Real-time laser coal checking system and method thereof

Abstract

The invention discloses a real-time laser coal checking system and method, and relates to the technical field of material management and inventory checking, and the system comprises the following components: a laser detection module, a data processing module, a communication module, a monitoring module and a server module. The laser detection module is adopted to perform high-frequency scanning on the coal pile to obtain the three-dimensional point cloud data of the coal pile, the crown block displacement data measured by the laser range finder and the material and density information of the material pile are combined, the volume and weight of the coal pile are calculated through an algorithm, the outer contour of the coal pile can be determined more accurately, and the accuracy of the coal pile is improved. According to the shape characteristics and the density distribution condition of the coal pile, the coal pile is divided into a plurality of small geometries for volume calculation, and compared with traditional manual measurement or simple geometric shape approximation, the method has the advantages that the measurement precision of the volume and the weight of the coal pile can be remarkably improved; and more accurate data support is provided for material management and inventory checking of enterprises.

Description

Technical Field

[0001] The invention belongs to the technical field of material management and inventory counting, and in particular relates to a real-time laser coal counting system and method thereof. Background Art

[0002] In material management and inventory counting, the volume and weight measurement of coal piles is an important link, which is of great significance to the production operation and cost control of enterprises. However, the traditional coal pile measurement method has many shortcomings, such as low measurement accuracy and low efficiency, which can hardly meet the needs of modern enterprises for material management.

[0003] There are shortcomings in the traditional coal pile measurement method. The irregularity of the coal pile shape and the difference in density distribution are ignored, resulting in large errors in the measurement results. The data processing module usually relies on manual operation, which is not only inefficient but also easily affected by human factors, further increasing the measurement error.

[0004] In summary, the traditional coal pile measurement method has many shortcomings and needs a more accurate, efficient and real-time coal pile measurement system to replace it. Therefore, the real-time laser coal disc system and method proposed in the present invention are particularly important. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a real-time laser coal counting system and method. The system uses a laser detection module to perform high-frequency scanning on the coal pile to obtain three-dimensional point cloud data of the coal pile, and combines algorithms and data processing technology to achieve accurate measurement of the volume and weight of the coal pile, thereby providing more accurate data support for the company's material management and inventory counting.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A real-time laser coal-discharging system, the system comprises a laser detection module, a data processing module, a communication module, a monitoring module and a server module;

[0008] The laser detection module: a laser detector installed on the top of the overhead crane facing downward and a target set at one end of the material area. The laser detector has a built-in GIMP program to perform high-frequency scanning on the material pile, support the output of radar data points per frame and three-dimensional scanning data points, and the laser rangefinder uses a pulse method to emit laser to the target, and then measures the displacement of the overhead crane according to the speed of light and time;

[0009] The data processing module receives the material pile scanning results of the laser detector and the overhead crane displacement data measured by the laser rangefinder, and calculates the volume and weight of the material pile by combining the material and density information of the material pile through the following algorithm: Assume that the point cloud data set of the material pile in three-dimensional space is P = {p1, p2, p3, ..., pn}, where each point p i =(x i ,y i , z i ), x i ,y i 、z i are the coordinates of the point in the three-dimensional coordinate system, and define a distance threshold d based on the surface roughness characteristics of the pile. th , which is determined by: performing laser scanning on sample piles of different types of coal under different stacking conditions, obtaining their surface point cloud data, and calculating the Euclidean distance from each point to its adjacent points The distribution of these distances is counted, and the kth percentile of the distribution is taken, where k is determined to be 75 based on a large number of experiments, as the distance threshold d th , by calculating the Euclidean distance of each point to its neighboring points, when d(p i , p j )>d th When , the point is considered as a boundary point. All boundary points are connected to get the outer contour of the pile. The pile is divided into multiple small geometric bodies according to the outer contour. For each small geometric body, its approximate geometric shape and volume are determined by the following method: Let E = {e1, e2, ..., e m} is the set of small geometric bodies obtained by segmentation. For each e l ∈E, define a shape feature vector S l =(s l1 ,s l2 ,…,s lq ), where s li Represents the i-th shape feature of the l-th small geometric body. By laser scanning and data analysis of a large number of standard geometric bodies with known shapes and volumes, a mapping relationship M(S)→G between shape feature vectors and geometric shapes is established, where M is the mapping function, S is the shape feature vector, and G is the geometric shape. According to the mapping relationship, each small geometric body e is determined l The approximate geometry of G l ;

[0010] For a small geometric body that is determined to be a rectangular parallelepiped, its volume Among them, l l 、w l 、h l They are the length, width and height of the cuboid respectively, and are determined by the following method: principal component analysis is performed on the point cloud data of the stockpile surface in the corresponding area of ​​the small geometric body to obtain three principal component directions, whose lengths correspond to the length, width and height of the cuboid respectively. The length values ​​are determined according to the distribution range of the point cloud data in the principal component directions. For the small geometric body determined to be in the shape of a cone, its volume Among them, rl is the radius of the cone base, h l The cone height is determined by analyzing the point cloud data of the pile surface in the corresponding area of ​​the small geometric body, and the center position of the cone bottom is determined. The average distance from the center to the boundary point is calculated as the bottom radius r l , determine the cone height h according to the distribution range of the point cloud data in the direction perpendicular to the bottom surface l ;

[0011] Total volume of the stockpile Among them, V l is the volume of the lth small geometric body, assuming the density of the pile material is ρ, then the weight of the pile is W = V × ρ;

[0012] The communication module adopts 5G wireless communication to achieve real-time data transmission and interconnection between systems;

[0013] The monitoring module; video monitoring and panoramic video monitoring equipment installed next to the laser detector, used to provide real-time monitoring images of the material yard;

[0014] The server module has a built-in GIMP-PF program, supports the access of multiple silo application units, supports inventory operation control, system status forwarding management, data storage, log management, and the platform system develops external database connection ports and publishes point cloud files through Http. The server can also connect to the DCS data within the enterprise, read the coal transportation flow data of each node in the feeding area and the discharging area, and dynamically form a material flow chart in combination with the coal yard inventory data.

