Construction site environment control method and automation equipment

Abstract

The invention provides a construction site environment control method and automation equipment. The method comprises the following steps: controlling a first camera at a construction site to shoot a panoramic image of the construction site, determining a shooting view angle with an optimal light condition according to the panoramic image, and determining a plurality of second cameras according to the shooting view angle; controlling each second camera to shoot a high-definition image of the construction site, and analyzing according to the high-definition image to obtain the magnitude of dust in the air of the construction site; and deciding whether to start dust falling treatment or not and deciding the dust falling treatment intensity according to the dust magnitude. According to the scheme, whether dust fall processing needs to be carried out or not can be automatically decided on the basis of the high-definition image, automatic control over the dust fall equipment can be achieved, and compared with an existing mode of manually starting the dust fall equipment, the dust fall response rate is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of smart construction sites, and in particular to a construction site environment control method and automation equipment. Background Art

[0002] With the acceleration of industrialization, the dust pollution problem at construction sites is becoming increasingly serious, and dust reduction equipment is needed for dust reduction. Existing dust reduction equipment generally requires manual control, that is, the staff decides whether dust reduction treatment is needed based on the observation of the dust situation on site, and then turns on the dust reduction equipment. This method requires a lot of manpower and the response speed is too slow.

[0003] Therefore, how to achieve automated dust reduction treatment for dust at construction sites is a technical problem that urgently needs to be solved. Summary of the invention

[0004] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a construction site environment control method, automation equipment, electronic equipment, computer storage medium and computer program product.

[0005] The present invention provides a construction site environment control method, the method comprising the following steps: Controlling a first camera at the construction site to capture a panoramic image of the construction site, determining a shooting angle with the best light conditions according to the panoramic image, and determining a plurality of second cameras according to the shooting angles; Controlling each of the second cameras to capture a high-definition image of the construction site, and analyzing the high-definition image to obtain a dust level in the air of the construction site; Whether to start dust reduction processing and the intensity of dust reduction processing are determined according to the dust level.

[0006] Optionally, before analyzing the panoramic image and the high-definition image, the method further includes: The panoramic image and the high-definition image are subjected to image preprocessing, including but not limited to denoising, brightness adjustment, and cropping.

[0007] Optionally, obtaining the dust level in the air at the construction site according to the high-definition image analysis includes: Extracting image parameter data from the high-definition image, the image parameter data including color component ratio, contrast, edge detection data, and texture analysis data; The image parameter data is analyzed using a machine learning model, which outputs an estimate of the dust level in the construction site air.

[0008] Optionally, the using a machine learning model to analyze the image parameter data, wherein the machine learning model outputs an estimated dust level in the air at the construction site, including: Integrating the image parameter data into a feature vector, and inputting the feature vector into the machine learning model; The machine learning model specifically performs: Input layer: takes the feature vector as input; Residual block processing: The feature vector is processed in turn through multiple residual blocks; in each residual block, the feature vector is first extracted through a convolutional layer, then normalized through a batch normalization layer, and then nonlinearly transformed through a ReLU activation function, and finally the input data is added to the output of the convolutional layer through an identity connection to obtain a feature map; Global average pooling layer: After the residual block is processed, the feature map is converted into a one-dimensional feature vector using the global average pooling layer; Fully connected layer: performs feature fusion and classification on the one-dimensional feature vector to obtain and output the estimated dust level in the air at the construction site.

[0009] Optionally, determining a shooting angle of view with the best lighting conditions according to the panoramic image, and determining a plurality of second cameras according to the shooting angle of view include: Extracting brightness distribution data and shadow direction from the panoramic image, and determining the direction of sunlight in the panoramic image based on the brightness distribution data and the shadow direction; Determine the shooting angle range of the panoramic image facing away from the sunlight according to the direction of the sunlight obtained by analysis, that is, determine the shooting angle range corresponding to the opposite direction of the sunlight in the panoramic image; According to the layout positions of the cameras in the construction site, several cameras belonging to the shooting angle range are screened and obtained as the second cameras.

