IoT-based smart construction site management methods and systems

Abstract

This invention discloses a smart construction site management method and system based on the Internet of Things (IoT), relating to the field of smart construction site environmental control technology. The method includes performing vector projection on the point cloud of the material storage area's surface to obtain the tangential wind speed components of the surface points, calculating the voxel center elevation difference of the three-dimensional point cloud, screening wind erosion active areas, uniformly dividing the wind erosion active areas into sub-zones, calculating the horizontal drift distance, predicting the landing center coordinates, and generating a predicted landing circle; calculating the fog cannon coverage circle, determining the spatial intersection of the predicted landing circle and the fog cannon coverage circle, calculating the fog cannon activation time in the control area, and sending the activation time to the fog cannon for execution. This invention improves the dynamic simulation capability of dust particle falling behavior by combining particle size distribution quantification with a settling velocity estimation model, and improves the accuracy of dust fall range prediction by calculating the horizontal drift of dust particles combined with a landing circle prediction model.

Description

Technical Field

[0001] This invention relates to the field of smart construction site environmental control technology, and in particular to a smart construction site management method and system based on the Internet of Things. Background Technology

[0002] With the continuous expansion of construction projects and the ongoing advancement of smart city systems, the application of Internet of Things (IoT) technology in construction site management is becoming increasingly widespread. Currently, smart construction sites mainly rely on various types of sensor networks, video surveillance systems, environmental monitoring equipment, and digital interfaces of construction machinery to achieve multi-dimensional information management such as construction progress monitoring, personnel positioning, safety early warning, and environmental quality assessment.

[0003] Existing smart construction site management methods still have shortcomings. Traditional wind field acquisition methods usually rely on a limited number of anemometer measurement points and estimate the wind field distribution through linear extrapolation or empirical formulas. This results in the inability to recover the true three-dimensional wind speed distribution when there are local structural obstructions, building wind shadow areas, and areas with significant changes in the vertical wind speed gradient. This makes it difficult to support precise judgment of wind erosion behavior. Most existing technologies for predicting dust particle movement use simplified models, such as two-dimensional planar diffusion models or empirical attenuation models, which fail to fully consider the coupling effect between turbulent diffusion and settling velocity, resulting in large deviations in the predicted dust settling range. Summary of the Invention

[0004] In view of the aforementioned existing problems, the inventors have proposed the present invention.

[0005] Therefore, this invention provides a smart construction site management method and system based on the Internet of Things, which solves the problem that traditional wind field acquisition methods usually rely on a limited number of anemometer measurement points and estimate the wind field distribution through linear extrapolation or empirical formulas. This results in the inability to recover the true three-dimensional wind speed distribution when there are local structural obstructions, building wind shadow areas, and areas with significant changes in the vertical wind speed gradient. This makes it difficult to support precise judgment of wind erosion behavior. In the prior art, the prediction of dust particle movement mostly adopts simplified models, such as two-dimensional planar diffusion models or empirical attenuation models, which fail to fully consider the coupling effect between turbulent diffusion and settling velocity, resulting in a large deviation in the prediction of dust settling range.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a smart construction site management method based on the Internet of Things, which includes the following steps:

[0008] Collect and preprocess multi-source data, construct a construction coordinate system, divide the construction site area into rectangular grids, generate a unit vector of horizontal wind direction, and perform trilinear interpolation to generate a three-dimensional wind speed field.

[0009] Vector projection is performed on the point cloud on the surface of the material stacking area to obtain the tangential wind speed component of the surface points. The elevation difference of the voxel center of the three-dimensional point cloud is calculated, the wind erosion active area is screened, the wind erosion active area is uniformly divided into sub-zones, the horizontal drift distance is calculated, the landing center coordinates are predicted, and the predicted landing circular area is generated.

[0010] The system calculates the coverage area of ​​the fog cannon, determines the spatial intersection between the predicted landing area and the coverage area of ​​the fog cannon, calculates the start time of the fog cannon in the control area, sends the start time to the fog cannon and executes it, and builds a visualization interface to display the execution results.

[0011] As a preferred embodiment of the IoT-based smart construction site management method of the present invention, the step of constructing a construction coordinate system, dividing the construction site area into rectangular grids, generating a unit vector of horizontal wind direction, and performing trilinear interpolation to generate a three-dimensional wind speed field includes:

[0012] A construction coordinate system is constructed with the permanent control point at the southwest corner of the construction site as the origin. Multi-source data is then mapped to the construction coordinate system of the construction site using the rigid body coordinate transformation method.

