Method, device and equipment for intelligently generating building outer wall repair vertical greening design based on yolk algorithm and medium

Abstract

The invention discloses an intelligent generation building outer wall repair vertical greening design method, device and equipment based on a yolk algorithm and a medium, and relates to the technical field of building outer wall repair and vertical greening, and the method comprises the steps: inputting a preprocessed target building outer wall image into a trained YOLO detection model, and recognizing and marking an outer wall defect position and a defect type; according to the defect position and the defect type of the outer wall, on the basis of the structure and material characteristics of the building outer wall and the environmental condition, a repair scheme is generated in combination with information in the vertical greening database; and planning the layout of the vertical greening system according to the characteristics of the building outer wall facade and in combination with the outer wall defect position and defect type identified by the YOLO detection model. According to the method, the damage condition of the building outer wall is automatically recognized through the AI technology, efficient and accurate target detection is achieved in combination with the YOLO algorithm, then a personalized repair scheme and vertical greening design are intelligently generated, the repair efficiency and the design quality are improved, and the labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of building exterior wall repair and vertical greening, and specifically to a design method, device, equipment and medium for building exterior wall repair and vertical greening generated by AI in combination with a YOLO algorithm. The method automatically identifies exterior wall defects and plans a repair plan through AI technology, while optimizing the layout of vertical greening to enhance the aesthetics and ecological benefits of the building's appearance. Background Art

[0002] With the acceleration of urbanization, the damage of building exterior walls has become increasingly prominent, such as wall cracks, peeling, water seepage, etc., which seriously affect the aesthetics and safety of buildings. At the same time, vertical greening, as an important means of urban greening, can improve the urban ecological environment and enhance the quality of life of residents. However, traditional exterior wall repair and vertical greening design often rely on manual experience, and have problems such as low efficiency, high cost, and poor effect. The maintenance and repair of building exterior walls has become an important topic.

[0003] Existing building exterior wall repair and vertical greening design mostly rely on manual survey and design, which is inefficient, costly, and difficult to accurately match the building appearance and functional requirements. With the development of computer vision and artificial intelligence technology, especially the widespread application of deep learning algorithms in the field of image recognition and processing, it is possible to automatically and intelligently detect and design building exterior wall repair solutions. However, there is currently a lack of systematic solutions on the market that closely integrate advanced AI technologies such as the YOLO algorithm with building exterior wall repair and vertical greening design.

[0004] As an advanced real-time target detection algorithm, the YOLO algorithm can achieve rapid target recognition and positioning while maintaining high accuracy, making it possible for intelligent building exterior wall restoration and vertical greening design. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, equipment and medium for intelligently generating vertical greening design for building exterior wall repair based on the YOLO algorithm. The method automatically identifies exterior wall defects through AI technology and generates a repair plan. At the same time, the layout of the vertical greening system is optimized in combination with the characteristics of the building facade, so as to realize the intelligentization and efficiency of exterior wall repair and vertical greening.

[0006] In a first aspect, the present invention provides a method for intelligently generating vertical greening design for building exterior wall restoration based on a YOLO algorithm, comprising:

[0007] Acquire a building exterior wall image, and preprocess the acquired building exterior wall image to obtain a target building exterior wall image;

[0008] Input the target building exterior wall image into the trained YOLO detection model to identify and mark the exterior wall defect locations and defect types;

[0009] Generate a repair plan based on the location and type of exterior wall defects, the structural and material characteristics of the building exterior wall, and environmental conditions, combined with the information in the vertical greening database;

[0010] According to the characteristics of the building's exterior wall facade, combined with the exterior wall defect locations and defect types identified by the YOLO detection model, the layout of the vertical greening system is planned.

[0011] In some examples, the training method of the YOLO detection model includes:

[0012] A training data set is established by using building exterior wall images and corresponding defect information, and the YOLO detection model is trained using the training data set.

[0013] In some examples, the method further comprises:

[0014] The target building exterior wall image is converted into a three-dimensional model, the identified exterior wall defect locations and defect types are marked on the three-dimensional model, and displayed through virtual reality technology.

