Intelligent Method and System for Suppressing Dust from Building Demolition

Abstract

This invention discloses an intelligent method and system for suppressing dust from building demolition, relating to the field of dust suppression during building demolition. The method includes: acquiring wind field data and spatial location information of structures in the demolition area; acquiring the arrangement parameters of each nozzle unit in the spray system; constructing a wind barrier interference area model within the target area based on the spatial location information and wind field data; calculating the spray range of each nozzle unit based on the arrangement parameters; spatially superimposing the spray range with the wind barrier interference area model to calculate the predicted distribution value of spray particles generated by each nozzle unit in three-dimensional space, generating a spatial unit matrix of the work area; traversing the spatial unit matrix to determine the uniformity of particle distribution; and maintaining or adjusting the nozzle unit arrangement parameters based on the determination results. This invention achieves dynamic reconfiguration of spray resources in a multi-nozzle system, solving the problem of low dust suppression efficiency caused by the coexistence of overspray and underspray.

Description

Technical Field

[0001] This invention relates to the field of dust suppression during building demolition, specifically to an intelligent method and system for suppressing dust during building demolition. Background Technology

[0002] During building demolition, high-intensity mechanical impacts or structural collapses can easily generate large amounts of inhalable dust. These dust particles are extremely fine and easily disperse and migrate under the influence of airflow, causing not only a sharp deterioration in the air quality around the construction site but also posing a serious threat to the respiratory health of construction workers and affecting the overall environmental quality of the city.

[0003] To control this type of pollution, spray systems are currently widely used for dust suppression. However, existing spray systems have several technical shortcomings: First, the design of the droplet coverage area is too simplistic and crude, only considering the spraying effect in a static environment, and cannot accurately respond to the complex distribution of structures and dynamic wind conditions on site; second, the spray control strategy mainly relies on manual experience for adjustment, lacking scientific and quantitative control basis, making it difficult to make precise real-time adjustments for uneven particle distribution; third, existing technologies lack effective particle distribution prediction models, and cannot intelligently optimize the nozzle arrangement parameters, resulting in frequent coexistence of local overspray and underspray in the spray area, which wastes water resources and fails to achieve the desired dust suppression effect.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for intelligent suppression of dust from building demolition.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] In a first aspect, this invention discloses an intelligent method for suppressing dust from building demolition, comprising the following steps:

[0008] Obtain wind field data and spatial location information of structures in the building demolition area, and obtain the layout parameters of each nozzle unit in the spray system;

[0009] Based on the spatial location information and the wind field data, a wind barrier interference area model is constructed within the target area; the spray range of each nozzle unit is calculated based on the arrangement parameters.

[0010] The spray range is spatially superimposed with the wind barrier interference area model, and the predicted value of the spray particle distribution generated by each nozzle unit in three-dimensional space is calculated. Based on the predicted value of the spray particle distribution, a spatial unit matrix of the working area is generated, and each spatial unit contains the number of spray particles per unit volume.

[0011] Traverse the spatial unit matrix and determine whether the number of spray particles in each spatial unit is within a preset threshold range. If so, the particle distribution is considered uniform; otherwise, the particle distribution is considered uneven.

[0012] When the particle distribution is uniform, maintain the current spray state of all nozzle units;

[0013] When the particle distribution is uneven, the spatial units with a spray particle count value higher than the upper limit of the threshold interval are marked as overspray units, and the spatial units with a spray particle count value lower than the lower limit of the threshold interval are marked as underspray units.

[0014] The set of nozzle units that corresponds to the set of overspray units is determined. Based on the predicted spray particle distribution of the nozzle units in the set of nozzle units, the nozzle units to be adjusted are determined. The arrangement parameters of the nozzle units to be adjusted are adjusted according to the set of underspray units.

[0015] Secondly, this invention discloses an intelligent dust suppression system for building demolition, comprising:

[0016] The data acquisition module is used to acquire wind field data in the building demolition area, spatial location information of structures, and layout parameters of each nozzle unit in the spray system;

[0017] The interference model construction module is used to construct a wind barrier interference area model within the target area based on the spatial location information and wind field data.

[0018] The spray range calculation module is used to calculate the spray range of each nozzle unit based on the nozzle arrangement parameters.

[0019] The particle distribution prediction module is used to spatially overlay the spray range with the wind barrier interference area model and calculate the predicted value of the spray particle distribution generated by each nozzle unit in three-dimensional space.

[0020] The spatial cell matrix generation module is used to generate a spatial cell matrix of the work area based on the predicted spray particle distribution value. Each spatial cell in the spatial cell matrix contains the number of spray particles per unit volume.

[0021] The particle distribution judgment module is used to traverse the spatial unit matrix and determine whether the number of spray particles in each spatial unit is within a preset threshold range. If so, it outputs a judgment result that the particle distribution is uniform; otherwise, it outputs a judgment result that the particle distribution is uneven.

[0022] The spray state analysis module is used to maintain the current spray state of all nozzle units when the particle distribution is uniform; when the particle distribution is uneven, spatial units with a spray particle number value higher than the upper limit of the threshold interval are marked as overspray unit sets, and spatial units with a spray particle number value lower than the lower limit of the threshold interval are marked as underspray unit sets.

[0023] The nozzle control module is used to determine the set of nozzle units that corresponds to the set of overspray units, determine the nozzle unit to be adjusted based on the predicted value of the spray particle distribution of the nozzle units in the set of nozzle units, and adjust the arrangement parameters of the nozzle unit to be adjusted according to the set of underspray units.

