A method and system for increasing or decreasing coal volume based on a crane

Abstract

This invention discloses a method and system for increasing or decreasing coal loads based on a crane. The method includes: Step 1: Measuring the load of coal mining vehicles and collecting pose data of the coal mining vehicles and the material humps on them using lidar; Step 2: Processing the collected pose data using an extended Kalman filter algorithm to obtain the pose state of the coal mining vehicles and the material humps on them; Step 3: Constructing an occupancy grid map based on the pose state of the coal mining vehicles and the material humps on them; Step 4: Processing the material on the coal mining vehicles using a gantry crane with a grab bucket, based on the load of the coal mining vehicles and the occupancy grid map. This invention obtains the pose state of the coal mining vehicles and the material on them using an extended Kalman filter algorithm, and constructs an occupancy grid map based on the pose state of the coal mining vehicles and the material, facilitating the automatic increase or decrease of material loads by the gantry crane with a grab bucket based on the occupancy grid map.

Description

Technical Field

[0001] This invention relates to the field of material transportation technology, and in particular to a method and system for increasing or decreasing coal volume based on a crane. Background Technology

[0002] During coal mining vehicle transportation, inaccurate weight of transported coal often fails to meet the national standards for highway truck weight, necessitating adjustments to the material to ensure the vehicles meet load requirements and avoid overloading or underloading. Current methods typically involve manual adjustments to ensure the weight is within acceptable limits; however, this manual process is slow, time-consuming, and inefficient. Summary of the Invention

[0003] To at least partially address the problem of low efficiency in adding and removing materials on coal mine vehicles, this invention provides a method and system for adding and removing coal based on a crane. The method uses an extended Kalman filter algorithm to obtain the pose state of the coal mine vehicle and the materials on it, and constructs an occupancy grid map based on the pose state of the coal mine vehicle and the materials on it, so that the hoist grab bucket gantry crane can add or remove materials according to the occupancy grid map.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows:

[0005] The first aspect of this invention proposes a method for increasing or decreasing coal volume based on a crane, comprising:

[0006] Step 1: Measure the load of coal mine vehicles. Collect the pose data of coal mine vehicles and the material humps on them using lidar to facilitate the subsequent acquisition of the pose status of coal mine vehicles and the material humps on them.

[0007] Step 2: Process the collected pose data using the extended Kalman filter algorithm to obtain the pose state of the coal mine vehicle and the material humps on the coal mine vehicle, which will facilitate the subsequent construction of the occupancy grid map.

[0008] Step 3: Construct an occupation grid map based on the position and orientation of coal mine vehicles and the material humps on them, so as to facilitate the addition or removal of materials according to the occupation grid map;

[0009] Step 4: Based on the load status of the coal mine vehicles and the occupied grid map, the materials on the coal mine vehicles are processed using a hoist grab bucket gantry crane.

[0010] Furthermore, step two specifically includes:

[0011] The pose data of the coal mine vehicle and the material hump on the coal mine vehicle are input into the model of the extended Kalman filter algorithm.

[0012] The model of the extended Kalman filter algorithm is predicted and updated to obtain the updated pose state, which is used to represent the pose state of the coal mine vehicle and the material hump on the coal mine vehicle.

[0013] The updated pose state is repeatedly predicted and updated to obtain the final pose state. The final pose state is used as the pose state of the coal mine vehicle and the material hump on the coal mine vehicle, which facilitates obtaining the accurate pose state of the coal mine vehicle and the material on the coal mine vehicle.

[0014] Furthermore, the model of the extended Kalman filter algorithm is expressed by the following formula:

[0015]

[0016] Where, x k The pose state when x is k, f(x) k-1 u k ) is a nonlinear state function, u k For control input k, v k and w k These represent different zero mean values. Let h(x) be the observation vector. k ) is the observation function;

[0017] The covariance matrix of the predicted pose state and pose state vector is expressed by the following formula:

[0018]

[0019] in, To predict the pose state after k, g(x) k-1 u k () is the Taylor expansion of the nonlinear state function. Let be the covariance matrix of the pose state vector at time k after prediction. J is the covariance matrix of the pose state vector at time k after the update. k The Jacobian matrix of the nonlinear state function. For J k The transpose of , where Q is the noise covariance;

[0020] The covariance matrix of the updated pose state and pose state vector is expressed by the following formula:

[0021]

[0022] Among them, K k For Kalman gain, J H Let R be the Jacobian matrix of the observation function, and R be the covariance matrix of the measured values. This represents the pose state at time k after the update.