[0013] Project the construction site area onto the XY plane in the construction coordinate system. Use a uniform grid division method to divide the projected construction site area into rectangular grids to generate grid sub-blocks. Use bilinear interpolation to generate a continuous horizontal wind speed field within the sub-blocks and perform vector normalization to generate a unit vector of horizontal wind direction.

[0014] Based on the wind speed at the top of the tower crane, the friction speed is calculated, and the wind speed at various heights is calculated using the boundary layer logarithmic law formula.

[0015] The height of the anemometer measuring points is divided into layers at fixed intervals. The median value of the vertical wind speed of the measuring points in the horizontal wind speed field is extracted as the representative vertical wind speed of each layer. If all anemometers have no vertical output, zero value filling is performed. Otherwise, the representative vertical wind speed is used as the corrected vertical wind speed field to generate the corrected vertical wind speed.

[0016] Construct a three-dimensional wind speed vector and perform trilinear interpolation to generate a three-dimensional wind speed field.

[0017] As a preferred embodiment of the IoT-based smart construction site management method of the present invention, the step of performing vector projection on the point cloud of the material stacking area surface to obtain the tangential wind speed component of the surface points, calculating the voxel center elevation difference of the three-dimensional point cloud, and screening for wind erosion active areas includes:

[0018] Based on the three-dimensional wind speed field, vector projection is performed on the point cloud on the surface of the material stacking area to obtain the tangential wind speed components of each surface point. A wind erosion initiation threshold is set, and grid sub-blocks with tangential wind speed components greater than or equal to the wind erosion initiation threshold are selected and defined as potential wind erosion areas.

[0019] Extract three-dimensional point clouds from potential wind erosion areas, calculate the voxel center elevation difference of the three-dimensional point clouds, set a point cloud measurement accuracy threshold, and filter three-dimensional point clouds with voxel center elevation differences greater than the point cloud measurement accuracy threshold to obtain wind erosion active areas.

[0020] Extract the elevation difference of all voxel centers within the active wind erosion area and calculate the wind erosion volume loss.

[0021] As a preferred embodiment of the IoT-based smart construction site management method of the present invention, the step of uniformly dividing the wind-erosion active area into sub-zones, calculating the horizontal drift distance, predicting the landing center coordinates, and generating a predicted landing circular region includes:

[0022] Calculate the total dust particle mass based on wind erosion volume loss;

[0023] The wind speeds in the active wind erosion areas are sorted in ascending order and evenly divided to generate sub-bands. The total dust particle mass is then distributed to each sub-band to generate the dust particle release mass.

[0024] Based on particle size, the average equivalent sphere diameter of the sub-band is calculated, a dust threshold is set, and sub-bands with an average equivalent sphere diameter greater than the dust threshold are selected as a set of settleable sub-bands.

[0025] For sub-zones within the set of settleable sub-zones, Stokes' law is used to calculate the terminal settling velocity. Based on the elevation difference of the voxel centers, the initial height of the dust particles off the ground is calculated. The initial height of the dust particles off the ground is divided by the terminal settling velocity to generate the sub-zone settling time, and the horizontal drift distance is calculated.

[0026] The product of the dust particle release mass and the horizontal drift distance is calculated and defined as the drift risk index, which is then normalized to generate a mass weight.

[0027] Based on the horizontal drift distance, the coordinates of the landing center are predicted, and a predicted landing circle is generated.

[0028] As a preferred embodiment of the IoT-based smart construction site management method of the present invention, the step of calculating the coverage area of ​​the fog cannon involves determining the spatial intersection of the predicted landing area and the coverage area of ​​the fog cannon, calculating the start-up time of the fog cannon in the control area, sending the start-up time to the fog cannon and executing it, including:

[0029] Based on the location of the fog cannon, the fog cannon coverage area is calculated. The circle-circle intersection method is used to determine the spatial intersection between the predicted landing circle and the fog cannon coverage area. The intersection area is defined as the control area.