[0024] The beneficial effects of this invention are as follows:

[0025] 1. By constructing a wind barrier interference area model and a spray cone model, and spatially superimposing the two, combined with a particle motion trajectory model, high-precision prediction of spray particle distribution is achieved, overcoming the problem of coarse design of droplet coverage range in existing technologies;

[0026] 2. By establishing a particle concentration contribution matrix of the nozzle to the spatial unit, a feedback path between the spray state and particle distribution is formed, enabling the system to automatically adjust the nozzle parameters according to the particle distribution, thus eliminating the dependence on empirical adjustments.

[0027] 3. By introducing a priority function to sort and select nozzles, dynamic reconfiguration of spray resources in a multi-nozzle system is realized, solving the problem of low dust suppression efficiency caused by the coexistence of overspray and underspray.

[0028] 4. The system can automatically construct an interference model based on the real-time wind field and structure layout, which has good on-site adaptability and improves the adaptability of the spray dust suppression system to complex environments. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is an overall block diagram of the method in Embodiment 1 of the present invention;

[0031] Figure 2 This is a flowchart of the method according to Embodiment 1 of the present invention;

[0032] Figure 3 This is an overall block diagram of the system in Embodiment 2 of the present invention. Detailed Implementation

[0033] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Application Overview: In existing technologies, the large amounts of inhalable dust generated during building demolition are easily dispersed by airflow. Traditional spray systems suffer from problems such as poorly designed droplet coverage, inability to respond to changes in wind field, and reliance on experience for adjustments. For example, in areas with high walls or dense tower cranes, spray systems often experience uneven dust suppression due to obstruction by these structures, resulting in both over-spraying and under-spraying. At a demolition site, strong crosswinds interfered with the existing spray system, which could not adjust its coverage according to the real-time wind field, causing dust to spread to surrounding roads and affecting construction safety.

[0035] To address these issues, researchers discovered that existing spray systems lack the ability to predict particle distribution in three-dimensional space, necessitating the development of a dynamic response model. First, they analyzed the impact of the spatial relationship between the wind field and structures on dust diffusion paths, considering how to integrate nozzle parameters with windbreak interference areas. By studying the motion patterns of spray particles in the wind field, they proposed a method for spatially superimposing a windbreak interference area model with the spray range. Further research explored how to quantify particle distribution uniformity and establish a dynamic correlation mechanism between nozzle parameters and under-sprayed areas.

[0036] Example 1:

[0037] like Figure 1-2 As shown, the intelligent dust suppression method for building demolition includes the following steps:

[0038] Obtain wind field data and spatial location information of structures in the building demolition area, and obtain the layout parameters of each nozzle unit in the spray system;

[0039] Based on the spatial location information and the wind field data, a wind barrier interference area model is constructed within the target area; the spray range of each nozzle unit is calculated based on the arrangement parameters.

[0040] The spray range is spatially superimposed with the wind barrier interference area model, and the predicted value of the spray particle distribution generated by each nozzle unit in three-dimensional space is calculated. Based on the predicted value of the spray particle distribution, a spatial unit matrix of the working area is generated, and each spatial unit contains the number of spray particles per unit volume.

[0041] Traverse the spatial unit matrix and determine whether the number of spray particles in each spatial unit is within a preset threshold range. If so, the particle distribution is considered uniform; otherwise, the particle distribution is considered uneven.

[0042] When the particle distribution is uniform, maintain the current spray state of all nozzle units;

[0043] When the particle distribution is uneven, the spatial units with a spray particle count value higher than the upper limit of the threshold interval are marked as overspray units, and the spatial units with a spray particle count value lower than the lower limit of the threshold interval are marked as underspray units.

[0044] The set of nozzle units that corresponds to the set of overspray units is determined. Based on the predicted spray particle distribution of the nozzle units in the set of nozzle units, the nozzle units to be adjusted are determined. The arrangement parameters of the nozzle units to be adjusted are adjusted according to the set of underspray units.

[0045] Among these, wind field data refers to the dynamic parameters of airflow within the demolition area. This can be achieved by using wind speed sensors and wind vanes to collect average wind speed and prevailing wind direction data in real time, used to construct a model of the impact of airflow on dust diffusion. Spatial location information refers to the geometric parameters of the structure in a three-dimensional coordinate system. This can be achieved by extracting the outline coordinates of high walls and tower cranes through laser scanning or BIM models, used to determine the spatial boundaries of the windbreak interference area. Layout parameters refer to the spatial configuration parameters of the nozzles, specifically including nozzle installation height, spray cone angle, and water supply pressure, used to calculate spray range and coverage volume. The windbreak interference area model refers to the disturbance area formed by airflow obstruction by the structure, specifically constructed using the structure's windward side projection extension method, used to identify potential areas where spray particles are affected by the wind field. The spatial unit matrix refers to a three-dimensional grid system that discretizes the work area, specifically generated using a cubic unit division method. Each unit records the particle concentration value within a unit volume, used to quantitatively analyze the uniformity of spray coverage.

[0046] Specifically, a 3D model incorporating airflow disturbance characteristics is constructed by real-time acquisition of wind speed, direction, and structure coordinates. The effective spray cone range for each nozzle is calculated based on its installation parameters, and the spray cone is spatially superimposed with the wind barrier interference area for analysis. The trajectory of spray particles in the complex wind field is simulated based on fluid dynamics principles to predict the particle concentration distribution in each spatial unit. By traversing the 3D mesh, areas with excessively high or low concentrations are automatically identified, and the nozzle units requiring adjustment are located accordingly. When an under-spraying area is detected, the spray angle or pressure parameters of the corresponding nozzle are adjusted to bring the particle distribution back to the preset threshold range.