[0023] Furthermore, step three specifically includes:

[0024] A grid map is constructed based on the environment surrounding the coal mine vehicles under the gourd grab bucket gantry crane, which is used to initially construct the occupied grid map.

[0025] The state of the grid in the grid map is determined based on the pose of the coal mine vehicle and the material humps on the coal mine vehicle, which is used to determine whether the grid is occupied.

[0026] Complete the construction of the occupied grid map based on the grid status.

[0027] Furthermore, the state of the grid is represented by the following formula:

[0028] S + =S - +lofree

[0029] Among them, S + To measure the post-grid state, S - Lofree is a model for measuring the grid state before it is in use.

[0030] Furthermore, step four specifically includes:

[0031] The weight of materials on coal mine vehicles is determined based on their load conditions, which is used to determine whether the weight of materials on the coal mine vehicles is within acceptable limits.

[0032] If the material weight is overloaded, the main grab bucket of the hoist grab bucket gantry crane is controlled according to the grid map to initially unload the material on the coal mine vehicle. Then, according to the material weight, the quantitative grab bucket of the hoist grab bucket gantry crane is used to perform secondary processing on the material on the coal mine vehicle to make the material weight on the coal mine vehicle qualified.

[0033] If the material weight is insufficient, the main grab bucket of the hoist grab bucket gantry crane is controlled according to the grid map to load the material on the coal mine vehicle. Then, according to the material weight, the quantitative grab bucket of the hoist grab bucket gantry crane is used to perform secondary processing on the material on the coal mine vehicle to make the material weight on the coal mine vehicle qualified.

[0034] Furthermore, the material on the coal mine vehicle is then subjected to secondary processing using the quantitative grab bucket of the hoist grab gantry crane according to the material weight. Specifically, this includes: quantitatively increasing or decreasing the material on the coal mine vehicle and eliminating material humps on the coal mine vehicle to facilitate coal mine vehicle transportation.

[0035] A second aspect of the present invention provides a crane-based coal replenishment / reduction system, comprising:

[0036] The data collection module is used to measure the load of coal mine vehicles. It collects the pose data of coal mine vehicles and the material humps on them using lidar, which facilitates the subsequent acquisition of the pose status of coal mine vehicles and the material humps on them.

[0037] The pose module is used to process the collected pose data according to the extended Kalman filter algorithm to obtain the pose state of the coal mine vehicle and the material hump on the coal mine vehicle, which is convenient for subsequent construction of the occupancy grid map.

[0038] The Occupied Grid Map module is used to construct an occupied grid map based on the pose of coal mine vehicles and the material humps on them, making it easy to add or remove materials based on the occupied grid map.

[0039] The processing module is used to process materials on coal mine vehicles using a hoist grab bucket gantry crane, based on the load status of the coal mine vehicles and the occupied grid map.

[0040] A third aspect of the present invention provides a terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a crane-based method for increasing or decreasing coal volume as described in the first aspect above.

[0041] A fourth aspect of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device on which the storage medium is located to perform a crane-based method for increasing or decreasing coal volume as described in the first aspect above.

[0042] The beneficial effects of this invention are:

[0043] (1) This invention proposes a method for increasing or decreasing coal based on a crane. It can control the main grab bucket and the quantitative grab bucket of the hoist grab bucket gantry crane to automatically increase or decrease the material in the coal mine vehicle and eliminate the material hump. The whole process does not require manual operation, saving manpower and improving the efficiency of increasing or decreasing materials.

[0044] (2) This invention uses the extended Kalman filter algorithm to repeatedly predict and update the pose data obtained by the lidar until the final pose state of the material hump on the mining vehicle and coal mining vehicle is obtained, which ensures the accuracy of positioning and facilitates the processing of materials.

[0045] (3) The present invention first constructs a grid map of the environment around the coal mine vehicle, and then obtains the state of each grid based on the pose state of the mine vehicle and the material hump on the coal mine vehicle obtained by the extended Kalman filter algorithm, thus completing the construction of the grid map. It can accurately display the state of the coal mine vehicle and the material on the coal mine vehicle, which is convenient for the hoist grab bucket gantry crane to increase or decrease the material through the main grab bucket and the quantitative grab bucket, and to eliminate the material hump. Attached Figure Description

[0046] Figure 1 A flowchart of a method for increasing or decreasing coal volume based on a crane, provided as an embodiment of the present invention.

[0047] Figure 2 A flowchart of material handling provided for an embodiment of the present invention.

[0048] Figure 3 This is a schematic diagram of a lidar setup provided in an embodiment of the present invention.