[0030] Calculate the start time of the fog cannons in the control area, send the start time to the fog cannons via the MQTT protocol and execute it;

[0031] Spray mist from the fog cannons in the controlled area, set a PM10 concentration threshold, and stop spraying when the PM10 concentration in the controlled area is lower than the PM10 concentration threshold.

[0032] As a preferred embodiment of the IoT-based smart construction site management method of the present invention, the step of constructing a visual interface to display the execution results includes:

[0033] Build a visual human-computer interaction interface to display the control area and execution results in real time.

[0034] As a preferred embodiment of the IoT-based smart construction site management method of the present invention, the step of collecting and preprocessing multi-source data includes:

[0035] Smart sensors are used to collect multi-source data from the construction site, and time synchronization, noise reduction, and standardization processing are performed.

[0036] The intelligent sensors include an ultrasonic anemometer, a laser scanner, a laser particle size analyzer, and a PM10 sensor.

[0037] The multi-source data includes wind speed, three-dimensional point cloud, fog cannon location, particle size, and PM10 concentration data.

[0038] Secondly, the present invention provides a smart construction site management system based on the Internet of Things, comprising:

[0039] The data collection and processing module is used to collect multi-source data and perform time synchronization, noise reduction, and standardization.

[0040] The coordinate wind field module is used to construct the construction coordinate system, divide the construction site area into rectangular grids, generate unit vectors of horizontal wind direction, and perform trilinear interpolation to generate a three-dimensional wind speed field.

[0041] The filtering and prediction module is used to perform vector projection on the point cloud on the surface of the material stacking area to obtain the tangential wind speed component of the surface points, calculate the voxel center elevation difference of the three-dimensional point cloud, filter the wind erosion active area, uniformly divide the wind erosion active area into sub-zones, calculate the horizontal drift distance, predict the landing center coordinates, and generate the predicted landing circular area.

[0042] The management and display module is used to calculate the coverage area of ​​the fog cannon, determine the spatial intersection between the predicted landing area and the coverage area of ​​the fog cannon, calculate the start time of the fog cannon in the control area, send the start time to the fog cannon and execute it, and build a visual interface to display the execution results.

[0043] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the Internet of Things-based smart construction site management method as described in the first aspect of the present invention.

[0044] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the Internet of Things-based smart construction site management method as described in the first aspect of the present invention.

[0045] The beneficial effects of this invention are as follows: By constructing a horizontal wind direction unit vector and combining it with trilinear interpolation to reconstruct a three-dimensional wind speed field, this invention improves the recovery capability of the vertical gradient of wind speed and the local disturbance wind field, upgrading the wind field description from two-dimensional empirical extrapolation to dynamic reconstruction of the real three-dimensional wind field. The particle size distribution quantification combined with the settling velocity estimation model improves the dynamic simulation capability of dust particle falling behavior. It can fully consider the influence of factors such as vertical wind speed and turbulent diffusion on dust particle migration. The calculation of dust particle horizontal drift combined with the landing circular domain prediction model improves the accuracy of dust settling range prediction and overcomes the problem of large deviation in traditional two-dimensional diffusion models. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the operation of the IoT-based smart construction site management method in Example 1.

[0048] Figure 2 This is a schematic diagram of the IoT-based smart construction site management system in Example 1. Detailed Implementation

[0049] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0050] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0051] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0052] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a smart construction site management method based on the Internet of Things, including the following steps:

[0053] S1. Collect and preprocess multi-source data, construct a construction coordinate system, divide the construction site area into rectangular grids, generate a unit vector of horizontal wind direction, and perform trilinear interpolation to generate a three-dimensional wind speed field.

[0054] Specifically, this involves collecting and preprocessing multi-source data, including:

[0055] Smart sensors are used to collect multi-source data from the construction site, and time synchronization, noise reduction, and standardization processing are performed.

[0056] The intelligent sensors include an ultrasonic anemometer, a laser scanner, a laser particle size analyzer, and a PM10 sensor.

[0057] The multi-source data includes wind speed, three-dimensional point cloud, fog cannon location, particle size, and PM10 concentration data.

[0058] Standardized data is suitable for real-time computing, enabling the system to update wind field models and environmental conditions at a frequency of seconds or minutes, enhancing the system's responsiveness to dynamic environments and improving the accuracy of multi-source data fusion.