[0010] Optionally, the selecting, according to the layout positions of the cameras in the construction site, several cameras belonging to the shooting angle range as the second camera includes: A color diversity value of the construction site is extracted based on the panoramic image, a target quantity is determined based on the color diversity value and a corresponding positive correlation, and cameras of the target quantity that belong to the shooting viewing angle range are used as the second cameras.

[0011] The present invention also provides an automation device, comprising a processor and a memory, wherein the processor calls and executes a computer program in the memory to implement the following steps: Controlling a first camera at the construction site to capture a panoramic image of the construction site, determining a shooting angle with the best light conditions according to the panoramic image, and determining a plurality of second cameras according to the shooting angles; Controlling each of the second cameras to capture a high-definition image of the construction site, and analyzing the high-definition image to obtain a dust level in the air of the construction site; Whether to start dust reduction processing and the intensity of dust reduction processing are determined according to the dust level.

[0012] The present invention also provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the method as described in any of the preceding items.

[0013] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, any of the above methods is executed.

[0014] The present invention also provides a computer program product, comprising a computer program stored on a non-transitory computer-readable medium, wherein the computer program is executed by a processor to execute the method as described in any of the preceding items.

[0015] The beneficial effects of the present invention are: On the one hand, the solution of the present invention can automatically decide whether dust reduction treatment is needed based on high-definition images and can realize automatic control of dust reduction equipment. Compared with the existing method of manually turning on the dust reduction equipment, the dust reduction response rate is higher. On the other hand, the present invention can also select the most suitable camera for shooting high-definition images for analyzing the dust level in the air of the construction site based on the panoramic image of the construction site, thereby ensuring the accuracy of the dust level obtained by analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a flow chart of a construction site environment control method disclosed in an embodiment of the present invention.

[0018] Figure 2 It is a structural schematic diagram of an automation device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0021] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0022] In the description of the present invention, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. appear to indicate an orientation or position relationship, they are based on the orientation or position relationship shown in the accompanying drawings, or are the orientation or position relationship in which the product of the invention is usually placed when used. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0023] It should be noted that, in the absence of conflict, the features in the embodiments of the present invention may be combined with each other.

[0024] like Figure 1 As shown, an embodiment of the present invention discloses a construction site environment control method, the method comprising the following steps: Controlling a first camera at the construction site to capture a panoramic image of the construction site, determining a shooting angle with the best light conditions according to the panoramic image, and determining a plurality of second cameras according to the shooting angles; Controlling each of the second cameras to capture a high-definition image of the construction site, and analyzing the high-definition image to obtain a dust level in the air of the construction site; Whether to start dust reduction processing and the intensity of dust reduction processing are determined according to the dust level.

[0025] The present invention deploys multiple cameras in different areas of the construction site, and these cameras can capture image data of the construction site at different shooting angles. Among them, these cameras are divided into panoramic cameras and ordinary cameras. Specifically, the panoramic camera, i.e., the first camera mentioned above, is first controlled to capture a panoramic image of the construction site. The shooting angle with the best light conditions can be determined based on the panoramic image, and then a number of second cameras that meet the shooting angle can be screened. Finally, these second cameras are controlled to capture high-definition images of the construction site. The dust level in the air of the construction site can be analyzed from the captured high-definition images, so as to decide whether to turn on the dust reduction treatment and the specific dust reduction treatment intensity. Specifically, when the dust level is higher than the set level, it is decided to turn on the dust reduction treatment, and the dust reduction treatment intensity is decided according to the extent to which the dust level is higher than the set level. The dust reduction treatment intensity is, for example, the amount of water mist sprayed per unit time, the adsorption rate of the dust suction equipment, etc.

[0026] Therefore, the solution of the present invention can automatically decide whether dust reduction treatment is needed based on high-definition images and can realize automatic control of dust reduction equipment. Compared with the existing method of manually turning on the dust reduction equipment, the dust reduction response rate is higher. On the other hand, the present invention can also select the most suitable camera for shooting high-definition images for analyzing the dust level in the air of the construction site based on the panoramic image of the construction site, thereby ensuring the accuracy of the dust level obtained by analysis.

