Mine management method based on intelligent Internet of Things and management system thereof

Abstract

The invention discloses a mine management method and a mine management system based on an intelligent internet of things, particularly relates to the field of mine vehicle cleaning, and is used for solving the problems of precise distribution of cleaning resources and optimization of the cleaning effect of a high-pollution area. Vehicle pollution gears are divided in combination with the global dirt index, and a dynamic partition speed limiting strategy is generated, so that the cleaning coverage of a high-pollution area and the resource saving of a low-pollution area are improved; by utilizing a programmable spraying controller and a dynamic regulation and control algorithm, the cleaning equipment can adaptively adjust the nozzle angle and the water flow pressure according to the actual passing speed, the pollution gear and the local dirt distribution of a vehicle, so that layered and differentiated cleaning is realized, and the comprehensiveness of the cleaning effect and the economical efficiency of resource utilization are ensured; therefore, mine area operation delay caused by vehicle queuing or insufficient cleaning is reduced, and reliable technical support is provided for environmental protection and high efficiency of mine area operation.

Description

Technical Field

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

[0002] Mine vehicle cleaning is an important part of mine area management. During transportation, the surface and chassis of mine vehicles are often seriously polluted due to the accumulation of dirt, mineral powder and other materials. This not only affects the service life and operating efficiency of the vehicle, but may also cause secondary pollution to the mining environment due to the shedding of dirt. Therefore, effective cleaning of vehicles when entering and exiting the mine gate has become a necessary means. However, due to the large size of mine vehicles and the complex distribution of pollution, there are significant differences in the degree of dirt in various parts. Conventional fixed spray cleaning methods are difficult to achieve efficient and accurate 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 mining vehicle cleaning technologies generally adopt fixed cleaning structures, which cannot be dynamically adjusted according to the actual pollution situation of the vehicle, resulting in problems of wasted cleaning resources and insufficient cleaning. On the one hand, the fixed spray equipment is not strong enough to clean high-pollution areas, making it difficult to ensure the cleaning effect in key areas; on the other hand, excessive cleaning of low-pollution areas causes waste of water resources and electricity. In addition, the existing technology lacks precise control of vehicle passing speed, and the cleaning time and cleaning intensity cannot be matched, resulting in reduced vehicle traffic efficiency and unstable cleaning effect. Especially during the peak period of mining operations, the problem of resource scheduling of vehicle queuing and cleaning process is particularly prominent. These problems seriously restrict the intelligence and efficiency of the vehicle cleaning process in mining areas.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a mine management method and a management system based on an intelligent Internet of Things. By acquiring multimodal information of vehicle appearance and dirt distribution, the vehicle pollution gear is divided into a global dirt index and a dynamic partition speed limit strategy is generated, thereby improving the cleaning coverage of high-pollution areas and resource conservation in low-pollution areas; using a programmable spray controller and a dynamic control algorithm, the cleaning equipment can adaptively adjust the nozzle angle and water flow pressure according to the actual passing speed, pollution gear and local dirt distribution of the vehicle, thereby achieving hierarchical and differentiated cleaning, ensuring the comprehensiveness of the cleaning effect and the economy of resource utilization; thereby reducing mine operation delays caused by vehicle queuing or insufficient cleaning, and providing reliable technical support for the environmental protection and efficiency of mine operations, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A mine management method based on intelligent Internet of Things, comprising the steps of: S1: Use a data acquisition device to synchronously scan the vehicle to be cleaned at the gate to obtain multimodal data on vehicle appearance and dirt distribution and generate a preliminary information matrix; S2: Based on the preliminary information matrix, the vehicle parts are regionalized and the dirt coverage is calculated. After the global dirt index is generated, the vehicle is divided into lightly polluted or heavily polluted gears, and the corresponding zone speed limit strategy is determined accordingly; S3: According to the zoning speed limit strategy, the cleaning area is divided into multiple independently controlled spray zones through a programmable spray controller, and the nozzle angle adjustment algorithm and water flow pressure control strategy are used to implement key cleaning in high-pollution areas.

