Intelligent construction site flying dust real-time monitoring and early warning method and system based on Internet of Things

Abstract

The invention provides an intelligent construction site flying dust real-time monitoring and early warning method and system based on the Internet of Things, and the method comprises the steps: obtaining flight state parameters of a plurality of unmanned aerial vehicles when the unmanned aerial vehicles hover at different spatial positions of a construction site, calculating wind speed data corresponding to each spatial position according to the flight state parameters based on a pre-constructed unmanned aerial vehicle airflow model; wherein a dust raising sensor is mounted at a specified position of each unmanned aerial vehicle; obtaining flying dust data collected by a flying dust sensor when each unmanned aerial vehicle hovers, and determining a real flying dust concentration corresponding to each spatial position based on a pre-constructed sensor wind speed response model according to the wind speed data and the flying dust data; and determining the position of a dust source based on the real dust concentration corresponding to each spatial position, performing early warning on the position of the dust source, and controlling an execution mechanism matched with the position of the dust source to perform dust falling operation on the dust source, so that the accuracy and reliability of monitoring the position of the dust source by the unmanned aerial vehicle are improved.

Description

Technical Field

[0001] This invention relates to the field of dust monitoring technology, and in particular to a method and system for real-time monitoring and early warning of dust at construction sites based on Internet of Things intelligence. Background Technology

[0002] Dust pollution at construction sites is becoming increasingly prominent, posing a significant threat to the environment and human health. Traditional dust monitoring methods mainly rely on sensors deployed at fixed locations. While continuous monitoring is possible, these methods have obvious limitations: limited monitoring range, insufficient spatial representativeness, and an inability to comprehensively reflect the spatial distribution and dynamic changes of dust at construction sites, as well as the inability to quickly track and locate pollution sources. To improve monitoring flexibility, drone technology equipped with sensors has been introduced in recent years, enabling regionalized and three-dimensional dust distribution perception through its mobile measurement capabilities. However, the strong airflow generated by the drone rotors during operation can affect the distribution of particulate matter in the environment, leading to a significant deviation between the measured dust concentration and the actual concentration, severely impacting the accuracy and reliability of the monitoring data. Summary of the Invention

[0003] This invention provides a method and system for real-time monitoring and early warning of dust at construction sites based on Internet of Things (IoT) intelligence, to solve the problems existing in related technologies. The technical solution is as follows: In a first aspect, embodiments of the present invention provide a method for real-time monitoring and early warning of dust pollution at construction sites based on Internet of Things (IoT) intelligence, including: The flight status parameters of multiple drones hovering at different spatial locations on the construction site are obtained. Based on the pre-built drone airflow model, the wind speed data corresponding to each spatial location is calculated according to the flight status parameters. A dust sensor is installed at a designated location on each drone. The dust data collected by the dust sensor when each drone is hovering is acquired, and the actual dust concentration at each spatial location is determined based on the wind speed data and dust data according to the pre-built sensor wind speed response model. The location of the dust source is determined based on the actual dust concentration at each spatial location. The location of the dust source is then given an early warning, and the actuator matched to the location of the dust source is controlled to perform dust suppression operations.

[0004] In one implementation, it further includes: Acquire drone parameters and current environmental wind speed data, and output the minimum flight altitude in real time based on the trained flight prediction model according to the drone parameters and environmental wind speed data. Flight restriction commands are generated based on the minimum flight altitude and sent to each drone to ensure that the drone maintains a flight altitude above the minimum flight altitude when flying or hovering.

[0005] In one implementation, it further includes: Obtain a construction site map and divide the map into grids to obtain multiple grid areas; The center coordinates of the grid area are determined as the spatial position for drone hovering measurement. Based on the starting position and hovering spatial position of the drone, the flight path of each drone is planned and generated. The flight path of each drone must avoid the spatial positions of other drones hovering measurement.

[0006] In one implementation, dividing the construction site map into grids includes: The airflow influence range of a single UAV in the horizontal direction is determined based on the UAV airflow model; During the process of dividing the construction site map into grids according to the input grid density, if the airflow influence range of a single drone in the horizontal direction is greater than the horizontal size of a single grid, an alarm will be generated.

[0007] In one implementation, calculating wind speed data for each spatial location based on a pre-built UAV airflow model according to flight state parameters includes: The wind speed around the drone is calculated based on the flight state parameters using a pre-built drone airflow model. The system acquires the current ambient wind speed, merges and superimposes the wind speed around the host and the current ambient wind speed to obtain the wind speed data corresponding to the spatial location.