[0059] Furthermore, a construction coordinate system is constructed, the construction site area is projected into a rectangular grid, a unit vector of horizontal wind direction is generated, and trilinear interpolation is performed to generate a three-dimensional wind speed field, including:

[0060] A construction coordinate system is constructed with the permanent control point at the southwest corner of the construction site as the origin. The coordinate system includes the X-axis extending eastward along the south side of the construction site fence, the Y-axis extending northward along the west side of the construction site fence, and the Z-axis pointing vertically upward.

[0061] Multi-source data is mapped to the construction coordinate system of the construction site using rigid body coordinate transformation.

[0062] Project the construction site area onto the XY plane in the construction coordinate system. Divide the projected area into 5×5m rectangular grids using a uniform grid method to generate grid sub-blocks. Use bilinear interpolation to generate horizontal wind speed vectors within the sub-blocks, and then perform vector normalization to generate unit vectors of horizontal wind direction. The formula is as follows:

[0063] ,

[0064] in Let be the horizontal wind speed vector at time t and location (x, y). The interpolation weights for the m-th neighboring measurement points are... Let m be the horizontal wind speed at the m-th neighboring measuring point;

[0065] Using the wind speed sensor located at the highest point on the construction site (such as the top of a tower crane) as a reference, the friction speed is calculated based on the wind speed at the top of the tower crane, using the following formula:

[0066] ,

[0067] in For friction speed, Let be the von Kármán constant, taken as 0.4. Let the horizontal wind speed at the top of the tower crane be at time t. Let d be the height of the top of the tower crane, t be time, and d be the zero-plane displacement. For surface roughness, it is obtained by looking up a table based on the surface type;

[0068] Using the logarithmic wind profile formula, the average wind speed modulus at various heights can be calculated. The formula is as follows:

[0069] ,

[0070] in Let z be the average wind speed modulus at height z;

[0071] The height of the anemometer measuring points is divided into layers according to a fixed interval (e.g., 0.5m). The median value of the vertical wind speed of the measuring points in the horizontal wind speed field is extracted as the representative vertical wind speed of each layer. If all anemometers have no vertical output, zero value filling is performed; otherwise, the representative vertical wind speed is used as the corrected vertical wind speed field to generate the corrected vertical wind speed.

[0072] Construct a three-dimensional wind speed vector and perform trilinear interpolation to generate a three-dimensional wind speed field. The formula is as follows:

[0073] ,

[0074] in Let z be the three-dimensional wind speed vector at time t, and z be the height. Let x and y be the unit vector of the horizontal wind direction at time t, and x and y be the center coordinates of the grid sub-block. This represents the corrected vertical wind speed at height z.

[0075] Mapping dispersed sensor data to the same reference system avoids computational errors caused by inconsistencies in the coordinate systems of different sensors, improving the spatial resolution and continuity of the wind field model. After normalization, the horizontal wind direction unit vector can more accurately express the main trend of wind direction. The gridded wind field can capture the disturbance effect of structures such as buildings, tower cranes, and fences on wind flow, which is beneficial for subsequent prediction of dust particle drift paths. The logarithmic law-based wind field expression can more closely resemble the actual atmospheric boundary layer characteristics, improving the model's credibility. Compensating for missing vertical data through a physical model is key to achieving three-dimensional reconstruction of the wind field. The median strategy can suppress extreme point interference and improve the stability of vertical wind speed estimation. Trilinear interpolation overcomes the limitations of discrete measurement points, making the three-dimensional field continuous, which is beneficial for simulating wind erosion processes.

[0076] S2. Perform vector projection on the point cloud on the surface of the material stacking area to obtain the tangential wind speed component of the surface points, calculate the elevation difference of the voxel center of the three-dimensional point cloud, screen the wind erosion active area, divide the wind erosion active area into sub-zones evenly, calculate the horizontal drift distance, predict the landing center coordinates, and generate the predicted landing circular area.

[0077] Specifically, vector projection is performed on the point cloud on the surface of the material storage area to obtain the tangential wind speed components of the surface points. The voxel center elevation difference of the three-dimensional point cloud is calculated to screen for wind erosion active areas, including:

[0078] Based on the three-dimensional wind speed field, vector projection is performed on the point cloud on the surface of the material stacking area to obtain the tangential wind speed components of each surface point, as shown in the formula:

[0079] ,

[0080] in Let be the tangential wind speed component at time t and location (x, y). The surface normal vector at any point (x,y) on the surface of the pile is set using the covariance analysis method, based on the constructed three-dimensional wind speed field and the point cloud data of the pile surface obtained through three-dimensional laser scanning;

[0081] The wind tunnel sand-lifting test method was used to set the wind erosion initiation threshold, and grid sub-blocks with tangential wind speed components greater than or equal to the wind erosion initiation threshold were screened and defined as potential wind erosion zones.