[0007] In a preferred embodiment, step S1 includes the following contents: A1. Deploy 3D laser scanning devices and multi-eye imaging devices at the mine gate to synchronously scan the vehicles to be cleaned and obtain point cloud data sets on the vehicle surface. and a collection of high-definition grayscale images ,in Indicates the vehicle surface The spatial coordinates of the points, For the image Line The grayscale value of the column pixels; point cloud data is used to describe the geometry 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 non-polluting vehicles With the reference gray matrix ; Use the point cloud registration algorithm to align the point cloud data with the reference point cloud data in the same coordinate system.

[0008] In a preferred embodiment, A3. Pollution metric calculation: Geometry Variation: Calculates the geometry variation of the vehicle , the formula is as follows: ;in, The first The coordinates of the points, Reflects the degree of pollution of the vehicle geometry relative to the reference state; Grayscale deviation value: The grayscale image set obtained by the multi-eye imaging device is compared pixel by pixel with the reference grayscale matrix to calculate the grayscale deviation value. , the formula is as follows: ;in, is the reference gray value, Indicates the degree of optical deviation of dirt on the vehicle surface; Pollution index: Combine the geometric variation and grayscale deviation value to generate the vehicle's pollution index through nonlinear processing. If the pollution index is lower than the initial threshold, the vehicle will be subjected to a fast cleaning mode of ordinary flight transition analysis. Otherwise, it is necessary to prioritize the regional segmentation of the vehicle and the multi-level dirt index calculation and analysis mode at the scheduling level to save waiting resources.

[0009] In a preferred embodiment, step S2 includes the following contents: B1. Based on point cloud data and grayscale images , using the nested method of coarse segmentation + fine segmentation: Coarse segmentation: Define the main areas according to the geometric characteristics of the vehicle and extract the point cloud subsets corresponding to each area and grayscale sub-image ; Fine segmentation: In each main area, the grayscale gradient Based on the local changes of ; The final output nested structure ,in Indicates the main A main area.

[0010] In a preferred embodiment, B2. for each sub-region , combining the point cloud subset and grayscale sub-image in the corresponding sub-region to fit the pollution feature surface , the formula is as follows: ;in, and are the point cloud height value and grayscale value respectively. and is the base value, and They respectively represent the main area number into which the vehicle is divided and the local sub-area number within each main area.

[0011] In a preferred embodiment, B3. Local sub-region pollution index: In each local sub-region, the degree of deviation between the local geometric shape and the grayscale is accumulated using the pollution characteristic surface fitted in the corresponding region to form a comprehensive pollution intensity index, and the pollution level of the corresponding region is reflected by calculating the overall energy of the characteristic surface; Global regional pollution index: The pollution index of all local sub-regions in the main region is weighted averaged according to the proportion of their relative areas to obtain the overall pollution level of the corresponding main region; Global dirt index: The pollution index of all main areas is weighted averaged and then logarithm is taken to generate a comprehensive global dirt index.

[0012] In a preferred embodiment, B4. According to the global fouling index , classifying vehicles into lightly polluted and heavily polluted gears, and dynamically assigning speed limits for passing through the wash area , definition: 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, the speed limit value is dynamically calculated based on the gear signal through the following formula: ;in, is the basic speed limit corresponding to the gear position, It is a dynamic adjustment coefficient.

[0013] In a preferred embodiment, step S3 includes the following contents: C1. Read the vehicle identification and the corresponding pollution gear information, and design the cleaning area into multiple spray zones Each spray zone has independent nozzle angle and pressure control interface the number of C2. In each spray zone Internally, a set of rotating nozzles are used to achieve angle adaptive control; setting the angle control matrix ,in Representative nozzle In the spray zone The swing angle of They represent the spray zone numbers of the cleaning areas respectively; Indicates the number of the nozzle; defines the following angle adjustment logic: ;in, is the reference angle of the corresponding spray belt; represents the global dirtiness index, which is used to measure the overall pollution level of the vehicle; is a regional identification variable, which is 1 if it is a highly polluted area, otherwise it is 0; and It is an empirical coefficient used to adjust the sensitivity of the swing angle.

[0014] In a preferred embodiment, C3. Combine the speed limit value and the pollution gear to define the spray zone pressure control vector ,in For nozzle In the spray zone the pressure of the water flow applied; If the vehicle is marked as lightly polluted, the pressure of the spray belt is regulated to 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 improvement: ;in, For spray belt Base pressure; represents the global dirt index; is the maximum passing speed; is the current vehicle passing speed; To prevent small values ​​of zero denominator; is the basic gain coefficient of water flow pressure.

