Self-adaptive unmanned silo loading method and device, upper computer and medium

Abstract

The invention discloses a self-adaptive unmanned silo loading method, and belongs to the technical field of unmanned silo loading, in response to a silo loading request instruction, hopper size and hopper structure data are obtained, a control instruction set is generated based on the hopper structure data, and the control instruction set is used for controlling a transport vehicle to run to a preset vehicle position. The control instruction set is used for controlling the discharging direction adjusting device to move to the position corresponding to the car hopper. Based on the car hopper size and the car hopper structure data, a material level detection area and a material level limiting height are determined; acquiring material level data in a car hopper, and determining the running speed of the car; and the position of a hopper rear baffle and the position of a discharging port are obtained, whether the vehicle travels to the discharging ending position or not is judged based on the position of the hopper rear baffle and the position of the discharging port, and if yes, the discharging port is closed, and the vehicle travels away. Aiming at the deviation degree of a vehicle, self-adaptive adjustment of the discharging opening is carried out, and the stability of loading work is improved.

Description

Technical Field

[0001] The present invention discloses an adaptive unmanned silo loading method, belonging to the technical field of unmanned silo loading. Background Art

[0002] Silo loading is a common operation scenario in industry, agriculture and logistics transportation. Traditional loading methods usually rely on manual operation or fixed mechanical systems, which have problems such as low efficiency, insufficient loading accuracy and safety hazards. