A mine management method and its management system based on the intelligent Internet of Things

Abstract

The present invention discloses a mine management method and its management system based on the intelligent Internet of Things, specifically related to the field of mine vehicle cleaning, which is used to solve the problems of accurate allocation of cleaning resources and optimization of cleaning effects in highly polluted areas. By obtaining multimodal information on vehicle appearance and dirt distribution, and combining the global dirt index to divide vehicle pollution grades and generate a dynamic zoning speed limit strategy, the cleaning coverage of highly polluted areas is improved and resource conservation in low-polluted areas is achieved; using a programmable spray controller and a dynamic regulation algorithm, the cleaning equipment can adaptively adjust the nozzle angle and water flow pressure according to the actual passing speed, pollution grade and local dirt distribution of the vehicle, so as to achieve hierarchical and differentiated cleaning, ensuring the comprehensiveness of cleaning effects and the economy of resource utilization; furthermore, it reduces the delay of mining area operations caused by vehicle queuing or insufficient cleaning, providing reliable technical support for the environmental protection and high efficiency of mining area operations.

Description

Technical Field

[0001] The present invention relates to the field of mine vehicle cleaning, and more specifically, to a mine management method and its management system based on the intelligent Internet of Things. Background Art

[0002] The cleaning of mine vehicles is an important link in mine area management. During transportation, mine trucks are often severely contaminated on the body surface and chassis due to the accumulation of substances such as mud and ore powder. This not only affects the service life and operation efficiency of the vehicles but also may cause secondary pollution to the mine area environment due to the shedding of dirt. Therefore, effective cleaning when the vehicle enters and exits the mine gate has become a necessary measure. However, due to the large size of mine trucks and the complex distribution of pollution, there are significant differences in the dirt levels of each part. Conventional fixed spray cleaning methods are difficult to achieve efficient and precise cleaning, resulting in unsatisfactory cleaning effects and serious waste of resources, affecting the economy and environmental protection of the overall mine cleaning management.

[0003] Existing mine vehicle cleaning technologies generally adopt fixed cleaning structures and cannot be dynamically adjusted according to the actual pollution situation of the vehicles, resulting in problems of waste of cleaning resources and insufficient cleaning. On the one hand, the fixed spray equipment has insufficient cleaning power for high-pollution areas and it is difficult to ensure the cleaning effect of key areas; on the other hand, the over-cleaning of low-pollution areas causes waste of water resources and electricity. In addition, the existing technologies lack precise control of the vehicle passing speed, and the cleaning time and cleaning intensity cannot be matched, resulting in a decrease in vehicle passing efficiency and unstable cleaning effects. Especially during the peak mine operation period, the resource scheduling problems of vehicle queuing and the cleaning process are particularly prominent. These problems severely restrict the intelligentization and high efficiency of the vehicle cleaning process in the mine area.

[0004] To solve the above problems, a technical solution is provided now. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a mine management method and its management system based on the intelligent Internet of Things. By obtaining multi-modal information of the vehicle shape and dirt distribution, dividing the vehicle pollution levels in combination with the global dirt index and generating a dynamic zoning speed limit strategy, the cleaning coverage of high-pollution areas and resource conservation in low-pollution areas are improved; using a programmable spray controller and a dynamic regulation algorithm, the cleaning equipment can adaptively adjust the nozzle angle and water flow pressure according to the actual passing speed, pollution level and local dirt distribution of the vehicle, so as to achieve hierarchical and differentiated cleaning, ensuring the comprehensiveness of the cleaning effect and the economy of resource utilization; thereby reducing the delays in mine area operations caused by vehicle queuing or insufficient cleaning, providing reliable technical support for the environmental protection and high efficiency of mine area operations, so as to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A mine management method based on the intelligent Internet of Things, comprising the steps of:

[0008] S1: At the gate, use a data acquisition device to synchronously scan the vehicle to be cleaned, obtain multi-modal data of the vehicle's shape and dirt distribution, and generate a preliminary information matrix;

[0009] S2: Based on the preliminary information matrix, regionalize and segment each part of the vehicle, calculate the dirt coverage, generate a global dirt index, divide the vehicle into a lightly polluted or heavily polluted gear, and determine the corresponding partition speed limit strategy accordingly;

[0010] S3: According to the partition speed limit strategy, divide the cleaning area into multiple independently controlled spray zones through a programmable spray controller, and use the nozzle angle adjustment algorithm and water flow pressure control strategy to focus on cleaning the highly polluted areas.

[0011] In a preferred embodiment, step S1 includes the following contents:

[0012] A1. Deploy a three-dimensional laser scanning device and a multi-view imaging device at the mine gate to synchronously scan the vehicle to be cleaned, and respectively obtain the point cloud data set P L ={(x i , y i , z i ) | i = 1, 2,..., n} and the high-definition grayscale image set G I ={g m,n | m = 1,..., M; n = 1,..., N}, where (x i , y i , z i ) represents the spatial coordinates of the i-th point on the vehicle surface, and g m,n is the grayscale value of the pixel in the m-th row and n-th column of the image; the point cloud data is used to describe the geometric shape of the vehicle, and the grayscale image is used to reflect the dirt density on the vehicle surface;

[0013] A2. Alignment of reference data: Define the reference point cloud data set B L ={(x bi , y bi , z bi ) | i = 1, 2,..., n} and the reference grayscale matrix B I ={b m,n | m = 1,..., M; n = 1,..., N}; Use the point cloud registration algorithm to align the point cloud data with the reference point cloud data in the same coordinate system.