[0015] A mine management system based on intelligent Internet of Things, comprising: a multi-mode acquisition module, a partition empowerment module and a dynamic spray control module; Multi-mode acquisition module: Use the data acquisition device to synchronously scan the vehicle to be cleaned, collect multi-modal data on the vehicle's geometry and surface dirt distribution, including three-dimensional point cloud and grayscale image, and generate a preliminary information matrix; the output preliminary information matrix is ​​transmitted to the partition empowerment module for regional segmentation of various parts of the vehicle and calculation of dirt coverage; Zoning empowerment module: Based on the preliminary information matrix, the vehicle parts are segmented and the dirt coverage is calculated. After the global dirt index is generated, the vehicle is divided into lightly polluted or heavily polluted gears, and the corresponding zoning 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 programmable spray controller divides the cleaning area into multiple independently controlled spray zones, and uses the nozzle angle adjustment algorithm and water flow pressure control strategy to implement key cleaning on high-pollution areas.

[0016] The technical effects and advantages of a mine management method and management system based on intelligent Internet of Things of the present invention are as follows: The present invention realizes the precise and intelligent management of the mine vehicle cleaning process through the technical means of combining multimodal data acquisition, regional segmentation and dynamic cleaning control, and effectively solves the problems of low cleaning efficiency, serious resource waste and uneven cleaning effect in the prior art. The multimodal information of vehicle appearance and dirt distribution is obtained by using a data acquisition device, and the vehicle pollution gear is divided into a dynamic partition speed limit strategy in combination with the global dirt index, which significantly improves the cleaning coverage of high-pollution areas and resource conservation in low-pollution areas. Through a programmable spray controller and a dynamic control algorithm, the cleaning equipment can adaptively adjust the nozzle angle and water flow pressure according to the actual passing speed of the vehicle, the pollution gear and the local dirt distribution, thereby achieving 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 operating efficiency of mine vehicle cleaning, optimizes the allocation of cleaning resources, and reduces the delay of mine operations caused by vehicle queuing or insufficient cleaning, providing reliable technical support for the environmental protection and efficiency of mine operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a process of a mine management method based on intelligent Internet of Things according to the present invention; Figure 2 The present invention is a structural schematic diagram of a mine management system based on intelligent Internet of Things. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1: Figure 1 The present invention provides a mine management method based on intelligent Internet of Things, comprising: S1: Use a data acquisition device to synchronously scan the vehicle to be cleaned at the gate to obtain multimodal data on vehicle appearance and dirt distribution and generate a preliminary information matrix; S2: Based on the preliminary information matrix, the vehicle parts are regionalized and the dirt coverage is calculated. After the global dirt index is generated, the vehicle is divided into lightly polluted or heavily polluted gears, and the corresponding zone speed limit strategy is determined accordingly; S3: According to the zoning speed limit strategy, the cleaning area is divided into multiple independently controllable spray zones through a programmable spray controller, and the nozzle angle adjustment algorithm and water flow pressure control strategy are used to implement key cleaning in high-pollution areas.

[0020] At the mine gate, the frequent entry and exit of vehicles causes a large amount of mud, mineral powder and other dirt to accumulate on the surface and chassis of the vehicle body. These dirt not only affect the normal operation of the vehicle and increase maintenance costs, but may also cause secondary pollution to the environment. Therefore, accurately detecting and evaluating the distribution and degree of dirt on the vehicle is of great significance for optimizing the cleaning process, improving cleaning efficiency, reducing resource waste and ensuring environmental protection. Step S1 combines three-dimensional laser scanning and multi-eye imaging technology to obtain multimodal data of vehicle appearance and dirt distribution in real time, and generates a detailed preliminary information matrix to provide an accurate basis for subsequent regional segmentation and speed limit strategies.

[0021] Step S1 includes the following contents: A1. Data collection and preprocessing: Deploy 3D laser scanning devices and multi-eye imaging devices at the mine gate to synchronously scan the vehicles to be cleaned and obtain point cloud data sets on the vehicle surface. and a collection of high-definition grayscale images ,in Indicates the vehicle surface The spatial coordinates of the points, For the image Line The grayscale value of the column pixels. The point cloud data is used to describe the geometry of the vehicle, and the grayscale image is used to reflect the density of dirt on the vehicle surface.

