A method and system for detecting dust on mining area transport roads

Abstract

The application relates to the technical field of environmental monitoring, in particular to a mine area transportation road surface dust emission detection method and system. The method comprises the following steps: acquiring real-time point cloud data collected by a laser radar and vehicle information of a mine area transportation vehicle, analyzing the point cloud data, and determining dust accumulation area information; calling dust accumulation data after last cleaning; determining the current dust accumulation situation according to the dust accumulation data and the dust accumulation area information; predicting the dust emission amount according to the vehicle information and the dust accumulation situation; and determining whether to clean the road surface according to the dust emission amount.

Description

Technical Field

[0001] This application relates to the field of environmental monitoring technology, and in particular to a method and system for detecting dust on the road surface of a mining area transportation road. Background Technology

[0002] Currently, domestic and international research on dust monitoring on mining area transportation roads mainly focuses on monitoring particulate matter concentration and identifying dust sources. Methods for monitoring particulate matter concentration primarily include gravimetric analysis, light scattering, and light absorption methods. While these methods can reflect the degree of dust pollution to some extent, they cannot accurately determine the source of dust, thus hindering effective dust control at its source. Summary of the Invention

[0003] This application provides a method and system for detecting dust on the road surface of mining transportation roads to solve the above-mentioned problems.

[0004] In a first aspect, this application provides a method for detecting dust pollution on mining area transport roads. The method is applied to a dust detection device, which includes a server and a lidar system. The method is applied to the server and includes:

[0005] The system acquires real-time point cloud data collected by the lidar and vehicle information of the mining area transport vehicles, analyzes the point cloud data, and determines the dust accumulation area information.

[0006] Retrieve dust accumulation data after the last cleaning; determine the current dust accumulation status based on the dust accumulation data and the dust accumulation area information;

[0007] Based on the vehicle information and the dust accumulation, the amount of dust is predicted; and based on the amount of dust, it is determined whether to clean the road surface.

[0008] By acquiring real-time point cloud data collected by the lidar and vehicle information of mining transport vehicles, the system can monitor road dust in real time and accurately identify dust accumulation areas. This helps to understand the distribution and changes of dust in a timely manner, thereby enabling effective control measures to be taken. Retrieving dust accumulation data after the last cleaning and combining it with current dust accumulation area information can provide data-driven decision support for decision-makers. By analyzing this data, cleaning time and frequency can be scientifically planned, improving cleaning efficiency. Determining the current dust accumulation situation helps to take timely measures to reduce dust pollution to the surrounding environment of the mining area. By predicting dust volume, intervention can be carried out before dust is generated, thereby reducing pollutant emissions. Dust not only pollutes the environment but also reduces visibility, affecting road driving safety. By predicting dust volume and deciding whether to clean the road surface accordingly, the impact of dust on visibility can be effectively reduced, improving road driving safety. Based on the predicted dust volume, cleaning resources, such as cleaning vehicles and personnel, can be allocated in a targeted manner to avoid resource waste while ensuring effective cleaning work is carried out when necessary.

[0009] Optionally, analyzing the point cloud data to determine the dust accumulation area information includes:

[0010] Obtain transportation information from the mining area; analyze the transportation information from the mining area to determine the type of dust accumulation;

[0011] Acquire meteorological data within a first preset time period; analyze the meteorological data to determine the meteorological impact of different weather conditions within the preset time period on the dust accumulation type;

[0012] Based on the vehicle information, determine the type of transport vehicle;

[0013] The dust accumulation pattern is determined based on the type of transport vehicle.

[0014] Analyze the dust accumulation morphology to determine the data type mapped to the point cloud;

[0015] Based on the data type and the meteorological impact, the point cloud data is analyzed to determine the dust accumulation area information.

[0016] By analyzing point cloud data, the dust accumulation areas on roads can be more accurately identified and located, thereby improving the accuracy and reliability of dust detection. Identifying dust accumulation areas helps optimize the allocation of cleaning resources, concentrating cleaning efforts on the areas with the most severe dust accumulation, improving cleaning efficiency, and reducing resource waste. Timely cleaning of dust accumulation areas can effectively reduce dust generation and emissions, improve the environmental quality around the mining area, and reduce air pollution.

[0017] Optionally, determining the current dust accumulation status based on the dust accumulation data and the dust accumulation area information includes:

[0018] Based on the dust accumulation pattern, determine the dust density at the current moment;

[0019] Based on the dust accumulation area information, determine the dust accumulation range;

[0020] Based on the dust accumulation data, determine the current change in dust accumulation;

[0021] The dust accumulation situation is determined based on the dust density, the dust range, and the current dust change.

[0022] The method provided in this embodiment, which analyzes dust morphology to determine dust density, allows for more precise monitoring of road dust conditions, providing a basis for developing targeted cleaning and maintenance plans. Determining the extent of dust accumulation helps managers understand its distribution, enabling them to allocate cleaning resources more effectively and improve cleaning efficiency. Monitoring current dust accumulation changes allows for timely detection of increasing or decreasing trends, facilitating prompt responses and preventing the worsening of dust pollution. Accurate analysis of dust accumulation helps reduce dust emissions, improve the environmental quality around mining areas, and protect public health. Monitoring and timely cleanup of dust accumulation can reduce road dust, improve visibility, and enhance driving safety.

[0023] Optionally, determining the dust accumulation status based on the dust density, the dust range, and the current dust change includes:

[0024] Obtain road traffic data for the second preset time period;

[0025] Analyze the road traffic data to determine the types of vehicles passing through;

[0026] Determine the total load capacity of the vehicles based on the types of vehicles passing through;

[0027] Based on the total weight of the vehicle, predict the change in dust density.

[0028] The dust accumulation situation is determined based on the dust density, the dust range, the current dust change, and the dust density change.

[0029] By analyzing the types and total weight of passing vehicles, the method provided in this embodiment can more accurately predict changes in dust density caused by vehicle traffic, thereby improving the accuracy of dust accumulation prediction. Understanding road traffic conditions and dust changes at different times helps optimize the allocation of cleaning resources, ensuring that more cleaning efforts are deployed in areas and periods with higher dust levels. Predicting changes in dust density can help relevant departments take preventative measures in advance, such as increasing sweeping frequency and using water trucks to reduce dust, thereby minimizing the impact of dust on the environment and road safety. Combining road traffic data with the cleaning and maintenance of mining area transport roads can be managed more effectively, improving management efficiency.

[0030] Optionally, the step of analyzing the point cloud data based on the data type and according to the meteorological impact to determine the dust accumulation area information includes:

[0031] Analyze the meteorological impacts to determine the first point cloud morphology after the initial meteorological change;

[0032] Analyze the impact of other different weather conditions on the first point cloud morphology to determine the final point cloud morphology;

[0033] Based on the final point cloud morphology, the point cloud data is analyzed to determine the characteristics of the dust accumulation point cloud;

[0034] Based on the characteristics of the dust point cloud, the dust accumulation area information is determined;

[0035] Determining the dust density at the current moment based on the dust accumulation morphology includes:

[0036] Based on the final point cloud morphology, the dust density at the current moment is determined.