[0015] Furthermore, the dimensions of the laser detector in the laser detection module are D1=130×102×157mm when the line is at the rear or D2=108×102×180mm when the line is at the bottom, the weight of the whole machine is W1=1.5kg, the types of external interfaces include power supply, network, YX, YK, synchronization, RS232 / RS485, the number of external interfaces are respectively power supply interface I1=1, network interface I2=1, YXI3=2, YKI4=2, synchronization I5=1, RS232 / RS485I6=1, and the external interface type is power interface is J1=5-core aviation socket, The network interface is J2 = 4-core aviation socket, 10 interface 1 is J3 = 8-core aviation socket, IO interface 2 is J4 = 8-core aviation socket, the working voltage is V1 = DC24V ± 4V, the starting current is A1 = 2A@DC24V, the heating power supply is V2 = DC24V, the power consumption of the whole machine is typically P1 ≤ 15W, the heating is P2 ≤ 55W, the target reflectivity is R1 = 2% ~ 255%, the scanning angle is θ1 = 270°, the scanning frequency is f1 = 25Hz, 50Hz, 100Hz, the transmission frequency is f2 = 144K, the angular resolution is α1 = 0.0625°, 0.12 5°, 0.25°, the spot characteristic is a circular spot, the divergence angle is β1=6±1mrad, the umbrella is β2=±5mrad, the measuring distance is L1=30m@10%, 80m@90%, the measuring accuracy is ΔL1=±30mm, the repeatability is ΔL2=±20mm, the response time is T1=90±20ms, the power adaptability is V3=DC24V±4V, the remote signal interface voltage is V4=DC6~12V, the remote control interface voltage is V5≤DC60V, the installation requirements are outdoor with a protective cover, the altitude is H1≤6000m, and the working temperature is T2=-55ひ+70び, working humidity is H2=0%95%, alternating damp heat is resistant to damp heat, salt spray corrosion is resistant to corrosion, vibration resistance is A2=IEC60068-2-6:2007, IP protection level is IP1=IP67, anti-strong light interference is I1=80000lx, electromagnetic compatibility, electrostatic discharge immunity is contact E1=2 level 4KV air E2=3 level 8KV, electrical fast transient pulse group immunity is F1=3 level, power supply V6=2KV signal V7=1KV, surge impact immunity is F2=3 level: line-line V8=1KV line-ground V9=2KV, network port line-ground V 10 =6KV.

[0016] Furthermore, the power supply voltage of the laser rangefinder in the laser detection module is V 11 =DC10V-30V, residual ripple is V 12≤5V, power consumption P3≤2.1W, initialization time T3≤250ms, preheating time T4≤10s, shell material aluminum alloy AL, organic glass PMMA, connection type J5 = M12 waterproof connector lead, display D3 = 5-digit digital tube, 5xLED, weight W2 = 360g, shell protection level IP2 = IP65, light source is red laser, laser level L2 = 1 (IEC60825-1: 2014, EN60825-1: 2014), measurement range L3 = 0-200m adjustable accuracy It is ΔL3=1.5mm+d0.5%, the resolution is ΔL4=1mm, the output time is T5≥4ms, the output is analog (4-1)20MA)+485, the operating environment temperature is T6=-15℃ to +50℃, the storage environment temperature is T7=-20℃ to +60℃, the suitable maximum relative air humidity is H3≤85%, the type of ambient light resistance is I2=40,000lx, the vibration resistance is A3=EN60068-2-6, EN60068-2-64, and the impact resistance is A4=EN60068-2-27.

[0017] Furthermore, the radar application unit in the laser detection module has an overall size of D4=445mm×360mm×158mm, a bottom outlet, and a power supply voltage of V 13 =165VAC-265VAC, typical V 14 =220VAC, the supply current is I7≥2.5A@220VAC, the typical operating power is P4≤15W@without external devices, the heating power consumption at low temperature is P5≤150W, and the output voltage is V 15 =DC24V, the output power of a single 24V interface is typically P6≤70W, the total output power is typically P7≤290W, the network bandwidth is B1=100M, and the remote signal interface is V 16 =0VDC-5VDC low level input, 8VDC-30VDC high level input, remote control interface is relay output, normally open and normally closed optional, contact load current maximum I8=1A, TF card capacity standard default C1=64G, other capacities optional, communication interface includes network port: I9=6; RS485: I 10 =1; RS232: I 11 =1; remote signal / remote control includes YX:I 12 =4-way; YK:I 13 =2-way, output power interface includes DC24V:I 14 = 5 channels, of which I 15 = 2-way direct output, I 16=3-way relay controllable output, can power N1=5 normal temperature laser detectors within the working temperature range, the Wi-Fi interface communication distance is D5≤10 meters, the working environment temperature is T8=-40℃~+65℃, the working environment humidity is H4=35, the storage environment temperature is T9=-40℃~+65℃, the storage environment soil layer is H5=35, the product protection level is IP3=IP55, and the use environment is outdoor.

[0018] Furthermore, the panoramic video monitoring sensor types in the monitoring module include panoramic S1 = 1 / 2.8" ProgressiveScanCMOS, detail S2 = 1 / 2.8" ProgressiveScanCMOS, and shutter is T 10 =1s1 / 30,000s, minimum illumination for panoramic: L4=0.03Lux@(F2.0, AGCON); black and white: L5=0.015Lux@(F2.0, AGCON); 0LuxwitIR;

[0019] L6=0.03Lux@(F1.6, AGCON); Black and white: L7=0.015Lux@(F1.6, AGCON); 0LuxwithIR slow shutter support, focus mode is semi-automatic / manual / automatic, day and night conversion mode is automatic ICR color to black, day and night conversion mode is day, night, automatic, timed switching, backlight compensation support, strong light suppression support, 3D noise reduction support, regional exposure support, image settings include saturation S3, brightness S4, contrast S5, sharpness S6, image parameter switching support, white balance includes automatic white balance W3, sodium lamp W4, fluorescent lamp, indoor W6, outdoor W7, manual white balance W8, locked white balance W9, privacy mask up to N2=24 blocks, support multiple color settings, quadrilateral area, signal-to-noise ratio is R2>52dB, general functions include mirror M1, password protection P8, watermark technology W 10 , IP address filter F3, lens focal length is panoramic f3 = 2.8mm; detail f4 = 8mm, field of view angle is panoramic horizontal field of view angle: θ2 = 99.5:116.1; horizontal field of view angle: θ3 = 37.5:43.3, maximum aperture number panoramic is F1 = F2.0; F2 = F1.6, infrared irradiation distance is infrared: L8 = 15m; white light L9 = 10m.