[0008] In one implementation, the formula for the sensor wind speed response model is: C corrected = [ C measured -ΔC dynamic ] / [ A + B * (V eff ) n ] * F attitude ; ΔC dynamic = τ * (dC measured / dt); F attitude = 1 + c1 * sinθ + c2 * sinφ; Among them, C corrected The corrected actual dust concentration; C measured Dust data collected by dust sensors; dC measured / dt is the rate of change of the dust sensor reading over time; τ is the time constant of the dust sensor, determined experimentally; A is the offset between the sensor reading and the true value at zero wind speed; n is the exponent; B is the nonlinear coefficient; V eff This refers to the wind speed data corresponding to the spatial location; F attitudeφ is the attitude compensation factor; c1 and c2 are coefficients determined by fitting through tests under different attitudes; θ is the pitch angle of the UAV; and φ is the roll angle of the UAV.

[0009] In one implementation, determining the location of a dust source based on the actual dust concentration corresponding to each spatial location includes: The actual dust concentration at each location on the construction site map is determined by an interpolation algorithm, and a concentration distribution map is generated. Based on the concentration distribution map, the location with the highest actual dust concentration is determined as the location of the dust source.

[0010] Secondly, embodiments of the present invention provide a real-time monitoring and early warning system for construction site dust based on Internet of Things intelligence, including: drone swarms; A cloud server, connected to the drone swarm, executes the aforementioned IoT-based intelligent construction site dust real-time monitoring and early warning method; the cloud server includes: The wind speed prediction module is used to acquire the flight status parameters of multiple drones hovering at different spatial locations on the construction site. Based on the pre-built drone airflow model, the wind speed data corresponding to each spatial location is calculated according to the flight status parameters. A dust sensor is installed at a designated location on each drone. The dust prediction module is used to acquire dust data collected by dust sensors when each drone hovers. Based on a pre-built sensor wind speed response model, it determines the actual dust concentration at each spatial location according to the wind speed data and dust data. The early warning and dust suppression module is used to determine the location of the dust source based on the actual dust concentration at each spatial location, issue early warnings for the location of the dust source, and send control commands to the actuators matched to the location of the dust source to carry out dust suppression operations.

[0011] Thirdly, embodiments of the present invention provide an electronic device comprising a memory and a processor. The memory and the processor communicate with each other via an internal connection path. The memory stores instructions, and the processor executes the instructions stored in the memory. When the processor executes the instructions stored in the memory, it causes the processor to perform the method described in any of the above embodiments.

[0012] Fourthly, embodiments of the present invention provide a computer-readable storage medium that stores a computer program, wherein when the computer program is run on a computer, the methods in any of the embodiments described above are executed.

[0013] The advantages or beneficial effects of the above technical solutions include at least the following: This invention simulates the airflow and wind speed generated by a drone during rotor operation using an airflow model. Based on a sensor wind speed response model, it corrects the dust data collected by the sensor according to the wind speed data, thereby determining the actual dust concentration corresponding to each spatial location. Based on the actual dust concentration, the location of the dust source is determined to achieve the monitoring and early warning effect, thereby improving the accuracy and reliability of the drone in detecting the location of dust sources.

[0014] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0015] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in the invention and should not be construed as limiting the scope of the invention.

[0016] Figure 1 This is a flowchart illustrating the method for real-time monitoring and early warning of construction site dust based on Internet of Things intelligence according to the present invention. Figure 2 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0017] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0018] Example 1 This embodiment provides a method for real-time monitoring and early warning of dust at construction sites based on the Internet of Things (IoT). This method can be implemented in a server or cloud server, which communicates with a drone swarm to send flight commands to the drone swarm and receive dust data collected by the drone swarm for dust monitoring.

[0019] In this embodiment, the drone swarm consists of multiple drones of different models. Each drone has a dust sensor fixed at the same designated location, and each drone communicates with a server or cloud server via its built-in network unit. Figure 1 As shown, the specific methods for real-time monitoring and early warning of construction site dust using drones include: Step S1: Obtain the flight status parameters of multiple drones hovering at different spatial locations on the construction site, and calculate the wind speed data corresponding to each spatial location based on the pre-built drone airflow model and the flight status parameters.