[0037] The method provided in this embodiment allows for the generation of detailed dust distribution maps through point cloud data analysis, showcasing the thickness and density of dust in different areas. Combined with meteorological data, changes in dust accumulation areas can be dynamically monitored, such as dust accumulation or removal over time. By identifying dust sources, targeted measures can be taken to control dust generation at its source. Analyzing point cloud data can identify long-term dust trends, providing a basis for long-term planning. Analyzing point cloud data allows for more precise identification and location of dust accumulation areas, thereby improving the accuracy and efficiency of dust monitoring. Timely identification of dust accumulation areas helps in rapid response to dust problems, reducing the impact on the environment and road safety. Precise dust accumulation area information can guide cleaning and maintenance work, optimize resource allocation, and avoid resource waste. Determining dust accumulation area information helps assess the environmental impact of dust, including air quality and ecosystem health. Understanding dust accumulation areas helps reduce road driving safety risks, such as reduced visibility and vehicle skidding.

[0038] Optionally, predicting the dust volume based on the vehicle information and the dust accumulation includes:

[0039] Based on the vehicle information, determine the vehicle type and vehicle load capacity;

[0040] Obtain historical passage records, and determine the average speed of each type of vehicle passing through the mining area transportation road based on the historical passage records;

[0041] Analyze the final point cloud morphology to determine the amount of particulate dust;

[0042] Obtain basic information about the transportation roads in the mining area and determine the road width;

[0043] The dust-generating range is determined based on the road width and the dust accumulation area information;

[0044] The amount of dust is predicted based on the amount of particulate matter, the dust emission range, the average speed, and the vehicle load.

[0045] The method provided in this embodiment allows for the implementation of effective dust control measures in advance based on predicted dust levels. Targeted measures can be implemented in specific areas or time periods, significantly reducing particulate matter concentrations in the air surrounding mining areas. This helps environmental managers better understand the spatiotemporal distribution of dust, thereby optimizing environmental management strategies. It also reduces dust damage to roads, extends road lifespan, and lowers road maintenance costs. Furthermore, it reduces the impact of dust on visibility and road friction coefficients, improving driver visibility and vehicle braking performance.

[0046] Optionally, determining whether to clean the road surface based on the amount of dust includes:

[0047] The dust height and dust volume are determined based on the dust volume and the vehicle load.

[0048] The degree of visual interference is determined based on the dust volume and the dust height.

[0049] Based on the degree of visual obstruction, determine whether to clear the road surface.

[0050] The method provided in this embodiment allows for real-time monitoring of dust levels, enabling timely detection of dust problems and issuing warnings when dust levels reach a certain threshold, allowing for proactive cleanup measures. Based on dust volume data, the timing and scope of road cleaning can be precisely controlled, avoiding over-cleaning and unnecessary resource waste. Timely dust removal reduces the risks of slippery roads and decreased visibility, thereby improving driver safety and reducing traffic accidents. Determining the cleaning frequency based on dust volume optimizes the allocation of human and equipment resources, improving work efficiency. When visibility interference is low, frequent cleaning may not be necessary, saving costs. When visibility interference is high, timely cleaning can quickly restore road visibility and reduce safety hazards.

[0051] Optionally, determining whether to clean the road surface based on the amount of dust includes:

[0052] The dust height and dust volume are determined based on the dust volume and the vehicle load.

[0053] The severity of the dust pollution is determined based on the dust volume and the dust height.

[0054] Based on the severity of the dust pollution, determine whether road cleaning is necessary.

[0055] The method provided in this embodiment helps to understand the actual distribution of dust, providing data support for subsequent cleanup work. By calculating the severity of dust pollution, the impact of dust on the environment and traffic can be understood more intuitively. Timely cleanup when dust reaches a certain level can prevent its spread and reduce pollution. When dust levels are below a certain standard, unnecessary cleanup can be avoided, saving costs.

[0056] Optionally, the dust accumulation situation is determined based on the dust density, the dust accumulation range, the current dust accumulation change, and the dust density change, calculated according to the following formula:

[0057] ;

[0058] in, Indicator of stacking conditions; This indicates the weight of the dust accumulation density on the accumulation index; This indicates the dust density; This indicates the weight of the dust accumulation range on the accumulation index; Indicates the range of dust accumulation; This indicates the weight of the influence of the change in dust density on the accumulation index; This represents the change in dust density over a relatively long period of time. This indicates the length of the first preset time period.

[0059] The formula provided in this embodiment offers a comprehensive indicator for assessing dust accumulation on mining area transport roads, including the total amount of dust, its distribution range, and its changing trend. By adjusting coefficients, the influence of dust density, dust accumulation range, and changes in dust density can be weighted according to actual conditions and needs. The introduction of the time factor allows the formula to reflect changes in dust accumulation over time, which is helpful for the dynamic monitoring and management of dust pollution.

[0060] Secondly, this application provides a dust monitoring system for mining area transportation roads, the system comprising:

[0061] The information acquisition module is used to acquire real-time point cloud data collected by lidar and vehicle information of mining transport vehicles, analyze the point cloud data, and determine the dust accumulation area information.

[0062] The dust accumulation analysis module is used to retrieve dust accumulation data after the last cleaning; based on the dust accumulation data and the dust accumulation area information, the current dust accumulation status is determined.

[0063] The prediction module is used to predict the amount of dust based on the vehicle information and the dust accumulation situation; and to determine whether to clean the road surface based on the amount of dust.

[0064] Optionally, when the information acquisition module analyzes the point cloud data to determine the dust accumulation area information, it is used for:

[0065] Obtain mining area transportation information; analyze the mining area transportation information to determine the type of dust accumulation;

[0066] Acquire meteorological data within a first preset time period; analyze the meteorological data to determine the meteorological impact of different weather conditions within the preset time period on the dust accumulation type;

[0067] Based on the vehicle information, determine the type of transport vehicle;

[0068] The dust accumulation pattern is determined based on the type of transport vehicle.

[0069] Analyze the dust accumulation morphology to determine the data type mapped to the point cloud;

[0070] Based on the data type and the meteorological impact, the point cloud data is analyzed to determine the dust accumulation area information.

[0071] Optionally, when the accumulation analysis module determines the current dust accumulation status based on the dust accumulation data and the dust accumulation area information, it is used to:

[0072] Based on the dust accumulation pattern, determine the dust density at the current moment;

[0073] Based on the dust accumulation area information, determine the dust accumulation range;

[0074] Based on the dust accumulation data, determine the current change in dust accumulation;

[0075] The dust accumulation situation is determined based on the dust density, the dust range, and the current dust change.

[0076] Optionally, when the accumulation analysis module determines the dust accumulation situation based on the dust density, the dust accumulation range, and the current dust accumulation change, it is used for:

[0077] Obtain road traffic data for the second preset time period;

[0078] Analyze the road traffic data to determine the types of vehicles passing through;

[0079] Determine the total load capacity of the vehicles based on the types of vehicles passing through;

[0080] Based on the total weight of the vehicle, predict the change in dust density.

[0081] The dust accumulation situation is determined based on the dust density, the dust range, the current dust change, and the dust density change.