[0082] Extract the 3D point cloud from the potential wind erosion zone and calculate the voxel center elevation difference of the 3D point cloud using the following formula:

[0083] ,

[0084] in Let be the elevation difference of the voxel center at time t and location (x, y). Let the elevation of the 3D point cloud at time t and location (x, y) be given. The point cloud elevation at location (x, y) is set using a rule of thumb.

[0085] The mean of historical data plus twice the standard deviation was calculated using statistical analysis. A point cloud measurement accuracy threshold was set, and three-dimensional point clouds with voxel center elevation differences greater than the point cloud measurement accuracy threshold were screened to obtain wind erosion active areas.

[0086] Extract the elevation difference of all voxel centers within the active wind erosion area, and calculate the wind erosion volume loss using the following formula:

[0087] ,

[0088] in Let be the volume loss due to wind erosion over time t. For the wind erosion active region at time t, The area is the projected area at the voxel level.

[0089] When the elevation difference exceeds the point cloud measurement accuracy threshold, it means that observable material migration has occurred on the surface. This avoids misjudgment, enables precise location of wind erosion, and allows for estimation of wind erosion volume loss, laying the foundation for subsequent dust particle mass inversion.

[0090] Furthermore, the wind-erosion active area is uniformly divided into sub-zones, the horizontal drift distance is calculated, the landing center coordinates are predicted, and a predicted landing circle is generated, including:

[0091] The total dust particle mass is calculated based on wind erosion volume loss using the following formula:

[0092] ,

[0093] in Let be the total mass of dust particles at time t. The dry density of the packing material is determined by the ring cutter method. This refers to the 3D point cloud acquisition cycle;

[0094] The wind speeds in the active wind erosion areas are sorted in ascending order and uniformly divided into sub-zones. The total dust particle mass is then distributed to each sub-zone according to its area ratio, generating the dust particle release mass. The formula is as follows:

[0095] ,

[0096] in Let be the mass of dust particles released in the k-th subband at time t. and Let K and J represent the areas of the k-th and j-th sub-bands, respectively, where K=5 is the total number of sub-bands.

[0097] Based on the particle size, the average equivalent spherical diameter of the sub-band is calculated using the following formula:

[0098] ,

[0099] in Let be the average equivalent sphere diameter of the k-th sub-band. Let be the volume fraction of the i-th particle size range in the measured particle size distribution of the k-th sub-band. Let be the center value of the interval for the i-th particle size;

[0100] The dust threshold is set at 90% of the average equivalent sphere diameter calculated using the percentile method. Sub-bands with an average equivalent sphere diameter greater than the dust threshold are selected as the set of settleable sub-bands.

[0101] For sub-zones within a set of settleable sub-zones, Stokes' law is used to calculate the terminal settlement velocity;

[0102] Based on the elevation difference of the voxel centers, the initial height of dust particles above the ground is calculated using the following formula:

[0103] ,

[0104] in This represents the initial height of the dust particles above the ground. The absolute elevation (Z-axis coordinate) of the wind erosion voxel in the construction coordinate system. This is the ground plane reference elevation of the construction site (if the origin of the construction coordinate system is on the ground, this item is 0).

[0105] Divide the initial height of dust particles from the ground by the terminal settling velocity to generate the sub-zone settling time, and calculate the horizontal drift distance using the following formula:

[0106] ,

[0107] ,

[0108] in Let k be the horizontal drift distance of the kth sub-band. Let k be the settling time of the kth subzone. Let be the three-dimensional wind speed vector of the k-th sub-zone at time t and location (x, y). Let be the representative wind speed of the k-th sub-zone;

[0109] The product of the dust particle release mass and the horizontal drift distance is calculated and defined as the drift risk index, which is then normalized to generate a mass weight.