[0020] This embodiment pre-divides the construction site map to be monitored into a grid. The monitoring range and grid density of the construction site can be flexibly set according to actual monitoring needs, taking into account factors such as the area of ​​the construction area, monitoring accuracy requirements, UAV endurance, and sensor performance. The construction site map is divided into grids according to the grid density input by the user. After grid division, a unified planar or three-dimensional coordinate system is established, resulting in multiple grid regions. Each grid region is numbered and spatially calibrated, thus providing a basis for structured path planning and spatial registration of monitoring data for the UAV.

[0021] When dividing the construction site into grids, if drones are too close together, their airflow will interfere with each other, causing complex changes in the drone's airflow. Therefore, to avoid drones getting too close together on the construction site, the airflow influence range of a single drone in the horizontal direction is determined based on the airflow model of a single drone during grid division. The horizontal size of the divided grid is compared with the horizontal airflow influence range of a single drone. If the horizontal airflow influence range of a drone is greater than the horizontal size of a single grid, it means that the airflow influence ranges of drones in adjacent grids will overlap, and an alarm will be generated. If the horizontal airflow influence range of a drone is less than or equal to the horizontal size of a single grid, it means that there will be no airflow interference between adjacent drones, and the following steps can continue.

[0022] After the grid is divided, the coordinates of the center point of each grid are the spatial positions for drone hovering and measurement. Different drones hover over different grids. The drones are equipped with dust sensors to monitor dust at the local construction site corresponding to the grid.

[0023] Each drone flies from its initial position to its corresponding spatial location and hovers along a pre-set flight path. It should be noted that the flight path can be planned using existing genetic algorithms or A* algorithms, which will not be described in detail here. However, it is essential to ensure that each drone's flight path avoids the spatial locations where other drones are hovering and measuring. This is to prevent the dust distribution at those locations from being disturbed by the drone's airflow, thus affecting the accuracy of the dust sensor's measurement.

[0024] Meanwhile, because the airflow generated by the drone when it flies at low altitudes stirs up ground dust, the measurements of the dust sensor become more unstable and inaccurate. Therefore, in order to avoid this situation, this embodiment needs to pre-determine the minimum flight altitude of the drone. Flying at an altitude below the minimum flight altitude will stir up ground dust, while flying at an altitude above or equal to the minimum flight altitude will not stir up ground dust, thereby improving the accuracy of drone atmospheric monitoring.

[0025] In this embodiment, the method for determining the minimum flight altitude includes: Acquire drone parameters and current environmental wind speed data, and output the minimum flight altitude in real time based on the trained flight prediction model according to the drone parameters and environmental wind speed data. Flight restriction commands are generated based on the minimum flight altitude and sent to each drone to ensure that the drone maintains a flight altitude greater than or equal to the minimum flight altitude when flying or hovering.

[0026] The flight prediction model is trained based on a large number of training samples. These training samples can be obtained through experiments. The experiments involve setting up a high-precision anemometer on the ground and allowing different types of drones to hover directly above it. The hovering height is then systematically increased gradually (for example, starting from 1 meter and increasing by 0.5 meters or 1 meter each time until it reaches 5-6 meters). After the drone has stabilized at each height, the readings of the ground anemometer are recorded. At the same time, the wind speed in the experimental environment is also recorded. The drone parameters, environmental wind speed data, altitude data, and ground anemometer readings are used as a dataset. This dataset is then used as training samples to train a known neural network model, learning the relationship between drone parameters, environmental wind speed, flight altitude, and wind speed, thus obtaining a trained flight prediction model.

[0027] Based on a trained flight prediction model, the minimum flight altitude for zero ground wind speed is determined using known UAV parameters and current environmental wind speed data. The UAV is then allowed to hover at an altitude higher than or equal to the minimum flight altitude to its corresponding spatial location for measurement, ensuring that the airflow generated by the UAV does not stir up ground dust, thus preventing instability in the dust sensor measurements.

[0028] Once the drone flies to its corresponding grid area, its positioning data is acquired. This positioning data is then compared with the coordinates of the center point within the grid area. Based on the difference, the drone's position is fine-tuned to ensure it hovers accurately in the center of its grid area. After the drone has stabilized, its flight status parameters are acquired, including rotor speed / RPM, throttle, drone weight, and drone attitude.