[0047] Traditional methods rely solely on two-dimensional planar nozzle placement, neglecting the combined effects of three-dimensional wind fields and structures. This solution, by constructing a model of the wind barrier interference area and spatially overlaying it with the spray range, achieves particle distribution prediction in dynamic environments. Existing technologies depend on manual experience to judge coverage effectiveness, while this solution, through spatial unit matrix quantitative analysis, can automatically trigger a nozzle parameter adjustment mechanism.

[0048] Through the above technical solutions, this application can dynamically adapt to the complex wind field environment at the demolition site and precisely control the uniformity of spray particle distribution in three-dimensional space. By establishing a correlation model between the wind barrier interference area and the nozzle parameters, under-spraying on the leeward side and over-spraying on the windward side of the structure are effectively avoided. The automatic detection mechanism based on the spatial unit matrix significantly improves dust suppression efficiency and reduces water waste.

[0049] This application further proposes wind field data including average wind speed data and wind direction data, structures including high walls, tower cranes or enclosures, and layout parameters including nozzle position, spray angle and spray pressure.

[0050] The data includes: average wind speed (meaning the average speed of airflow per unit time, which can be collected in real time using a wind speed sensor to quantify the lateral migration effect of the wind field on spray particles); wind direction data (meaning the vector information of airflow direction, which can be obtained using a wind vane or electronic compass to determine the spatial extension direction of the wind barrier interference area model); structures including high walls, tower cranes, or fences (meaning obstacles with significant volume within the construction site, whose spatial coordinates can be extracted using 3D laser scanning or building information modeling to establish the geometric benchmark of the wind barrier interference area model); nozzle position (meaning the coordinates of the spray device in 3D space, which can be determined using GPS positioning or a laser rangefinder to construct the spatial reference point of the spray cone model); spray angle (meaning the divergence angle of the nozzle atomization cone, which can be achieved by adjusting the nozzle orifice diameter using a stepper motor to control the lateral distribution of the spray coverage); and spray pressure (meaning the pressure parameter driving liquid atomization, which can be achieved by adjusting the flow rate using a variable frequency water pump to control the spray range and particle diffusion density).

[0051] Specifically, wind speed sensors and wind vanes are deployed at the construction site to collect wind field data in real time. Simultaneously, 3D scanning is used to obtain the spatial coordinates of structures such as high walls and tower cranes. After the nozzle positions are determined by a positioning device, the effective coverage area of ​​each nozzle is calculated based on the spray angle and pressure parameters. When the windward profile of a structure forms an angle with the prevailing wind direction, the corresponding type coefficient is selected according to the structure type to calculate the buffer zone width, thereby establishing an accurate model of the wind barrier interference area. Adjusting the combination of spray angle and pressure parameters can change the geometry of the spray cone, dynamically matching it with the wind barrier interference area.

[0052] Traditional methods typically consider only a single wind speed parameter and ignore differences in structure types, resulting in insufficient accuracy in wind barrier models. This solution integrates vector data of wind speed and direction, combined with the spatial characteristics of different types of structures, to make the calculation of wind barrier interference areas more closely resemble actual airflow distribution. Furthermore, by using nozzle position, angle, and pressure as linked adjustment parameters, it overcomes the limitations of traditional fixed spray devices.

[0053] Through the above technical solutions, this application can accurately identify the differentiated interference patterns of different structures on the wind field and establish a wind barrier model that matches the site environment. Based on the coordinated control of nozzle position, angle, and pressure, the matching degree between the spray coverage area and the wind barrier interference area can be dynamically optimized, thereby effectively suppressing spray blind spots or excessive spraying caused by structure obstruction or sudden changes in the wind field.

[0054] This application further proposes a process for constructing a windbreak interference area model, including determining the dominant wind direction vector based on wind direction data, extending a projected shading area in the opposite direction of the dominant wind direction vector based on the outline of the windward side of the structure, and expanding the buffer zone outward to form a windbreak interference area; the projection shading area is generated by discretizing the outer outline of the structure into several feature points, translating each feature point in the opposite direction of the dominant wind direction vector, and connecting the translated feature points to form a closed polygonal area; the buffer zone width is calculated by multiplying the average wind speed data by a preset structure type coefficient.

[0055] The dominant wind direction vector refers to the data on the main airflow direction in the demolition area obtained through meteorological monitoring equipment. Specifically, this can be achieved in real-time using a combination of wind speed sensors and wind vanes, used to determine the main path of dust diffusion. The projected obstruction zone refers to the geometric coverage area formed by the windward side of the structure extending in the opposite direction. This can be generated by vector calculations after acquiring the structure's outline point cloud data using a 3D laser scanner, used to simulate the obstruction effect of the structure on the wind field. The buffer zone width refers to the dynamically adjusted extension distance based on wind speed and structure type. Specifically, it can be calculated using aerodynamic coefficient tables corresponding to different structure types, for example, a coefficient of 0.8 for high walls and 1.2 for tower cranes, used to compensate for the dust diffusion effect caused by wind around the structure.