[0014] In a preferred embodiment, A3. Calculation of pollution measurement:

[0015] Geometric shape variation: Calculate the geometric shape variation V of the vehicle L , and the formula is as follows: where (x bi , y bi , z bi ) is the coordinate of the i-th point in the reference point cloud, and V L reflects the degree of contamination of the vehicle's geometric shape relative to the reference state;

[0016] Gray-scale deviation value: Use the set of gray-scale images obtained by the multi-view imaging device to compare with the reference gray-scale matrix pixel by pixel, and calculate the gray-scale deviation value D I , and the formula is as follows: where b m,n is the reference gray-scale value, and D I represents the optical deviation degree of the dirt on the vehicle surface;

[0017] Contamination index: Combine the geometric shape variation and the gray-scale deviation value, and generate the contamination index of the vehicle through a non-linear processing method; if the contamination index is lower than the preliminary threshold, implement a normal fast cleaning mode for the vehicle; otherwise, it is necessary to preferentially perform regional segmentation and multi-level dirt index calculation and analysis mode on the vehicle at the scheduling level, so as to save waiting resources.

[0018] In a preferred embodiment, step S2 includes the following contents:

[0019] B1. Based on the point cloud data P L and the gray-scale image G I , adopt a nested method of coarse segmentation + refined segmentation:

[0020] Coarse segmentation: According to the geometric characteristics of the vehicle, define the main region, and extract the corresponding point cloud subset P L,o and the gray-scale sub-image G I,o ;

[0021] Refined segmentation: In each main region, based on the local change of the gray-scale gradient , identify the local high-contamination region and generate the sub-region R o,v ;

[0022] Finally, output the nested structure R = {R o , R o,v}.

[0023] In a preferred embodiment, B2. For each sub-region R o,v , combine the point cloud subset and the gray-scale sub-image under the corresponding sub-region, and fit the contamination feature surface S o,v (x, y), and the formula is as follows: Among them, z(x, y) and g(x, y) are the height value and grayscale value of the point cloud respectively, and z ref (x, y) and g ref (x, y) are reference values, and o and v respectively represent the main area number divided by the vehicle and the local sub-area number inside each main area.

[0024] In a preferred embodiment, B3. Local sub-area pollution index: within each local sub-area, using the pollution characteristic surface fitted to the corresponding area, accumulate the deviation degree between the local geometric shape and the grayscale to form a comprehensive pollution intensity index, and reflect the pollution level of the corresponding area by calculating the overall energy of the characteristic surface;

[0025] Global area pollution index: Weight and average the pollution indices of all local sub-areas within the main area according to the proportion of their relative areas to obtain the overall pollution level of the corresponding main area;

[0026] Global dirt index: Take the logarithm after weighting and averaging the pollution indices of all main areas to generate a comprehensive global dirt index.

[0027] In a preferred embodiment, B4. According to the global dirt index C g , divide the vehicle into a light pollution and a heavy pollution gear, and dynamically allocate the speed limit value v pass passing through the cleaning area. Define: If the global dirt index is less than the overall threshold, the vehicle is classified as lightly polluted; if the global dirt index is not less than the overall threshold, the vehicle is classified as heavily polluted; if it is heavily polluted, then based on the gear signal, dynamically calculate the speed limit value through the following formula: Among them, v base is the basic speed limit corresponding to the gear, and K is the dynamic adjustment coefficient.

[0028] In a preferred embodiment, step S3 includes the following content:

[0029] C1. Read the vehicle identification and the corresponding pollution gear information, and design the cleaning area as multiple spray belts Each spray belt has an independent nozzle angle and pressure control interface The number of spray belts;

[0030] C2. Inside each spray belt Realize angle adaptive control through a group of rotating nozzles; set the angle control matrix Among them represents the nozzle in the spray belt The swing angle, where respectively represent the spray belt numbers of the cleaning area; Indicates the nozzle number in the spray belt; define the following angle adjustment logic: Among them, is the reference angle of the corresponding spray belt; C g represents the global fouling index, which is used to measure the overall pollution degree of the vehicle; regionFlag is a regional identification variable, which is 1 for high-pollution areas and 0 otherwise; k1 and k2 are empirical coefficients used to adjust the sensitivity of the swing angle.

[0031] In a preferred embodiment, C3. Combine the speed limit value with the pollution gear to define the spray belt pressure control vector Among them is the nozzle in the spray belt the water flow pressure applied;

[0032] If the vehicle is marked as lightly polluted, the water pressure for controlling the spray belt is the standard water flow pressure value to save water resources and shorten the cleaning time; if the vehicle is marked as heavily polluted, the following logic is used for dynamic boosting: Among them, p base,a is the spray belt basic pressure; G g represents the global fouling index; v max is the maximum passing speed; v pass is the current vehicle passing speed; ∈ is a small value to prevent the denominator from being zero; k3 is the basic gain coefficient of the water flow pressure.