[0029] Based on the pre-built UAV airflow model, the wind speed around the UAV is determined according to the UAV's flight state parameters. By combining the wind speed around the UAV and the current ambient wind speed, the wind speed data corresponding to the UAV's spatial location is obtained.

[0030] It should be noted that the UAV airflow model is based on physical principles (such as momentum theory) or computational fluid dynamics (CFD) simulations to establish a parameterized model that reflects the basic laws of airflow. This UAV airflow model can predict the downwash wind speed generated by the UAV during high-altitude flight based on its real-time flight parameters. The wind speed around the UAV is mainly dominated by the strong downwash generated by the UAV rotor, especially in the area below and near the drone. During model construction, real airflow data can be measured through wind tunnel or field testing to calibrate and correct the empirical coefficients in the theoretical model, ultimately resulting in a high-precision UAV airflow model that can be used for real-time prediction.

[0031] To obtain the current ambient wind speed, the wind speed around the drone, output by the drone's airflow model, is fused and superimposed with the current ambient wind speed to obtain the wind speed data corresponding to the spatial location. The core method for fusing the wind speed around the drone and the current ambient wind speed is vector superposition, and its expression is: V eff = V downwash + V environment Here, "+" refers to vector addition, and the calculated V eff It is the wind speed near the drone itself, and can also be approximated as the wind speed data corresponding to the spatial location of the drone.

[0032] Step S2: Obtain dust data collected by dust sensors when each drone is hovering, and determine the actual dust concentration at each spatial location based on the pre-built sensor wind speed response model and the wind speed data and dust data.

[0033] In this embodiment, the dust sensor can be installed on the upper surface of the drone's fuselage. Although the strong downdraft generated by the drone blows downwards, when the drone hovers, this downward airflow continuously "washes" the drone and the surrounding air, causing dust particles accumulated above the drone and near its surface to be blown away, diluted, or pushed downwards. At this time, the value measured by the sensor will be much lower than the true average dust concentration of the surrounding environment. In other words, when the drone reaches its corresponding spatial position and hovers to measure dust data, this dust data has actually been affected by the airflow generated by the drone itself, and the dust concentration at this point will be lower than the true average dust concentration.

[0034] To this end, the actual dust data was corrected based on a pre-built sensor wind speed response model to simulate the true dust concentration at that location before it was affected by the airflow from the drone.

[0035] It is important to note that the dust data mainly refers to the data detected by the dust sensor when the drone is hovering stably. Since the drone is already in a stable state, the dust data detected by the dust sensor will be relatively stable at this time.

[0036] The sensor wind speed response model can be established in advance through controlled experiments (wind tunnel calibration) to establish the mathematical relationship between the sensor's original readings, ambient wind speed, and actual concentration, and ultimately determine specific coefficients in the sensor wind speed response model formula. In this embodiment, the formula for the sensor wind speed response model is: C corrected = [ C measured - ΔC dynamic ] / [ A + B * (V eff ) n ] * F attitude ; Among them, C corrected The corrected actual dust concentration; C measured Dust data collected by dust sensors; ΔC dynamic V is the dynamic delay compensation term; A is the offset between the sensor reading and the true value at zero wind speed, ideally A should be close to 1; n is the exponent, usually n>1 (e.g. 1.2~1.8); B is the nonlinearity coefficient, obtained empirically; V eff This refers to the wind speed data corresponding to the spatial location, specifically the vector superposition of the wind speed around the drone and the current ambient wind speed; F attitude This is the attitude compensation factor.

[0037] The expression for the dynamic delay compensation term is: ΔC dynamic = τ * (dC measured / dt); Among them, dC measured / dt is the rate of change of the dust sensor reading over time; τ is the time constant of the dust sensor, which is determined experimentally.

[0038] The expression for the attitude compensation factor is: F attitude = 1 + c1 * sin(θ) + c2 * sin(φ); Where c1 and c2 are coefficients determined by fitting through tests under different attitudes; θ is the pitch angle of the UAV; and φ is the roll angle of the UAV.

[0039] Step S3: Determine the location of the dust source based on the actual dust concentration corresponding to each spatial location, issue an early warning for the location of the dust source, and control the actuator matched to the location of the dust source to perform dust suppression operation.

