Construction site flying dust management and control system and method based on unmanned aerial vehicle

Abstract

The invention provides a construction site flying dust management and control system and method based on an unmanned aerial vehicle, the system comprises a control center and an unmanned aerial vehicle carrying a high-definition camera, the control center plans an unmanned aerial vehicle flight route and a photo shooting point according to a construction area and construction stage data, and controls the unmanned aerial vehicle to fly and shoot; shooted image data are identified and judged by using an image identification algorithm, a color identification algorithm and the like, whether a wheel washing machine is installed on a construction site, whether a construction site fence is complete and whether dust screen coverage meets requirements are judged, corresponding project inspection information is generated, and finally statistical arrangement and reporting to a superior are performed to promote rectification. According to the invention, the dust raising condition of each construction site can be supervised in real time, the labor cost is greatly saved, the troubleshooting period is shortened, and intelligent dust raising management and control of the construction site are realized.

Description

Technical Field

[0001] This invention belongs to the field of dust control technology, and in particular relates to a construction site dust control system and method based on unmanned aerial vehicles (UAVs). Background Technology

[0002] Ensuring high urban air quality is crucial for improving residents' happiness index. Therefore, construction sites in cities must strictly implement five 100% dust control measures: 1. 100% perimeter fencing around the construction site; 2. 100% coverage of exposed soil and gravel at the construction site; 3. 100% washing of vehicle wheels and bodies at the construction site; 4. 100% hardening of road surfaces at the construction site; 5. 100% watering of dusty construction operations. Construction companies also need to manage and supervise dust control measures for projects under construction. However, traditional inspection methods involve sending personnel to each construction site, which is inefficient, time-consuming, and manpower-intensive. Therefore, this invention designs a system and method for dust control at construction sites based on unmanned aerial vehicles (UAVs). Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a construction site dust control system and method based on unmanned aerial vehicles (UAVs), which is efficient and saves manpower.

[0004] The present invention achieves the above-mentioned technical objectives through the following technical means.

[0005] A construction site dust control system based on drones includes a control center and drones equipped with high-definition cameras; The control center includes a control unit and a processing unit. The processing unit includes an instruction input module, a data processing module, and a display module. The control unit is interconnected with the instruction input module, data processing module, and display module to exchange information and control the UAV according to instructions. It controls the UAV to fly along a designated route and take photos at designated locations along the route. The instruction input module is used to input instruction data, including the project boundary location, manually planned flight routes, and dust control requirements.

[0006] The data processing module is used to process image data captured by high-definition cameras, enabling the identification of specific items in the images, including fences, wheel washing machines, fog cannons, and dust nets. It also compares the identified items with the dust control requirements input by the instruction input module to determine whether they meet the dust control requirements. The display module is used to visualize and output data.

[0007] A method for controlling dust pollution at a construction site using the aforementioned drone-based construction site dust control system includes the following steps: Step 1: Divide the project into various areas according to the project distribution, and set up a dust control system in each area; then first obtain the red line address and construction progress stage data of each construction project in the corresponding area, and input them into the processing unit through the instruction input module; Step 2: Based on the data information obtained in Step 1, the data processing module plans the flight path of the drone and the shooting points for photo data. Then, the shooting points in the key control areas are added manually through the command input module to ensure sufficient image data and prevent misjudgment. Step 3: The control unit controls the drone equipped with a high-definition camera to fly along the flight path and take pictures in the designated area; Step 4: The photo data captured by the high-definition camera is transmitted to the data processing module. The data processing module judges the information in the photo using the dust control judgment method to determine whether the construction site meets the local dust control requirements. There are three items to be judged, including: whether the construction site is equipped with a wheel washing machine; whether the construction site fence is complete and whether the area enclosed by the fence is consistent with the construction area; and whether the dust net coverage of the construction site meets the requirements. In each judgment process, if the judgment is qualified, the inspection qualification information of the item is generated; otherwise, the non-compliance information of the item is generated, and the reasons for non-compliance and non-compliance images are listed. Step 5: After all the control sub-items of each construction site have been judged, the data processing module generates a statistical table for all the non-compliant items of the non-compliant construction site and displays it visually through the display module. The statistical table includes the construction project name, non-compliant item category, screenshot of the non-compliant photo, and shooting time information. Step 6: After generating the dust control inspection statistics table, each jurisdiction shall submit it to the company. The company shall publicize the dust control inspection results and urge non-compliant projects to rectify them.

