset recognition model to obtain the structural damage location of urban buildings and determine the potential safety hazard areas; Generate a safety assessment report based on the structural damage location and the potential safety hazard area; The safety assessment report is returned to the detection equipment of the on-site staff, and the report content is displayed on the detection equipment.

2. The urban building safety detection method according to claim 1 is characterized in that: The process of establishing the preset recognition model includes: Collect images of urban buildings with marked structural damage locations and potential safety hazards, temperature distribution data, humidity distribution data, and structural deformation data to form a training data set; Use deep learning algorithms to build recognition models; The training data set is input into the recognition model, and the model parameters are adjusted through the back propagation algorithm until the model reaches a preset accuracy rate on the verification data set.

3. The urban building safety detection method according to claim 1 is characterized in that: Before obtaining the image, temperature distribution data, humidity distribution data and structural deformation data of the urban building to be detected, the method further includes the following steps: Acquiring characteristic information of the urban building, wherein the characteristic information includes the structure type, building material and service life of the urban building; According to the feature information, selecting a recognition model that matches the urban building structure from a preset recognition model library; The selected recognition model is used as the preset recognition model to be used currently.

4. The urban building safety detection method according to claim 3 is characterized in that: After the selected recognition model is used as the preset recognition model to be currently used, the method further comprises the following steps: According to the feature information, matching and querying the detection strategy and detection data corresponding to the feature information in a preset detection type library; Automatically generate test recommendation information based on the test strategy and test data; The detection recommendation information is sent to the detection equipment of the on-site staff, so that the on-site staff can detect the urban building house according to the detection recommendation information.

5. The urban building safety detection method according to claim 4 is characterized in that: After automatically generating the detection recommendation information according to the detection strategy and the detection data, the method further comprises the following steps: According to the temperature distribution data and the humidity distribution data, identifying the abnormal temperature and humidity area of ​​the urban building house, and determining the environmental risk index of the abnormal temperature and humidity area; According to the environmental risk index ranking of each of the abnormal temperature and humidity areas, the detection process in the detection recommendation information is adjusted.

6. The urban building safety detection method according to claim 1 is characterized in that: After generating a safety assessment report based on the structural damage location and the potential safety hazard area, the method further comprises the following steps: Obtaining architectural geometry information of the urban buildings; Determining the safety level of each area of ​​the urban building according to the structural damage location and the potential safety hazard area; Establishing a three-dimensional model according to the building geometry information, the location of structural damage and the potential safety hazard area, and displaying corresponding safety level identification in each area of ​​the three-dimensional model according to the safety level of each area; The three-dimensional model with the safety level identification is added to the safety assessment report.

7. A safety detection device for urban buildings, characterized in that: include: A data acquisition module, used to acquire images, temperature distribution data, humidity distribution data and structural deformation data of urban buildings to be inspected; A preprocessing module, used for preprocessing the image, temperature distribution data, humidity distribution data and structural deformation data; The recognition module is used to input the pre-processed image, temperature distribution data, humidity distribution data and structural deformation data into a preset recognition model to obtain the structural damage location of urban buildings and determine the potential safety hazard area; A report generation module, used to generate a safety assessment report according to the structural damage location and the safety hazard area; The display module is used to return the safety assessment report to the detection equipment of the on-site staff and display the report content on the detection equipment.

8. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the urban building safety detection method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the urban building safety detection method described in any one of claims 1-6 are implemented.

Images