[0033] A mine management system based on the intelligent Internet of Things, including: a multi-mode acquisition module, a zoning empowerment module, and a dynamic spray control module;

[0034] Multi-mode acquisition module: Use data acquisition devices to synchronously scan the vehicle to be cleaned, collect multi-modal data on the geometric shape and surface dirt distribution of the vehicle, including 3D point clouds and grayscale images, and generate a preliminary information matrix; the output preliminary information matrix is transmitted to the zoning empowerment module for regional segmentation and dirt coverage calculation of each part of the vehicle;

[0035] Zoning empowerment module: Based on the preliminary information matrix, perform regional segmentation on each part of the vehicle and calculate the dirt coverage, generate the global dirt index, and then divide the vehicle into lightly polluted or heavily polluted gears, and determine the corresponding zoning speed limit strategy accordingly; the generated global dirt index, pollution gear information, and speed limit strategy are transmitted to the dynamic spray control module for dynamic configuration of the cleaning equipment and adjustment of the cleaning intensity;

[0036] Dynamic spray control module: Divide the cleaning area into multiple independently controlled spray belts through a programmable spray controller, and use the nozzle angle adjustment algorithm and water flow pressure control strategy to focus on cleaning high-pollution areas.

[0037] Technical effects and advantages of a mine management method and its management system based on the intelligent Internet of Things according to the present invention:

[0038] Through technical means combining multi-modal data collection, regional segmentation and dynamic cleaning control, the present invention realizes precise and intelligent management of the mine vehicle cleaning process, effectively solving the problems of low cleaning efficiency, serious resource waste and uneven cleaning effect in the prior art. By using a data collection device to obtain multi-modal information on the vehicle appearance and dirt distribution, dividing the vehicle pollution grades in combination with the global dirt index and generating a dynamic zoning speed limit strategy, the cleaning coverage of high-pollution areas is significantly improved and resource conservation in low-pollution areas is achieved. Through a programmable spray controller and a dynamic regulation algorithm, the cleaning equipment can adaptively adjust the nozzle angle and water flow pressure according to the actual passing speed, pollution grade and local dirt distribution of the vehicle, so as to achieve hierarchical and differentiated cleaning, ensuring the comprehensiveness of the cleaning effect and the economy of resource utilization. The present invention significantly improves the automation level and operation efficiency of mine vehicle cleaning, optimizes the allocation of cleaning resources, and at the same time reduces the delays in mining area operations caused by vehicle queuing or insufficient cleaning, providing reliable technical support for the environmental protection and high efficiency of mining area operations. Brief Description of the Drawings

[0039] Figure 1 It is a schematic flow chart of a mine management method based on the intelligent Internet of Things according to the present invention;

[0040] Figure 2 It is a schematic structural diagram of a mine management system based on the intelligent Internet of Things according to the present invention. Detailed Embodiments

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] Embodiment 1: Figure 1 A mine management method based on the intelligent Internet of Things according to the present invention is given, including:

[0043] S1: At the gate, use a data collection device to synchronously scan the vehicle to be cleaned, obtain multi-modal data on the vehicle appearance and dirt distribution, and generate a preliminary information matrix;

[0044] S2: Based on the preliminary information matrix, regionalize and segment each part of the vehicle, calculate the dirt coverage, generate a global dirt index, divide the vehicle into a lightly polluted or heavily polluted gear, and determine the corresponding zonal speed limit strategy accordingly.

[0045] S3: According to the zonal speed limit strategy, divide the washing area into multiple independently controllable spray zones through a programmable spray controller, and implement key cleaning on highly polluted areas using the spray head angle adjustment algorithm and water flow pressure regulation strategy.

[0046] At the mine gate, the frequent entry and exit of vehicles cause a large amount of soil, ore powder, and other dirt to accumulate on the vehicle body surface and chassis. These dirt not only affect the normal operation of the vehicle, increase maintenance costs, but also may cause secondary pollution to the environment. Therefore, accurately detecting and evaluating the dirt distribution and degree of the vehicle is of great significance for optimizing the cleaning process, improving cleaning efficiency, reducing resource waste, and ensuring environmental protection. Step S1 combines 3D laser scanning and multi-view imaging technologies to obtain multi-modal data of the vehicle's shape and dirt distribution in real time, and generates a detailed preliminary information matrix, providing an accurate basis for subsequent regional segmentation and speed limit strategies.

[0047] Step S1 includes the following:

[0048] A1. Data acquisition and preprocessing: Deploy a 3D laser scanning device and a multi-view imaging device at the mine gate to synchronously scan the vehicle to be cleaned, and respectively obtain the point cloud data set P L ={(x i ,y i ,z i )∣i=1,2,…,n} and the high-definition grayscale image set G I ={g m,n ∣m=1,…,M;n=1,…,N}, where (x i ,y i ,z i ) represents the spatial coordinates of the i-th point on the vehicle surface, and g m,n is the grayscale value of the pixel at the m-th row and n-th column in the image. The point cloud data is used to describe the geometric shape of the vehicle, and the grayscale image is used to reflect the dirt density on the vehicle surface.