[0020] Furthermore, the server module product type is enterprise-level, the product category is rack-mounted, the product structure is H6=2U, the CPU type is Intel Xeon Bronze, eight cores, the memory capacity is M2=16GB, the memory type is DDR4, the RAID mode is R3=RAID1, the hard disk is H7=1.2T, and the interface includes I 17 =2×RJ45 network interface, I18 =1×RJ45 dedicated management port, power quantity is I 19 =1, power supply power is P9=750W, voltage is V 17 =AC220V, built-in program is built-in GIMP program, supports access to multiple silo application units, supports inventory operation control, system status forwarding management, data storage, log management, platform system external development database connection port, point cloud file is published through Http.

[0021] Furthermore, the monitoring terminal product type is a commercial computer, the product category is a desktop computer, the CPU type is Intel Core i510500, the memory capacity is M3=8G, the memory type is DDR4, the graphics card is G1=2G independent graphics card, the hard disk is H8=1TB, and the interface includes I 20 =1×RJ45, other interfaces include I 21 =1×power interface, I 22 =1×COM serial port, I 23 =2×PS / 2, USB is N3=4, power supply quantity is I 24 =1, the power supply is P 10 =260W, voltage is V 18 =AC220V.

[0022] Furthermore, the communication module 5G wireless communication has the characteristics of high speed, low latency and large capacity. The specific parameters of its frequency band, modulation mode and coding mode are determined according to the system's requirements for data transmission and the 5G technical standards. In the pan coal system, the coal pile data scanned by the laser detector, the overhead crane displacement data measured by the laser rangefinder and the monitoring screen collected by the video monitoring equipment need to be transmitted to the server module in a timely manner for processing and storage. The high speed of 5G wireless communication can meet the needs of rapid transmission of large amounts of data. Its low latency characteristics can ensure the timeliness of data transmission and avoid system errors and instability caused by transmission delays. The large capacity characteristics can meet the future expansion needs of the system without affecting the normal operation of the system due to the limitation of communication bandwidth.

[0023] The frequency band selection for 5G wireless communication is based on the electromagnetic spectrum conditions of the system environment and the availability and performance characteristics of the 5G frequency band. The modulation and coding methods are determined based on the reliability and efficiency requirements of data transmission. Through a large number of experiments and tests, and combined with the specific requirements of the system for data transmission, the most suitable modulation and coding methods are determined to ensure high quality and high efficiency of data transmission.

[0024] Furthermore, the method for dividing the coal pile into small geometric bodies in the data processing module is that when dividing the coal pile into small geometric bodies, in addition to determining the approximate geometric shapes of different regions according to the shape characteristics of the coal pile and the empirical rules in practical applications, the density distribution inside the coal pile and the texture characteristics on the surface of the coal pile are also considered;

[0025] As for the density distribution inside the coal pile, the density distribution inside the coal pile is inferred by analyzing the point cloud data on the surface of the coal pile, combining the penetration ability of laser scanning and the study of the optical characteristics of coal materials with different densities. For areas with relatively uniform density, simpler geometric shapes such as rectangular blocks or cylinders are used for approximation. For areas with uneven density, they are divided into multiple small areas with different geometric shapes according to the density change trend. As for the texture features of the coal pile surface, the texture feature vector is extracted by analyzing the texture of the point cloud data on the surface of the coal pile. According to the mapping relationship between the texture feature vector and the geometric shape, the approximate geometric shape of each small geometric body is further determined.

[0026] This method of segmenting the coal pile according to its internal density distribution and surface texture features can more accurately calculate the volume of the coal pile and improve the measurement accuracy of the system. At the same time, by continuously updating and optimizing the mapping relationship between texture feature vectors and geometric shapes, and adjusting the segmentation method based on feedback from actual applications, the system can be adapted to different types of coal piles and different stacking conditions, further improving the applicability and accuracy of the system.

[0027] On the other hand, a real-time laser coal disc method is characterized in that the method comprises the following specific steps:

[0028] S100, laser detection: a laser detector facing downwards installed on the top of the overhead crane and a target set at one end of the material area. The laser detector has a built-in GIMP program to perform high-frequency scanning of the material pile, support the output of radar data points per frame and 3D scanning data points. The laser rangefinder uses the pulse method to emit laser to the target, and then measures the displacement of the overhead crane based on the speed of light and time.

[0029] S200, data processing: receiving the material pile scanning results from the laser detector and the overhead crane displacement data measured by the laser rangefinder, and combining the material and density information of the material pile, calculating the volume and weight of the material pile through the following algorithm: Assume that the point cloud data set of the material pile in three-dimensional space is P = {p1, p2, p3, ..., p n}, where each point p i =(x i ,y i , z i ), x i ,y i 、z iare the coordinates of the point in the three-dimensional coordinate system, and define a distance threshold d based on the surface roughness characteristics of the pile. th , which is determined by: performing laser scanning on sample piles of different types of coal under different stacking conditions, obtaining their surface point cloud data, and calculating the Euclidean distance from each point to its adjacent points The distribution of these distances is counted, and the kth percentile of the distribution is taken, where k is determined to be 75 based on a large number of experiments, as the distance threshold d th , by calculating the Euclidean distance of each point to its neighboring points, when d(p i , p j )>d th When , the point is considered as a boundary point. All boundary points are connected to get the outer contour of the pile. The pile is divided into multiple small geometric bodies according to the outer contour. For each small geometric body, its approximate geometric shape and volume are determined by the following method: Let E = {e1, e2, ..., e m} is the set of small geometric bodies obtained by segmentation. For each e l ∈E, define a shape feature vector S l =(s l1 ,s l2 ,…,s lq ), where s li Represents the i-th shape feature of the l-th small geometric body. By laser scanning and data analysis of a large number of standard geometric bodies with known shapes and volumes, a mapping relationship M(S)→G between shape feature vectors and geometric shapes is established, where M is the mapping function, S is the shape feature vector, and G is the geometric shape. According to the mapping relationship, each small geometric body e is determined l The approximate geometry of G l ;