[0082] Optionally, when the information acquisition module analyzes the point cloud data based on the data type and the meteorological impact to determine the dust accumulation area information, it is used for:

[0083] Analyze the meteorological impacts to determine the first point cloud morphology after the initial meteorological change;

[0084] Analyze the impact of other different weather conditions on the first point cloud morphology to determine the final point cloud morphology;

[0085] Based on the final point cloud morphology, the point cloud data is analyzed to determine the characteristics of the dust accumulation point cloud;

[0086] Based on the characteristics of the dust point cloud, the dust accumulation area information is determined;

[0087] When determining the dust density at the current moment based on the dust accumulation morphology, it is used for:

[0088] Based on the final point cloud morphology, the dust density at the current moment is determined.

[0089] Optionally, when the prediction module predicts the amount of dust based on the vehicle information and the dust accumulation, it is used for:

[0090] Based on the vehicle information, determine the vehicle type and vehicle load capacity;

[0091] Obtain historical passage records, and determine the average speed of each type of vehicle passing through the mining area transportation road based on the historical passage records;

[0092] Analyze the final point cloud morphology to determine the amount of particulate dust;

[0093] Obtain basic information about the transportation roads in the mining area and determine the road width;

[0094] The dust range is determined based on the road width and the dust accumulation area information;

[0095] The amount of dust is predicted based on the amount of particulate matter, the dust emission range, the average speed, and the vehicle load.

[0096] Optionally, when the prediction module determines whether to clean the road surface based on the dust volume, it is used to:

[0097] The dust height and dust volume are determined based on the dust volume and the vehicle load.

[0098] The degree of visual interference is determined based on the dust volume and the dust height.

[0099] Based on the degree of visual obstruction, determine whether to clear the road surface.

[0100] Optionally, when the prediction module determines whether to clean the road surface based on the dust volume, it is used to:

[0101] The dust height and dust volume are determined based on the dust volume and the vehicle load.

[0102] The severity of the dust pollution is determined based on the dust volume and the dust height.

[0103] Based on the severity of the dust pollution, determine whether road cleaning is necessary.

[0104] Optionally, the accumulation analysis module determines the dust accumulation situation based on the dust density, the dust accumulation range, the current dust accumulation change, and the dust density change, and calculates it according to the following formula:

[0105] ;

[0106] in, Indicator of stacking conditions; This indicates the weight of the dust accumulation density on the accumulation index; This indicates the dust density; This indicates the weight of the dust accumulation range on the accumulation index; Indicates the range of dust accumulation; This indicates the weight of the influence of the change in dust density on the accumulation index; This represents the change in dust density over a relatively long period of time. This indicates the length of the first preset time period. Attached Figure Description

[0107] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0108] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application;

[0109] Figure 2 A flowchart illustrating a method for detecting road surface dust in a mining area, provided as an embodiment of this application;

[0110] Figure 3 This is a schematic diagram of a dust detection system for a mining area transportation road, provided as an embodiment of this application. Detailed Implementation

[0111] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0112] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0113] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0114] Currently, domestic and international research on dust monitoring on mining area transportation roads mainly focuses on monitoring particulate matter concentration and identifying dust sources. Methods for monitoring particulate matter concentration primarily include gravimetric analysis, light scattering, and light absorption methods. While these methods can reflect the degree of dust pollution to some extent, they cannot accurately determine the source of dust, thus hindering effective dust control at its source.

[0115] Based on this, this application provides a method and system for detecting road dust on mining transportation roads. The system acquires real-time point cloud data collected by lidar and vehicle information of mining transport vehicles; analyzes the point cloud data to determine dust accumulation areas; retrieves dust accumulation data after the last cleaning; determines the current dust accumulation status based on the dust accumulation data and dust accumulation area information; predicts the dust volume based on vehicle information and dust accumulation status; and determines whether road cleaning is necessary based on the dust volume. By acquiring real-time point cloud data collected by lidar and vehicle information of mining transport vehicles, the system can monitor road dust in real time and accurately identify dust accumulation areas. This helps to understand the distribution and changes of dust in a timely manner, thereby enabling the implementation of effective control measures. Retrieving dust accumulation data after the last cleaning and combining it with current dust accumulation area information can provide data-driven decision support for decision-makers. By analyzing this data, cleaning time and frequency can be scientifically planned, improving cleaning efficiency. Determining the current dust accumulation status helps to take timely measures to reduce dust pollution to the surrounding environment of the mining area. By predicting dust levels, intervention can be carried out before dust is generated, thereby reducing pollutant emissions.

[0116] Figure 1 This application provides a schematic diagram of an application scenario. In dust detection on mining transport roads, the method provided in this application is used. Specifically, the method is applied to any server, where the server interacts with a lidar system. By acquiring real-time point cloud data collected by the lidar and vehicle information of mining transport vehicles, the system can monitor road dust in real time and accurately identify dust accumulation areas. This helps to understand the distribution and changes of dust in a timely manner, thereby enabling effective control measures. Retrieving dust accumulation data after the last cleaning and combining it with current dust accumulation area information can provide data-driven decision support for decision-makers. By analyzing this data, cleaning time and frequency can be scientifically planned, improving cleaning efficiency. Determining the current dust accumulation situation helps to take timely measures to reduce dust pollution to the surrounding environment of the mining area. By predicting dust volume, intervention can be carried out before dust occurs, thereby reducing pollutant emissions.

[0117] For specific implementation details, please refer to the following examples.

[0118] Figure 2 This is a flowchart illustrating a method for detecting road dust on a mining area transport road, provided as an embodiment of this application. The method of this embodiment can be applied to servers in the above-described scenario. Figure 2 As shown, the method includes:

[0119] S201. Acquire real-time point cloud data collected by lidar and vehicle information of mining transport vehicles, analyze the point cloud data, and determine the dust accumulation area information.

[0120] Point cloud data can be a set of three-dimensional coordinate points on the surface of a mining area transportation road captured by LiDAR at a specific moment. It can reflect the shape and characteristics of the road surface, including dust accumulation.

[0121] Vehicle information can include various attributes of mining transport vehicles, such as vehicle type (e.g., truck, bus), license plate number, vehicle size, load capacity, and speed.

[0122] Dust accumulation area information can be obtained by analyzing point cloud data to show the distribution of dust on the road, including the location, range, and shape of the dust.

[0123] Specifically, to monitor dust levels on mining area transport roads in real time, it is necessary to install sensor equipment such as lidar at monitoring stations along the roads to capture point cloud data of the transport roads in real time. Point cloud data provides three-dimensional information about the road surface, providing the foundation for subsequent dust accumulation area identification. Vehicle information, including vehicle type, license plate number, and load, is collected from monitoring stations or transport vehicles using cameras, RFID, and license plate recognition devices. Image processing and machine learning algorithms are used to process the point cloud data to identify dust accumulation areas. The algorithms are trained to distinguish between dust-accumulated and non-dust-accumulated areas. Combining vehicle trajectories with mining area transport information, the specific location and extent of dust accumulation areas are further determined.

[0124] S202. Retrieve dust accumulation data after the last cleaning; determine the current dust accumulation status based on the dust accumulation data and dust accumulation area information.