[0049] Figure 4 This is a schematic diagram illustrating the effective positioning area for coal mine vehicles provided in an embodiment of the present invention.

[0050] Figure 5 This is a schematic diagram of lidar detection provided in an embodiment of the present invention.

[0051] Figure 6 This is a schematic diagram of the lidar detection setup provided in an embodiment of the present invention.

[0052] Figure 7 This is a schematic diagram of the lidar detection area provided in an embodiment of the present invention.

[0053] Figure 8 This is a schematic diagram of material hump detection provided in an embodiment of the present invention.

[0054] Figure 9 This is a schematic diagram of a hoist grab bucket gantry crane provided in an embodiment of the present invention.

[0055] Figure 10 This is a schematic diagram of a storage system provided in an embodiment of the present invention.

[0056] Figure 11 This is an architecture diagram of a crane-based coal replenishment / reduction system provided for an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0058] Example 1

[0059] like Figure 1 As shown, a method for increasing or decreasing coal volume based on a crane includes:

[0060] S101: Measure the load of coal mine vehicles and collect the pose data of the coal mine vehicles and the material humps on them using lidar.

[0061] S102: The collected pose data is processed according to the extended Kalman filter algorithm to obtain the pose state of the coal mine vehicle and the material hump on the coal mine vehicle.

[0062] S103: Construct an occupied grid map based on the pose of coal mine vehicles and the material humps on them.

[0063] S104: Based on the load status of coal mine vehicles and the occupied grid map, the materials on the coal mine vehicles are processed by a hoist grab bucket gantry crane.

[0064] This invention collects the pose data of coal mining vehicles and the material humps on them using lidar. Then, an extended Kalman filter algorithm is used to obtain the pose state of the coal mining vehicles and the material humps, facilitating accurate positioning. An occupancy grid map is constructed based on the pose state of the coal mining vehicles and the material humps to guide the hoist grab bucket gantry crane in adding or removing materials. This invention achieves a high degree of intelligence and enables unattended material handling. The weighing deviation can be controlled within ±1%, and it is suitable for ground transportation and coal mine transportation in dusty environments, with an ambient temperature between -25 and 60°C.

[0065] Example 2

[0066] Based on the above embodiments, such as Figure 2 As shown in the figure, this embodiment of the invention provides a specific process for a method of increasing or decreasing coal volume based on a crane. The specific process is as follows:

[0067] S201: Measure the load of coal mine vehicles and collect the pose data of the coal mine vehicles and the material humps on them using lidar.

[0068] Specifically, the coal mine vehicle is driven to the weighbridge for weighing, and then driven to the bottom of the hoist grab bucket gantry crane, where the lidar scans it to obtain the position and orientation data of the coal mine vehicle and the material hump on the vehicle.

[0069] LiDAR level scanners are suitable for working environments with explosive gases such as methane and coal dust, and their explosion-proof marking is "Exib I Mb". Based on frequency modulated continuous wave (FMCW) radar level measurement technology, the LiDAR level scanner employs a proprietary radar beam scanning scheme to achieve multi-point scanning and 3D imaging of materials.

[0070] LiDAR level scanners can be installed on various types of material silos to scan materials inside the silo using electromagnetic beams. Through an RS485 data interface, the multi-point scan data is uploaded to a computer, where imaging software algorithms calculate and reproduce a 3D image of the materials inside the silo, while also obtaining information such as the highest point of the materials.

[0071] In some applications where imaging display is not required, the embedded software of the LiDAR level scanner can calculate and transmit specific information about the material that the user is concerned with to the industrial control system.

[0072] LiDAR level scanners utilize high-frequency circuits to generate electromagnetic waves with a specific modulation frequency (FMCW). These waves are transmitted via an antenna, and reflected waves are received from the target. The level height at the target point is determined by the phase difference between the transmitted and received electromagnetic waves. High-frequency electromagnetic waves exhibit good measurement performance even under harsh conditions such as strong dust, foam, water vapor, crystallization, and spraying.

[0073] The lidar level scanner uses a proprietary electromagnetic beam scanning scheme to obtain the three-dimensional coordinate information of the current scanning position. After summarizing all the scanning points within a scanning cycle, the imaging algorithm calculates and fits the data to accurately restore the material's level and shape.

[0074] like Figure 3 As shown, the position of the lidar in the XYZ coordinate system includes the settings of the X, Y, Z dimensions and rotation angles (X, Y, Z, AngleA, AngleB, AngleC). The position of the lidar is set using the lidar origin coordinates and Euler angles.