[0027] Optionally, before analyzing the panoramic image and the high-definition image, the method further includes: The panoramic image and the high-definition image are subjected to image preprocessing, including but not limited to denoising, brightness adjustment, and cropping.

[0028] In this embodiment, the panoramic image and high-definition image captured cannot be used directly for the above analysis, but need to be preprocessed first. For example, denoising and brightness adjustment can be used to improve the recognizability of dust; and the panoramic image and high-definition image need to be cropped to a certain extent, such as cropping the image of the area above the ground, that is, removing the image of the ground area of ​​the panoramic image and high-definition image, which is conducive to improving the accuracy of the dust level obtained by subsequent detection.

[0029] Optionally, obtaining the dust level in the air at the construction site according to the high-definition image analysis includes: Extracting image parameter data from the high-definition image, the image parameter data including color component ratio, contrast, edge detection data, and texture analysis data; The image parameter data is analyzed using a machine learning model, which outputs an estimate of the dust level in the construction site air.

[0030] In this embodiment, the dust level analysis in the air at the construction site can be based on a comprehensive analysis of three types of image parameter data: color component ratio, contrast, edge detection data, and texture analysis data. The specific analysis principle is as follows: Color component ratio: Dust in the air at the construction site changes the propagation path of light, which in turn causes the ratio of the red, green, and blue color components in the image to change. By analyzing these changes, the dust concentration can be estimated.

[0031] Contrast: Dust in the air at the construction site will reduce the contrast of the image, making the image look more blurred. Therefore, by analyzing the change in contrast, the concentration of dust can also be inferred.

[0032] Edge detection data: Dust in the air at the construction site will blur the edges in the image, reducing the number of edges and edge corners. By analyzing the changes in edge detection data, the dust concentration can also be estimated.

[0033] Texture analysis data: Dust in the air at the construction site will change the texture characteristics of the image, making the texture look more blurred and unclear. By analyzing the changes in texture analysis data, the concentration of dust can also be estimated.

[0034] The above image parameter data is input into the preset machine learning model, which performs in-depth comprehensive analysis to estimate the dust level in the air at the construction site. The machine learning model is preferably built based on ResNet, which is a deep learning architecture that introduces "residual learning" to solve the gradient vanishing and gradient exploding problems encountered by traditional deep neural networks when increasing the number of layers. The main structure of ResNet includes the following key parts: Initial convolution layer: The ResNet model usually starts with an initial convolution layer, which is used to perform preliminary feature extraction and spatial downsampling of the input image. This convolution layer usually has a larger convolution kernel and stride to reduce the spatial dimension that needs to be processed by subsequent layers and reduce computational complexity.

[0035] Residual Blocks: The core of ResNet is composed of multiple residual blocks, which can be BasicBlock or Bottleneck. Each residual block contains two or more convolutional layers and one or more skip connections, allowing the input to skip some layers and be added to the output.

[0036] BasicBlock: It consists of two 3x3 convolutional layers and a residual connection. If the dimension of the feature map needs to be changed, an additional convolutional layer will be added to the residual block for downsampling.

[0037] Bottleneck: In deeper ResNet (such as ResNet-152), in order to reduce the amount of computation, a "bottleneck" structure is usually used, that is, first reduce the dimension through a small convolution kernel (such as 1x1), then perform 3x3 convolution, and finally restore the dimension through 1x1 convolution.

[0038] Global Average Pooling: After the residual block, ResNet usually has a global average pooling layer to average the spatial dimensions of each channel of the feature map, thereby reducing the dimension in preparation for the fully connected layer.

[0039] Fully Connected Layer: Finally, ResNet contains one or more fully connected layers to perform classification or other tasks on the features after global average pooling.

[0040] Skip Connections: Each residual block in ResNet contains one or more skip connections that add the input of the block directly to the output of the block. This design allows the network to learn an identity mapping so that performance does not suffer when increasing the depth of the network.

[0041] Batch Normalization: ResNet usually uses batch normalization after each convolutional layer to speed up the training process and improve the generalization ability of the model.

[0042] ReLU activation function: After the convolutional layer and batch normalization, ResNet uses the ReLU activation function to introduce non-linearity.