[0110] Based on the horizontal drift distance, the landing center coordinates are predicted. Integrating lateral turbulence diffusion, a predicted landing circular region is generated, using the following formula:

[0111] ,

[0112] ,

[0113] in The predicted horizontal coordinates of the landing of dust particles in the k-th sub-zone. Let be the horizontal coordinates of the geometric center point of the k-th sub-zone. This is the normalized unit vector of wind direction. For the predicted landing circle at time t, The lateral diffusion radius of the k-th subband is determined by experimental calibration. The diffusion radius set for the landing center of the k-th sub-band is used to characterize the range of landing point dispersion caused by turbulence, and is set using the three sigma rule. This is a set of settleable sub-zones.

[0114] Sub-zone segmentation captures wind speed gradients, enabling the model to simulate differentiated dust release levels in different wind speed regions. Mass conservation is maintained by using area ratio allocation. Wind speed sorting results in higher dust content in sub-zones of high-speed regions, more closely resembling the dynamic mechanism of aeolian landforms. Settlement velocity calculations are performed only on settleable sub-zones, improving efficiency and prediction accuracy. Compared to empirical formulas, Stokes' law has a clear dynamic basis. Initial ground clearance modeling can reflect the actual sand-raising location and height differences on the pile surface. A complete dynamic chain of dust particle drift distance and settling process is constructed, forming a probabilistic landing point prediction model suitable for turbulent uncertainty scenarios. The position and radius of the landing circle can be used to calculate the lead time, direction, and duration of the fog cannon spray.

[0115] S3. Calculate the coverage area of ​​the fog cannon, determine the spatial intersection between the predicted landing area and the coverage area of ​​the fog cannon, calculate the start time of the fog cannon in the control area, send the start time to the fog cannon and execute it, and build a visual interface to display the execution results.

[0116] Specifically, the coverage area of ​​the fog cannons is calculated, the spatial intersection of the predicted landing area and the coverage area of ​​the fog cannons is determined, the start-up time of the fog cannons in the control area is calculated, the start-up time is sent to the fog cannons and executed, including:

[0117] Based on the location of the fog cannon (the effective spray radius of the fog cannon is extracted from the equipment factory test report), the circular coverage area of ​​the fog cannon is calculated using a circular coverage model. The circle-circle intersection determination method is used to determine the spatial intersection between the predicted landing circle and the fog cannon coverage circle, and the intersection area is defined as the control area.

[0118] The formula for calculating the start-up time of the fog cannon in the control area is:

[0119] ,

[0120] in Let be the start time of the i-th fog cannon spray pulse. For the current time, The particle drift time for the i-th controlled sub-band is set using Stokes' law. and These are the water mist lead diffusion time and the duration of a single pulse jet, respectively, set by experimental calibration.

[0121] The startup time is sent to the fog cannon via the MQTT protocol and then executed.

[0122] The fog cannons will spray in the controlled area, and the PM10 concentration threshold will be set based on the ambient air quality standard GB 3095-2012. Spraying will stop when the PM10 concentration in the controlled area is lower than the PM10 concentration threshold.

[0123] Traditional construction site fog cannons rely mainly on manual experience for deployment and lack precise coverage area modeling. This invention provides a clear spatial boundary for the fog cannon's affected area, allowing it to directly participate in algorithmic inference. Spraying is only triggered when dust particles are likely to enter the fog cannon's sprayable area, avoiding ineffective activation. The calculation of the control area ensures precise spatial coupling between the fog cannon's spraying behavior and the predicted landing point, reducing the low governance efficiency caused by traditional blind zone spraying. By combining Stokes' law to calculate drift time, the control timing has physical consistency. In situations with high wind speeds or complex terrain, dust particle drift paths are difficult to predict manually. Lead-ahead control can minimize dust diffusion caused by delayed response. The lightweight protocol avoids redundant communication overhead, enabling stable operation of the equipment in harsh environments. A feedback loop is constructed, making the governance adaptive.

[0124] Furthermore, a visual interface is constructed to display the execution results, including:

[0125] Build a visual human-computer interaction interface to display the control area and execution results in real time.

[0126] Visualization can intuitively show the relationship between the predicted landing point and the fog cannon coverage, making the system behavior transparent and supporting rapid troubleshooting and maintenance decisions for abnormal situations.