[0040] In this embodiment, the actual dust concentration corresponding to each spatial location is a relatively discrete point. The dust concentration value of unknown locations is estimated by interpolation algorithm. A concentration distribution map is generated based on the actual dust concentration of each location in the construction site map. On the generated concentration distribution map, the point with the maximum concentration is found by algorithm search or visual interpretation and its coordinates are recorded. The coordinates corresponding to the maximum concentration are determined as the location of the dust source.

[0041] Actuators are pre-installed at different locations on the construction site. Control commands are generated based on the location of the dust source, including the location of the dust source. These commands are then sent to the actuator closest to the dust source. The actuator calculates the spray angle and spray range based on the dust source location and its own coordinates, and then executes the spraying operation according to the specified angle and range to reduce dust at the dust source.

[0042] It should be noted that the actuator in this embodiment is a spray device with an adjustable spray angle. The specific structure of the spray device has been disclosed in the prior art and will not be described again here.

[0043] Example 2 This embodiment provides a real-time monitoring and early warning system for construction site dust based on the Internet of Things (IoT). The system specifically includes: drone swarms; A cloud server, connected to the drone swarm, executes the aforementioned IoT-based intelligent construction site dust real-time monitoring and early warning method. Specifically, the cloud server includes: The wind speed prediction module is used to acquire the flight status parameters of multiple drones hovering at different spatial locations on the construction site. Based on the pre-built drone airflow model, the wind speed data corresponding to each spatial location is calculated according to the flight status parameters. A dust sensor is installed at a designated location on each drone. The dust prediction module is used to acquire dust data collected by dust sensors when each drone hovers. Based on a pre-built sensor wind speed response model, it determines the actual dust concentration at each spatial location according to the wind speed data and dust data. The early warning and dust suppression module is used to determine the location of the dust source based on the actual dust concentration at each spatial location, issue early warnings for the location of the dust source, and send control commands to the actuators matched to the location of the dust source, thereby performing dust suppression operations on the dust source.

[0044] It should be noted that the functions of each module in this embodiment system can be found in the corresponding descriptions in the above methods, and will not be repeated here.

[0045] Example 3 This embodiment provides an electronic device. Figure 2 A structural block diagram of an electronic device according to an embodiment of the present invention is shown. Figure 2 As shown, the electronic device includes a memory 100 and a processor 200. The memory 100 stores a computer program that can run on the processor 200. When the processor 200 executes the computer program, it implements the IoT-based intelligent construction site dust real-time monitoring and early warning method described in the above embodiments. The number of memories 100 and processors 200 can be one or more.

[0046] The electronic device also includes: The communication interface 300 is used to communicate with external devices and perform data exchange and transmission.

[0047] If the memory 100, processor 200, and communication interface 300 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc.

[0048] Optionally, in a specific implementation, if the memory 100, processor 200, and communication interface 300 are integrated on a single chip, then the memory 100, processor 200, and communication interface 300 can communicate with each other through an internal interface.

[0049] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method provided in this invention.

[0050] This invention also provides a chip, which includes a processor for calling and executing instructions stored in a memory, causing a communication device on which the chip is installed to perform the method provided in this invention.

[0051] This invention also provides a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, output interface, processor, and memory are connected through an internal connection path. The processor is used to execute code in the memory. When the code is executed, the processor is used to execute the method provided in this invention.

[0052] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting the Advanced Reduced Instruction Set Computing (RISC) machine (ARM) architecture.

[0053] Further, optionally, the aforementioned memory may include read-only memory and random access memory, and may also include non-volatile random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0054] In the above embodiments, implementation can be achieved, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0055] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0056] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0057] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for real-time monitoring and early warning of dust at construction sites based on Internet of Things (IoT) intelligence, characterized in that, include: The flight status parameters of multiple drones hovering at different spatial locations on the construction site are obtained. Based on a pre-built drone airflow model, the wind speed data corresponding to each spatial location is calculated according to the flight status parameters. A dust sensor is installed at a designated location on each drone. The dust data collected by the dust sensor when each drone is hovering is acquired, and the actual dust concentration corresponding to each spatial location is determined based on the wind speed data and the dust data according to the pre-built sensor wind speed response model. The location of the dust source is determined based on the actual dust concentration corresponding to each spatial location. The location of the dust source is given an early warning and the actuator matched to the location of the dust source is controlled to perform dust suppression operation on the dust source.