[0015] In some examples, after generating the repair solution, the method further includes:

[0016] Use AI technology to optimize the repair plan and prepare a repair measures report.

[0017] In some examples, after planning the layout of the vertical greening system, the method further includes:

[0018] Select plant species and arrange them according to environmental conditions, plant growth habits and aesthetic requirements.

[0019] In some examples, the method further includes designing a planting container and an irrigation system.

[0020] In some examples, the method further comprises:

[0021] The YOLO detection model is continuously optimized through user feedback and big data analysis.

[0022] In a second aspect, the present invention provides an intelligent generation device for building exterior wall repair vertical greening design based on the YOLO algorithm, comprising:

[0023] A preprocessing module is used to obtain a building exterior wall image, and preprocess the obtained building exterior wall image to obtain a target building exterior wall image;

[0024] The defect recognition module is used to input the target building exterior wall image into the trained YOLO detection model to identify and mark the exterior wall defect location and defect type;

[0025] A repair plan generation module is used to generate a repair plan based on the location and type of exterior wall defects, the structural and material characteristics of the building exterior wall, and environmental conditions, combined with the information in the vertical greening database;

[0026] The vertical greening design module is used to plan the layout of the vertical greening system according to the characteristics of the building's exterior wall facade and the location and type of exterior wall defects identified by the YOLO detection model.

[0027] In a third aspect, the present invention provides a computer 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 any one of the above methods when executing the computer program.

[0028] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0029] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0030] The AI ​​intelligent generation of building exterior wall repair and vertical greening system design based on the YOLO algorithm provided by the present invention realizes the intelligentization of exterior wall repair and the optimized design of vertical greening system, and has broad application prospects and promotion value. It is specifically reflected in the following aspects:

[0031] Improve repair efficiency: Automated detection and intelligent solution design greatly shorten the design cycle of building exterior wall repair solutions.

[0032] Reduce costs: reduce manual survey and design costs and improve resource utilization efficiency.

[0033] Improve design quality: AI algorithms can accurately match building characteristics with environmental requirements and generate personalized restoration and greening plans.

[0034] Intelligence: The YOLO algorithm is used to automatically identify exterior wall defects and intelligently generate repair plans, improving repair efficiency and accuracy.

[0035] Efficiency: Optimize the layout of the vertical greening system, reducing design costs and construction difficulties.

[0036] Aesthetics: Combining composition design and color design to enhance the aesthetics and ecological benefits of the building’s exterior.

[0037] Flexibility: It can be flexibly adjusted and optimized according to actual conditions to meet the needs of different buildings.

[0038] Customization: Combined with the vertical greening database, we provide customized restoration and greening solutions for different buildings to meet diverse needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 It is a schematic diagram of a method for intelligently generating vertical greening design for building exterior wall restoration based on the YOLO algorithm provided in an embodiment of the present invention;

[0041] Figure 2 It is a schematic diagram of a method for intelligently generating vertical greening design for building exterior wall restoration based on the YOLO algorithm provided in an embodiment of the present invention;

[0042] Figure 3 It is a schematic diagram of a device for intelligently generating vertical greening design for building exterior wall repair based on the YOLO algorithm provided in an embodiment of the present invention;

[0043] Figure 4 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0045] In the following description, specific embodiments of the present invention will be described with reference to steps and symbols performed by one or more computers, unless otherwise specified. Therefore, these steps and operations will be mentioned several times as being performed by a computer, and computer execution referred to herein includes operations by a computer processing unit of electronic signals representing data in a structured form. This operation converts the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise change the operation of the computer in a manner familiar to testers in the field. The data structure maintained by the data is a physical location in the memory, which has specific characteristics defined by the data format. However, the principles of the present invention are described in the above text, which does not represent a limitation, and testers in the field will understand that the following various steps and operations can also be implemented in hardware.

[0046] The term "module" or "unit" used herein can be regarded as a software object executed on the computing system. The different components, modules, engines and services in this article can be regarded as implementation objects on the computing system. The apparatus and method in this article are preferably implemented in software, but can also be implemented in hardware, all within the scope of protection of the present invention.