[0030] For a small geometric body that is determined to be a rectangular parallelepiped, its volume Among them, l l 、w l 、h l They are the length, width and height of the cuboid respectively, and are determined by the following method: principal component analysis is performed on the point cloud data of the stockpile surface in the corresponding area of ​​the small geometric body to obtain three principal component directions, whose lengths correspond to the length, width and height of the cuboid respectively. The length values ​​are determined according to the distribution range of the point cloud data in the principal component directions. For the small geometric body determined to be in the shape of a cone, its volume Among them, r l is the radius of the cone base, h l The cone height is determined by analyzing the point cloud data of the pile surface in the corresponding area of ​​the small geometric body, and the center position of the cone bottom is determined. The average distance from the center to the boundary point is calculated as the bottom radius r l, determine the cone height h according to the distribution range of the point cloud data in the direction perpendicular to the bottom surface l ;

[0031] Total volume of the stockpile Among them, V l is the volume of the lth small geometric body, assuming the density of the pile material is ρ, then the weight of the pile is W = V × ρ;

[0032] S300, communication module; uses 5G wireless communication for real-time data transmission and interconnection between systems;

[0033] S400, monitoring module; video monitoring and panoramic video monitoring equipment installed next to the laser detector, used to provide real-time monitoring images of the material yard;

[0034] S500, server module; built-in GIMP-PF program, supports access to multiple silo application units, supports inventory operation control, system status forwarding management, data storage, log management, platform system external development database connection port, point cloud file is published through Http, and the server can connect to the DCS data within the enterprise, read the coal transportation flow data of each node in the feeding area and the discharging area, and combine the coal yard worm material data to dynamically form a material flow chart.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] (1) The present invention uses a laser detection module to perform high-frequency scanning on the coal pile to obtain three-dimensional point cloud data of the coal pile. Combined with the overhead crane displacement data measured by the laser rangefinder and the material and density information of the material pile, the volume and weight of the coal pile are calculated through an algorithm. The outer contour of the coal pile can be determined more accurately, and the coal pile can be divided into multiple small geometric bodies for volume calculation according to the shape characteristics and density distribution of the coal pile. Compared with traditional manual measurement or simple geometric shape approximation, this method can significantly improve the measurement accuracy of the volume and weight of the coal pile, and provide more accurate data support for the company's material management and inventory counting.

[0037] (2) The present invention realizes the real-time transmission of coal pile data and the interconnection between systems by adopting a 5G wireless communication module. The coal pile data scanned by the laser detector, the overhead crane displacement data measured by the laser rangefinder, and the monitoring images collected by the video monitoring equipment can be timely transmitted to the server module for processing and storage. The real-time monitoring images provided by the monitoring module enable the operator to grasp the dynamic situation of the material yard at any time. These functions not only improve the operating efficiency, but also make the enterprise's material management more convenient and efficient. The server module also supports the access of multiple silo application units, supports inventory operation control, system status forwarding management, data storage and log management functions, and further improves the enterprise's material management level. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 An operation flow chart of a real-time laser coal discing system and method thereof. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0041] Embodiment 1 :

[0042] like Figure 1 As shown, this embodiment describes that in a large thermal power plant, the coal yard plays a core role in storing and producing fuel. Accurate inventory of coal piles is crucial to ensuring power generation efficiency and optimizing fuel management. However, traditional inventory methods are often inefficient and have large errors, which makes it difficult to meet the precise fuel management requirements of modern thermal power plants. For this reason, we have introduced a real-time laser coal counting system, which aims to achieve real-time and accurate inventory of coal piles through high-tech means.

[0043] A laser detector facing the coal pile is installed on the top of the overhead crane, and a target is set at one end of the coal yard. The laser detector has a built-in GIMP program, which can perform high-frequency scanning on the coal pile and obtain three-dimensional scanning data points of the material pile. These data points can accurately reflect the shape and height of the coal pile. The laser rangefinder uses the pulse method to emit laser to the target and accurately measures the displacement of the overhead crane according to the speed of light and time, thereby achieving precise control of the coal pile scanning process.

[0044] The data processing module receives the scanning results from the laser detector and the displacement data measured by the laser rangefinder. Combined with the material and density information of the coal pile, the data processing module calculates the volume and weight of the coal pile through advanced algorithms. The point cloud data set of the coal pile in three-dimensional space is P = {p1, p2, p3, ..., p n}, where each point p i =(x i ,y i , z i), x i ,y i 、z i are the coordinates of the point in the three-dimensional coordinate system, and define a distance threshold d based on the surface roughness characteristics of the pile. th , which is determined by: performing laser scanning on sample piles of different types of coal under different stacking conditions, obtaining their surface point cloud data, and calculating the Euclidean distance from each point to its adjacent points The distribution of these distances is counted, and the kth percentile of the distribution is taken, where k is determined to be 75 based on a large number of experiments, as the distance threshold d th , by calculating the Euclidean distance of each point to its neighboring points, when d(p i , p j )>d th When , the point is considered as a boundary point. All boundary points are connected to get the outer contour of the pile. The pile is divided into multiple small geometric bodies according to the outer contour. For each small geometric body, its approximate geometric shape and volume are determined by the following method: Let E = {e1, e2, ..., e m} is the set of small geometric bodies obtained by segmentation. For each e l ∈E, define a shape feature vector S l =(s l1 ,s l2 ,…,s lq ), where s li Represents the i-th shape feature of the l-th small geometric body. By laser scanning and data analysis of a large number of standard geometric bodies with known shapes and volumes, a mapping relationship M(S)→G between shape feature vectors and geometric shapes is established, where M is the mapping function, S is the shape feature vector, and G is the geometric shape. According to the mapping relationship, each small geometric body e is determined l The approximate geometry of G l ;

[0045] For a small geometric body that is determined to be a rectangular parallelepiped, its volume Among them, l l 、w l 、h l They are the length, width and height of the cuboid respectively, and are determined by the following method: principal component analysis is performed on the point cloud data of the stockpile surface in the corresponding area of ​​the small geometric body to obtain three principal component directions, whose lengths correspond to the length, width and height of the cuboid respectively. The length values ​​are determined according to the distribution range of the point cloud data in the principal component directions. For the small geometric body determined to be in the shape of a cone, its volume Among them, r l is the radius of the cone base, h lThe cone height is determined by analyzing the point cloud data of the pile surface in the corresponding area of ​​the small geometric body, and the center position of the cone bottom is determined. The average distance from the center to the boundary point is calculated as the bottom radius r l , determine the cone height h according to the distribution range of the point cloud data in the direction perpendicular to the bottom surface l ;

[0046] Total volume of the stockpile Among them, V l is the volume of the lth small geometric body. Assuming the density of the pile material is ρ, the weight of the pile is W = V × ρ.