[0008] Furthermore, in step 1, the red line address is the construction area where the construction site is located, and the area within the red line covers the entire construction area of ​​this construction project; the construction progress stage data refers to the current construction stage, including the earthwork excavation stage, the foundation stage, the main structure stage, and the decoration and finishing stage.

[0009] Furthermore, in step 4, the method for determining whether a wheel washing machine is installed on the construction site is as follows: The data processing module first uses an image recognition algorithm to find the wheel washer in the photo. If it is found, it is determined to be qualified; if not, it is determined to not meet the dust control regulations. The image recognition algorithm is implemented through machine deep learning. First, it learns to recognize the wheel washer, and then compares and searches in the photos taken at the construction site. That is, using the graphic comparison function, it searches for similar points within the site range with the photos of the existing wheel washers to check if there is a wheel washer in the site. If the wheel washer is not missing, it generates the inspection qualified information for this project. If there is a missing part, it generates the non-compliant violation information for this project, and lists the reasons for non-compliance and the non-compliant image photos.

[0010] Further, in step 4, the method for judging whether the construction site fence is complete and whether the area enclosed by the fence is the same as the construction area of the construction site is as follows: The data processing module uses an image recognition algorithm to identify and judge the fence of the construction site, and then connects the fence. Then it judges whether the fence connection is closed and whether the area within the closed area is the same as the construction area of the construction site. If it is closed and the area is the same, it can be determined that this item is qualified and generates the inspection qualified information for this project; otherwise, it generates the non-compliant violation information for this project, and lists the reasons for non-compliance and the non-compliant image photos.

[0011] Further, in step 4, the method for judging whether the dust-proof net coverage of the construction site meets the requirements is as follows: The data processing module uses a color recognition algorithm to identify the dust-proof net and the soil in the earth excavation area in the picture. It identifies green as the dust-proof net and yellowish-brown as the land. Then, through the area comparison method, it judges whether the green ratio in the excavation area reaches the local dust control coverage requirements. If so, it judges that this item is qualified and generates the inspection qualified information for this project; otherwise, it generates the non-compliant violation information for this project, and lists the reasons for non-compliance and the non-compliant image photos.

[0012] Further, in step 4, when the data processing module encounters a situation where it cannot make a judgment during the recognition and determination process, it automatically pops up an artificial recognition mark for manual judgment. It makes a one-by-one manual judgment on the wheel washer, fence, and dust-proof net coverage projects. The manual judgment method is the same as the system judgment method, and the judgment result is used for the system to learn. When encountering a similar situation, the system can make an independent judgment.

[0013] The present invention has the following beneficial effects: This invention can generate drone flight routes and photo collection locations by inputting information such as project locations. After obtaining the collected photos, it can determine whether the project violates regulations through appropriate judgment methods, thereby determining whether the construction of the project meets dust control requirements. This invention can inspect all projects in the area within one hour, greatly improving inspection efficiency. This invention can also replace manual on-site inspections, avoiding the waste of human resources. Attached Figure Description

[0014] Figure 1 This is a flowchart of the construction site dust control method based on drones described in this invention. Detailed Implementation

[0015] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the scope of protection of the present invention is not limited thereto.

[0016] The construction site dust control system based on drones described in this invention includes a drone and a control center connected by wireless communication.

[0017] The drone is equipped with a high-definition camera that is connected to the control center to collect data and transmit it to the control center for data processing.

[0018] The control center includes a control unit and a processing unit; the control unit controls the UAV according to the instructions from the processing unit; the processing unit includes an instruction input module, a data processing module, and a display module. The control unit is interconnected with the instruction input module, data processing module, and display module, and can exchange information.

[0019] The control unit can control the drone to fly along the designated route and take photos at the designated locations along the route after the route planning is completed.

[0020] The instruction input module can input instruction data, enabling the input of information such as the project boundary location, manually planned flight routes, and dust control requirements.

[0021] The data processing module can process image data captured by high-definition cameras, enabling the identification of specific items in the photos such as fences, wheel washing machines, fog cannons, and dust nets. It can also compare the identified items with the dust control requirements input by the instruction input module to determine whether they meet the dust control requirements.

[0022] The display module can visualize and output data, facilitating human-computer interaction.