[0075] Preferably, vehicles are required to reverse into designated spaces using methods such as signs, marked parking spaces, and specified vehicle directions. An effective area for positioning coal mine vehicles is determined by a rectangular prism within a spatial dimension. Figure 4 As shown.

[0076] like Figure 5As shown, the detection of the edge of the coal mine vehicle's cargo box using lidar is employed. Specifically, the parameters of the lidar in the coal mine vehicle's cargo box area are set, and the lidar cargo box positioning function of each line of the 16-line or single-line lidar can be disabled. Figure 6 As shown. The location determination of coal mine vehicles on each line consists of four 2D spatial regions (East Region 1, West Region 2, South Region 3, and North Region 4), each composed of cuboids, as shown. Figure 7 As shown.

[0077] The left-side area 1 is configured with XY coordinates and width / length settings (X1, Y1, XW1, YH1, Y / N1). Y / N1 represents the AND and NOT relationship with the carriage area and the right-side area. The actual X-coordinate offset (XX1) is displayed in real time, fed from an external PLC. The actual X-coordinate of the left-side area is obtained by adding the set X1 coordinate to the offset XX1. (The offset XX1 is the same for all threads in the 16-line configuration.)

[0078] The XY coordinates and width / length settings for left region 2 are (X2, Y2, XW2, YH2, Y / N2). Y / N is the same as above. Offset (XX1).

[0079] The XY coordinates and width / length settings for right-side region 3 are (X3, Y3, XW3, YH3, Y / N3). Y / N is the same as above. Offset (XX1).

[0080] The XY coordinates and width / length settings for region 4 on the right are (X4, Y4, XW4, YH4, Y / N4). Y / N is the same as above. Offset (XX1).

[0081] The system confirms the position of the coal mine vehicle under the grab bucket gantry crane and outputs a signal indicating that the coal mine vehicle's carriage has arrived.

[0082] Preferably, the area selectable by the lidar is set to avoid the area of ​​the vehicle's cab. For example, a standard parking space is 12.5m × 2.5m × 4.2m. When a coal mining vehicle reverses into the parking space as required, the lidar captures a 10m × 2m planar area that can be grabbed, excluding the cab area, to prevent the grab bucket from accidentally touching the cab.

[0083] When regions 1, 2, 3, and 4 meet the conditions, a coal mine vehicle arrival signal (C1) is output. The coal mine vehicle arrival signal C1 = [ <y n1>(There are laser spots in area 1) <y n2>(There are laser points in area 2)]&[ <y n3>(There are laser points in area 3). The total number of coal mine vehicles arriving is C = C1 & C2 & ... & C16.

[0084] Specifically, the location of the coal mine vehicle is determined by 2D scanning of each line of the lidar, identifying key points. The system scans the front and rear gaps and the vehicle body, covering four regions (Region 1, Region 2, Region 3, and Region 4), thus confirming the vehicle's position. When there are no detection points in Region 1, Region 2, Region 3, or Region 4 (more than three points are considered a lidar point), a confirmation signal indicating the coal mine vehicle is at its loading position is output based on NAND logic.

[0085] For example, when Y / N is selected as valid, it means the value is the inverse of the previous value. The locations of the coal mine vehicles are as follows:

[0086] (!Area 1)&(!Area 2)&(!Area 3)&(!Area 4)——> Coal mine vehicles are at the set location.

[0087] Regions 1, 2, 3, and 4 are all rectangular in shape, and their coordinates relative to the lidar, as well as their length, width, and inversion, can be set.

[0088] The parameters of the lidar for material hump detection are set. Both 16-line and single-line lidar can detect and determine the highest point of the material hump in the truck bed (B1, B2, B3, B4…). Assuming the material level detection consists of three 3-dimensional lines, such as… Figure 8 As shown, the first surface line is the first detected curved surface line D1, the second surface line is the second curved surface line D2, and the third surface line is the third detected curved surface line D3. The heights of the three points on the Z-axis are D2>D1>D3.

[0089] Specifically, the XYZ coordinates and shape parameters of curve 1 are (X1, Y1, Z1, W1, P1, H1, Angle1).

[0090] XYZ coordinates and shape parameters (X2, Y2, Z2, W2, P2, H2, Angle2) of surface line 2.

[0091] The XYZ coordinates and shape parameters (X3, Y3, Z3, W3, P3, H3, Angle3) of surface line 3.

[0092] The X-coordinate position parameter settings (F1, F2) of the front and rear edges are based on the edge position of the coal mine vehicle, i.e. the position of the car body, to avoid the grab bucket from grabbing the wrong thing.