2. The method for real-time monitoring and early warning of construction site dust based on Internet of Things intelligence according to claim 1, characterized in that, Also includes: Acquire drone parameters and current environmental wind speed data, and output the minimum flight altitude in real time based on the trained flight prediction model according to the drone parameters and the environmental wind speed data. Flight restriction commands are generated based on the minimum flight altitude and sent to each UAV to ensure that the UAV maintains a flight altitude greater than the minimum flight altitude when flying or hovering.

3. The method for real-time monitoring and early warning of construction site dust based on Internet of Things intelligence as described in claim 1, characterized in that, Also includes: Obtain a construction site map and divide the map into grids to obtain multiple grid areas; The center coordinates of the grid area are determined as the spatial position for drone hovering measurement. Based on the starting position and hovering spatial position of the drone, the flight path of each drone is planned and generated. The flight path of each drone must avoid the spatial positions of other drones hovering measurement.

4. The method for real-time monitoring and early warning of construction site dust based on Internet of Things intelligence according to claim 3, characterized in that, The process of dividing the construction site map into grids includes: The airflow influence range of a single UAV in the horizontal direction is determined based on the UAV airflow model; During the process of dividing the construction site map into grids according to the input grid density, if the airflow influence range of a single drone in the horizontal direction is greater than the horizontal size of a single grid, an alarm will be generated.

5. The method for real-time monitoring and early warning of construction site dust based on Internet of Things intelligence as described in claim 1, characterized in that, Based on the pre-built UAV airflow model, the wind speed data corresponding to each spatial location is calculated according to the flight state parameters, including: The wind speed around the drone is calculated based on the flight state parameters using a pre-built drone airflow model. The current ambient wind speed is obtained, and the wind speed around the subject and the current ambient wind speed are fused and superimposed to obtain the wind speed data corresponding to the spatial location.

6. The method for real-time monitoring and early warning of construction site dust based on Internet of Things intelligence according to claim 1, characterized in that, The formula for the sensor wind speed response model is: C corrected = [ C measured -ΔC dynamic ] / [ A + B * (V eff ) n ] * F attitude ; ΔC dynamic = τ * (dC measured / dt); F attitude = 1 + c1 * sinθ+ c2 * sinφ; Among them, C corrected The corrected actual dust concentration; C measured Dust data collected by dust sensors; dC measured / dt is the rate of change of the dust sensor reading over time; τ is the time constant of the dust sensor, determined experimentally; A is the offset between the sensor reading and the true value at zero wind speed; n is the exponent; B is the nonlinear coefficient; V eff This refers to the wind speed data corresponding to the spatial location; F attitude φ is the attitude compensation factor; c1 and c2 are coefficients determined by fitting through tests under different attitudes; θ is the pitch angle of the UAV; and φ is the roll angle of the UAV.

7. The method for real-time monitoring and early warning of construction site dust based on Internet of Things intelligence according to claim 1, characterized in that, Determining the location of the dust source based on the actual dust concentration at each spatial location includes: The actual dust concentration at each location on the construction site map is determined by an interpolation algorithm, and a concentration distribution map is generated. Based on the concentration distribution map, the location with the highest actual dust concentration is determined as the location of the dust source.

8. A real-time monitoring and early warning system for construction site dust based on Internet of Things intelligence, characterized in that, include: drone swarms; A cloud server, connected to the drone swarm, executes the IoT-based intelligent construction site dust real-time monitoring and early warning method as described in any one of claims 1 to 7; the cloud server includes: The wind speed prediction module is used to acquire the flight status parameters of multiple drones hovering at different spatial locations on the construction site. Based on the pre-built drone airflow model, the module calculates the wind speed data corresponding to each spatial location according to the flight status parameters. A dust sensor is installed at a designated location on each drone. The dust prediction module is used to acquire dust data collected by dust sensors when each drone hovers, and to determine the actual dust concentration at each spatial location based on the wind speed data and the dust data according to the pre-built sensor wind speed response model. The early warning and dust suppression module is used to determine the location of the dust source based on the actual dust concentration corresponding to each spatial location, issue an early warning for the location of the dust source, and send control commands to the actuator matched to the location of the dust source to carry out dust suppression operations.

9. An electronic device, characterized in that, include: A processor and a memory, wherein the memory stores instructions that are loaded and executed by the processor to implement the IoT-based intelligent construction site dust real-time monitoring and early warning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the IoT-based intelligent construction site dust real-time monitoring and early warning method as described in any one of claims 1 to 7.

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