[0125] Dust accumulation data can be historical data that records the physical characteristics (such as thickness, density, particle size, etc.) and distribution of dust accumulation.

[0126] The final cleaning can be the cleaning work carried out on the mining area's transport roads last time, including sweeping, rinsing and other operations to remove accumulated dust from the road surface.

[0127] Dust accumulation can refer to the current state of dust accumulation on the road surface, including the thickness, density, and distribution range of the dust.

[0128] Specifically, dust accumulation data from the last cleaning is extracted from the database, including dust thickness and distribution area. This data is analyzed to compare with the current dust accumulation situation. By comparing the current point cloud data with the data after the last cleaning, the increase in dust accumulation is calculated. The density, range, and change of dust accumulation are analyzed to determine the current dust accumulation status.

[0129] S203. Based on vehicle information and dust accumulation, predict the amount of dust; and based on the amount of dust, determine whether to clean the road surface.

[0130] Dust volume can be the total amount of dust particles suspended from the road surface into the air due to vehicle movement or other factors (such as wind).

[0131] Road cleaning can be the work of cleaning the surface of transportation roads in mining areas to reduce the generation and emission of dust, including sweeping and washing with equipment such as sweepers and water trucks.

[0132] Specifically, vehicle information, including vehicle type and load, is analyzed, as this information is directly related to dust levels. Combined with dust accumulation, predictive models are used to estimate dust levels. Threshold standards for dust levels are established; when the predicted dust level exceeds the threshold, a cleanup order is issued. For example, the impact of dust on the surrounding environment, such as visibility obstruction and air quality, is considered to determine the necessity and urgency of cleanup.

[0133] By acquiring real-time point cloud data collected by the lidar and vehicle information of mining transport vehicles, the system can monitor road dust in real time and accurately identify dust accumulation areas. This helps to understand the distribution and changes of dust in a timely manner, thereby enabling effective control measures to be taken. Retrieving dust accumulation data after the last cleaning and combining it with current dust accumulation area information can provide data-driven decision support for decision-makers. By analyzing this data, cleaning time and frequency can be scientifically planned, improving cleaning efficiency. Determining the current dust accumulation situation helps to take timely measures to reduce dust pollution to the surrounding environment of the mining area. By predicting dust volume, intervention can be carried out before dust is generated, thereby reducing pollutant emissions. Dust not only pollutes the environment but also reduces visibility, affecting road driving safety. By predicting dust volume and deciding whether to clean the road surface accordingly, the impact of dust on visibility can be effectively reduced, improving road driving safety. Based on the predicted dust volume, cleaning resources, such as cleaning vehicles and personnel, can be allocated in a targeted manner to avoid resource waste while ensuring effective cleaning work is carried out when necessary.

[0134] In some embodiments, the following steps are taken: acquiring mining area transportation information; analyzing the mining area transportation information to determine the dust accumulation type; acquiring meteorological data within a first preset time period; analyzing the meteorological data to determine the meteorological impact of different weather conditions on the dust accumulation type within the first preset time period; determining the transport vehicle type based on vehicle information; determining the dust accumulation morphology based on the transport vehicle type; analyzing the dust accumulation morphology to determine the data type mapped to the point cloud; and analyzing the point cloud data based on the data type and meteorological impact to determine the dust accumulation area information.

[0135] Mining area transportation information can be any information related to mining area transportation activities, including but not limited to the type of transport vehicle, license plate number, load, speed, and route.

[0136] Dust accumulation can be categorized into different types of road surface deposits, depending on the type of cargo transported in the mining area and the characteristics of vehicle travel. For example, coal transport may produce black coal dust, while ore transport may produce ore dust of different colors and particle sizes.

[0137] The first preset time period can be the time period from the last cleaning to the present, which is preset for the purpose of analyzing dust conditions and stored in a preset database.

[0138] Meteorological data can be measurements of meteorological elements that affect the generation and spread of dust, including wind speed, wind direction, humidity, temperature, precipitation, etc.

[0139] Meteorological influences can refer to the effects of meteorological conditions on the generation and spread of dust. For example, wind speed and direction can determine the direction and distance of dust propagation, humidity can affect the settling speed of dust, and temperature can affect the volatility and stability of dust.

[0140] The type of transport vehicle can be any category of vehicle used for transportation in the mining area, such as heavy-duty trucks, light-duty trucks, dump trucks, trailers, etc. Different types of vehicles have different impacts on dust generation due to their size, load capacity, and driving characteristics.

[0141] Dust morphology refers to the physical state and structure of road surface deposits, including particle size, shape, density, and distribution. Dust morphology affects the generation and dispersion characteristics of dust.

[0142] Specifically, sensors and GPS positioning systems installed on mining transport vehicles collect transportation information such as vehicle trajectories, speeds, and loads. License plate recognition systems or RFID technology are used to automatically identify and record vehicle information passing through mining transport roads. Based on vehicle load, type, and frequency of travel, the types and characteristics of dust accumulation on road surfaces from different vehicles are analyzed. The size, shape, and density of potential dust particles are determined by considering the type of goods transported (e.g., coal, ore). Meteorological data such as wind speed, wind direction, humidity, and temperature are collected through weather stations or mobile meteorological monitoring equipment. The collection period for meteorological data is ensured to match the peak periods of mining transport activities. The impact of wind speed and direction on dust dispersion is analyzed to determine which meteorological conditions may lead to increased dust levels, such as the effect of humidity on dust particle adsorption and settling, and the effect of temperature on dust particle volatility. The collected vehicle information is categorized to distinguish different types of transport vehicles, and the morphology of dust accumulation on road surfaces is inferred by combining vehicle type and load information. This study analyzes potential dust patterns generated during vehicle operation, such as dust emitted from vehicle exhaust and under tires. Based on the characteristics of dust accumulation morphology, appropriate point cloud data processing methods are selected, including filtering, segmentation, and feature extraction. The data types used to identify dust accumulation areas are determined, such as height, intensity, and echo count in the point cloud. The selected point cloud data processing methods are then used to process real-time point cloud data and extract information about dust accumulation areas. Combined with meteorological data, the distribution and changes of dust accumulation areas under different meteorological conditions are analyzed to determine the specific location and extent of these areas.

[0143] By analyzing point cloud data, the dust accumulation areas on roads can be more accurately identified and located, thereby improving the accuracy and reliability of dust detection. Identifying dust accumulation areas helps optimize the allocation of cleaning resources, concentrating cleaning efforts on the areas with the most severe dust accumulation, improving cleaning efficiency, and reducing resource waste. Timely cleaning of dust accumulation areas can effectively reduce dust generation and emissions, improve the environmental quality around the mining area, and reduce air pollution.

[0144] In some embodiments, the dust density at the current moment is determined based on the dust accumulation morphology; the dust accumulation range is determined based on dust accumulation area information; the current dust accumulation change is determined based on dust accumulation data; and the dust accumulation situation is determined based on dust accumulation density, dust accumulation range, and current dust accumulation change.

[0145] Dust density can be the mass of dust per unit area or volume. It is an indicator that measures the thickness and concentration of dust and reflects the degree of dust accumulation.