[0093] The validity of the 3n lines, including the XYZ coordinates and shape parameters of D1, D2, D3…, is extracted by the PLC. Once the three coordinates are obtained, the crane's movement and the grab bucket's grasping action are controlled. The grasping priority is based on the Z-axis coordinate parameters: D2 > D1 > D3.

[0094] S202: The collected pose data is processed according to the extended Kalman filter algorithm to obtain the pose state of the coal mine vehicle and the material hump on the coal mine vehicle.

[0095] Specifically, the Extended Kalman Filter (EKF) algorithm mainly consists of two parts: prediction and update. The EKF algorithm must first transform the nonlinear model into a linear model. During this transformation, a first-order Taylor expansion is used on the nonlinear model. Then, the Kalman filter algorithm is employed for the filtering calculation. Through this transformation, the EKF algorithm successfully solves the problem of tracking nonlinear targets.

[0096] The pose data of the coal mine vehicle and the material humps on it are input into the model of the extended Kalman filter algorithm. The model of the extended Kalman filter algorithm is expressed by the following formula:

[0097]

[0098] Where, x k The pose state when x is k, f(x) k-1 u k ) is a nonlinear state function, u k For control input k, v k and w k These represent different zero mean values. Let h(x) be the observation vector. k ) is the observation function.

[0099] Then, the extended Kalman filter (EPF) algorithm model is predicted based on the pose state at time k. The EPF prediction formula includes the state vector equation and the state vector covariance equation. The predicted state vector is expressed by the following formula:

[0100]

[0101] in, To predict the pose state after k, g(x) k-1 u k ) is the Taylor expansion of the nonlinear state function.

[0102] The state vector covariance of the prediction part is expressed by the following formula:

[0103]

[0104] in, Let be the covariance matrix of the pose state vector at time k after prediction. J is the covariance matrix of the pose state vector at time k after the update. k The Jacobian matrix of the nonlinear state function. For J k The transpose of , where Q is the noise covariance.

[0105] Next, the Kalman gain needs to be calculated for updating the subsequent prediction part. The measured covariance matrix R in the Kalman gain is related to sensor performance and does not change over time. However, in practical applications, the measured covariance matrix R will change due to environmental interference. Therefore, after obtaining the measured covariance matrix R, the Kalman gain is calculated based on the state vector covariance of the prediction part at time k. The Kalman gain is expressed by the following formula:

[0106]

[0107] Among them, K k For Kalman gain, J H Let Jacobi be the observation function. For J H The transpose of , where R is the covariance matrix of the measured values.

[0108] The updated state equation is obtained from the Kalman gain, and the updated pose state is expressed by the following formula:

[0109]

[0110] in, This represents the pose state at time k after the update.

[0111] The updated pose state vector covariance is expressed by the following formula:

[0112]

[0113] The updated pose state is repeatedly predicted and updated to obtain the final pose state, which is then used as the pose state of the coal mine vehicle and the material hump on the coal mine vehicle.

[0114] S203: Construct an occupied grid map based on the pose of coal mine vehicles and the material humps on them.

[0115] Specifically, a grid map is constructed based on the environment surrounding the coal mine vehicles under the gantry crane. A grid map divides a defined area into multiple equally sized grids, each containing one or more different grids. The advantage of this method is its ease of creation and maintenance of as much environmental information as possible, and it enables the location and planning of coal mine vehicles and the materials on them.

[0116] The state of the grid in the grid map is determined based on the pose of the coal mine vehicles and the material humps on them.

[0117] Specifically, for each grid cell, the probability of being idle and the probability of being occupied are summed to 1. However, using two values ​​to represent the state of a grid cell is somewhat inappropriate, so a ratio between the two is introduced to represent the grid cell state. The grid cell state is represented by the following formula:

[0118]

[0119] Where Odds(s|z) is the state of the grid, p(s=1) is the probability of being in an idle state, and p(s=0) is the probability of being in an occupied state.

[0120] Assuming the state of the grid is denoted as odd(s) without considering the data collected by the lidar, then the state of the grid is updated when considering the data collected by the lidar:

[0121]

[0122] Where Odds(s|z) represents the state of the grid under the condition that there are or are not coal mining vehicles or materials on this grid, z ~ {0, 1}, z = 0 indicates that there are coal mining vehicles or materials on this grid, and z = 1 indicates that there are no coal mining vehicles or materials on this grid.

[0123] By applying Bayes' theorem to p(s=1|z) and p(s=0|z), the following two probability equations are obtained:

[0124]

[0125] Where p(s=1|z) is the probability that the grid is in an idle state given the presence or absence of coal mining vehicles or materials at this grid, p(s=0|z) is the probability that the grid is in an occupied state given the presence or absence of coal mining vehicles or materials at this grid, and p(z) is the probability that there are or are not coal mining vehicles or materials at this grid.