[0022] A2. Benchmark data alignment: Define the benchmark point cloud data set of non-polluting vehicles With the reference gray matrix 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. The spatial alignment eliminates the differences in vehicle positions and angles, making the calculation of morphological variation and grayscale deviation values ​​comparable.

[0023] A3. Pollution measurement calculation: Geometry Variation: Calculates the geometry variation of the vehicle , the formula is as follows: ;in, The first The coordinates of the points, Reflects the degree of contamination of the vehicle geometry relative to the reference state. This calculation is used to quantify the changes in the vehicle geometry caused by dirt adhesion and provides a basis for dirt assessment.

[0024] Grayscale deviation value: The grayscale image set obtained by the multi-eye imaging device is compared pixel by pixel with the reference grayscale matrix to calculate the grayscale deviation value. , the formula is as follows: ;in, is the reference gray value, Indicates the degree of optical deviation of dirt on the vehicle surface. This calculation is used to quantify the optical changes caused by dirt adhesion on the vehicle surface and assist in assessing the degree of contamination.

[0025] Pollution index: Combine the geometric variation and grayscale deviation value to generate the pollution index of the vehicle through nonlinear processing. For example, the pollution index is calculated by the following method: : ;in, and To enhance sensitivity to high pollution levels, the denominator It is used to balance the combined effects of geometric shape and optical deviation to avoid excessive amplification of extreme values. This pollution index is used to comprehensively evaluate the pollution level of the vehicle and ensure accurate classification of vehicles with different pollution levels.

[0026] If the pollution index is lower than the preliminary threshold, the vehicle will be subjected to a rapid cleaning mode of ordinary flight transition analysis; otherwise, the vehicle will be given priority at the scheduling level to be sent to the subsequent regional segmentation and multi-level dirt index calculation and analysis mode, thereby saving waiting resources and optimizing the overall traffic flow.

[0027] A4. Preliminary information matrix construction: The pollution index, point cloud data set, grayscale image set and vehicle unique identification information are structured and stored as a preliminary information matrix .

[0028] Through step S1, the multimodal data collection and fusion of the vehicle appearance and surface dirt distribution are realized by combining the three-dimensional laser scanning device and the multi-eye imaging device, and a data set containing pollution index and structured information matrix is ​​generated. This process not only ensures the high accuracy and comprehensiveness of dirt detection, but also effectively distinguishes vehicles with different pollution levels by accurately quantifying the degree of pollution. This processing method greatly improves the accuracy of the formulation of cleaning strategies, optimizes the allocation of cleaning resources, reduces the waste of cleaning time and water resources, and significantly improves the intelligence and automation level of the mine car cleaning process, thereby effectively ensuring the cleanliness of the mining environment and the efficient operation of vehicles.

[0029] The preliminary information matrix output by step S1 includes the overall pollution degree of the vehicle, as well as geometric and grayscale information for detailed analysis. In the mine car cleaning scenario, it is difficult to accurately locate the local high-pollution areas of the vehicle by relying solely on the overall pollution degree, and the speed limit strategy of a single gear cannot take into account the actual pollution differences. To this end, step S2 realizes accurate identification and quantitative evaluation of various parts of the vehicle through multi-level nested area division and pollution feature surface construction, and generates a speed limit strategy that can be dynamically adjusted according to the value based on the global dirt index, providing a more flexible decision-making basis for the key cleaning of step S3.

[0030] Step S2 includes the following contents: B1. Dynamic regional segmentation: The dirt distribution on the vehicle surface usually shows uneven characteristics, and global evaluation alone may ignore local high dirt areas. By performing hierarchical segmentation on the vehicle, the pollution characteristics of different parts and local details can be extracted separately to form structured data for input into subsequent steps.

[0031] Based on point cloud data and grayscale images , using the nested method of coarse segmentation + fine segmentation: Coarse segmentation: Define the main areas (such as roof, side, wheels, chassis) according to the geometric characteristics of the vehicle, and extract the point cloud subsets corresponding to each area and grayscale sub-image .

[0032] Fine segmentation: In each main area, the grayscale gradient Based on the local changes of .