[0043] Optionally, the using a machine learning model to analyze the image parameter data, wherein the machine learning model outputs an estimated dust level in the air at the construction site, including: Integrating the image parameter data into a feature vector, and inputting the feature vector into the machine learning model; The machine learning model specifically performs: Input layer: takes the feature vector as input; Residual block processing: The feature vector is processed in sequence through multiple residual blocks; in each residual block, the feature vector is first extracted through a convolutional layer, then normalized through a batch normalization layer, and then nonlinearly transformed through a ReLU activation function, and finally the input data is added to the output of the convolutional layer through an identity connection (to retain the gradient information of the input data and avoid gradient disappearance) to obtain a feature map; Global average pooling layer: After the residual block is processed, the feature map is converted into a one-dimensional feature vector using the global average pooling layer; Fully connected layer: performs feature fusion and classification on the one-dimensional feature vector to obtain and output the estimated dust level in the air at the construction site.

[0044] Optionally, determining a shooting angle of view with the best lighting conditions according to the panoramic image, and determining a plurality of second cameras according to the shooting angle of view include: Extracting brightness distribution data and shadow direction from the panoramic image, and determining the direction of sunlight in the panoramic image based on the brightness distribution data and the shadow direction; Determine the shooting angle range of the panoramic image facing away from the sunlight according to the direction of the sunlight obtained by analysis, that is, determine the shooting angle range corresponding to the opposite direction of the sunlight in the panoramic image; According to the layout positions of the cameras in the construction site, several cameras belonging to the shooting angle range are screened and obtained as the second cameras.

[0045] In this embodiment, the present invention extracts brightness distribution data and shadow direction from the panoramic image, and determines the direction of the sunlight in the panoramic image based on the data, thereby determining the shooting angle range facing away from the sunlight, and using multiple cameras within the shooting angle range as the above-mentioned second cameras. The above-mentioned second cameras obtained by screening can reduce image overexposure or shadow occlusion caused by direct sunlight, thereby providing clearer and more accurate high-definition image data of the construction site, which is conducive to obtaining more accurate dust levels.

[0046] Optionally, the selecting, according to the layout positions of the cameras in the construction site, several cameras belonging to the shooting angle range as the second camera includes: A color diversity value of the construction site is extracted based on the panoramic image, a target quantity is determined based on the color diversity value and a corresponding positive correlation, and cameras of the target quantity that belong to the shooting viewing angle range are used as the second cameras.

[0047] In this embodiment, there may be a variety of targets of other colors in the construction site, such as other buildings, plants, fence panels, etc., and the colors of these targets may also be diverse. However, the more colors of the target objects as the background, the more difficult it is to accurately extract the above-mentioned dust level. In response to this technical problem, the present invention is configured to extract a color diversity value of the construction site based on a panoramic image. The color diversity value is used to characterize the number and distribution of colors of background targets in the construction site. The more colors there are and the more scattered the distribution is, the higher the color diversity value is; the fewer colors there are and the more concentrated the distribution is, the lower the color diversity value is. The number of targets can be determined based on the color diversity value and the corresponding positive correlation, and the camera that obtains the number of targets in the aforementioned determined shooting angle range can be selected as the second camera.

[0048] However, the present invention does not limit the specific expression form of the positive correlation, such as formula expression or comparison table expression.

[0049] like Figure 2 As shown, an embodiment of the present invention further discloses an automation device, including a processor and a memory, wherein the processor calls and executes a computer program in the memory to implement the following steps: Controlling a first camera at the construction site to capture a panoramic image of the construction site, determining a shooting angle with the best light conditions according to the panoramic image, and determining a plurality of second cameras according to the shooting angles; Controlling each of the second cameras to capture a high-definition image of the construction site, and analyzing the high-definition image to obtain a dust level in the air of the construction site; Whether to start dust reduction processing and the intensity of dust reduction processing are determined according to the dust level.

[0050] An embodiment of the present invention further discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the method described in the above embodiment.

[0051] The embodiment of the present invention further discloses a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in the above embodiment is executed.