[0047] The communication module adopts 5G wireless communication technology to realize real-time transmission of coal pile data and interconnection between systems. This technology not only has the characteristics of high speed and low latency, but also can meet the future expansion needs of the system. The monitoring module includes video monitoring and panoramic video monitoring equipment, which can provide real-time monitoring images of the coal yard, which helps to ensure the safety and accuracy of the operation process, and at the same time facilitates managers to remotely monitor and manage the coal yard.

[0048] The server module has built-in GIMP-PF program, which supports the access and inventory operation control of multiple silo application units. The server is also responsible for the forwarding management, data storage and log management of the system status. Through the built-in database connection port and Http publishing function, the server can easily connect with the enterprise's internal DCS system, read the coal flow data of each node in the feeding area and the discharging area, and combine with the coal yard inventory data. The server can dynamically form a material flow chart to provide strong data support for the fuel management of thermal power plants.

[0049] Embodiment 2 :

[0050] This embodiment describes a large port with a busy coal transshipment business. The coal yard is an important part of the port operation. In order to improve the management efficiency and safety of the coal yard, the port introduced a real-time laser coal panning system.

[0051] A high-precision laser detector is installed on the top of the port's overhead crane. The detector has a built-in GIMP program and can perform high-frequency, high-precision three-dimensional scanning of the coal yard. The laser rangefinder uses the pulse method to emit laser to the target set at one end of the yard, and calculates the displacement of the overhead crane by measuring the speed of light and time, thereby ensuring the accuracy of the scanning data. The data processing module receives the scanning results from the laser detector and the displacement data of the laser rangefinder, and calculates the volume and weight of the coal yard through an algorithm based on the material properties and density information of the coal. The point cloud data set of the material pile in three-dimensional space is P = {p1, p2, p3, ..., p n}, where each point p i =(xi ,y i , z i ), x i ,y i 、z i are the coordinates of the point in the three-dimensional coordinate system, and define a distance threshold d based on the surface roughness characteristics of the pile. th , which is determined by: performing laser scanning on sample piles of different types of coal under different stacking conditions, obtaining their surface point cloud data, and calculating the Euclidean distance from each point to its adjacent points The distribution of these distances is counted, and the kth percentile of the distribution is taken, where k is determined to be 75 based on a large number of experiments, as the distance threshold d th , by calculating the Euclidean distance of each point to its neighboring points, when d(p i , p j )>d th When , the point is considered as a boundary point. All boundary points are connected to get the outer contour of the pile. The pile is divided into multiple small geometric bodies according to the outer contour. For each small geometric body, its approximate geometric shape and volume are determined by the following method: Let E = {e1, e2, ..., e m} is the set of small geometric bodies obtained by segmentation. For each e l ∈E, define a shape feature vector S l =(s l1 ,s l2 ,…,s lq ), where s li Represents the i-th shape feature of the l-th small geometric body. By laser scanning and data analysis of a large number of standard geometric bodies with known shapes and volumes, a mapping relationship M(S)→G between shape feature vectors and geometric shapes is established, where M is the mapping function, S is the shape feature vector, and G is the geometric shape. According to the mapping relationship, each small geometric body e is determined l The approximate geometry of G l ;

[0052] For a small geometric body that is determined to be a rectangular parallelepiped, its volume Among them, l l 、w l 、h l They are the length, width and height of the cuboid respectively, and are determined by the following method: principal component analysis is performed on the point cloud data of the stockpile surface in the corresponding area of ​​the small geometric body to obtain three principal component directions, whose lengths correspond to the length, width and height of the cuboid respectively. The length values ​​are determined according to the distribution range of the point cloud data in the principal component directions. For the small geometric body determined to be in the shape of a cone, its volume Among them, r l is the radius of the cone base, hl The cone height is determined by analyzing the point cloud data of the pile surface in the corresponding area of ​​the small geometric body, and the center position of the cone bottom is determined. The average distance from the center to the boundary point is calculated as the bottom radius r l , determine the cone height h according to the distribution range of the point cloud data in the direction perpendicular to the bottom surface l ;

[0053] Total volume of the stockpile Among them, V l is the volume of the lth small geometric body. Assuming the density of the pile material is ρ, the weight of the pile is W = V × ρ.

[0054] The monitoring module is equipped with video monitoring and panoramic video monitoring equipment next to the laser detector to provide real-time monitoring images of the coal yard, ensuring that staff can grasp the dynamic situation of the yard at any time. When abnormal conditions occur in the coal yard, the system will automatically issue an early warning to remind staff to take timely measures to ensure the safety and efficiency of port operations.

[0055] The communication module uses high-speed, low-latency, large-capacity 5G wireless communication technology to transmit real-time data to the server module, which ensures the timeliness and accuracy of the data and provides strong support for subsequent data processing and analysis. The server module has a built-in GIMP-PF program to support the access and data management of multi-silo application units. At the same time, the module is deeply connected with the port's logistics management system to achieve real-time sharing and updating of coal yard data, which helps the port to formulate more reasonable transshipment plans and inventory strategies, improve operational efficiency and reduce costs.

[0056] Based on the real-time coal-in-hand data and the information provided by the logistics management system, the port can formulate more accurate transshipment plans and inventory strategies. For example, based on the volume and weight data of the coal yard, the port can reasonably arrange the working time and task volume of the transshipment vehicles and loaders; at the same time, combined with the cargo information and transportation plan in the logistics management system, the port can optimize the coal stacking location and transshipment route to reduce transshipment time and cost. In addition, the port can also use real-time coal-in-hand data for inventory management and cost control. By accurately grasping the inventory situation of the coal yard, the port can reasonably arrange procurement and sales plans to avoid inventory backlogs and capital occupation. At the same time, through data analysis, potential waste and loss points can be discovered, and measures can be taken to improve and optimize.