[0146] Dust accumulation range can be the area on the road covered by dust, which can be continuous or scattered.

[0147] The current change in dust accumulation can be the change in dust accumulation over a certain period of time, which can be calculated by comparing the current dust density with the previous dust density.

[0148] Specifically, using real-time point cloud data, the point cloud density distribution within the dust accumulation area is analyzed. The current dust density is determined by calculating the number of points per unit area or using specialized algorithms to estimate the volume or mass density of the dust. Based on the dust accumulation area information, Geographic Information System (GIS) or computer vision algorithms are used to determine the specific extent of the dust accumulation, including identifying the boundaries of the dust accumulation area and calculating its area. By comparing the current dust accumulation data with the data after the last cleaning, the change in dust accumulation is determined by calculating the difference in dust density or the change in the area of ​​the dust accumulation area. The dust density, dust accumulation range, and dust accumulation change data obtained from the above steps are integrated to provide comprehensive information for analyzing dust accumulation. Based on the dust density and dust accumulation range, the total volume or mass of the dust is calculated. The trend of dust accumulation change over time is analyzed to determine whether the dust accumulation is continuously increasing, decreasing, or remaining stable. Based on the calculated dust volume or mass and the dust accumulation change trend, the current dust accumulation situation is determined. This may include the severity of the accumulation, the accumulation rate, and the potential risk of dust emission.

[0149] The method provided in this embodiment, which analyzes dust morphology to determine dust density, allows for more precise monitoring of road dust conditions, providing a basis for developing targeted cleaning and maintenance plans. Determining the extent of dust accumulation helps managers understand its distribution, enabling them to allocate cleaning resources more effectively and improve cleaning efficiency. Monitoring current dust accumulation changes allows for timely detection of increasing or decreasing trends, facilitating prompt responses and preventing the worsening of dust pollution. Accurate analysis of dust accumulation helps reduce dust emissions, improve the environmental quality around mining areas, and protect public health. Monitoring and timely cleanup of dust accumulation can reduce road dust, improve visibility, and enhance driving safety.

[0150] In some embodiments, road traffic data for a second preset time period is obtained; the road traffic data is analyzed to determine the types of passing vehicles; the total load weight of the vehicles is determined based on the types of passing vehicles; the change in dust density is predicted based on the total load weight of the vehicles; and the dust accumulation situation is determined based on the dust density, dust accumulation range, current change in dust accumulation, and change in dust density.

[0151] The second preset time period can be the duration from the last weather change to the present, such as from the last precipitation to the present.

[0152] Road traffic data can be various forms of same-direction data of vehicles passing through the road, including vehicle count, vehicle speed, vehicle type, and travel time.

[0153] The types of vehicles allowed to pass can be different kinds of vehicles, classified according to their structure, purpose or load capacity, such as trucks, buses, motorcycles, etc.

[0154] The gross vehicle weight can be the total weight of a vehicle when it is fully loaded, including the weight of the vehicle itself and the weight of the goods or passengers it carries.

[0155] The change in dust density can be the increase or decrease in dust density over a certain time interval.

[0156] Specifically, traffic data is collected on roads during a second preset time period using sensors, cameras, and other equipment installed on the roads. The collected traffic data is processed, and image recognition or sensor data analysis techniques are used to identify and classify the types of passing vehicles. Based on the identified vehicle types, and referring to vehicle load standards or actual measurement data, the total load of each vehicle is estimated. For different types of vehicles, preset average load values ​​can be used, or calculations can be made based on actual data. Combining the total vehicle load and the dust generation patterns of mining area transportation roads, statistical models or machine learning algorithms are used to analyze the relationship between load and dust accumulation, predicting the change in dust density caused by vehicle traffic. LiDAR technology is used to determine the specific area covered by dust accumulation. The dust density, dust accumulation range, current dust accumulation change, and predicted dust density change data are integrated. Based on the dust density and dust accumulation range, the total volume or mass of dust is calculated. Taking into account both the current dust accumulation change and the predicted dust density change, the trend of dust accumulation increase or decrease is analyzed. Based on the above data and analysis results, the current dust accumulation situation is determined, including the dust accumulation rate, accumulation density, and possible environmental impact.

[0157] By analyzing the types and total weight of passing vehicles, the method provided in this embodiment can more accurately predict changes in dust density caused by vehicle traffic, thereby improving the accuracy of dust accumulation prediction. Understanding road traffic conditions and dust changes at different times helps optimize the allocation of cleaning resources, ensuring that more cleaning efforts are deployed in areas and periods with higher dust levels. Predicting changes in dust density can help relevant departments take preventative measures in advance, such as increasing sweeping frequency and using water trucks to reduce dust, thereby minimizing the impact of dust on the environment and road safety. Combining road traffic data with the cleaning and maintenance of mining area transport roads can be managed more effectively, improving management efficiency.

[0158] In some embodiments, meteorological influences are analyzed to determine the first point cloud morphology after the initial meteorological change; the influence of other different meteorological conditions on the first point cloud morphology is analyzed to determine the final point cloud morphology; based on the final point cloud morphology, point cloud data is analyzed to determine dust accumulation point cloud characteristics; based on the dust accumulation point cloud characteristics, dust accumulation area information is determined; and based on the final point cloud morphology, the dust accumulation density at the current moment is determined.

[0159] The first meteorological change can be the first significant change in meteorological conditions within a certain period of time. For example, the first meteorological change can be a sudden strong wind or a heavy rain.

[0160] The first point cloud morphology can be the shape of the mining area's transportation roads as shown by point cloud data after the first meteorological change. For example, the first point cloud morphology can show that dust accumulated due to strong winds is blown away, forming an uneven distribution pattern.

[0161] The final point cloud morphology can be the road morphology under stable conditions reflected by point cloud data after comprehensively considering the influence of all meteorological factors. For example, the final point cloud morphology may show that dust accumulates in low-lying areas of the road, forming a thick dust layer.

[0162] Point cloud data can be a collection of a large number of spatial points used to represent the three-dimensional shape of an object. In dust monitoring, point cloud data can be detailed three-dimensional coordinates of the road surface and its surrounding environment collected using lidar or other scanning equipment.

[0163] Dust point cloud features can be dust-related features extracted from point cloud data, including dust density (number of points / unit area), height variation of dust layer, and dust distribution pattern.

[0164] Information on dust accumulation areas can be specific information about the distribution of dust accumulation, including the location, extent, thickness, and amount of dust accumulation. For example, in a 100-square-meter area at the entrance of a mine, the average thickness of the dust accumulation is 2 centimeters.

[0165] The morphology of dust accumulation can refer to the distribution and appearance of dust in space. For example, dust accumulation can be a uniformly distributed thin layer, or it can be a non-uniform blocky or striped distribution.

[0166] Dust density can be the mass of dust per unit area or volume. For example, in a specific area, there are 30 grams of dust per square meter.