[0127] Example 2, refer to Figure 2 As a second embodiment of the present invention, a smart construction site management system based on the Internet of Things includes:

[0128] The data collection and processing module is used to collect multi-source data and perform time synchronization, noise reduction, and standardization.

[0129] The coordinate wind field module is used to construct the construction coordinate system, divide the construction site area into rectangular grids, generate unit vectors of horizontal wind direction, and perform trilinear interpolation to generate a three-dimensional wind speed field.

[0130] The filtering and prediction module is used to perform vector projection on the point cloud on the surface of the material stacking area to obtain the tangential wind speed component of the surface points, calculate the voxel center elevation difference of the three-dimensional point cloud, filter the wind erosion active area, uniformly divide the wind erosion active area into sub-zones, calculate the horizontal drift distance, predict the landing center coordinates, and generate the predicted landing circular area.

[0131] The management and display module is used to calculate the coverage area of ​​the fog cannon, determine the spatial intersection between the predicted landing area and the coverage area of ​​the fog cannon, calculate the start time of the fog cannon in the control area, send the start time to the fog cannon and execute it, and build a visual interface to display the execution results.

[0132] This embodiment also provides a computer device applicable to the Internet of Things (IoT)-based smart construction site management method, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the IoT-based smart construction site management method proposed in the above embodiment.

[0133] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0134] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the IoT-based smart construction site management method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0135] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart construction site management method based on the Internet of Things, characterized by: Includes the following steps: Multi-source data is collected and preprocessed to construct a construction coordinate system. The construction site area is projected into a rectangular grid, a unit vector for the horizontal wind direction is generated, and trilinear interpolation is performed to generate a three-dimensional wind speed field, including: Based on the wind speed at the top of the tower crane, the friction speed is calculated, and the wind speed at various heights is calculated using the boundary layer logarithmic law formula. The height of the anemometer measuring points is divided into layers at fixed intervals. The median value of the vertical wind speed of the measuring points in the horizontal wind speed field is extracted as the representative vertical wind speed of each layer. If all anemometers have no vertical output, zero value filling is performed. Otherwise, the representative vertical wind speed is used as the corrected vertical wind speed field to generate the corrected vertical wind speed. Construct a three-dimensional wind speed vector and perform trilinear interpolation to generate a three-dimensional wind speed field. The formula is as follows: ， in Let z be the three-dimensional wind speed vector at time t, location (x, y), and height z, where z is the height. Let x be the unit vector of the horizontal wind direction at time t and position (x, y), where x and y are the center coordinates of the grid sub-block. This represents the corrected vertical wind speed at height z. Let z be the average wind speed modulus at height z; Vector projection is performed on the point cloud on the surface of the material stacking area to obtain the tangential wind speed components of the surface points. The voxel center elevation difference of the 3D point cloud is calculated, active wind erosion areas are screened, and these areas are uniformly divided into sub-zones. The horizontal drift distance is calculated, the landing center coordinates are predicted, and a predicted landing circular region is generated, including: Calculate the total dust particle mass based on wind erosion volume loss; The wind speeds in the active wind erosion areas are sorted in ascending order and evenly divided to generate sub-bands. The total dust particle mass is then distributed to each sub-band to generate the dust particle release mass. Based on particle size, the average equivalent sphere diameter of the sub-band is calculated, a dust threshold is set, and sub-bands with an average equivalent sphere diameter greater than the dust threshold are selected as a set of settleable sub-bands. For sub-zones within the set of settleable sub-zones, Stokes' law is used to calculate the terminal settling velocity. Based on the elevation difference of the voxel centers, the initial height of the dust particles off the ground is calculated. The initial height of the dust particles off the ground is divided by the terminal settling velocity to generate the sub-zone settling time, and the horizontal drift distance is calculated. The product of the dust particle release mass and the horizontal drift distance is calculated and defined as the drift risk index, which is then normalized to generate a mass weight. Based on the horizontal drift distance, predict the landing center coordinates and generate a predicted landing circle. Calculate the coverage area of ​​the fog cannon, determine the spatial intersection between the predicted landing area and the coverage area of ​​the fog cannon, calculate the start time of the fog cannon in the control area, send the start time to the fog cannon and execute it, and build a visual interface to display the execution results.