[0049] A2. Alignment of reference data: Define the reference point cloud data set B L ={(x bi ,y bi ,z bi )∣i=1,2,…,n} and the reference grayscale matrix B I ={b m,n{|m = 1, …, M; n = 1, …, N}. The point cloud registration algorithm is used to align the point cloud data with the reference point cloud data in the same coordinate system to ensure accurate comparison of geometric shapes. By spatial alignment, the differences in vehicle position and angle are eliminated, making the calculation of the morphological variation and gray deviation values comparable.

[0050] A3. Pollution metric calculation:

[0051] Geometric morphological variation: Calculate the geometric morphological variation V of the vehicle L , the formula is as follows: where, (x bi , y bi , z bi ) is the coordinate of the i-th point in the reference point cloud, and V L reflects the pollution degree of the vehicle's geometric shape relative to the reference state. This calculation is used to quantify the geometric shape changes of the vehicle caused by dirt attachment and provides a basis for dirt evaluation.

[0052] Gray deviation value: Using the set of gray images obtained by the multi-view imaging device, compare with the reference gray matrix pixel by pixel to calculate the gray deviation value D I , the formula is as follows: where, b m,n is the reference gray value, and D I represents the optical deviation degree of the dirt on the vehicle surface. This calculation is used to quantify the optical changes of the vehicle surface caused by dirt attachment and assist in evaluating the pollution degree.

[0053] Pollution index: Combining the geometric morphological variation and the gray deviation value, generate the pollution index of the vehicle through a non-linear processing method. For example, the pollution index G is calculated in the following way pollution : where, and are used to enhance the sensitivity to high pollution levels, and in the denominator is used to balance the combined effects of geometric shape and optical deviation and avoid excessive amplification of extreme values. This pollution index is used to comprehensively evaluate the pollution degree of the vehicle and ensure accurate classification of vehicles with different pollution levels.

[0054] If the pollution index is lower than the preliminary threshold, implement the ordinary fast cleaning mode for the vehicle; otherwise, it is necessary to preferentially send the vehicle to the subsequent regional segmentation and multi-level dirt index calculation and analysis mode at the scheduling level, so as to save waiting resources and optimize the overall traffic flow.

[0055] A4. Construction of the preliminary information matrix: Structurally store the pollution index, the set of point cloud data, the set of gray images, and the vehicle unique identification information as the preliminary information matrix M o。

[0056] Through step S1, the integration of the 3D laser scanning device and the multi-camera imaging device realizes the multi-modal data acquisition and fusion of the vehicle's external shape and surface dirt distribution, generating a data set containing contamination index and structured information matrix. This process not only ensures the high precision and comprehensiveness of dirt detection, but also effectively differentiates vehicles with different contamination levels by accurately quantifying the contamination degree. This processing method greatly improves the precision of formulating cleaning strategies, optimizes the allocation of cleaning resources, reduces the waste of cleaning time and water resources, significantly enhances the intelligence and automation level of the mining truck cleaning process, and thus effectively guarantees the cleanliness of the mining area environment and the efficient operation of the vehicles.

[0057] The preliminary information matrix output by step S1 includes the overall contamination degree of the vehicle, as well as geometric and grayscale information for fine analysis. In the mining truck cleaning scenario, it is difficult to accurately locate the high-contamination areas of the vehicle locally relying only on the overall contamination degree, and a single-gear speed limit strategy cannot take into account the actual contamination differences. Therefore, step S2 realizes the accurate identification and quantitative evaluation of each part of the vehicle through multi-level nested regional division and construction of contamination characteristic surfaces, and generates a speed limit strategy that can be dynamically adjusted according to the numerical value on the basis of the global dirt index, providing a more flexible decision-making basis for the key cleaning in step S3.

[0058] Step S2 includes the following:

[0059] B1. Dynamic regional segmentation:

[0060] The dirt distribution on the vehicle surface usually shows non-uniform characteristics, and local high-dirt areas may be ignored by relying only on global evaluation. By hierarchically segmenting the vehicle, the contamination characteristics of different parts and local details can be separately extracted to form structured data for input into subsequent steps.

[0061] Based on the point cloud data P L and the grayscale image G I , a nested method of coarse segmentation + refined segmentation is adopted:

[0062] Coarse segmentation: According to the geometric characteristics of the vehicle, define the main regions (such as the roof, vehicle side, wheels, chassis), and extract the corresponding point cloud subsets P L,o and grayscale sub-images G I,o .

[0063] Refined segmentation: In each main region, based on the local change of the grayscale gradient , identify the local high-contamination areas and generate sub-regions R o,v .

[0064] The final output nested structure R = {R o , R o,v}, ensuring the consideration of both large - scale areas and detailed pollution characteristics.