[0047] Those skilled in the art will appreciate that, unless expressly stated otherwise, the singular forms "a", "an", "" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other elements, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" as used herein includes all or any unit and all combinations of one or more associated listed items.

[0048] In the first embodiment of the present invention, the present invention provides a method for intelligently generating vertical greening design for building exterior wall repair based on YOLO algorithm, such as Figure 1 As shown, the following steps are included:

[0049] S101: Acquire a building exterior wall image, and preprocess the acquired building exterior wall image to obtain a target building exterior wall image;

[0050] S102: Input the target building exterior wall image into the trained YOLO detection model to identify and mark the exterior wall defect locations and defect types;

[0051] S103: Generate a repair plan based on the location and type of defects on the exterior wall, the structural and material properties of the building exterior wall and environmental conditions, combined with the information in the vertical greening database;

[0052] S104: Plan the layout of the vertical greening system based on the characteristics of the building's exterior wall facade and the exterior wall defect locations and defect types identified by the YOLO detection model.

[0053] In the embodiment of the present invention, Figure 2 As shown, the database can be established in the following ways:

[0054] Collect and organize a large number of building exterior wall images and corresponding defect information to establish a training data set;

[0055] Introduce a vertical greening database, which contains information on different types of green plants, planting methods, maintenance knowledge, etc.

[0056] In an embodiment of the present invention, exterior wall defect detection can be performed in the following manner:

[0057] Data collection: Use high-definition fixed cameras or drones to take all-round photos of the building's exterior walls to obtain high-definition image data of the exterior walls. Pre-process the collected images, including denoising, contrast enhancement, color correction, etc., to improve the accuracy of subsequent image recognition and processing.

[0058] 3D model construction: Using Neural Radiance Field (NeRF), a deep learning model for 3D space modeling, drone aerial photos are converted into high-precision 3D models to intuitively display the building exterior wall shape.

[0059] Defect identification: exterior wall damage detection based on YOLO algorithm

[0060] Build a YOLO detection model and train it on common damage types of building exterior walls (such as cracks, peeling, stains, etc.).

[0061] The preprocessed image is input into the trained YOLO detection model to automatically identify and mark the damaged area of ​​the exterior wall, and output information such as the location, type and severity of the damage, such as primary defect information such as wall cracks, peeling, and water seepage, and secondary defect information such as crack type and size, and mark the defect location and type. The identified defects and their classification information are directly marked on the 3D model to achieve intuitive presentation of the defects and form an intuitive 3D defect distribution map. Through virtual reality VR or augmented reality AR technology, users can view the exterior wall defects from multiple angles and in all directions. This facilitates the planning and implementation of subsequent repair work.

[0062] Defect analysis: Based on the identification results, analyze the severity and impact of the defects to provide a basis for the formulation of repair plans.

[0063] In the embodiment of the present invention, the repair solution can be generated in the following manner:

[0064] Solution planning: Based on the damage information identified by the YOLO detection model, combined with the structure and material characteristics of the building's exterior wall and environmental conditions, combined with the information in the vertical greening database, the AI ​​algorithm is used to intelligently and automatically generate a repair plan, including the selection of repair materials, repair processes and repair sequence steps, as well as expected effect evaluation, to ensure that the repair plan is both economical and meets the safety and aesthetic requirements of the building.

[0065] Solution optimization: Use AI algorithms to optimize the repair plan to ensure the best repair effect, while taking into account factors such as cost and construction period.

[0066] Report preparation: Prepare a detailed restoration measures report, including plant species selection, planting layout, maintenance suggestions, etc., for users' reference and implementation.

[0067] In the embodiment of the present invention, the vertical greening system design can be performed in the following ways:

[0068] Layout planning: According to the characteristics of the building facade and the recognition results of the wall space by the YOLO detection model, the layout of the vertical greening system suitable for the area is planned, including the location, size and shape of the greening module.

[0069] Plant configuration: According to environmental conditions, plant growth habits and aesthetic requirements, select appropriate plant species and configure them reasonably to form a diversified green landscape.