[0167] Specifically, meteorological data related to dust, including wind speed, wind direction, temperature, humidity, and rainfall, is typically collected from weather stations or automatic weather observation equipment. This data is analyzed to determine the impact of meteorological factors on dust propagation and deposition. For example, wind speed and direction affect the direction and speed of dust dispersion. Point cloud data of mining area transport roads is collected; this data can be obtained through laser scanning, UAV aerial photography, or other 3D scanning techniques. Point cloud data following the first meteorological change (such as a strong wind event) is analyzed to determine changes in point cloud morphology, which may include the thickness and distribution of the dust layer. Point cloud data under different meteorological conditions are compared to analyze the impact of meteorological factors on point cloud morphology, such as the effects of wind speed, wind direction, and precipitation on dust deposition. Finally, by combining all meteorological conditions and point cloud data, the final point cloud morphology is determined, reflecting the long-term deposition pattern of dust under normal meteorological conditions. Features of dust accumulation points, such as point cloud density, distribution, and color, are extracted from point cloud data. Based on these features, combined with meteorological data and point cloud morphology, specific information about the dust accumulation area is determined. Point cloud analysis software is used to calculate the average thickness of the dust layer based on the final point cloud morphology, thereby estimating the dust accumulation density. If available, the dust accumulation density can be calculated directly from the point cloud data at the current moment. The analysis results are then combined with other data, such as particulate matter concentration monitoring data, to verify the accuracy of the dust accumulation area information.

[0168] The method provided in this embodiment allows for the generation of detailed dust distribution maps through point cloud data analysis, showcasing the thickness and density of dust in different areas. Combined with meteorological data, changes in dust accumulation areas can be dynamically monitored, such as dust accumulation or removal over time. By identifying dust sources, targeted measures can be taken to control dust generation at its source. Analyzing point cloud data can identify long-term dust trends, providing a basis for long-term planning. Analyzing point cloud data allows for more precise identification and location of dust accumulation areas, thereby improving the accuracy and efficiency of dust monitoring. Timely identification of dust accumulation areas helps in rapid response to dust problems, reducing the impact on the environment and road safety. Precise dust accumulation area information can guide cleaning and maintenance work, optimize resource allocation, and avoid resource waste. Determining dust accumulation area information helps assess the environmental impact of dust, including air quality and ecosystem health. Understanding dust accumulation areas helps reduce road driving safety risks, such as reduced visibility and vehicle skidding.

[0169] In some embodiments, the vehicle type and vehicle load are determined based on vehicle information; historical traffic records are obtained, and the average speed of each type of vehicle passing through the mining area transportation road is determined based on the historical traffic records; the final point cloud morphology is analyzed to determine the amount of particulate dust; basic information of the mining area transportation road is obtained to determine the road width; the dust emission range is determined based on the road width and dust accumulation area information; and the amount of dust emission is predicted based on the amount of particulate dust, the dust emission range, the average speed, and the vehicle load.

[0170] Historical traffic records can be data that documents the history of a vehicle's travel on a specific road.

[0171] Particulate matter quantity can be the number or mass of suspended particulate matter in a unit volume or area of ​​air, derived from point cloud data analysis, such as the total amount of particulate matter in dust caused by vehicle movement or wind.

[0172] Basic information about transportation roads in mining areas can be data about the basic attributes and characteristics of the roads.

[0173] Dust range can be the area affected by dust caused by vehicle movement or other factors.

[0174] Specifically, based on vehicle information such as license plate number and vehicle registration information, vehicle types are identified, such as trucks, buses, and dump trucks. The rated and actual load capacity of each vehicle are determined using the vehicle type and registration information. Historical traffic records of vehicles on mining area transport roads are obtained from the traffic monitoring system, including travel time and frequency. These historical records are analyzed to calculate the average speed of each type of vehicle on the mining area transport roads. Point cloud data analysis technology is used to analyze the final point cloud morphology, determine the amount of particulate dust, and obtain basic information about the mining area transport roads, including road width and road material. Based on the road width and dust accumulation area information, GIS technology is used to create a dust distribution map to determine the dust range. Based on the amount of particulate dust, dust range, average speed, and vehicle load, mathematical models or statistical methods are used to predict the amount of dust. Alternatively, the influence of meteorological factors such as wind speed, wind direction, and humidity can be considered in addition to the above factors when using mathematical models or statistical methods to predict the amount of dust.

[0175] The method provided in this embodiment allows for the implementation of effective dust control measures in advance based on predicted dust levels. Targeted measures can be implemented in specific areas or time periods, significantly reducing particulate matter concentrations in the air surrounding mining areas. This helps environmental managers better understand the spatiotemporal distribution of dust, thereby optimizing environmental management strategies. It also reduces dust damage to roads, extends road lifespan, and lowers road maintenance costs. Furthermore, it reduces the impact of dust on visibility and road friction coefficients, improving driver visibility and vehicle braking performance.

[0176] In some embodiments, the dust height and dust volume are determined based on the amount of dust and the vehicle load; the degree of visual interference is determined based on the dust volume and dust height; and whether to clean the road surface is determined based on the degree of visual interference.

[0177] Dust height can be the height at which dust particles are suspended in the air, starting from the ground and extending to the height at which dust particles begin to settle due to gravity, air resistance, and meteorological conditions.

[0178] Dust volume can be defined as the size of the three-dimensional space occupied by dust particles.

[0179] The degree of visual interference can be defined as the extent to which dust affects visual clarity. Dust particles suspended in the air can reduce the transmission of light, thereby reducing visibility and causing blurred vision.

[0180] Specifically, environmental monitoring equipment (such as particulate matter monitors) or point cloud data analysis are used to collect data on the changes in dust volume over different time periods. Since the dust height and volume vary among different types of vehicles, statistical analysis or mathematical models are used to analyze the relationship between dust volume and vehicle load, determining the degree of influence of vehicle load on dust volume. Based on the dust volume data, combined with meteorological conditions and vehicle driving conditions, fluid dynamics models or empirical formulas are used to estimate the height and volume of dust. The dust height can be calculated using the following formula (1);

[0181] (1)

[0182] in, It is the height of the dust cloud; It refers to the amount of dust; It is air density; It refers to the vehicle's load capacity; It is a reference load (usually taken as a standard value); , , It is a weighting coefficient constant.

[0183] The volume of dust can be calculated using the following formula (2);

[0184] (2)

[0185] in, It is the volume of dust. It refers to the amount of dust; It refers to the density of dust particles.

[0186] The impact of dust on visibility is assessed based on the height and volume of the dust. The same dust height and volume will have different effects on the visibility of different vehicles. The degree of dust interference on visibility is calculated in this way. The degree of visibility interference can be calculated using the following formula (3):

[0187] (3)

[0188] in, It refers to the degree of visual interference; It is the volume of dust. It is the height of the dust cloud; , It is the weighting coefficient.

[0189] Based on the degree of visual obstruction and safety standards, thresholds for road surface cleaning are set according to the "Ambient Air Quality Standard" (GB 3095-2012) and the "Technical Standard for Highway Engineering" (JTG B01-2014). For example, cleaning is required when dust causes visibility to fall below a certain standard. The amount of dust and the degree of visual obstruction are monitored in real time. When the amount of dust exceeds the preset threshold or the degree of visual obstruction reaches the cleaning standard, road surface cleaning operations are triggered. Based on the decision, road surface cleaning work is implemented, including measures such as water spraying to reduce dust, sweeping accumulated dust, and using dust suppressants.