[0052] An embodiment of the present invention further discloses a computer program product, including a computer program stored on a non-transitory computer-readable medium, wherein the computer program is executed by a processor to perform any of the methods described in the preceding items.

[0053] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0054] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0055] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0056] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0057] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0058] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0059] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document is not limited here.

[0060] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A construction site environment control method, characterized in that: The method comprises the following steps: Controlling a first camera at the construction site to capture a panoramic image of the construction site, determining a shooting angle with the best light conditions according to the panoramic image, and determining a plurality of second cameras according to the shooting angles; Controlling each of the second cameras to capture a high-definition image of the construction site, and analyzing the high-definition image to obtain a dust level in the air of the construction site; Whether to start dust reduction processing and the intensity of dust reduction processing are determined according to the dust level.

2. A construction site environment control method according to claim 1, characterized in that: Before analyzing the panoramic image and the high-definition image, the method further includes: The panoramic image and the high-definition image are subjected to image preprocessing, including but not limited to denoising, brightness adjustment, and cropping.

3. A construction site environment control method according to claim 2, characterized in that: The dust level in the air at the construction site obtained by analyzing the high-definition image includes: Extracting image parameter data from the high-definition image, the image parameter data including color component ratio, contrast, edge detection data, and texture analysis data; The image parameter data is analyzed using a machine learning model, which outputs an estimate of the dust level in the construction site air.

4. A construction site environment control method according to claim 3, characterized in that: The using of a machine learning model to analyze the image parameter data, wherein the machine learning model outputs an estimated dust level in the air at the construction site, comprises: Integrating the image parameter data into a feature vector, and inputting the feature vector into the machine learning model; The machine learning model specifically performs: Input layer: takes the feature vector as input; Residual block processing: The feature vector is processed in turn through multiple residual blocks; in each residual block, the feature vector is first extracted through a convolutional layer, then normalized through a batch normalization layer, and then nonlinearly transformed through a ReLU activation function, and finally the input data is added to the output of the convolutional layer through an identity connection to obtain a feature map; Global average pooling layer: After the residual block is processed, the feature map is converted into a one-dimensional feature vector using the global average pooling layer; Fully connected layer: performs feature fusion and classification on the one-dimensional feature vector to obtain and output the estimated dust level in the air at the construction site.

5. A construction site environment control method according to claim 4, characterized in that: The method of determining a shooting angle with the best light condition according to the panoramic image, and determining a plurality of second cameras according to the shooting angle, comprises: Extracting brightness distribution data and shadow direction from the panoramic image, and determining the direction of sunlight in the panoramic image based on the brightness distribution data and the shadow direction; Determine the shooting angle range of the panoramic image facing away from the sunlight according to the direction of the sunlight obtained by analysis, that is, determine the shooting angle range corresponding to the opposite direction of the sunlight in the panoramic image; According to the layout positions of the cameras in the construction site, several cameras belonging to the shooting angle range are screened and obtained as the second cameras.

6. A construction site environment control method according to claim 5, characterized in that: The method of screening a plurality of cameras belonging to the shooting angle range as the second camera according to the layout positions of the cameras in the construction site includes: A color diversity value of the construction site is extracted based on the panoramic image, a target quantity is determined based on the color diversity value and a corresponding positive correlation, and cameras of the target quantity that belong to the shooting viewing angle range are used as the second cameras.

7. An automation device, comprising a processor and a memory, characterized in that: The processor calls and executes the computer program in the memory to implement the following steps: Controlling a first camera at the construction site to capture a panoramic image of the construction site, determining a shooting angle with the best light conditions according to the panoramic image, and determining a plurality of second cameras according to the shooting angles; Controlling each of the second cameras to capture a high-definition image of the construction site, and analyzing the high-definition image to obtain a dust level in the air of the construction site; Whether to start dust reduction processing and the intensity of dust reduction processing are determined according to the dust level.

8. An electronic device comprising: A memory storing executable program code; A processor coupled to the memory; characterized in that: the processor calls the executable program code stored in the memory to execute the method according to any one of claims 1-6.

9. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is executed.

10. A computer program product comprising a computer program stored on a non-transitory computer readable medium, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images