[0065] The segmentation strategy enables the precise capture of local pollution hotspots of the vehicle, providing more fine - grained data support and making the subsequent allocation of cleaning resources more targeted.

[0066] B2. Construction of regional pollution characteristic surface:

[0067] Independent analysis of single - point data or gray - scale values is difficult to reflect the spatial characteristics of pollution distribution. Through surface fitting, the local pollution distribution can be transformed into a two - dimensional function model, which more intuitively reflects the regional pollution intensity. For each sub - region R o,v , combining the point - cloud subset and gray - scale sub - image corresponding to the sub - region, fit the pollution characteristic surface S o,v (x, y), and the formula is as follows: Among them, z(x, y) and g(x, y) are the point - cloud height value and gray - scale value respectively, z ref (x, y) and g ref (x, y) are the reference values, o and v respectively represent the main region numbers (such as the roof, wheels, side walls, etc.) divided by the vehicle and the local sub - region numbers (such as the grooves on the wheels, the edges of the roof, etc.) within each main region. Surface fitting can intuitively quantify the local dirt degree and its spatial distribution, which helps the subsequent accurate calculation of the regional pollution index and the dynamic identification of high - pollution regions.

[0068] B3. Calculation of multi - level pollution index:

[0069] The layer - by - layer synthesis of the pollution index can achieve a comprehensive assessment from local details to the global pollution degree, and suppress the influence of extreme values through non - linear processing.

[0070] Local sub - region pollution index: Within each local sub - region, using the pollution characteristic surface fitted in this region, accumulate the deviation degree between the local geometry and gray - scale, and form a comprehensive pollution intensity index. This index reflects the pollution level of this region by calculating the overall energy of the characteristic surface (i.e., the accumulated deviation degree). Specifically: Based on the surface S o,v (x, y), calculate the pollution index Z o,v : The local sub - region pollution index is calculated based on the pollution characteristic surface. Its core idea is to accumulate the deviation between the shape and gray - scale within the region, and the result represents the pollution intensity of this sub - region. The higher the overall deviation degree of the characteristic surface, the larger the pollution index of this region.

[0071] Global area pollution index: The pollution indices of all local sub - areas within the main area are weighted and averaged according to the proportion of their relative areas to obtain the overall pollution level of the main area. This method can balance the influence of local high - pollution areas and low - pollution areas on the overall pollution degree, ensuring that the result can reflect the overall trend while not losing the detailed characteristics. Specifically: The sub - area pollution index Z o,v is integrated into the corresponding main area The formula is as follows: The global area pollution index is obtained by area - weighted accumulation of the sub - area pollution indices. Its core idea is to comprehensively calculate the overall pollution intensity of the main area based on the area proportion of each sub - area. Sub - areas with a larger area proportion contribute more to the overall pollution index.

[0072] Global fouling index: The pollution indices of all main areas are weighted and averaged and then logarithmized to generate a comprehensive global fouling index. The purpose of logarithmizing is to suppress the excessive influence of local extreme pollution areas on the overall result and at the same time enhance the ability to distinguish medium and light pollution. The result is used to measure the pollution level of the entire vehicle. Specifically: The pollution indices of all main areas are weighted and averaged, and the logarithm of the result is taken to generate the global fouling index C g : The global fouling index is based on the average of the pollution indices of all main areas and is the result optimized by logarithmic transformation. Its core idea is to comprehensively consider the pollution levels of each main area and at the same time use logarithmic transformation to balance the influence of extreme values, forming an accurate and stable global pollution assessment index.

[0073] The layer - by - layer synthesis method ensures the accurate transmission of the pollution index from local to global. At the same time, logarithmic processing improves the ability to distinguish medium and light pollution and suppresses the influence of extreme values on the result.

[0074] B4. Pollution gear determination and dynamic speed limit adjustment:

[0075] The speed limit strategy should not only be based on the classification of pollution gears but also combined with the specific value of the global fouling index to achieve dynamic adjustment for vehicles with different pollution degrees, thereby balancing the cleaning effect and passing efficiency.

[0076] According to the global fouling index C g , the vehicle is divided into a light - pollution gear and a heavy - pollution gear, and the speed limit value v pass for passing through the cleaning area is dynamically allocated. Definition:

[0077] If the global fouling index is less than the overall threshold, the vehicle is classified as lightly polluted;

[0078] If the global fouling index is not less than the overall threshold, the vehicle is classified as heavily polluted.

[0079] If it is severe pollution, based on the gear signal, the speed limit value is dynamically calculated through the following formula: where v base is the basic speed limit corresponding to the gear, and K is the dynamic adjustment coefficient. This formula realizes the dynamic adjustment logic that the higher the pollution index, the lower the speed limit. The dynamic speed limit adjustment combined with specific values can ensure the cleaning effect of high-pollution vehicles while avoiding resource and time waste of low-pollution vehicles due to excessive speed limit, and improving the overall passing efficiency.