[0033] The final output nested structure , ensuring that both large-scale area and detailed pollution characteristics are taken into account, among which Indicates the main A main area.

[0034] The segmentation strategy enables accurate capture of local pollution hotspots in vehicles, provides more fine-grained data support, and makes subsequent cleaning resource allocation more targeted.

[0035] B2. Construction of regional pollution characteristic surface: The independent analysis of single-point data or grayscale 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 can more intuitively reflect the regional pollution intensity. , combining the point cloud subset and grayscale sub-image in the corresponding sub-region to fit the pollution feature surface , the formula is as follows: ;in, and are the point cloud height value and grayscale value respectively. and is the base value, and They represent the main area numbers of the vehicle (such as the roof, wheels, sidewalls, etc.) and the local sub-area numbers within each main area (such as the grooves on the wheels, the edges of the roof, etc.). Surface fitting can intuitively quantify the local dirt level and its spatial distribution, which is helpful for the precise calculation of the subsequent regional pollution index and the dynamic identification of high-pollution areas.

[0036] B3. Calculation of multi-level pollution index: The layer-by-layer synthesis of pollution indices can achieve a comprehensive assessment of pollution levels from local details to the global level, and suppress the influence of extreme values ​​through nonlinear processing.

[0037] Local sub-region pollution index: In each local sub-region, the pollution characteristic surface fitted in the region is used to accumulate the deviation between the local geometric shape and the grayscale to form a comprehensive pollution intensity index. This index reflects the pollution level of the region by calculating the overall energy of the characteristic surface (i.e. the cumulative deviation). Specifically: based on the surface , calculate the pollution index of the local sub-region The local sub-region pollution index is calculated based on the pollution characteristic surface. The core idea is to accumulate the morphology and grayscale deviations in the region, and the result is expressed as the pollution intensity of the sub-region. The higher the overall deviation of the characteristic surface, the greater the pollution index of the region.

[0038] Global regional pollution index: The pollution index of all local sub-regions in the main region is weighted averaged according to the proportion of their relative areas to obtain the overall pollution level of the main region. This method can balance the impact of local high-pollution areas and low-pollution areas on the overall pollution level, ensuring that the results can reflect the overall trend without losing the details. Specifically: Sub-regional pollution index Integrate into the corresponding main area , the formula is as follows: The global regional pollution index is obtained by accumulating the sub-regional pollution index by area weight. The core idea is to comprehensively calculate the overall pollution intensity of the main area based on the area proportion of each sub-region. The sub-region with a large area proportion contributes more to the overall pollution index.

[0039] Global Dirt Index: The pollution index of all main areas is weighted averaged and then logarithmized to generate a comprehensive global dirt index. The purpose of taking the logarithm is to suppress the excessive impact of local extreme pollution areas on the overall results, while improving the ability to distinguish between moderate and mild pollution. The result is used to measure the pollution level of the entire vehicle. Specifically: the pollution index of all main areas is weighted averaged, and the result is logarithmized to generate the global dirt index. The global dirt index is based on the average pollution index of all main areas and is optimized through logarithmic transformation. The core idea is to comprehensively consider the pollution level of each main area and use logarithmic transformation to balance the influence of extreme values ​​to form an accurate and stable global pollution assessment index.

[0040] The layer-by-layer synthesis method ensures the accurate transmission of the pollution index from local to global. At the same time, the logarithmic processing is used to improve the ability to distinguish between moderate and light pollution and suppress the influence of extreme values ​​on the results.

[0041] B4. Pollution gear determination and dynamic speed limit adjustment: The speed limit strategy should not only be based on the classification of pollution gears, but also be combined with the specific value of the global dirt index to achieve dynamic adjustment of vehicles with different pollution levels, thereby balancing the cleaning effect and passing efficiency.

[0042] According to the global dirt index , classifying vehicles into lightly polluted and heavily polluted gears, and dynamically assigning speed limits for passing through the wash area .definition: 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.

[0043] If the pollution is severe, the speed limit is dynamically calculated based on the gear position signal using the following formula: ;in, is the basic speed limit corresponding to the gear position, is the dynamic adjustment coefficient. This formula implements the dynamic adjustment logic that the higher the pollution index, the lower the speed limit. The dynamic speed limit adjustment combined with specific values ​​ensures the cleaning effect of high-pollution vehicles while avoiding the waste of resources and time of low-pollution vehicles due to excessive speed limits, thereby improving the overall passing efficiency.