[0057] The real-time laser coal counting system not only improves the safety and efficiency of port operations, but also contributes to environmental protection and sustainable development. By accurately calculating the volume and weight of the coal yard, the port can reasonably control the stacking and transshipment volume of coal, reduce dust pollution and energy consumption. At the same time, the system can also provide data support for the port's environmental monitoring and governance. For example, by monitoring the dust concentration and gas emissions in the coal yard, the port can promptly discover and deal with environmental pollution problems. At the same time, combined with real-time coal counting data, it can also evaluate the effectiveness of different environmental protection measures and make optimization adjustments.

[0058] After verification in actual application, the real-time laser coal-counting system has significantly improved the management efficiency and safety of the port's coal yard. It enables the port to more accurately grasp the inventory situation and transshipment plan of the coal yard, reducing waste and loss. At the same time, through real-time monitoring and early warning functions, it also ensures the safety and efficiency of port operations. In the future, with the continuous advancement of technology and the continuous expansion of application scenarios, the real-time laser coal-counting system is expected to be widely used and promoted in more fields. For example, it can be applied to other types of cargo yard management, warehousing and logistics. At the same time, through deep integration and innovative application of the Internet of Things and big data technologies, it can also provide intelligent and efficient solutions for more industries.

[0059] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A real-time laser coal disc system, characterized in that: The system includes a laser detection module, a data processing module, a communication module, a monitoring module and a server module; The laser detection module: a laser detector installed on the top of the overhead crane facing downward and a target set at one end of the material area. The laser detector has a built-in GIMP program to perform high-frequency scanning on the material pile, support the output of radar data points per frame and three-dimensional scanning data points, and the laser rangefinder uses a pulse method to emit laser to the target, and then measures the displacement of the overhead crane according to the speed of light and time; The data processing module receives the material pile scanning results of the laser detector and the overhead crane displacement data measured by the laser rangefinder, and calculates the volume and weight of the material pile by combining the material and density information of the material pile through the following algorithm: Assume that the point cloud data set of the material pile in three-dimensional space is P = {p1, p2, p3, ..., p n }, where each point p i =(x i ,y i ,z i ), x i ,y i 、z i are the coordinates of the point in the three-dimensional coordinate system, and define a distance threshold d based on the surface roughness characteristics of the pile. th , which is determined by: performing laser scanning on sample piles of different types of coal under different stacking conditions, obtaining their surface point cloud data, and calculating the Euclidean distance from each point to its adjacent points The distribution of these distances is counted, and the kth percentile of the distribution is taken, where k is determined to be 75 based on a large number of experiments, as the distance threshold d th , by calculating the Euclidean distance of each point to its neighboring points, when d(p i ,p j )>d th When , the point is considered as a boundary point. All boundary points are connected to get the outer contour of the pile. The pile is divided into multiple small geometric bodies according to the outer contour. For each small geometric body, its approximate geometric shape and volume are determined by the following method: Let E = {e1, e2, …, e m } is the set of small geometric bodies obtained by segmentation. For each e l ∈E, define a shape feature vector S l =(s l1 ,s l2 ,…,s lq ), where s li Represents the i-th shape feature of the l-th small geometric body. By laser scanning and data analysis of a large number of standard geometric bodies with known shapes and volumes, a mapping relationship M(S)→G between shape feature vectors and geometric shapes is established, where M is the mapping function, S is the shape feature vector, and G is the geometric shape. According to the mapping relationship, each small geometric body e is determined l The approximate geometry of G l ; For a small geometric body that is determined to be a rectangular parallelepiped, its volume Among them, l l 、w l 、h l They are the length, width and height of the cuboid respectively, and are determined by the following method: principal component analysis is performed on the point cloud data of the stockpile surface in the corresponding area of ​​the small geometric body to obtain three principal component directions, whose lengths correspond to the length, width and height of the cuboid respectively. The length values ​​are determined according to the distribution range of the point cloud data in the principal component directions. For the small geometric body determined to be in the shape of a cone, its volume Among them, r l is the radius of the cone base, h l The cone height is determined by analyzing the point cloud data of the pile surface in the corresponding area of ​​the small geometric body, and the center position of the cone bottom is determined. The average distance from the center to the boundary point is calculated as the bottom radius r l , determine the cone height h according to the distribution range of the point cloud data in the direction perpendicular to the bottom surface l ; Total volume of the stockpile Among them, V l is the volume of the lth small geometric body, assuming the density of the pile material is ρ, then the weight of the pile is W = V × ρ; The communication module adopts 5G wireless communication to achieve real-time data transmission and interconnection between systems; The monitoring module; video monitoring and panoramic video monitoring equipment installed next to the laser detector, used to provide real-time monitoring images of the material yard; The server module has a built-in GIMP-PF program, supports the access of multiple silo application units, supports inventory operation control, system status forwarding management, data storage, log management, and the platform system develops external database connection ports and publishes point cloud files through Http. The server can also connect to the DCS data within the enterprise, read the coal transportation flow data of each node in the feeding area and the discharging area, and dynamically form a material flow chart in combination with the coal yard inventory data.

2. According to claim 1, a real-time laser coal disc system is characterized in that: The laser detector in the laser detection module has an overall size of D1 = 130 × 102 × 157 mm when the line is at the rear or D2 = 108 × 102 × 180 mm when the line is at the bottom, and a weight of W1 = 1.5 kg. The types of external interfaces include power supply, network, YX, YK, synchronization, and RS232 / RS485. The number of external interfaces is respectively power supply interface I1 = 1, network interface I2 = 1, YXI3 = 2, YKI4 = 2, synchronization I5 = 1, RS232 / RS485I6 = 1, and the external interface type is power interface J1 = 5-core aviation socket, network interface is J2 = 4-core aviation socket, 10 interface 1 is J3 = 8-core aviation socket, IO interface 2 is J4 = 8-core aviation socket, the working voltage is V1 = DC24V ± 4V, the starting current is A1 = 2A@DC24V, the heating power supply is V2 = DC24V, the power consumption of the whole machine is typically P1≤15W, the heating is P2≤55W, the target reflectivity is R1 = 2% ~ 255%, the scanning angle is θ1 = 270°, the scanning frequency is f1 = 25Hz, 50Hz, 100Hz, the transmission frequency is f2 = 144K, the angular resolution is α1 = 0.0625°, 0.125°, 0.2 5°, the spot characteristic is a circular spot, the divergence angle is β1=6±1mrad, the umbrella is β2=±5mrad, the measuring distance is L1=30m@10%, 80m@90%, the measuring accuracy is ΔL1=±30mm, the repeatability is ΔL2=±20mm, the response time is T1=90±20ms, the power adaptability is V3=DC24V±4V, the remote signal interface voltage is V4=DC6~12V, the remote control interface voltage is V5≤DC60V, the installation requirements are outdoor with a protective cover, the altitude is H1≤6000m, the working temperature is T2=-55ひ+70び, the working The operating humidity is H2=0%95%, the alternating damp heat is resistant to damp heat, the salt spray corrosion is resistant to corrosion, the vibration resistance is A2=IEC60068-2-6:2007, the IP protection level is IP1=IP67, the anti-strong light interference is I1=80000lx, the electromagnetic compatibility, the electrostatic discharge immunity is contact E1=2 level 4KV air E2=3 level 8KV, the electrical fast transient pulse group immunity is F1=3 level, the power supply V6=2KV signal V7=1KV, the surge impact immunity is F2=3 level: line-line V8=1KV line-ground V9=2KV, the network port line-ground V10=6KV.