[0023] The method for controlling dust at construction sites based on unmanned aerial vehicles (UAVs) described in this invention is as follows: Figure 1 As shown, the specific process includes the following: Step 1: Divide the projects into different areas according to their distribution. Set up a dust control system as described in this invention in each area. The person in charge of the area can use the dust control system to control dust in the projects within the area at any time. First, obtain the red line address and construction progress stage data of each construction project within the jurisdiction; the red line address is the construction area where the construction site is located, and the area within the red line can cover the entire construction area of ​​the project; the construction progress stage data refers to clarifying which specific stage of construction is currently in, such as earthwork excavation, foundation, main structure, decoration and finishing, so as to facilitate key management of earthwork excavation projects, because earthwork excavation projects generate a lot of dust.

[0024] Step 2: Based on the data obtained in Step 1, the processing unit plans the flight path of the drone from one construction site to another, along with the photo data capture points. Then, manual input via the command input module adds capture points to key control areas to ensure sufficient image data and prevent misjudgments. For example, locating a wheel washing machine requires photo capture at the construction site gate; determining whether the construction site fence meets dust control requirements necessitates photo capture along the red line.

[0025] Step 3: Once the flight path and photo shooting locations are planned, the control unit in the control center will control the drone equipped with a high-definition camera to fly along the flight path and take photos in the designated area.

[0026] Step 4: The photo data captured by the high-definition camera is transmitted to the data processing module of the processing unit. The module uses dust control judgment methods to determine whether certain information in the photos complies with local dust control requirements, thus determining whether the construction site violates dust control regulations. The specific judgment method is as follows: After obtaining a photo of a construction site, the first step is to determine whether the site has a wheel washing machine. Specifically, an image recognition algorithm is used to locate the wheel washing machine in the photo. If found, the site is deemed compliant; otherwise, it is deemed non-compliant with dust control regulations. The image recognition algorithm is implemented using machine deep learning. It first learns to recognize wheel washing machines and then compares them with photos taken at the construction site. That is, the image comparison function of the processing unit is used to find similarities within the site using existing photos of wheel washing machines to determine if a wheel washing machine is present. If no wheel washing machine is missing, inspection compliance information for the project is generated. If a machine is missing, non-compliance information for the project is generated, and the reasons for non-compliance and the non-compliant images are listed. Then, the same image recognition algorithm is used to identify and judge the construction site fence. The fence is connected by lines to determine whether the lines are closed and whether the area of ​​the closed area is consistent with the area of ​​the construction site. If the lines are closed and the area is consistent, the item is deemed qualified and the inspection qualification information of the item is generated. Otherwise, the non-compliance information of the item is generated and the reasons for non-compliance and non-compliance photos are listed. Finally, in the earthwork excavation area in the image, a color recognition algorithm is used to identify the dust control net and the soil. Green is identified as the dust control net and yellowish-brown as the soil. Then, the area comparison method is used to determine whether the green ratio of the excavation area meets the local dust control coverage requirements. If it does, the item is deemed qualified and the inspection qualification information of the item is generated. Otherwise, the non-compliance information of the item is generated and the reasons for non-compliance and non-compliance photos are listed. If any situation cannot be identified during the above identification and judgment process, a manual identification icon will automatically pop up for manual judgment. The wheel washing machine, enclosure, dust net coverage, and other items will be judged manually one by one. The manual judgment method is consistent with the system judgment method, and the judgment results will be used by the system to learn. If a similar situation is encountered again, the system can make independent judgments.

[0027] Step 5: After all the control sub-items at each construction site have been judged, the data processing module generates a statistical table for all non-compliant items at the non-compliant construction site and displays it visually through the display module. The statistical table includes information such as the construction project name, non-compliant item category, screenshot of the violation photo, and the time of the photo.

[0028] Step 6: After generating the dust control inspection statistics table, each jurisdiction shall submit it to the company. The company shall publicize the dust control inspection results and urge non-compliant projects to rectify them.

[0029] This completes the entire process of dust control inspection at the construction site, avoiding the time-consuming and labor-intensive nature of manual on-site inspections, as well as the possibility of subjective judgments by the inspectors.

[0030] The embodiments described above are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A construction site dust control system based on unmanned aerial vehicles (UAVs), characterized in that, This includes the control center and drones equipped with high-definition cameras; The control center includes a control unit and a processing unit. The processing unit includes an instruction input module, a data processing module, and a display module. The control unit is interconnected with the instruction input module, data processing module, and display module to exchange information and control the UAV according to instructions. It controls the UAV to fly along a designated route and take photos at designated locations along the route. The instruction input module is used to input instruction data, including the project boundary location, manually planned flight routes, and dust control requirements. The data processing module is used to process image data captured by high-definition cameras, enabling the identification of specific items in the images, including fences, wheel washing machines, fog cannons, and dust nets. It also compares the identified items with the dust control requirements input by the instruction input module to determine whether they meet the dust control requirements. The display module is used to visualize and output data.