[0056] Specifically, wind direction monitoring devices are deployed at the work site to collect real-time data on the prevailing wind direction. Using 3D modeling technology, the outlines of structures such as high walls and tower cranes are discretized into a dense set of feature points. Based on computational fluid dynamics principles, each feature point is vector-translated along the upwind direction. The translation distance can be dynamically set according to the structure's height; for example, the translation distance is equal to 0.3 times the structure's height. The translated feature points are topologically connected to form a polygonal projection area, which represents the range of the wind shadow zone generated by the structure in the upwind direction. Further combining real-time wind speed monitoring data, the buffer zone expansion width is calculated by multiplying the average wind speed by a preset structure type coefficient. For example, when the wind speed is 5 m / s and the structure is a high wall, the buffer zone width expands to 5 × 0.8 = 4 meters. The resulting wind barrier interference area model accurately reflects the range of interference that the structure causes to the dust diffusion path.

[0057] Traditional methods estimate the obstruction range based solely on the static dimensions of structures, neglecting the coupling effect between dynamic wind direction changes and the aerodynamic characteristics of the structures. This solution establishes an environmentally adaptable wind barrier interference model by introducing the dominant wind direction vector and structure type coefficients. For example, in tower crane operation scenarios, the type coefficient corresponding to its openwork structure can accurately reflect the wind flow effect, significantly improving the accuracy of dust diffusion path prediction compared to traditional rectangular obstruction models.

[0058] Through the above technical solution, this application can accurately quantify the interference range of structures on dust diffusion paths, providing a dynamic modeling basis for the spatial layout of spray systems. Based on the collaborative calculation of real-time wind field data and the aerodynamic characteristics of structures, it effectively solves the problem of spray coverage deviation caused by neglecting the wind field flow effect in traditional methods, making the spray particle distribution prediction model more consistent with the actual working environment.

[0059] This application further proposes a calculation process for the spray range, including: obtaining the nozzle position, spray angle, and spray pressure of each nozzle unit; calculating the spray range based on the spray angle and spray pressure; and constructing a spray cone model in three-dimensional space with the nozzle position as the vertex and the spray angle and spray range as the spray parameters. The spray cone model is used to characterize the effective spray range of the nozzle unit.

[0060] The nozzle position refers to the coordinate data of the nozzle unit in three-dimensional space, which can be obtained using a laser rangefinder or GPS positioning device, and is used to determine the starting point of the spray's effective range. The spray angle refers to the three-dimensional diffusion angle of the spray droplets, which can be monitored in real time using an angle sensor; this parameter directly affects the lateral distribution range of the spray coverage area. The spray pressure refers to the fluid pressure value that drives the nozzle to generate droplets, which can be collected using a pressure transmitter; this parameter is positively correlated with the spray range. The spray range refers to the maximum horizontal distance that droplets can reach in a windless state, which can be calculated using fluid dynamics formulas combined with the spray angle and pressure parameters, and is used to determine the longitudinal extension boundary of the spray's effective range. The spray cone model refers to a three-dimensional geometric body with the nozzle position as the vertex, the spray angle as the cone's apex angle, and the spray range as the generatrix length; it can be constructed using computer-aided design software and is used to visually represent the actual coverage area of ​​the nozzle unit.

[0061] Specifically, during implementation, the three-dimensional coordinates, spray angle, and pressure parameters of each nozzle unit are first collected in real time via a sensor network. Based on the jet equation in fluid mechanics, the spray angle and pressure are input into a preset mathematical model to automatically calculate the spray range corresponding to each nozzle. In the three-dimensional coordinate system, a conical spatial model is constructed along the spray direction with the nozzle position as the vertex. The diameter of the base of the cone is determined by both the spray angle and the range. This conical model can accurately reflect the droplet coverage range of the nozzle under ideal conditions, providing a spatial benchmark for subsequent particle distribution prediction. By spatially superimposing the conical model of each nozzle unit with the wind barrier interference area model, spray blind spots obstructed by structures or affected by wind can be accurately identified, providing data support for nozzle parameter optimization.

[0062] Traditional methods typically estimate spray coverage using two-dimensional planar projection, neglecting the combined effects of wind field and structures in three-dimensional space. This approach, by constructing a three-dimensional spray cone model and combining it with hydrodynamic parameter calculations, can more accurately simulate the diffusion path of spray particles in a real environment. This modeling method effectively solves the problem of coarse spray range calculations in existing technologies, laying an accurate geometric spatial foundation for subsequent particle distribution prediction.

[0063] Through the above technical solution, this application achieves accurate quantitative modeling of the effective range of the nozzle unit, overcoming the deficiency of traditional two-dimensional planar projection methods that ignore the spatial height dimension. By dynamically overlaying the three-dimensional cone model with the windbreak interference area, spray blind spots caused by structural obstruction can be detected in a timely manner, providing reliable spatial data support for the intelligent adjustment of nozzle angle and pressure parameters, thereby improving the spatial adaptability of the dust suppression system.

[0064] This application further proposes that the spatial unit matrix be divided into unit cubic grids, with each spatial unit not overlapping and collectively covering the entire working area.

[0065] The unit cube grid refers to dividing the working area into cube units with equal side lengths. This can be achieved by meshing in a three-dimensional coordinate system with a preset step size, such as 0.5 meters or 1 meter side lengths. This partitioning method ensures a unified benchmark for particle concentration calculation by standardizing the volume of spatial units. Non-overlapping and common coverage means that all spatial units are arranged without gaps or overlaps in three-dimensional space, which can be achieved using the Cartesian product operation of the cube grid. This feature ensures the completeness and accuracy of particle distribution prediction by eliminating spatial blind spots and redundant statistical areas.