[0080] Dividing vehicles into lightly polluted or severely polluted according to the overall pollution degree and dynamically adjusting the vehicle speed in combination with the global dirt index can effectively achieve the differential allocation of cleaning resources and the improvement of cleaning efficiency: adopting a higher vehicle speed for lightly polluted vehicles can reduce queuing time and resource waste; while severely polluted vehicles extend their stay time in the cleaning area through the speed limit strategy, enabling the high-pollution areas to be fully cleaned, thus taking into account both the cleaning effect and time management, while reducing the cost of low-pollution vehicles occupying cleaning equipment, and overall optimizing the resource utilization efficiency and passing efficiency of the mine cleaning process.

[0081] During the cleaning process of mine vehicles, the pollution distribution on the vehicle surface is usually significantly uneven. High-pollution areas such as the chassis and tire grooves are more difficult to clean, while the pollution in other areas is relatively light. The unified cleaning mode often leads to resource waste or incomplete cleaning. Step S3, through precise control based on the vehicle's global dirt index, pollution gear, and dynamic speed limit strategy, divides the cleaning area into multiple independently controllable spray zones, and combines the adaptive adjustment of the nozzle angle and water flow pressure to implement differential cleaning for different pollution areas, effectively achieving key coverage of high-pollution parts, while reducing resource consumption in low-pollution parts, and significantly improving the overall cleaning efficiency and resource utilization rate.

[0082] Step S3 includes the following content:

[0083] C1. Read the vehicle identification and the corresponding pollution gear information, and design the cleaning area as multiple spray zones according to the area division result of step S2 (such as the roof, wheels, side walls, chassis) Each spray zone has an independent nozzle angle and pressure control interface, the number of spray zones. Align these spray zones with the vehicle passing trajectory in the physical layout to ensure that when the vehicle enters the cleaning area at the speed limit value v pass each high-pollution part corresponds accurately to the corresponding spray zone.

[0084] C2. Inside each spray zone achieve angle adaptive adjustment through a group of rotatable nozzles. Set the angle control matrix where represents the nozzle At the spray belt The swing angle, where respectively represent the spray belt numbers in the cleaning area (corresponding to different physical positions and covering different parts of the vehicle); represents the nozzle number in the spray belt (each spray belt usually contains multiple nozzles). To highlight key cleaning, the following angle adjustment logic is defined: Among them, is the reference angle for the corresponding spray belt; C g represents the global dirt index, which is used to measure the overall pollution degree of the vehicle; regionFlag is the region identification variable, which is 1 for high-pollution regions and 0 otherwise; k1 and k2 are empirical coefficients used to adjust the sensitivity of the swing angle.

[0085] For high-pollution regions (regionFlag = 1), the function adopts a logarithmic logic to avoid excessive angle adjustment while ensuring the coverage depth.

[0086] For lightly polluted regions (regionFlag = 0), the function adopts a square root logic to slow down the angle swing and avoid wasting resources.

[0087] C3. Combine the speed limit value and the pollution level to define the spray belt pressure control vector Among them is the nozzle at the spray belt the water flow pressure applied.

[0088] If the vehicle is marked as lightly polluted, the spray belt pressure control water pressure is the standard water flow pressure value to save water resources and shorten the cleaning time.

[0089] If the vehicle is marked as severely polluted, the following logic is used for dynamic boosting:

[0090] Among them, p base,a is the spray belt base pressure; G g represents the global dirt index; v max is the maximum passing speed; v pass is the current vehicle passing speed; ∈ is a small value to prevent the denominator from being zero; k3 is the water flow pressure base gain coefficient.

[0091] Enhance the response sensitivity to highly polluted vehicles. The higher the pollution index, the greater the water flow pressure.

[0092] The above formula takes into account the influence of the vehicle passing speed on the cleaning time. When the speed is higher, the pressure is increased to make up for the insufficient cleaning time. When the speed is lower, the pressure is reduced to save resources.

[0093] C4. When the vehicle passes through each spray zone at the current vehicle passing speed, if the sensor detects that the highly polluted area has not been effectively cleaned (for example, a residual threshold is set for judgment), a key cleaning instruction can be temporarily triggered: where Δ α and Δ p are the real-time correction amounts respectively, which are used to further increase the swing angle of the nozzle and the water pressure to achieve secondary strengthening.

[0094] Step S3 realizes the precise allocation and dynamic adjustment of cleaning resources through intelligent control based on the vehicle's global dirt index and the zonal speed limit strategy. By dividing the cleaning area into spray zones that can be independently controlled and combining the adaptive control logic of the nozzle angle and water flow pressure, it is possible to implement differential cleaning for vehicles and areas with different pollution levels: intensively clean the highly polluted areas to ensure the complete removal of dirt; reasonably reduce the cleaning intensity in lightly polluted areas to reduce the waste of water and electricity resources. In addition, the real-time cleaning monitoring and the dynamic compensation mechanism for key areas further improve the reliability and efficiency of the cleaning effect. The implementation of this step not only optimizes the overall process of mine vehicle cleaning, significantly reduces the waste of resources caused by inefficient cleaning, but also ensures the cleaning effect of heavily polluted vehicles through the key cleaning strategy, providing an efficient and environmentally friendly solution for mine operation, and ultimately realizing the intelligent and refined management of vehicle cleaning.