3. According to claim 1, a real-time laser coal disc system is characterized in that: The power supply voltage of the laser rangefinder in the laser detection module is V 11 =DC10V-30V, residual ripple is V 12 ≤5V, power consumption P3≤2.1W, initialization time T3≤250ms, preheating time T4≤10s, shell material aluminum alloy AL, organic glass PMMA, connection type J5=M12 waterproof connector lead, display D3=5-digit digital tube, 5xLED, weight W2=360gg, shell protection level IP2=IP65, light source is red laser, laser level L2=1 (IEC60825-1:2014, EN60825-1:2014), measurement range L3=0-200m adjustable accuracy It is ΔL3=1.5mm+d0.5%, the resolution is ΔL4=1mm, the output time is T5≥4ms, the output is analog (4-1)20MA)+485, the operating environment temperature is T6=-15℃ to +50℃, the storage environment temperature is T7=-20℃ to +60℃, the suitable maximum relative air humidity is H3≤85%, the type of ambient light resistance is I2=40,000lx, the vibration resistance is A3=EN60068-2-6, EN60068-2-64, and the impact resistance is A4=EN60068-2-27.

4. A real-time laser coal discing system according to claim 1, characterized in that: The radar application unit in the laser detection module has an overall size of D4 = 445mm × 360mm × 158mm, a bottom-out wiring method, and a power supply voltage of V 13 =165VAC-265VAC, typical V 14 =220VAC, the supply current is I7≥2.5A@220VAC, the typical operating power is P4≤15W@without external devices, the heating power consumption at low temperature is P5≤150W, and the output voltage is V 15 =DC24V, the output power of a single 24V interface is typically P6≤70W, the total output power is typically P7≤290W, the network bandwidth is B1=100M, and the remote signal interface is V 16 =0VDC-5VDC low level input, 8VDC-30VDC high level input, remote control interface is relay output, normally open and normally closed optional, contact load current maximum I8=1A, TF card capacity standard default C1=64G, other capacities optional, communication interface includes network port: I9=6; RS485: I 10 =1; RS232: I 11 =1; remote signal / remote control includes YX:I 12 =4-way; YK:I 13 =2-way, output power interface includes DC24V:I 14 = 5 channels, of which I 15 = 2-way direct output, I 16 =3-way relay controllable output, can power N1=5 normal temperature laser detectors within the working temperature range, the Wi-Fi interface communication distance is D5≤10 meters, the working environment temperature is T8=-40℃~+65℃, the working environment humidity is H4=35, the storage environment temperature is T9=-40℃~+65℃, the storage environment soil layer is H5=35, the product protection level is IP3=IP55, and the use environment is outdoor.

5. The real-time laser coal discing system according to claim 1, characterized in that: The panoramic video monitoring sensor types in the monitoring module include panoramic S1 = 1 / 2.8" ProgressiveScanCMOS, detail S2 = 1 / 2.8" ProgressiveScanCMOS, and shutter is T 10 =1s1 / 30,000s, minimum illumination for panoramic: L4 = 0.03Lux@(F2.0, AGC0N); black and white: L5 = 0.015Lux@(F2.0, AGCON); 0LuxwitIR; L6=0.03Lux@(F1.6, AGCON); Black and white: L7=0.015Lux@(F1.6, AGCON); 0LuxwithIR slow shutter support, focus mode is semi-automatic / manual / automatic, day and night conversion mode is automatic ICR color to black, day and night conversion mode is day, night, automatic, timed switching, backlight compensation support, strong light suppression support, 3D noise reduction support, regional exposure support, image settings include saturation S3, brightness S4, contrast S5, sharpness S6, image parameter switching support, white balance includes automatic white balance W3, sodium lamp W4, fluorescent lamp, indoor W6, outdoor W7, manual white balance W8, locked white balance W9, privacy mask up to N2=24 blocks, support multiple color settings, quadrilateral area, signal-to-noise ratio is R2>52dB, general functions include mirror M1, password protection P8, watermark technology W 10 , IP address filter F3, lens focal length is panoramic f3 = 2.8mm; detail f4 = 8mm, field of view angle is panoramic horizontal field of view angle: θ2 = 99.5:116.1; horizontal field of view angle: θ3 = 37.5:43.3, maximum aperture number panoramic is F1 = F2.0; F2 = F1.6, infrared irradiation distance is infrared: L8 = 15m; white light L9 = 10m.

6. The real-time laser coal mining system according to claim 1, characterized in that: The server module product type is enterprise-level, the product category is rack-mounted, the product structure is H6=2U, the CPU type is Intel Xeon Bronze, eight cores, the memory capacity is M2=16GB, the memory type is DDR4, the RAID mode is R3=RAID1, the hard disk is H7=1.2T, and the interfaces include I 17 =2×RJ45 network interface, I 18 =1×RJ45 dedicated management port, power quantity is I 19 =1, power supply power is P9=750W, voltage is V 17 =AC220V, built-in program is built-in GIMP program, supports access to multiple silo application units, supports inventory operation control, system status forwarding management, data storage, log management, platform system external development database connection port, point cloud file is published through Http.