[0070] Planting container design.

[0071] Irrigation system design: Design an efficient irrigation system to ensure the normal growth needs of plants while taking into account water and energy conservation.

[0072] In the embodiment of the present invention, system integration can be performed in the following manner:

[0073] System integration: Integrate the repair plan and the vertical greening system design plan into the same platform to achieve coordinated optimization of repair and greening design. Use the big model again to classify defects and iterate the plan, and combine the information in the vertical greening database to automatically complete customized vertical greening repair product plans and repair measures reports. Through user feedback and big data analysis, continuously optimize the AI ​​algorithm model to improve the system intelligence level and user experience.

[0074] In the embodiment of the present invention, it can be used in the following scenarios:

[0075] Real estate industry: Cooperate with real estate developers to apply AI intelligent inspection systems to self-inspection and self-repair of real estate projects to improve building quality and market competitiveness.

[0076] Community property: Cooperate with community property to ensure the safety of buildings while improving the overall image of the community through vertical greening and creating a livable environment.

[0077] Government projects: Cooperate with the government to carry out urban renewal, renovation of historical and cultural blocks, and reconstruction and renovation of old communities at the lowest cost to promote sustainable urban development.

[0078] Urban design: Applied to urban design micro-transformation and micro-stitching projects, through refined management and design, to enhance the aesthetics and functionality of urban space.

[0079] In the second embodiment of the present invention, in order to better implement the method provided in the embodiment of the present invention, the embodiment of the present invention also provides a device based on the above method. The meanings of the terms are the same as those in the above method, and the specific implementation details can refer to the description in the method embodiment.

[0080] See also Figure 3 , Figure 3 A schematic diagram of the structure of a device provided in an embodiment of the present invention, wherein the device 300 may include a pre-processing module 301, a defect identification module 302, a repair solution generation module 303 and a vertical greening design module 304, wherein:

[0081] The preprocessing module 301 is used to obtain a building exterior wall image, and preprocess the obtained building exterior wall image to obtain a target building exterior wall image;

[0082] The defect recognition module 302 is used to input the target building exterior wall image into the trained YOLO detection model to identify and mark the exterior wall defect location and defect type;

[0083] The repair plan generation module 303 is used to generate a repair plan according to the location and type of the exterior wall defects, based on the structure and material characteristics of the building exterior wall and environmental conditions, combined with the information in the vertical greening database;

[0084] The vertical greening design module 304 is used to plan the layout of the vertical greening system according to the characteristics of the building's exterior wall facade and the exterior wall defect locations and defect types identified by the YOLO detection model.

[0085] In an embodiment of the present invention, the training method of the YOLO detection model includes:

[0086] A training data set is established by using building exterior wall images and corresponding defect information, and the YOLO detection model is trained using the training data set.

[0087] In an embodiment of the present invention, the above-mentioned device further includes:

[0088] The virtual display module is used to convert the target building exterior wall image into a three-dimensional model, mark the identified exterior wall defect locations and defect types on the three-dimensional model, and display it through virtual reality technology.

[0089] In an embodiment of the present invention, after generating the repair solution, the above-mentioned device further includes:

[0090] The repair plan optimization module is used to optimize the repair plan using AI technology and prepare a repair measures report.

[0091] In an embodiment of the present invention, after planning the layout of the vertical greening system, the above-mentioned device further includes:

[0092] The configuration module is used to select and configure plant species according to environmental conditions, plant growth habits and aesthetic requirements.

[0093] In an embodiment of the present invention, the above-mentioned device also includes: designing a planting container and an irrigation system.

[0094] In an embodiment of the present invention, the above-mentioned device further includes:

[0095] The model optimization module is used to continuously optimize the YOLO detection model through user feedback and big data analysis.