[0095] Embodiment 2: Figure 2 A mine management system based on the intelligent Internet of Things according to the present invention is given, including: a multi-mode acquisition module, a zonal empowerment module, and a dynamic spray control module;

[0096] Multi-mode acquisition module: Use a data acquisition device to synchronously scan the vehicle to be cleaned, collect multi-modal data on the geometric shape and surface dirt distribution of the vehicle, including 3D point clouds and grayscale images, and generate a preliminary information matrix; the output preliminary information matrix is transmitted to the zonal empowerment module for regional segmentation of each part of the vehicle and calculation of the dirt coverage;

[0097] Zonal empowerment module: Based on the preliminary information matrix, perform regional segmentation on each part of the vehicle and calculate the dirt coverage, generate a global dirt index, then divide the vehicle into a lightly polluted or heavily polluted gear, and determine the corresponding zonal speed limit strategy accordingly; the generated global dirt index, pollution gear information, and speed limit strategy are transmitted to the dynamic spray control module for dynamic configuration of the cleaning equipment and adjustment of the cleaning intensity;

[0098] Dynamic Spray Control Module: The cleaning area is divided into multiple independently controllable spray zones by a programmable spray controller, and key cleaning is implemented on highly polluted areas using the nozzle angle adjustment algorithm and water flow pressure control strategy.

[0099] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0100] Only some exemplary embodiments of the present invention have been described by way of illustration. Undoubtedly, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

[0101] It should be noted that in this article, if there are relational terms such as first and second, they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including the element.

[0102] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A mine management method based on the intelligent Internet of Things, characterized in that Including the steps: S1: At the gate, use a data acquisition device to synchronously scan the vehicle to be cleaned, obtain multimodal data on the vehicle's shape and dirt distribution, and generate a preliminary information matrix; Deploy a 3D laser scanning device and a multi-camera imaging device at the mine gate to synchronously scan the vehicle to be cleaned, and respectively obtain a point cloud data set and a high-definition grayscale image set on the vehicle surface; Define a reference point cloud data set and a reference grayscale matrix for a pollution-free vehicle, and use a point cloud registration algorithm to align the point cloud data with the reference point cloud data in the same coordinate system; Calculate the geometric form variation of the point cloud data , and the formula is as follows: ; where is the coordinate of the -th point in the reference point cloud, represents the spatial coordinate of the -th point on the vehicle surface, reflects the pollution degree of the vehicle geometric form relative to the reference state; Use the grayscale image set obtained by the multi-camera imaging device to compare pixel by pixel with the reference grayscale matrix, and calculate the grayscale deviation value; Combine the geometric shape variation amount and the grayscale deviation value, and generate a pollution degree index for the vehicle through a non-linear processing method; If the pollution degree index is lower than the preliminary threshold, implement an ordinary fast cleaning mode for the vehicle; otherwise, it is necessary to preferentially perform regional segmentation and multi-level dirt index calculation and analysis mode for the vehicle at the scheduling level; S2: Based on the preliminary information matrix, perform regional segmentation on each part of the vehicle and calculate the dirt coverage, generate a global dirt index, then divide the vehicle into a lightly polluted or heavily polluted gear, and determine the corresponding zoning speed limit strategy accordingly; S3: According to the zoning speed limit strategy, divide the cleaning area into multiple independently controlled spray zones through a programmable spray controller, and use the nozzle angle adjustment algorithm and the water flow pressure control strategy to focus on cleaning the highly polluted area.

2. The method for mine management based on the intelligent Internet of Things according to claim 1, wherein, Step S1 includes the following contents: A1. Deploy a three-dimensional laser scanning device and a multi-camera imaging device at the mine gate to synchronously scan the vehicle to be cleaned, and respectively obtain the point cloud data set of the vehicle surface and the high-definition grayscale image set , where is the grayscale value of the pixel at the th row and the th column in the image; the point cloud data is used to describe the geometric shape of the vehicle, and the grayscale image is used to reflect the dirt density on the vehicle surface; A2. Benchmark data alignment: Define the benchmark point cloud data set of pollution-free vehicles and the benchmark grayscale matrix ; Use the point cloud registration algorithm to align the point cloud data with the benchmark point cloud data in the same coordinate system, where is the benchmark grayscale value of the pixel in the th row and th column of the image.

3. The method for mine management based on the intelligent Internet of Things according to claim 2, wherein, A3. Pollution measurement calculation: Gray deviation value: A set of grayscale images obtained by a multi-camera imaging device is compared with a reference grayscale matrix pixel by pixel to calculate the gray deviation value , and the formula is as follows: ; where represents the optical deviation degree of the dirt on the vehicle surface; Pollution degree index: Combine the geometric shape variation amount and the grayscale deviation value, and generate a pollution degree index for the vehicle through a non-linear processing method; if the pollution degree index is lower than the preliminary threshold, implement an ordinary fast cleaning mode for the vehicle; otherwise, it is necessary to preferentially perform regional segmentation and multi-level dirt index calculation and analysis mode for the vehicle at the scheduling level, so as to save waiting resources.