7. The real-time laser coal discing system according to claim 1, characterized in that: The monitoring terminal product type is a commercial computer, the product category is a desktop computer, the CPU type is Intel Core i510500, the memory capacity is M3=8G, the memory type is DDR4, the graphics card is G1=2G independent graphics card, the hard disk is H8=1TB, and the interfaces include I 20 =1×RJ45, other interfaces include I 21 =1×power interface, I 22 =1×COM serial port, I 23 =2×PS / 2, USB is N3=4, power supply quantity is I 24 =1, the power supply is P 10 =260W, voltage is V 18 =AC220V.

8. The real-time laser coal discing system according to claim 1, characterized in that: The communication module 5G wireless communication has the characteristics of high speed, low latency and large capacity. The specific parameters of its frequency band, modulation mode and coding mode are determined according to the system's requirements for data transmission and the 5G technical standards. In the pan coal system, the coal pile data scanned by the laser detector, the overhead crane displacement data measured by the laser rangefinder and the monitoring screen collected by the video monitoring equipment need to be transmitted to the server module in time for processing and storage. The high speed of 5G wireless communication can meet the demand for rapid transmission of large amounts of data, its low latency characteristics can ensure the timeliness of data transmission, and its large capacity characteristics can meet the future expansion needs of the system without affecting the normal operation of the system due to the limitation of communication bandwidth. The frequency band selection for 5G wireless communication is based on the electromagnetic spectrum conditions of the system environment and the availability and performance characteristics of the 5G frequency band. The modulation and coding methods are determined based on the reliability and efficiency requirements of data transmission through a large number of experiments and tests.

9. The real-time laser coal processing system according to claim 1, characterized in that: The method for dividing the coal pile into small geometric bodies in the data processing module is that when dividing the coal pile into small geometric bodies, in addition to determining the approximate geometric shapes of different regions according to the shape characteristics of the coal pile and the empirical rules in practical applications, the density distribution inside the coal pile and the texture characteristics on the surface of the coal pile are also considered; As for the density distribution inside the coal pile, the density distribution inside the coal pile is inferred by analyzing the point cloud data on the surface of the coal pile, combining the penetration ability of laser scanning and the study of the optical properties of coal materials with different densities. For areas with relatively uniform density, simpler geometric shapes such as rectangles or cylinders are used for approximation. For areas with uneven density, they are divided into multiple small areas with different geometric shapes according to the density change trend. As for the texture features of the coal pile surface, the texture feature vectors are extracted by analyzing the texture of the point cloud data on the surface of the coal pile. According to the mapping relationship between the texture feature vectors and the geometric shapes, the approximate geometric shape of each small geometric body is further determined.

10. A real-time laser coal discing method, characterized in that: The specific steps of this method are: S100, laser detection: a laser detector facing downwards installed on the top of the overhead crane and a target set at one end of the material area. The laser detector has a built-in GIMP program to perform high-frequency scanning of the material pile, support the output of radar data points per frame and 3D scanning data points. The laser rangefinder uses the pulse method to emit laser to the target, and then measures the displacement of the overhead crane based on the speed of light and time. S200, data processing: receiving the material pile scanning results from the laser detector and the overhead crane displacement data measured by the laser rangefinder, and combining the material and density information of the material pile, calculating the volume and weight of the material pile through the following algorithm: Assume that the point cloud data set of the material pile in three-dimensional space is P = {p1, p2, p3, ..., p n }, where each point p i =(x i ,y i ,z i ), x i ,y i 、z i are the coordinates of the point in the three-dimensional coordinate system, and define a distance threshold d based on the surface roughness characteristics of the pile. th , which is determined by: performing laser scanning on sample piles of different types of coal under different stacking conditions, obtaining their surface point cloud data, and calculating the Euclidean distance from each point to its adjacent points The distribution of these distances is counted, and the kth percentile of the distribution is taken, where k is determined to be 75 based on a large number of experiments, as the distance threshold d th , by calculating the Euclidean distance of each point to its neighboring points, when d(p i ,p j )>d th When , the point is considered as a boundary point. All boundary points are connected to get the outer contour of the pile. The pile is divided into multiple small geometric bodies according to the outer contour. For each small geometric body, its approximate geometric shape and volume are determined by the following method: Let E = {e1, e2, …, e m } is the set of small geometric bodies obtained by segmentation. For each e l ∈E, define a shape feature vector S l =(s l1 ,s l2 ,…,s lq ), where s li Represents the oth shape feature of the lth small geometric body. By laser scanning and data analysis of a large number of standard geometric bodies with known shapes and volumes, a mapping relationship M(S)→G between shape feature vectors and geometric shapes is established, where M is the mapping function, S is the shape feature vector, and G is the geometric shape. According to the mapping relationship, each small geometric body e is determined l The approximate geometry of G l ; For a small geometric body that is determined to be a rectangular parallelepiped, its volume Among them, l l 、w l 、h l They are the length, width and height of the cuboid respectively, and are determined by the following method: principal component analysis is performed on the point cloud data of the stockpile surface in the corresponding area of ​​the small geometric body to obtain three principal component directions, whose lengths correspond to the length, width and height of the cuboid respectively. The length values ​​are determined according to the distribution range of the point cloud data in the principal component directions. For the small geometric body determined to be in the shape of a cone, its volume Among them, r l is the radius of the cone base, h l The cone height is determined by analyzing the point cloud data of the pile surface in the corresponding area of ​​the small geometric body, and the center position of the cone bottom is determined. The average distance from the center to the boundary point is calculated as the bottom radius r l , determine the cone height h according to the distribution range of the point cloud data in the direction perpendicular to the bottom surface l ; Total volume of the stockpile Among them, V l is the volume of the lth small geometric body, assuming the density of the pile material is ρ, then the weight of the pile is W = V × ρ; S300, communication module; uses 5G wireless communication for real-time data transmission and interconnection between systems; S400, monitoring module; video monitoring and panoramic video monitoring equipment installed next to the laser detector, used to provide real-time monitoring images of the material yard; S500, server module; built-in GIMP-PF program, supports access to multiple silo application units, supports inventory operation control, system status forwarding management, data storage, log management, platform system external development database connection port, point cloud file is published through Http, and the server can connect to the DCS data within the enterprise, read the coal transportation flow data of each node in the feeding area and the discharging area, and combine the coal yard worm material data to dynamically form a material flow chart.

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