[0066] Specifically, after generating the predicted spray particle distribution, the operating area is divided into multiple unit cubic grids. Each grid, as an independent spatial unit, has a fixed volume and well-defined boundaries. By traversing the overlapping areas of all spray cone models and windbreak interference regions, the number of spray particles is statistically analyzed and assigned to the corresponding spatial unit. Due to the uniform grid size, particle concentration calculations can be directly normalized based on unit volume, avoiding statistical errors caused by differences in grid shape or size. Furthermore, the non-overlapping nature of the grids ensures that the particle count in each spatial unit belongs solely to the contribution range of a single nozzle unit, providing a reliable data foundation for subsequent priority adjustment calculations.

[0067] Traditional methods typically use irregular polygons or fixed-size cuboids to divide the space, leading to volume differences and blurred boundaries in particle distribution statistics. This proposed solution, however, uses unit cube meshes, ensuring each spatial unit has the same volume parameters. This facilitates lateral comparison of particle concentration values ​​and threshold determination, while also simplifying the complexity of mesh generation and data storage in three-dimensional space.

[0068] Through the above technical solution, this application can achieve full coverage and accurate monitoring of dust suppression areas, eliminate the problem of misjudgment of particle concentration caused by uneven grid division, provide a consistent spatial reference for nozzle unit parameter adjustment, and thus improve the spray system's accuracy in identifying under-spray and over-spray areas.

[0069] This application further proposes the following steps for calculating the predicted value of spray particle distribution: Within the spray cone model, the particle diffusion behavior is discretized based on the emission direction and spray parameters of the spray particles; the motion path of the spray particles is corrected by combining wind field data, and the landing points of the spray particles in the three-dimensional grid of the target area are aggregated and statistically analyzed; the aggregated statistical results are normalized to obtain the number of spray particles per unit volume space, which is used as the predicted value of spray particle distribution.

[0070] Discrete modeling involves decomposing the diffusion process of spray particles into multiple independent computational steps. Specifically, numerical simulation methods can be used to mathematically describe the relationship between particle emission direction and spray parameters, iteratively calculating particle trajectories using discrete time steps. Path correction involves dynamically adjusting particle trajectories based on real-time wind field data. This can be achieved by superimposing the force of wind speed vectors using fluid dynamics equations to correct displacement deviations in three-dimensional space. Aggregate statistics involves mapping particle termination positions to three-dimensional grid cells. This can be done using spatial indexing algorithms to determine the region to which particle landing points belong and to count the number of particles within each grid cell. Normalization eliminates the influence of spatial cell volume differences on particle concentration. This can be achieved by dividing the particle count by the volume of the corresponding spatial cell to convert it into a standardized concentration index per unit volume.

[0071] Specifically, a discrete mathematical model of particle diffusion is established within the spray cone model. The initial emission direction and velocity are determined by setting the spray angle and pressure parameters. Each particle experiences trajectory deviation due to the wind field during its motion, and the particle coordinates are updated in real time by superimposing wind speed vector components. The termination positions of all particles are mapped to three-dimensional mesh cells. After counting the number of particles accumulated in each cell, the normalized concentration value is obtained by dividing by the cell volume. This predicted value is used to quantitatively evaluate the spatial distribution of spray particles in the operating area, providing a data foundation for subsequent nozzle parameter optimization.

[0072] Existing methods for predicting spray particles typically employ static coverage estimation, neglecting the impact of dynamic wind field changes on particle trajectories, leading to predictions that deviate from actual distributions. This proposed solution introduces wind field data correction and discrete modeling methods to accurately simulate particle diffusion behavior in complex airflow environments. Combined with 3D mesh aggregation statistics and normalization processing, it effectively improves the spatial resolution and computational accuracy of particle concentration prediction.

[0073] Through the above technical solution, this application can accurately predict the three-dimensional distribution of spray particles in the building demolition area, identify the spatial location of overspray and underspray areas, provide a reliable basis for the directional adjustment of the nozzle unit, thereby improving the uniformity of particle distribution and avoiding dust suppression blind spots or resource waste caused by wind field interference.

[0074] This application further proposes a method for determining the nozzle units to be adjusted, including establishing a spray particle contribution matrix in three-dimensional space based on the arrangement parameters of the nozzle units and the predicted values ​​of spray particle distribution. The rows of the matrix represent the nozzle unit numbers, the columns represent the spatial unit numbers, and the matrix element values ​​represent the particle concentration contribution values ​​of the corresponding nozzle unit to the spatial unit. After marking the overspray unit set and the underspray unit set, the total contribution value of each nozzle unit to the two sets is calculated to construct a comprehensive adjustment priority function. Nozzle units whose priority function values ​​exceed a set threshold are selected to form a candidate set, and the top N are selected as nozzle units to be adjusted according to priority.

[0075] Based on the arrangement parameters of each nozzle unit and the predicted value of spray particle distribution, a spray particle contribution matrix M[i][j] in three-dimensional space is established; where i represents the nozzle unit number; j represents the spatial unit number; M[i][j] represents the particle concentration contribution value of nozzle unit i to spatial unit j;

[0076] Mark the overspray unit set With underspray unit assembly Then, calculate the total contribution of each nozzle unit to the two sets mentioned above:

[0077]

[0078] Calculate the overall adjustment priority function for each nozzle unit:

[0079]

[0080] Wherein, α and β are set weighting coefficients used to balance the adjustment targets of overspray suppression and underspray compensation;

[0081] Select nozzle units whose P[i] is greater than a set threshold from all nozzle units as a set of candidate nozzle units to be adjusted;

[0082] The candidate nozzle units to be adjusted are further sorted from high to low according to P[i], and the top N nozzle units are selected as the nozzle units to be adjusted, where N is a positive integer.