[0190] The method provided in this embodiment allows for real-time monitoring of dust levels, enabling timely detection of dust problems and issuing warnings when dust levels reach a certain threshold, allowing for proactive cleanup measures. Based on dust volume data, the timing and scope of road cleaning can be precisely controlled, avoiding over-cleaning and unnecessary resource waste. Timely dust removal reduces the risks of slippery roads and decreased visibility, thereby improving driver safety and reducing traffic accidents. Determining the cleaning frequency based on dust volume optimizes the allocation of human and equipment resources, improving work efficiency. When visibility interference is low, frequent cleaning may not be necessary, saving costs. When visibility interference is high, timely cleaning can quickly restore road visibility and reduce safety hazards.

[0191] In some embodiments, the dust height and dust volume are determined based on the amount of dust and the vehicle load; the severity of dust pollution is determined based on the dust volume and dust height; and the need for road cleaning is determined based on the severity of dust pollution.

[0192] The severity of dust pollution can be measured by the degree to which dust particles on the road surface are lifted up and suspended in the air by the action of different forces.

[0193] Specifically, particulate matter monitoring equipment is used to collect dust data on transportation routes in the mining area. Load data of passing vehicles is also collected, which may require vehicle passage records or weighing equipment. Empirical formulas or numerical models are then used to analyze dust levels. and vehicle load To estimate the height of dust:

[0194] (4)

[0195] in, It is the height of the dust cloud; It refers to the amount of dust; It refers to the vehicle's load capacity; , , It is a weighting coefficient constant.

[0196] According to dust volume and the density of dust particles The volume of dust is estimated using formula (2).

[0197] Use dust volume and dust height To assess the severity of dust pollution:

[0198] (5)

[0199] in, It refers to the severity of dust pollution; These are weighting coefficients; It is the volume of dust. It refers to the height of the dust cloud.

[0200] in This is a weighting coefficient used to convert the relationship between dust volume and dust height into a severity index. A threshold for dust severity is set based on traffic regulations, road safety standards, and environmental quality requirements. If the calculated severity of dust pollution... Exceeding the threshold Then, road cleaning will be carried out. If Not exceeding the threshold If so, cleaning may not be necessary or other preventative measures may be taken.

[0201] The method provided in this embodiment helps to understand the actual distribution of dust, providing data support for subsequent cleanup work. By calculating the severity of dust pollution, the impact of dust on the environment and traffic can be understood more intuitively. Timely cleanup when dust reaches a certain level can prevent its spread and reduce pollution. When dust levels are below a certain standard, unnecessary cleanup can be avoided, saving costs.

[0202] In some embodiments, the dust accumulation situation is determined based on the dust density, dust accumulation range, current dust accumulation change, and dust density change, and is calculated according to the following formula (6):

[0203] (6)

[0204] in, Indicator of stacking conditions; This indicates the weight of the influence of dust density on the accumulation index; Indicates dust density; This indicates the weight of the dust accumulation range on the dust accumulation index. Indicates the area of ​​dust accumulation; This indicates the weight of the effect of changes in dust density on the accumulation index. This represents the change in dust density over a relatively long period of time. This indicates the length of the first preset time period.

[0205] Specifically, dust density It is usually proportional to the total amount of dust. Therefore, the square of the dust density... It can be used to represent the total amount of dust, assuming that the dust distribution is uniform. Coefficient This is a weight representing the degree to which dust density affects the accumulation condition index. Dust accumulation range. This represents the area covered by dust on the ground. Since a larger area indicates a larger area affected by the dust, the natural logarithm can be used. To reflect the impact of the dust accumulation range. Coefficient. This indicates the weight of the dust accumulation range on the accumulation index. Adding 1 here is to avoid the logarithmic function being affected by... Undefined case at time. Change in dust density. Indicates a period of time Changes in internal dust density. To account for the time factor, [the following is used]... To represent the rate of change per unit time. Coefficient. This indicates the weight of the impact of this change on the stacking exponent. Similarly, adding 1 is to avoid a denominator of zero.

[0206] The formula provided in this embodiment offers a comprehensive indicator for assessing dust accumulation on mining area transport roads, including the total amount of dust, its distribution range, and its changing trend. By adjusting coefficients, the influence of dust density, dust accumulation range, and changes in dust density can be weighted according to actual conditions and needs. The introduction of the time factor allows the formula to reflect changes in dust accumulation over time, which is helpful for the dynamic monitoring and management of dust pollution.

[0207] Figure 3 This application provides a schematic diagram of the structure of a dust detection system for mining area transportation roads, as shown in one embodiment. Figure 3 As shown, the dust detection system 300 for mining area transportation roads in this embodiment includes: an information acquisition module 301, an accumulation analysis module 302, and a prediction module 303.

[0208] The information acquisition module 301 is used to acquire real-time point cloud data collected by the lidar and vehicle information of the mining area transport vehicle, analyze the point cloud data, and determine the dust accumulation area information.

[0209] The accumulation analysis module 302 is used to retrieve the dust accumulation data after the last cleaning; and to determine the current dust accumulation status based on the dust accumulation data and the dust accumulation area information.

[0210] The prediction module 303 is used to predict the amount of dust based on the vehicle information and the dust accumulation situation; and to determine whether to clean the road surface based on the amount of dust.

[0211] Optionally, when the information acquisition module 301 analyzes the point cloud data to determine the dust accumulation area information, it is used for:

[0212] Obtain mining area transportation information; analyze the mining area transportation information to determine the type of dust accumulation;

[0213] Acquire meteorological data within a first preset time period; analyze the meteorological data to determine the meteorological impact of different weather conditions within the preset time period on the dust accumulation type;

[0214] Based on the vehicle information, determine the type of transport vehicle;

[0215] The dust accumulation pattern is determined based on the type of transport vehicle.

[0216] Analyze the dust accumulation morphology to determine the data type mapped to the point cloud;

[0217] Based on the data type and the meteorological impact, the point cloud data is analyzed to determine the dust accumulation area information.

[0218] Optionally, when the accumulation analysis module 302 determines the current dust accumulation status based on the dust accumulation data and the dust accumulation area information, it is used to:

[0219] Based on the dust accumulation pattern, determine the dust density at the current moment;

[0220] Based on the dust accumulation area information, determine the dust accumulation range;

[0221] Based on the dust accumulation data, determine the current change in dust accumulation;

[0222] The dust accumulation situation is determined based on the dust density, the dust range, and the current dust change.

[0223] Optionally, when the accumulation analysis module 302 determines the dust accumulation situation based on the dust density, the dust range, and the current dust change, it is used for:

[0224] Obtain road traffic data for the second preset time period;

[0225] Analyze the road traffic data to determine the types of vehicles passing through;

[0226] Determine the total load capacity of the vehicles based on the types of vehicles passing through;

[0227] Based on the total weight of the vehicle, predict the change in dust density.

[0228] The dust accumulation situation is determined based on the dust density, the dust range, the current dust change, and the dust density change.