[0096] In a third embodiment of the present invention, the present invention further provides a computer device, such as Figure 4 As shown, it shows a schematic diagram of the structure of a computer device involved in an embodiment of the present invention, specifically:

[0097] The computer device may include components such as a processor 401 with one or more processing cores, a memory 402 with one or more computer-readable storage media, a power supply 403, and an input unit 404. Those skilled in the art will appreciate that Figure 4 The computer device structure shown in the figure does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Among them:

[0098] The processor 401 is the control center of the computer device. It uses various interfaces and lines to connect various parts of the entire computer device. By running or executing software programs and / or modules stored in the memory 402 and calling data stored in the memory 402, it executes various functions of the computer device and processes data, thereby monitoring the computer device as a whole. Optionally, the processor 401 may include one or more processing cores; preferably, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operation storage medium, user interface and application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 401.

[0099] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating storage medium, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 402 may also include a controller to provide the processor 401 with access to the memory 402.

[0100] The computer device also includes a power supply 403 for supplying power to various components. Preferably, the power supply 403 can be logically connected to the processor 401 through a power management storage medium, so as to manage charging, discharging, and power consumption management through the power management storage medium. The power supply 403 can also include any components such as one or more DC or AC power supplies, recharge storage media, power failure detection circuits, power converters or inverters, and power status indicators.

[0101] The computer device may further include an input unit 404, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0102] Although not shown, the computer device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 401 in the computer device will load the executable files corresponding to the processes of one or more application programs into the memory 402 according to the following instructions, and the processor 401 will run the application programs stored in the memory 402, thereby implementing the steps in the above method embodiment.

[0103] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0104] To this end, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. The computer program is loaded by a processor to execute the steps in any method provided by the embodiment of the present invention.

[0105] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0106] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0107] Since the computer program stored in the computer-readable storage medium can execute the steps in any method provided in the embodiments of the present invention, the beneficial effects that can be achieved by any method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0108] The above is a detailed introduction to the intelligent generation of vertical greening design method, device, equipment and medium for building exterior wall repair based on YOLO algorithm provided in the embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for intelligently generating vertical greening design for building exterior wall restoration based on YOLO algorithm, characterized in that: include: Acquire a building exterior wall image, and preprocess the acquired building exterior wall image to obtain a target building exterior wall image; Input the target building exterior wall image into the trained YOLO detection model to identify and mark the exterior wall defect locations and defect types; Generate a repair plan based on the location and type of exterior wall defects, the structural and material characteristics of the building exterior wall, and environmental conditions, combined with the information in the vertical greening database; According to the characteristics of the building's exterior wall facade, combined with the exterior wall defect locations and defect types identified by the YOLO detection model, the layout of the vertical greening system is planned.

2. The method according to claim 1, characterized in that The training method of the YOLO detection model includes: A training data set is established by using building exterior wall images and corresponding defect information, and the YOLO detection model is trained using the training data set.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: The target building exterior wall image is converted into a three-dimensional model, the identified exterior wall defect locations and defect types are marked on the three-dimensional model, and displayed through virtual reality technology.

4. The method according to claim 3, characterized in that: After generating the repair plan, the method further includes: Use AI technology to optimize the repair plan and prepare a repair measures report.

5. The method according to claim 4, characterized in that After planning the layout of the vertical greening system, the method further includes: Select plant species and arrange them according to environmental conditions, plant growth habits and aesthetic requirements.

6. The method according to claim 5, characterized in that The method also includes: designing a planting container and an irrigation system.

7. The method according to claim 6, characterized in that The method further comprises: The YOLO detection model is continuously optimized through user feedback and big data analysis.

8. An intelligent generation building exterior wall repair vertical greening design device based on YOLO algorithm, characterized in that: include: A preprocessing module is used to obtain a building exterior wall image, and preprocess the obtained building exterior wall image to obtain a target building exterior wall image; The defect recognition module is used to input the target building exterior wall image into the trained YOLO detection model to identify and mark the exterior wall defect location and defect type; A repair plan generation module is used to generate a repair plan based on the location and type of exterior wall defects, the structural and material characteristics of the building exterior wall, and environmental conditions, combined with the information in the vertical greening database; The vertical greening design module is used to plan the layout of the vertical greening system according to the characteristics of the building's exterior wall facade and the location and type of exterior wall defects identified by the YOLO detection model.

9. A computer 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 method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

Images