2. The smart construction site management method based on the Internet of Things as described in claim 1, characterized in that: The construction coordinate system is constructed by projecting the construction site area into a rectangular grid and generating a unit vector for the horizontal wind direction, including: A construction coordinate system is constructed with the permanent control point at the southwest corner of the construction site as the origin. Multi-source data is then mapped to the construction coordinate system of the construction site using the rigid body coordinate transformation method. Project the construction site area onto the XY plane in the construction coordinate system. Use a uniform grid division method to divide the projected construction site area into rectangular grids to generate grid sub-blocks. Use bilinear interpolation to generate a continuous horizontal wind speed field within the sub-blocks and perform vector normalization to generate a unit vector of horizontal wind direction.

3. The smart construction site management method based on the Internet of Things as described in claim 2, characterized in that: The process of performing vector projection on the point cloud of the material stacking area surface to obtain the tangential wind speed components of the surface points, calculating the voxel center elevation difference of the three-dimensional point cloud, and screening for wind erosion-active areas includes: Based on the three-dimensional wind speed field, vector projection is performed on the point cloud on the surface of the material stacking area to obtain the tangential wind speed components of each surface point. A wind erosion initiation threshold is set, and grid sub-blocks with tangential wind speed components greater than or equal to the wind erosion initiation threshold are selected and defined as potential wind erosion areas. Extract three-dimensional point clouds from potential wind erosion areas, calculate the voxel center elevation difference of the three-dimensional point clouds, set a point cloud measurement accuracy threshold, and filter three-dimensional point clouds with voxel center elevation differences greater than the point cloud measurement accuracy threshold to obtain wind erosion active areas. Extract the elevation difference of all voxel centers within the active wind erosion area and calculate the wind erosion volume loss.

4. The smart construction site management method based on the Internet of Things as described in claim 3, characterized in that: The calculation of the fog cannon coverage circle involves determining the spatial intersection of the predicted landing circle and the fog cannon coverage circle, calculating the start-up time of the fog cannon in the control area, sending the start-up time to the fog cannon, and executing the calculation, including: Based on the location of the fog cannon, the fog cannon coverage area is calculated. The circle-circle intersection method is used to determine the spatial intersection between the predicted landing circle and the fog cannon coverage area. The intersection area is defined as the control area. Calculate the start time of the fog cannons in the control area, send the start time to the fog cannons via the MQTT protocol and execute it; Spray mist from the fog cannons in the controlled area, set a PM10 concentration threshold, and stop spraying when the PM10 concentration in the controlled area is lower than the PM10 concentration threshold.

5. The smart construction site management method based on the Internet of Things as described in claim 4, characterized in that: The construction of a visual interface to display the execution results includes: Build a visual human-computer interaction interface to display the control area and execution results in real time.

6. The smart construction site management method based on the Internet of Things as described in claim 1, characterized in that: The collection and preprocessing of multi-source data includes: Smart sensors are used to collect multi-source data from the construction site, and time synchronization, noise reduction, and standardization processing are performed. The intelligent sensors include an ultrasonic anemometer, a laser scanner, a laser particle size analyzer, and a PM10 sensor. The multi-source data includes wind speed, three-dimensional point cloud, fog cannon location, particle size, and PM10 concentration data.

7. A smart construction site management system based on the Internet of Things (IoT), used to implement the smart construction site management method based on the IoT as described in any one of claims 1 to 6, characterized in that: include: The data collection and processing module is used to collect multi-source data and perform time synchronization, noise reduction, and standardization. The coordinate wind field module is used to construct the construction coordinate system, divide the construction site area into rectangular grids, generate a unit vector of horizontal wind direction, and perform trilinear interpolation to generate a three-dimensional wind speed field. The filtering and prediction module is used to perform vector projection on the point cloud on the surface of the material stacking area to obtain the tangential wind speed component of the surface points, calculate the voxel center elevation difference of the three-dimensional point cloud, filter the wind erosion active area, uniformly divide the wind erosion active area into sub-zones, calculate the horizontal drift distance, predict the landing center coordinates, and generate the predicted landing circular area. The management and display module is used to calculate the coverage area of ​​the fog cannon, determine the spatial intersection between the predicted landing area and the coverage area of ​​the fog cannon, calculate the start time of the fog cannon in the control area, send the start time to the fog cannon and execute it, and build a visual interface to display the execution results.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the IoT-based smart construction site management method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the IoT-based smart construction site management method according to any one of claims 1 to 6.

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