[0126] Substituting the two probability equations obtained from Bayes' theorem into the state equation for updating the grid when considering the data collected by the lidar, we obtain the following state formula:

[0127]

[0128] Taking the logarithm of both sides of the state formula, the state of a single raster is represented by log Odd(s). The state of a single raster is expressed by the following formula:

[0129]

[0130] in, The model for the measured values ​​has two states: idle and occupied.

[0131] The model for the measured values ​​of the raster idle state is expressed by the following formula:

[0132]

[0133] Here, lofree is a model of the measured values ​​of the raster's idle state.

[0134] The model for the measured values ​​of grid occupancy status is expressed by the following formula:

[0135]

[0136] Here, looccu is a model of the measured values ​​of the grid occupancy state.

[0137] At this point, if we use log Odd(s) to express the raster state, the raster state is represented by the following formula:

[0138]

[0139] Among them, S + S- represents the state before lidar measurement.

[0140] Furthermore, the default probability of a grid being idle or occupied is 0.5. Therefore, the initial state of a grid is:

[0141]

[0142] Among them, S init This represents the initial state of a raster.

[0143] The final state of a raster is updated simply by adding the states together. The raster state is represented by the following formula:

[0144] S + =S - +lofree or S + =S - +looccu

[0145] At this point, the state number S of the grid + The larger the value, the greater the probability that it will be occupied. The occupation of the grid is completed based on the grid status value.

[0146] S204: Based on the load conditions of coal mine vehicles and the occupied grid map, materials on coal mine vehicles are processed by a hoist grab bucket gantry crane.

[0147] Specifically, this invention, when adding or removing materials, includes not only a hoist-grab gantry crane but also a storage system and an electrical control system. The hoist-grab gantry crane works in conjunction with a weighbridge and interacts with it to achieve intelligent and automated addition or removal of weights for transport vehicles. The hoist-grab gantry crane mainly consists of a main beam, outriggers, an electric hoist, an electric grab bucket, and a slewing device, etc. Figure 9 As shown, its main parameters are as follows:

[0148] 1. Lifting capacity: 2 / 0.2 tons (total weight of grab bucket + material)

[0149] 2. Grab bucket volume: 0.75 / 0.1m³ 3

[0150] 3. Opening and closing method: Electric hoist opening and closing

[0151] 4. Lifting speed: 8m / min

[0152] 5. Hoist operating speed: 20m / min

[0153] 6. Trolley travel speed: 20m / min

[0154] 7. Lifting height: 6000mm

[0155] 8. Job Level: A5

[0156] The storage system is located next to the grab bucket gantry crane. Its main purpose is to store materials and it calculates the weight of materials loaded or unloaded via a weighing system on the storage bin. The main external appearance of the storage system is as follows: Figure 10 As shown, the specific parameters are as follows:

[0157] 1. Storage volume: 5m³ 3

[0158] 2. Storage chamber diameter: φ2500mm

[0159] 3. Weighing system accuracy: 1%

[0160] 4. Main material of the storage warehouse: Q355B

[0161] 5. Storage warehouse dust collection system: bag filter pulse dust collector

[0162] The function of the electrical control system is to determine whether coal mine vehicles are overloaded or underloaded based on data from the weighbridge. When either of these conditions occurs, the control system performs logical judgments based on the weighbridge and vehicle location data, generates operating instructions, and controls the main grab bucket and quantitative grab bucket to automatically adjust the load on the transport vehicle, thus automatically resolving overload and underload issues and achieving intelligent and unmanned operation of coal loading and unloading. The electrical control system includes:

[0163] The Siemens SIMATIC S7-1200 controller is the control center of an electrical control system. It boasts powerful data processing capabilities and high reliability, can withstand harsh working environments, and can quickly execute complex calculations and task scheduling. It is expandable with multiple I / O and communication modules, allowing for flexible configuration as needed.

[0164] The communication system's function is to exchange data with other system modules, providing a communication interface for the control system. These interfaces include serial communication, Ethernet, etc., to ensure real-time information transmission and coordinated system operation.

[0165] Human-Machine Interface (HMI): As an internal component of the control center, the HMI serves as a bridge between operators and the control system. It provides a visual display of the system status and allows operators to intervene or make adjustments when necessary.

[0166] The drive control of the grab crane consists of three drive shafts. The drive of the three shafts is controlled in a closed loop by a frequency converter and a motor encoder, supplemented by a vision monitoring system for precise positioning and intelligent grabbing.