[0044] Classifying vehicles as lightly polluted or heavily polluted according to their overall pollution level and dynamically adjusting their speed in combination with the global dirt index can effectively achieve differentiated allocation of cleaning resources and improve cleaning efficiency: using a higher speed for lightly polluted vehicles can reduce queuing time and resource waste; and for heavily polluted vehicles, the speed limit strategy can extend their stay time in the cleaning area so that high-pollution areas can be fully cleaned, thus taking into account both cleaning effect and time management, while reducing the cost of low-pollution vehicles occupying cleaning equipment, and overall optimizing the resource utilization efficiency and traffic efficiency of the mine cleaning process.

[0045] During the cleaning process of mining vehicles, the surface pollution distribution of the vehicle usually shows significant unevenness. Highly polluted areas such as the chassis and tire grooves are difficult to clean, while other areas are relatively lightly polluted. The unified cleaning mode often leads to resource waste or incomplete cleaning. Step S3 divides the cleaning area into multiple independently controllable spray zones based on the precise control of the vehicle's global dirt index, pollution gear and dynamic speed limit strategy. Combined with the adaptive regulation of the nozzle angle and water flow pressure, differentiated cleaning is implemented for different polluted areas, effectively achieving key coverage of highly polluted areas, while reducing resource consumption in low-polluted areas, and significantly improving overall cleaning efficiency and resource utilization.

[0046] Step S3 includes the following contents: C1. Read the vehicle identification and the corresponding pollution gear information, and design the cleaning area into multiple spray zones according to the area division results of step S2 (such as roof, wheel, side wall, chassis) Each spray zone has independent nozzle angle and pressure control interface. The number of spray strips. These spray strips are physically aligned with the vehicle's passing trajectory to ensure that the vehicle moves at the speed limit When entering the cleaning area, each highly polluted area accurately corresponds to the corresponding spray zone.

[0047] C2. In each spray zone Internally, a set of rotatable nozzles are used to achieve angle adaptive control. Setting the angle control matrix ,in Representative nozzle In the spray zone The swing angle is They represent the spray zone numbers of the wash area (corresponding to different physical locations and covering different parts of the vehicle); Indicates the number of the nozzle (each spray zone usually contains multiple nozzles). To highlight the key points of cleaning, define the following angle adjustment logic: ;in, is the reference angle of the corresponding spray belt; represents the global dirtiness index, which is used to measure the overall pollution level of the vehicle; is a regional identification variable, which is 1 if it is a highly polluted area, otherwise it is 0; and It is an empirical coefficient used to adjust the sensitivity of the swing angle.

[0048] For highly polluted areas ( ), the function uses logarithmic logic to avoid excessive angle adjustment while ensuring coverage depth.

[0049] For lightly polluted areas ( ), the function uses square root logic to slow down the angle swing and avoid wasting resources.

[0050] C3. Combine the speed limit value and the pollution gear to define the spray zone pressure control vector ,in For nozzle In the spray zone The applied water pressure.

[0051] If the vehicle is marked as slightly contaminated, the spray belt pressure regulates the water pressure to the standard water flow pressure value to save water resources and shorten the cleaning time.

[0052] If the vehicle is marked as heavily polluted, the following logic is used for dynamic improvement: ;in, For spray belt Base pressure; represents the global dirt index; is the maximum passing speed; is the current vehicle passing speed; To prevent small values ​​of zero denominator; is the basic gain coefficient of water flow pressure.

[0053] : Enhance the response sensitivity to high-pollution vehicles. The higher the pollution index, the greater the water flow pressure.

[0054] The above formula takes into account the effect of vehicle speed on cleaning time. The faster the speed, the higher the pressure to make up for the lack of cleaning time. The slower the speed, the lower the pressure to save resources.

[0055] C4. When the vehicle passes through each spray zone at the current vehicle passing speed, if the sensor detects that the highly polluted area is not effectively cleaned (for example, by setting a residual threshold for judgment), a key cleaning instruction can be temporarily triggered: ;in and They are real-time correction amounts, which are used to further increase the nozzle swing angle and water pressure to achieve secondary enhancement.