4. A mine management method based on the intelligent Internet of Things according to claim 3, characterized in that, Step S2 includes the following contents: B1. Based on point cloud data and grayscale images , a nested method of rough segmentation + refined segmentation is adopted: Coarse segmentation: Define the main regions according to the geometric characteristics of the vehicle, and extract the corresponding subsets of point clouds for each region and grayscale sub-images ; Refined segmentation: In each main region, based on the local change of the gray-scale gradient identify local high-pollution regions and generate sub-regions , where and represent the main region number divided by the vehicle and the local sub-region number inside each main region respectively; Final output nested structure , where represents the th main area.

5. The method for mine management based on the intelligent Internet of Things according to claim 4, characterized in that, B2. For each sub-region , combine the point cloud subset and the grayscale sub-image under the corresponding sub-region to fit the pollution feature surface , and the formula is as follows: ; where and are the point cloud height value and the grayscale value respectively, and are reference values.

6. The mine management method based on the intelligent Internet of Things according to claim 5, characterized in that, B3. Local sub-region pollution index: In each local sub-region, use the pollution characteristic surface fitted to the corresponding region to accumulate the deviation degree of the local geometry and grayscale, form a comprehensive pollution intensity index, and reflect the pollution level of the corresponding region by calculating the overall energy of the characteristic surface; Global region pollution index: Weight and average the pollution indices of all local sub-regions in the main region according to their relative area ratios to obtain the overall pollution level of the corresponding main region; Global dirt index: Take the logarithm after weighting and averaging the pollution indices of all main regions to generate a comprehensive global dirt index.

7. The mine management method based on the intelligent Internet of Things according to claim 6, characterized in that, B4. According to the global dirt index , the vehicle is divided into a light pollution gear and a heavy pollution gear, and the speed limit value passing through the cleaning area is dynamically allocated . It is defined that if the global dirt index is less than the overall threshold, the vehicle is divided into light pollution; if the global dirt index is not less than the overall threshold, the vehicle is divided into heavy pollution; if it is heavy pollution, based on the gear signal, the speed limit value is dynamically calculated through the following formula: ; where is the basic speed limit corresponding to the gear, is the dynamic adjustment coefficient.

8. A mine management method based on the intelligent Internet of Things according to claim 7, characterized in that, Step S3 includes the following contents: C1. Read the vehicle identification and the corresponding pollution level information, and design the cleaning area as multiple spray zones , each spray zone has an independent nozzle angle and pressure control interface, represents the number of spray zones; C2. At each spray belt inside, angle adaptive control is achieved through a set of rotating nozzles; Set Angle Control Matrix , where represents the swing angle of the nozzle on the spray belt , represents the spray belt number in the cleaning area, represents the nozzle number; Define the following angle adjustment logic: ; where is the reference angle of the corresponding spray belt; represents the global fouling index, which is used to measure the overall pollution degree of the vehicle; is the area identification variable, which is 1 if it is a high-pollution area, otherwise 0; and are empirical coefficients, which are used to adjust the sensitivity of the swing angle.

9. A mine management method based on the intelligent Internet of Things according to claim 8, characterized in that, C3. Combine the speed limit value with the pollution level to define the spray zone pressure control vector , where is the nozzle at the spray zone the water flow pressure applied; If the vehicle is marked as lightly polluted, the water pressure regulation of the spray zone is the standard water flow pressure value to save water resources and shorten the cleaning time; If the vehicle is marked as heavily polluted, the following logic is used for dynamic boosting: ; where is the base pressure of the spray belt ; represents a non-linear transformation of the global fouling index to the 1.2th power; is the maximum passing speed; is the current vehicle passing speed; is a small value to prevent the denominator from being zero; is the base gain coefficient of the water flow pressure.

10. A mine management system based on the intelligent Internet of Things is used to implement a mine management method based on the intelligent Internet of Things according to any one of claims 1-9, characterized in that, Including: A multi-mode acquisition module, a zoning empowerment module, and a dynamic spray control module; Multi-mode acquisition module: Use a data acquisition device to synchronously scan the vehicle to be cleaned, collect multimodal data on the vehicle's geometric shape and surface dirt distribution, including 3D point clouds and grayscale images, and generate a preliminary information matrix; The initially output information matrix is transmitted to the partition empowerment module for regional segmentation of various parts of the vehicle and calculation of the dirt coverage; Partition empowerment module: Based on the initial information matrix, regional segmentation of various parts of the vehicle is performed and the dirt coverage is calculated. After generating the global dirt index, the vehicle is divided into mild pollution or severe pollution gears, and the corresponding partition speed limit strategy is determined accordingly; the generated global dirt index, pollution gear information, and speed limit strategy are transmitted to the dynamic spray control module for dynamic configuration of the cleaning equipment and adjustment of the cleaning intensity; Dynamic spray control module: The cleaning area is divided into multiple independently controlled spray zones through a programmable spray controller, and key cleaning is implemented on high-pollution areas using the spray head angle adjustment algorithm and the water flow pressure control strategy.

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