[0167] The detailed configuration list is shown in Table 1:

[0168] Table 1: Electrical Control System Configuration List

[0169] Serial Number Component Name Component model Number of components 1 CPU 1215C 6ES7215-1BG40-0XB0 1 2 CM 1241 6ES7241-1CH32-0XB0 1 3 Miniature circuit breakers 5SY6 2P C25A 1 4 Miniature circuit breakers 5SY6 2P C10A 2 5 Miniature circuit breakers 5SY6 2P C6A 3 6 Miniature circuit breakers 5SY6 1P C6A 3 7 Switching power supply 6EP1333-2BA01 1 8 touchscreen TPC1570Gi 1 9 switch UT-62206SM-SC 1 10 intermediate relay DRM270024LT+FS2COECO 25 11 Thermostat AK30 1 12 terminal SAKDU2.5N 150 13 Molded case switch 3VM1M400 5 14 frequency converter SIEMENS S120 5 15 encoder BRT38-R0 M 4096D150-DC24 5

[0170] When the weighbridge indicates that a coal mine vehicle is overloaded or underloaded, the electrical control system controls the hoist grab bucket gantry crane to add and unload bulk materials such as coal. Its operation is divided into the following two scenarios:

[0171] When the weighbridge indicates that a coal mine vehicle is overloaded, the weighbridge will alert the vehicle to overloading and require unloading. The coal mine vehicle will then proceed to the gantry crane with a grab bucket, where the appropriate grab bucket will be selected based on the overload weight. If the overload is too small, a fixed-weight grab bucket (with a lifting capacity of 200 kg and a grabbing capacity of approximately 100 kg) will be used. If the overload is too large, the main grab bucket of the gantry crane (with a grabbing capacity of approximately 1 ton) will be used. The main grab bucket will grab the material from the coal mine vehicle according to the grid map, performing preliminary processing and unloading the material into the storage system around the gantry crane. Simultaneously, the weighing system in the storage system will weigh the material grabbed by the main grab bucket. When the unloaded material just reaches the required amount, the main grab bucket will stop working. If the weighing system detects that the unloaded weight exceeds or falls short of the required amount, the fixed-weight grab bucket will be activated for secondary processing to make up the difference. When unloading materials, priority is given to capturing the material hump to eliminate it and facilitate subsequent transportation by coal mine vehicles.

[0172] When the weighbridge indicates that a vehicle is underloaded: the weighbridge will indicate that the vehicle is underloaded and material replenishment is required. When a coal mine vehicle travels under a gantry crane with a grab bucket, the crane will determine the underload based on the amount of material underload. If the underload exceeds the rated grabbing capacity of the main grab bucket, the main grab bucket will first grab material from the storage bin and unload it into an idle grid on the coal mine vehicle. This process continues until the underload is less than the rated grabbing capacity of the main grab bucket. Then, the quantitative grab bucket will activate to replenish the weight and make up the difference. When replenishing material, the main grab bucket and the quantitative grab bucket should first eliminate any existing material humps before evenly adding material to prevent the formation of humps on the coal mine vehicle.

[0173] Example 3

[0174] Corresponding to the above methods, such as Figure 11 As shown, this invention proposes a crane-based coal replenishment / reduction system, comprising:

[0175] The data collection module is used to measure the load of coal mine vehicles and collect the pose data of coal mine vehicles and the material humps on them using lidar.

[0176] The pose module is used to process the collected pose data according to the extended Kalman filter algorithm to obtain the pose state of the coal mine vehicle and the material hump on the coal mine vehicle.

[0177] The Occupied Grid Map module is used to construct an occupied grid map based on the pose of coal mining vehicles and the material humps on them.

[0178] The processing module is used to process materials on coal mine vehicles using a hoist grab bucket gantry crane, based on the load status of the coal mine vehicles and the occupied grid map.

[0179] It should be noted that the coal addition / reduction system based on a crane provided in this embodiment of the invention is for implementing the above-mentioned coal addition / reduction method based on a crane. Its specific functions can be referred to in the above-mentioned method embodiments, and will not be repeated here.

[0180] Example 4

[0181] Based on the above embodiments, this invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a crane-based coal addition / reduction method as described in the above embodiments.

[0182] Preferably, the present invention also provides a computer-readable storage medium, the storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to execute a crane-based coal addition / reduction method according to the above embodiments.