[0056] Step S3 realizes the precise allocation and dynamic adjustment of cleaning resources through intelligent control based on the vehicle's global dirt index and zoning speed limit strategy. By dividing the cleaning area into independently controllable spray zones and combining the adaptive control logic of nozzle angle and water flow pressure, differentiated cleaning can be implemented for vehicles and areas with different pollution levels: intensified cleaning of highly polluted areas to ensure that dirt is completely removed; reasonably reducing the cleaning intensity of lightly polluted areas to reduce the waste of water and electricity resources. In addition, real-time cleaning monitoring and dynamic compensation mechanisms 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 key cleaning strategies, providing efficient and environmentally friendly solutions for mining operations, and ultimately realizing the intelligent and refined management of vehicle cleaning.

[0057] Embodiment 2: Figure 2 The present invention provides a mine management system based on intelligent Internet of Things, including: a multi-mode acquisition module, a partition empowerment module and a dynamic spray control module; Multi-mode acquisition module: Use the data acquisition device to synchronously scan the vehicle to be cleaned, collect multi-modal data on the vehicle's geometry and surface dirt distribution, including three-dimensional point cloud and grayscale image, and generate a preliminary information matrix; the output preliminary information matrix is ​​transmitted to the partition empowerment module for regional segmentation of various parts of the vehicle and calculation of dirt coverage; Zoning empowerment module: Based on the preliminary information matrix, the vehicle parts are segmented and the dirt coverage is calculated. After the global dirt index is generated, the vehicle is divided into lightly polluted or heavily polluted gears, and the corresponding zoning 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 programmable spray controller divides the cleaning area into multiple independently controllable spray zones, and uses the nozzle angle adjustment algorithm and water flow pressure control strategy to implement key cleaning on high-pollution areas.

[0058] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0059] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0060] It should be noted that, in this article, if there are relational terms such as first and second, etc., 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 variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0061] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A mine management method based on intelligent Internet of Things, characterized in that: Includes steps: S1: Use a data acquisition device to synchronously scan the vehicle to be cleaned at the gate to obtain multimodal data on vehicle appearance and dirt distribution and generate a preliminary information matrix; S2: Based on the preliminary information matrix, the vehicle parts are regionalized and the dirt coverage is calculated. After the global dirt index is generated, the vehicle is divided into lightly polluted or heavily polluted gears, and the corresponding zone speed limit strategy is determined accordingly; S3: According to the zoning speed limit strategy, the cleaning area is divided into multiple independently controlled spray zones through a programmable spray controller, and the nozzle angle adjustment algorithm and water flow pressure control strategy are used to implement key cleaning in high-pollution areas.

2. A mine management method based on intelligent Internet of Things according to claim 1, characterized in that: Step S1 includes the following contents: A1. Deploy 3D laser scanning devices and multi-eye imaging devices at the mine gate to synchronously scan the vehicles to be cleaned and obtain point cloud data sets on the vehicle surface. and a collection of high-definition grayscale images ,in Indicates the vehicle surface The spatial coordinates of the points, For the image Line The grayscale value of the column pixels; point cloud data is used to describe the geometry 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 non-polluting vehicles With the reference gray matrix ; Use the point cloud registration algorithm to align the point cloud data with the reference point cloud data in the same coordinate system.

3. A mine management method based on intelligent Internet of Things according to claim 2, characterized in that: A3. Pollution measurement calculation: Geometry Variation: Calculates the geometry variation of the vehicle , the formula is as follows: ;in, The first The coordinates of the points, Reflects the degree of pollution of the vehicle geometry relative to the reference state; Grayscale deviation value: The grayscale image set obtained by the multi-eye imaging device is compared pixel by pixel with the reference grayscale matrix to calculate the grayscale deviation value. , the formula is as follows: ;in, is the reference gray value, Indicates the degree of optical deviation of dirt on the vehicle surface; Pollution index: Combine the geometric variation and grayscale deviation value to generate the vehicle's pollution index through nonlinear processing. If the pollution index is lower than the initial threshold, the vehicle will be subjected to a fast cleaning mode of ordinary flight transition analysis. Otherwise, it is necessary to prioritize the regional segmentation of the vehicle and the multi-level dirt index calculation and analysis mode at the scheduling level to save waiting resources.