[0083] Existing methods typically rely solely on local observation data to uniformly control the start and stop of nozzles, failing to differentiate the varying contributions of different nozzles to abnormal areas. This proposed solution, by establishing a concentration contribution matrix and a priority evaluation mechanism, dynamically identifies key control targets and optimizes the adjustment sequence, effectively avoiding resource waste caused by global parameter adjustments.

[0084] Through the above technical solution, this application solves the problem of coexistence of overspray and underspray caused by the lack of targeted control in existing spray systems. By using a priority sorting mechanism to accurately locate high-impact nozzle units, it can improve dust suppression efficiency while reducing equipment energy consumption.

[0085] This application further proposes the following steps for calculating the particle concentration contribution value: simulating the trajectory of spray particles based on the spray parameters and wind field data of the nozzle unit; corresponding each particle's termination position in the target area with a spatial unit, and counting the number of particles released by the nozzle unit to each spatial unit; and calculating the particle concentration contribution value of the nozzle unit to each spatial unit based on the number of particles and the volume of the spatial unit.

[0086] The particle concentration contribution value refers to the influence of a single nozzle unit on the number of spray particles per unit volume within a specific spatial unit. This can be achieved through fluid dynamics simulation or discrete element method (DEM) simulation, used to quantify the coverage effect of the nozzle unit on the target area. Spray parameters include the nozzle's spray angle, spray pressure, and jet flow rate, which can be acquired in real-time using pressure sensors and angle encoders to determine the spray range and diffusion area. Wind field data includes real-time wind speed, wind direction, and turbulence intensity, which can be dynamically monitored using ultrasonic anemometers or lidar to correct the trajectory of spray particles in the air. Trajectory simulation refers to establishing a three-dimensional equation of motion for spray particles under wind force based on Newtonian mechanics principles. This can be numerically calculated using an Euler-Lagrange coupling algorithm to predict the spatial distribution of particles.

[0087] Specifically, after the nozzle unit starts up, the initial spray direction and velocity are first determined based on its spray angle and pressure parameters, generating the initial motion vector of the spray particles. Then, combined with real-time wind field data, a particle motion equation is established in a three-dimensional coordinate system to calculate the displacement changes of the particles under wind force. The termination position of each particle is mapped through a spatial cell grid, and the number of particles in different spatial cells for each nozzle unit is counted. Finally, the statistical result is divided by the volume of the spatial cell to obtain the concentration contribution value of each nozzle unit to the corresponding cell. This process, by dynamically coupling spray parameters and wind field data, can accurately reflect the diffusion law of particles in complex airflow environments.

[0088] Traditional methods typically use fixed-range models to estimate spray coverage, neglecting the impact of dynamic wind field changes on particle trajectories, leading to significant errors in concentration contribution calculations. This proposed solution, however, establishes a dynamic simulation model of particle trajectories and combines it with real-time wind field data to correct particle paths, significantly improving the accuracy of concentration contribution calculations and providing a reliable basis for subsequent nozzle parameter optimization.

[0089] Through the above technical solution, this application can accurately quantify the dust suppression effect contribution of each nozzle unit to the target area and effectively identify areas with insufficient or redundant spray coverage. By dynamically adjusting the nozzle parameters, local overspray or underspray caused by wind disturbances can be avoided, thereby improving dust suppression efficiency and reducing water waste.

[0090] This application further proposes a process for adjusting the arrangement parameters of the nozzle unit to be adjusted based on the set of under-sprayed units, including determining the spatial centroid coordinates of the under-sprayed units, calculating the direction vector between the nozzle unit to be adjusted and the spatial centroid coordinates, adjusting the spray angle of the nozzle unit to be adjusted so that its spray direction is toward the spatial centroid, and further adjusting the spray pressure or nozzle spatial position when the adjusted spray angle is insufficient to cover the under-sprayed units.

[0091] Among them, the spatial centroid coordinates refer to the geometric center position of the under-spray unit set in three-dimensional space, which can be calculated by the weighted average of the coordinates of each under-spray unit, and is used to characterize the spatial distribution concentration trend of the under-spray area. The direction vector refers to the spatial vector from the position of the nozzle unit to be adjusted to the spatial centroid coordinates, which can be calculated by the difference between the coordinates of two points in the three-dimensional coordinate system, and is used to determine the reference axis for spray direction adjustment. The spray angle refers to the angular range of the nozzle spray cone, which can be achieved by using a servo motor-driven angle adjustment mechanism, and the spray coverage area is controlled by changing the nozzle deflection angle. The spray pressure refers to the power parameter that drives liquid atomization, which can be achieved by using a variable frequency water pump to adjust the flow rate and pressure, and the spray range and droplet size are controlled by changing the pressure value. The nozzle spatial position refers to the three-dimensional coordinates of the nozzle installation point, which can be achieved by using a track sliding mechanism or a robotic arm device, and the spray coverage range is expanded by changing the physical position of the nozzle.