[0183] In summary, this invention proposes a method for increasing and decreasing coal volume based on a crane. This method can automatically control the main grab bucket and quantitative grab bucket of a gantry crane to increase or decrease material volume on coal mine vehicles and eliminate material humps. The entire process requires no manual operation, saving manpower and improving material handling efficiency. This invention uses an extended Kalman filter algorithm to repeatedly predict and update the pose data obtained from LiDAR until the final pose state of the coal mine vehicles and the material humps on them is obtained, ensuring accurate positioning and facilitating material processing. This invention first constructs a grid map of the environment surrounding the coal mine vehicles. Then, based on the pose state of the coal mine vehicles and the material humps on them obtained through the extended Kalman filter algorithm, the state of each grid cell is obtained, completing the construction of the grid map. This accurately displays the state of the coal mine vehicles and the materials on them, facilitating the use of the main grab bucket and quantitative grab bucket of the gantry crane to increase or decrease material volume and eliminate material humps.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.< / y> < / y> < / y>

Claims

1. A method for increasing or decreasing coal volume based on a crane, characterized in that, include: Step 1: Measure the load of coal mine vehicles and collect the pose data of the coal mine vehicles and the material humps on them using lidar. Step 2: Process the collected pose data using the extended Kalman filter algorithm to obtain the pose state of the coal mine vehicle and the material humps on the coal mine vehicle. Step 3: Construct an occupied grid map based on the pose of the coal mine vehicles and the material humps on them. Step three specifically includes: A grid map is constructed based on the environment surrounding the coal mine vehicles under the gourd grab bucket gantry crane. The state of the grid in the grid map is determined based on the pose of the coal mine vehicle and the material humps on the coal mine vehicle. Complete the construction of the occupied grid map based on the grid status; The state of the grid is represented by the following formula: or ; ； ； in, To measure the state of the grid after measurement, To measure the front grid state, A model for measurements of the grid's idle state; A model for the measured values ​​of the grid occupancy state; The probability of being in an idle state; The probability of occupying a state; When z is 0, it means that there are coal mining vehicles or materials on this grid; when z is 1, it means that there are no coal mining vehicles or materials on this grid. Step 4: Based on the load status of the coal mine vehicles and the occupied grid map, process the materials on the coal mine vehicles using a hoist grab bucket gantry crane. Step four specifically includes: Determine the weight of materials on coal mine vehicles based on their load conditions. If the material weight is overloaded, the main grab bucket of the hoist grab bucket gantry crane is controlled according to the grid map to initially unload the material on the coal mine vehicle. Then, according to the material weight, the quantitative grab bucket of the hoist grab bucket gantry crane is used to perform secondary processing on the material on the coal mine vehicle to make the material weight on the coal mine vehicle qualified. If the material weight is insufficient, the main grab bucket of the hoist grab bucket gantry crane is controlled according to the grid map to load the material on the coal mine vehicle. Then, according to the material weight, the quantitative grab bucket of the hoist grab bucket gantry crane is used to perform secondary processing on the material on the coal mine vehicle to make the material weight on the coal mine vehicle qualified. The material on the coal mine vehicle is then subjected to secondary processing using the quantitative grab bucket of the hoist grab bucket gantry crane according to the material weight. Specifically, this includes: quantitatively increasing or decreasing the material on the coal mine vehicle and eliminating material humps on the coal mine vehicle.

2. The method for increasing or decreasing coal volume based on a crane according to claim 1, characterized in that, Step two specifically includes: The pose data of the coal mine vehicle and the material hump on the coal mine vehicle are input into the model of the extended Kalman filter algorithm. The model of the extended Kalman filter algorithm is predicted and updated to obtain the updated pose state; The updated pose state is repeatedly predicted and updated to obtain the final pose state, which is then used as the pose state of the coal mine vehicle and the material hump on the coal mine vehicle.

3. A coal feeding / reducing system based on a crane, characterized in that, This system is used to implement a crane-based method for increasing or decreasing coal volume as described in claim 1. The system includes: The data collection module is used to measure the load of coal mine vehicles and collect the pose data of coal mine vehicles and the material humps on them using lidar. The pose module is used to process the collected pose data according to the extended Kalman filter algorithm to obtain the pose state of the coal mine vehicle and the material hump on the coal mine vehicle. The Occupied Grid Map module is used to construct an occupied grid map based on the pose of coal mining vehicles and the material humps on them. The processing module is used to process materials on coal mine vehicles using a hoist grab bucket gantry crane, based on the load status of the coal mine vehicles and the occupied grid map.

4. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a crane-based method for increasing or decreasing coal volume as described in any one of claims 1 to 2.

5. A computer-readable storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform a crane-based method for increasing or decreasing coal volume as described in any one of claims 1 to 2.

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