4. A mine management method based on 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 , using the nested method of coarse segmentation + fine segmentation: Coarse segmentation: Define the main areas according to the geometric characteristics of the vehicle and extract the point cloud subsets corresponding to each area and grayscale sub-image ; Fine segmentation: In each main area, the grayscale gradient Based on the local changes of ; Final output nested structure ,in Indicates the main A main area.

5. A mine management method based on intelligent Internet of Things according to claim 4, characterized in that: B2. For each sub-region , combining the point cloud subset and grayscale sub-image in the corresponding sub-region to fit the pollution feature surface , the formula is as follows: ;in, and are the point cloud height value and grayscale value respectively. and is the base value, and They respectively represent the main area number into which the vehicle is divided and the local sub-area number within each main area.

6. A mine management method based on intelligent Internet of Things according to claim 5, characterized in that: B3. Local sub-region pollution index: In each local sub-region, the pollution characteristic surface fitted in the corresponding region is used to accumulate the deviation between the local geometric shape and the grayscale to form a comprehensive pollution intensity index. The pollution level of the corresponding region is reflected by calculating the overall energy of the characteristic surface. Global regional pollution index: The pollution index of all local sub-regions in the main region is weighted averaged according to the proportion of their relative areas to obtain the overall pollution level of the corresponding main region; Global dirt index: The pollution index of all main areas is weighted averaged and then logarithm is taken to generate a comprehensive global dirt index.

7. A mine management method based on intelligent Internet of Things according to claim 6, characterized in that: B4. Based on the global dirt index , classifying vehicles into lightly polluted and heavily polluted gears, and dynamically assigning speed limits for passing through the wash area , definition: 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, the speed limit value is dynamically calculated based on the gear signal through the following formula: ;in, is the basic speed limit corresponding to the gear position, It is a dynamic adjustment coefficient.

8. A mine management method based on 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 gear information, and design the cleaning area into multiple spray zones Each spray zone has independent nozzle angle and pressure control interface the number of C2. In each spray zone Internally, a set of rotating nozzles are used to achieve adaptive angle control; Setting the angle control matrix ,in Representative nozzle In the spray zone The swing angle is They represent the spray zone numbers of the cleaning areas respectively; Indicates the number of the nozzle; Define the following angle adjustment logic: ;in, is the reference angle of the corresponding spray belt; represents the global dirtiness index, which is used to measure the overall pollution level of the vehicle; is a regional identification variable, which is 1 if it is a highly polluted area, otherwise it is 0; and It is an empirical coefficient used to adjust the sensitivity of the swing angle.

9. A mine management method based on intelligent Internet of Things according to claim 8, characterized in that: C3. Combine the speed limit value and the pollution gear to define the spray zone pressure control vector ,in For nozzle In the spray zone the pressure of the water flow applied; If the vehicle is marked as slightly contaminated, the spray belt pressure will regulate the water pressure to 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 improvement: ;in, For spray belt Base pressure; represents the global dirt index; is the maximum passing speed; is the current vehicle passing speed; To prevent small values ​​of zero denominator; is the basic gain coefficient of water flow pressure.

10. A mine management system based on intelligent Internet of Things, used to implement a mine management method based on intelligent Internet of Things as claimed in any one of claims 1 to 9, characterized in that: include: Multi-mode acquisition module, partition empowerment module and dynamic spray control module; Multi-mode acquisition module: Use the data acquisition device to synchronously scan the vehicle to be cleaned, collect multi-modal data on the vehicle's geometry and surface dirt distribution, including three-dimensional point cloud and grayscale image, and generate a preliminary information matrix; The output preliminary information matrix is ​​passed to the partition empowerment module for regional segmentation of various parts of the vehicle and calculation of dirt coverage; Zoning empowerment module: Based on the preliminary information matrix, the vehicle parts are segmented and the dirt coverage is calculated. After the global dirt index is generated, the vehicle is divided into lightly polluted or heavily polluted gears, and the corresponding zoning 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 programmable spray controller divides the cleaning area into multiple independently controlled spray zones, and uses the nozzle angle adjustment algorithm and water flow pressure control strategy to implement key cleaning on high-pollution areas.

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