[0092] Specifically, after detecting a set of under-sprayed units, the average position of all spatial units within the set is first calculated using a three-dimensional coordinate system as the spatial centroid coordinates. Then, a spatial vector relationship is established between the installation position of the nozzle unit to be adjusted and these centroid coordinates, and the adjustment direction of the spray angle is determined by calculating the vector direction. When the current spray cone coverage of the nozzle unit cannot effectively reach the under-sprayed area, the spray angle is adjusted first to align the main spray axis with the spatial centroid coordinates. If a coverage blind spot still exists after angle adjustment, the spray range is extended by increasing the spray pressure, or the spatial distance to the under-sprayed area is shortened by moving the physical position of the nozzle.

[0093] In some specific implementations, when the underspray units are distributed across multiple discrete regions, the centroid coordinates of each sub-region can be calculated separately, and a corresponding set of direction vectors can be established. The final adjustment parameters of the nozzle units can then be determined using a multi-objective optimization algorithm. In cases where structures obstruct the flow, an obstacle penetration compensation coefficient can be introduced during the direction vector calculation to increase the spray pressure and offset particle attenuation caused by wind field interference.

[0094] Traditional methods improve spray coverage by adjusting only a single parameter, such as changing the nozzle angle or adjusting the spray pressure. This solution establishes the spatial correlation between the nozzle and under-sprayed areas through spatial vector analysis, employing a multi-level control strategy that prioritizes angle adjustment and supplements it with pressure and position adjustments. This enables more precise elimination of particle distribution blind spots in three-dimensional space. Existing technologies lack quantitative analysis of the spatial distribution characteristics of under-sprayed areas, while this solution achieves dynamic tracking and intelligent compensation of under-sprayed areas through centroid coordinate positioning technology.

[0095] Through the above technical solution, this application effectively solves the problem of insufficient response accuracy of spray systems to under-sprayed areas in complex spaces. By establishing the spatial vector relationship between the nozzle unit and the under-sprayed area, precise directional adjustment of the spray direction is achieved; through a multi-parameter collaborative adjustment mechanism, complete coverage of the under-sprayed area is ensured under different operating conditions. This technical solution can dynamically adapt to changes in the distribution of structures and wind field disturbances, significantly improving the uniformity of particle distribution while maintaining the stability of system operation, and avoiding the problem of reduced dust suppression efficiency caused by local under-spray.

[0096] Example 2:

[0097] like Figure 3 As shown, the intelligent dust suppression system for building demolition includes:

[0098] The data acquisition module is used to acquire wind field data in the building demolition area, spatial location information of structures, and layout parameters of each nozzle unit in the spray system;

[0099] The interference model construction module is used to construct a wind barrier interference area model within the target area based on the spatial location information and wind field data.

[0100] The spray range calculation module is used to calculate the spray range of each nozzle unit based on the nozzle arrangement parameters.

[0101] The particle distribution prediction module is used to spatially overlay the spray range with the wind barrier interference area model and calculate the predicted value of the spray particle distribution generated by each nozzle unit in three-dimensional space.

[0102] The spatial cell matrix generation module is used to generate a spatial cell matrix of the work area based on the predicted spray particle distribution value. Each spatial cell in the spatial cell matrix contains the number of spray particles per unit volume.

[0103] The particle distribution judgment module is used to traverse the spatial unit matrix and determine whether the number of spray particles in each spatial unit is within a preset threshold range. If so, it outputs a judgment result that the particle distribution is uniform; otherwise, it outputs a judgment result that the particle distribution is uneven.

[0104] The spray state analysis module is used to maintain the current spray state of all nozzle units when the particle distribution is uniform; when the particle distribution is uneven, spatial units with a spray particle number value higher than the upper limit of the threshold interval are marked as overspray unit sets, and spatial units with a spray particle number value lower than the lower limit of the threshold interval are marked as underspray unit sets.

[0105] The nozzle control module is used to determine the set of nozzle units that corresponds to the set of overspray units, determine the nozzle unit to be adjusted based on the predicted value of the spray particle distribution of the nozzle units in the set of nozzle units, and adjust the arrangement parameters of the nozzle unit to be adjusted according to the set of underspray units.

[0106] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0107] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," 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. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0108] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for intelligently suppressing dust from building demolition, characterized in that, Includes the following steps: Obtain wind field data and spatial location information of structures in the building demolition area, and obtain the layout parameters of each nozzle unit in the spray system; Based on the spatial location information and the wind field data, a wind barrier interference area model is constructed within the target area; the spray range of each nozzle unit is calculated based on the arrangement parameters. The calculation process of the spray range includes: obtaining the nozzle position, spray angle and spray pressure of each nozzle unit; calculating the spray range based on the spray angle and spray pressure, and constructing a spray cone model in three-dimensional space with the nozzle position as the vertex and the spray angle and spray range as the spray parameters. The spray cone model is used to characterize the effective spray range of the nozzle unit. The spray range is spatially superimposed with the wind barrier interference area model, and the predicted value of the spray particle distribution generated by each nozzle unit in three-dimensional space is calculated. Based on the predicted value of the spray particle distribution, a spatial unit matrix of the working area is generated, and each spatial unit contains the number of spray particles per unit volume. The spatial unit matrix is ​​divided into unit cubic grids, with each spatial unit not overlapping and collectively covering the entire working area. The calculation of the predicted value of the spray particle distribution includes the following steps: within the spray cone model, the particle diffusion behavior is discretized based on the spray particle emission direction and spray parameters; the spray particle motion path is corrected by combining wind field data, and the landing points of the spray particles in the three-dimensional grid of the target area are aggregated and statistically analyzed; the aggregated statistical results are normalized to obtain the number of spray particles per unit volume space, which is used as the predicted value of the spray particle distribution. Traverse the spatial unit matrix and determine whether the number of spray particles in each spatial unit is within a preset threshold range. If so, the particle distribution is considered uniform; otherwise, the particle distribution is considered uneven. When the particle distribution is uniform, maintain the current spray state of all nozzle units; When the particle distribution is uneven, the spatial units with a spray particle count value higher than the upper limit of the threshold interval are marked as overspray units, and the spatial units with a spray particle count value lower than the lower limit of the threshold interval are marked as underspray units. The set of nozzle units that corresponds to the set of overspray units is determined. Based on the predicted spray particle distribution of the nozzle units in the set of nozzle units, the nozzle units to be adjusted are determined. The arrangement parameters of the nozzle units to be adjusted are adjusted according to the set of underspray units.