[0229] Optionally, when the information acquisition module 301 analyzes the point cloud data based on the data type and the meteorological impact to determine the dust accumulation area information, it is used for:

[0230] Analyze the meteorological impacts to determine the first point cloud morphology after the initial meteorological change;

[0231] Analyze the impact of other different weather conditions on the first point cloud morphology to determine the final point cloud morphology;

[0232] Based on the final point cloud morphology, the point cloud data is analyzed to determine the characteristics of the dust accumulation point cloud;

[0233] Based on the characteristics of the dust point cloud, the dust accumulation area information is determined;

[0234] When determining the dust density at the current moment based on the dust accumulation morphology, it is used for:

[0235] Based on the final point cloud morphology, the dust density at the current moment is determined.

[0236] Optionally, when the prediction module 303 predicts the amount of dust based on the vehicle information and the dust accumulation, it is used to:

[0237] Based on the vehicle information, determine the vehicle type and vehicle load capacity;

[0238] Obtain historical passage records, and determine the average speed of each type of vehicle passing through the mining area transportation road based on the historical passage records;

[0239] Analyze the final point cloud morphology to determine the amount of particulate dust;

[0240] Obtain basic information about the transportation roads in the mining area and determine the road width;

[0241] The dust range is determined based on the road width and the dust accumulation area information;

[0242] The amount of dust is predicted based on the amount of particulate matter, the dust emission range, the average speed, and the vehicle load.

[0243] Optionally, when the prediction module 303 determines whether to perform road cleaning based on the dust volume, it is used to:

[0244] The dust height and dust volume are determined based on the dust volume and the vehicle load.

[0245] The degree of visual interference is determined based on the dust volume and the dust height.

[0246] Based on the degree of visual obstruction, determine whether to clear the road surface.

[0247] Optionally, when the prediction module determines whether to clean the road surface based on the dust volume, it is used to:

[0248] The dust height and dust volume are determined based on the dust volume and the vehicle load.

[0249] The severity of the dust pollution is determined based on the dust volume and the dust height.

[0250] Based on the severity of the dust pollution, determine whether road cleaning is necessary.

[0251] Optionally, the accumulation analysis module 302 determines the dust accumulation situation based on the dust density, the dust accumulation range, the current dust accumulation change, and the dust density change, and calculates it according to the following formula:

[0252] ;

[0253] in, Indicator of stacking conditions; This indicates the weight of the dust accumulation density on the accumulation index; This indicates the dust density; This indicates the weight of the dust accumulation range on the accumulation index; Indicates the range of dust accumulation; This indicates the weight of the influence of the change in dust density on the accumulation index; This represents the change in dust density over a relatively long period of time. This indicates the length of the first preset time period.

[0254] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A mine haul road surface dust emission detection method, characterized by, The mine area transportation road surface dust detection method is applied to a dust detection device, the dust detection device comprises a server and a laser radar, the method is applied to the server, and the method comprises: Obtaining real-time point cloud data collected by the laser radar and vehicle information of a mine area transport vehicle, analyzing the point cloud data, and determining dust accumulation area information; Calling dust accumulation data after last cleaning; determining the dust accumulation situation at the current moment according to the dust accumulation data and the dust accumulation area information, comprising: According to the dust shape, the dust density at the current moment is determined; According to the dust accumulation area information, the dust accumulation range is determined; According to the dust accumulation data, the current dust change amount is determined; According to the dust density, the dust range, the current dust change amount, the dust accumulation situation is determined, comprising: Obtaining road traffic data of a second preset period; Analyzing the road traffic data to determine the traffic vehicle type; Based on the traffic vehicle type, the total weight of the vehicle load is determined; According to the total weight of the vehicle load, the dust density change amount is predicted; According to the dust density, the dust range, the current dust change amount and the dust density change amount, the dust accumulation situation is determined, and the following formula is used to calculate: Wherein, S represents the accumulation situation index; α represents the influence weight of the dust density on the accumulation situation index; D represents the dust density; β represents the influence weight of the dust range on the accumulation situation index; R represents the dust range; γ represents the influence weight of the dust density change amount on the accumulation situation index; ΔD is the change amount of the dust density in a long time; T represents the time length of the first preset period; According to the vehicle information and the dust accumulation situation, the dust amount is predicted; and according to the dust amount, it is determined whether to clean the road surface.

2. The method of claim 1, wherein, The analysis of the point cloud data to determine the dust accumulation area information comprises: Obtaining mine area transportation information; analyzing the mine area transportation information to determine the dust type; Obtaining meteorological data in a first preset period; analyzing the meteorological data to determine the meteorological influence of different weather on the dust type in the preset period; According to the vehicle information, the transport vehicle type is determined; According to the transport vehicle type, the dust shape is determined; Analyzing the dust shape to determine the data type mapped to the point cloud; Based on the data type, according to the meteorological influence, the point cloud data is analyzed to determine the dust accumulation area information.

3. The method of claim 2, wherein, The analysis of the point cloud data to determine the dust accumulation area information based on the data type according to the meteorological influence comprises: Analyzing the meteorological influence to determine the first point cloud shape after the first meteorological change; Analyzing the influence of the remaining different weather on the first point cloud shape to determine the final point cloud shape; According to the final point cloud shape, the point cloud data is analyzed to determine the dust point cloud feature; According to the dust point cloud feature, the dust accumulation area information is determined; The determination of the dust density at the current moment according to the dust shape comprises: According to the final point cloud shape, the dust density at the current moment is determined.

4. The method of claim 3, wherein, The prediction of the dust amount according to the vehicle information and the dust accumulation situation comprises: According to the vehicle information, determine the vehicle type and the vehicle load; Obtain historical traffic records, and determine the average speed of each type of vehicle passing through the mining area transport road according to the historical traffic records; Analyze the final point cloud shape to determine the amount of particulate dust; Obtain the basic information of the mining area transport road to determine the road width; According to the road width and the dust accumulation area information, determine the dust raising range; According to the amount of particulate dust, the dust raising range, the average speed and the vehicle load, predict the amount of dust raising.

5. The method of claim 4, wherein, According to the amount of dust raising, determine whether to carry out road cleaning, including: According to the amount of dust raising and the vehicle load, determine the dust raising height and the dust raising volume; According to the dust raising volume and the dust raising height, determine the degree of visual interference; According to the degree of visual interference, determine whether to carry out road cleaning.

6. The method of claim 4, wherein, According to the amount of dust raising, determine whether to carry out road cleaning, including: According to the amount of dust raising and the vehicle load, determine the dust raising height and the dust raising volume; According to the dust raising volume and the dust raising height, determine the dust raising severity; According to the dust raising severity, determine whether to carry out road cleaning.

7. A mine haul road surface dust emission detection system, characterized by, Applied to the method of any one of claims 1-6, comprising: An information acquisition module for acquiring real-time point cloud data collected by a laser radar and vehicle information of a mining area transport vehicle, analyzing the point cloud data, and determining dust accumulation area information; An accumulation analysis module for calling dust accumulation data after the last cleaning, and determining the current dust accumulation situation according to the dust accumulation data and the dust accumulation area information; A prediction module for predicting the amount of dust raising according to the vehicle information and the dust accumulation situation, and determining whether to carry out road cleaning according to the amount of dust raising.

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