2. A method for controlling dust pollution at a construction site using the UAV-based construction site dust control system as described in claim 1, characterized in that, The process includes the following: Step 1: Divide the project into various areas according to the project distribution, and set up a dust control system in each area; then first obtain the red line address and construction progress stage data of each construction project in the corresponding area, and input them into the processing unit through the instruction input module; Step 2: Based on the data obtained in Step 1, the data processing module plans the flight path of the drone and the photo data shooting points. Then, the shooting points in the key control areas are added manually through the command input module. Step 3: The control unit controls the drone equipped with a high-definition camera to fly along the flight path and take pictures in the designated area; Step 4: The photo data captured by the high-definition camera is transmitted to the data processing module. The data processing module judges the information in the photo using the dust control judgment method to determine whether the construction site meets the local dust control requirements. There are three items to be judged, including: whether the construction site is equipped with a wheel washing machine; whether the construction site fence is complete and whether the area enclosed by the fence is consistent with the construction area; and whether the dust net coverage of the construction site meets the requirements. In each judgment process, if the judgment is qualified, the inspection qualification information of the item is generated; otherwise, the non-compliance information of the item is generated, and the reasons for non-compliance and non-compliance images are listed. Step 5: The data processing module generates a statistical table for all non-compliant items at the substandard construction site and displays it through the display module. The statistical table includes the construction project name, non-compliant item category, screenshot of the non-compliant photo, and the time of the photo being taken. Step 6: After generating the dust control inspection statistics table, each jurisdiction shall submit it to the company. The company shall publicize the dust control inspection results and urge non-compliant projects to rectify them.

3. The method for controlling dust at construction sites according to claim 2, characterized in that, In step 1, the red line address is the construction area where the construction site is located, and the area within the red line covers the entire construction area of ​​this construction project; the construction progress stage data refers to the current construction stage, including the earthwork excavation stage, the foundation stage, the main structure stage, and the decoration and finishing stage.

4. The method for controlling dust at construction sites according to claim 2, characterized in that, In step 4, the method for determining whether a wheel washing machine is installed on the construction site is as follows: The data processing module first uses an image recognition algorithm to find the wheel washing machine in the photo. If it is found, it is deemed qualified; otherwise, it is deemed not to comply with dust control regulations. The image recognition algorithm is implemented through machine deep learning. It first learns to recognize the wheel washing machine and then compares and searches for it in the photos taken at the construction site. That is, it uses the image comparison function to find similar points in the site area using photos of existing wheel washing machines to find whether there is a wheel washing machine on the site. If the wheel washing machine is complete, then the inspection pass information for this project will be generated. If any deficiencies are found, non-compliance information for the project will be generated, and the reasons for non-compliance and non-compliant images / photos will be listed.

5. The method for controlling dust at construction sites according to claim 2, characterized in that, In step 4, the method for determining whether the construction site fence is complete and whether the area enclosed by the fence is consistent with the area of ​​the construction site is as follows: The data processing module uses image recognition algorithms to identify and determine the construction site fences, connects the fences, and then determines whether the fence lines are closed and whether the area within the closed area is consistent with the area of ​​the construction site. If they are closed and the area is consistent, the item is deemed qualified and an inspection qualification information for the item is generated. Otherwise, a non-compliance information for the item is generated, and the reasons for non-compliance and non-compliance photos are listed.

6. The method for controlling dust at construction sites according to claim 2, characterized in that, In step 4, the method for determining whether the dust control netting coverage at the construction site meets the requirements is as follows: The data processing module uses a color recognition algorithm to identify dust control netting and soil in the earthwork excavation area of ​​the image. It identifies green as dust control netting and yellowish-brown as soil. Then, it uses an area comparison method to determine whether the green ratio of the excavation area meets the local dust control coverage requirements. If it does, the project is deemed qualified and an inspection qualification information is generated. Otherwise, a non-compliance information is generated and the reasons for non-compliance and non-compliant images are listed.

7. The method for controlling dust at construction sites according to claim 2, characterized in that, In step 4, when the data processing module encounters situations that cannot be identified during the identification and judgment process, it automatically pops up a manual identification icon for manual judgment. The wheel washing machine, enclosure, and dust net covering items are judged manually one by one. The manual judgment method is consistent with the system judgment method, and the judgment results are used by the system for learning.

Images