2. The intelligent dust suppression method for building demolition according to claim 1, characterized in that: The wind field data includes average wind speed data and wind direction data; the structures include high walls, tower cranes, or enclosures; the layout parameters include nozzle position, spray angle, and spray pressure.

3. The intelligent dust suppression method for building demolition according to claim 2, characterized in that: The process of constructing the wind barrier interference area model includes: Based on the wind direction data, determine the prevailing wind direction vector. Using the outline of the windward side of the structure as a reference, extend the projected shading area in the opposite direction of the prevailing wind direction vector. Expand the buffer zone outward from the projected shading area to form a wind barrier interference area. The projection occlusion area is generated by: discretizing the outer contour of the structure into several feature points, translating each feature point in the opposite direction of the prevailing wind direction vector, and connecting the translated feature points to form a closed polygonal region. The buffer zone width is calculated as follows: Buffer zone width = Average wind speed data * Preset structure type coefficient.

4. The intelligent dust suppression method for building demolition according to claim 3, characterized in that: The process of determining the nozzle unit to be adjusted includes: Based on the arrangement parameters of each nozzle unit and the predicted value of spray particle distribution, a spray particle contribution matrix M[i][j] in three-dimensional space is established; where i represents the nozzle unit number; j represents the spatial unit number; M[i][j] represents the particle concentration contribution value of nozzle unit i to spatial unit j; Mark the overspray unit set With underspray unit assembly Then, calculate the total contribution of each nozzle unit to the two sets mentioned above: Calculate the overall adjustment priority function for each nozzle unit: Wherein, α and β are set weighting coefficients used to balance the adjustment targets of overspray suppression and underspray compensation; Select nozzle units whose P[i] is greater than a set threshold from all nozzle units as a set of candidate nozzle units to be adjusted; The candidate nozzle units to be adjusted are further sorted from high to low according to P[i], and the top N nozzle units are selected as the nozzle units to be adjusted, where N is a positive integer.

5. The intelligent dust suppression method for building demolition according to claim 4, characterized in that: The calculation of the particle concentration contribution value includes the following steps: Based on the spray parameters and wind field data of the nozzle unit, the motion trajectory of the spray particles is simulated; The termination position of each particle in the target area is associated with a spatial unit, and the number of particles released by the nozzle unit to each spatial unit is counted. The contribution of the nozzle unit to the particle concentration of each spatial unit is calculated based on the number of particles and the volume of the spatial unit.

6. The intelligent dust suppression method for building demolition according to claim 5, characterized in that: The process of adjusting the arrangement parameters of the nozzle unit to be adjusted according to the set of under-sprayed units includes: determining the spatial centroid coordinates of the under-sprayed units; calculating the direction vector between the nozzle unit to be adjusted and the spatial centroid coordinates; adjusting the spray angle of the nozzle unit to be adjusted so that its spray direction is towards the spatial centroid; and when adjusting the spray angle is insufficient to cover the under-sprayed units, further adjusting the spray pressure or the spatial position of the nozzle.

7. An intelligent dust suppression system for building demolition, characterized in that: Using the intelligent dust suppression method for building demolition as described in any one of claims 1 to 6, comprising: The data acquisition module is used to acquire wind field data in the building demolition area, spatial location information of structures, and layout parameters of each nozzle unit in the spray system; The interference model construction module is used to construct a wind barrier interference area model within the target area based on the spatial location information and wind field data. The spray range calculation module is used to calculate the spray range of each nozzle unit based on the arrangement parameters of the nozzle unit. The particle distribution prediction module is used to spatially overlay the spray range with the wind barrier interference area model and calculate the predicted value of the spray particle distribution generated by each nozzle unit in three-dimensional space. The spatial cell matrix generation module is used to generate a spatial cell matrix of the work area based on the predicted spray particle distribution value. Each spatial cell in the spatial cell matrix contains the number of spray particles per unit volume. The particle distribution judgment module is used to traverse the spatial unit matrix and determine whether the number of spray particles in each spatial unit is within a preset threshold range. If so, it outputs a judgment result that the particle distribution is uniform; otherwise, it outputs a judgment result that the particle distribution is uneven. The spray state analysis module is used to maintain the current spray state of all nozzle units when the particle distribution is uniform; when the particle distribution is uneven, spatial units with a spray particle number value higher than the upper limit of the threshold interval are marked as overspray unit sets, and spatial units with a spray particle number value lower than the lower limit of the threshold interval are marked as underspray unit sets. The nozzle control module is used to determine the set of nozzle units that corresponds to the set of overspray units, determine the nozzle unit to be adjusted based on the predicted value of the spray particle distribution of the nozzle units in the set of nozzle units, and adjust the arrangement parameters of the nozzle unit to be adjusted according to the set of underspray units.

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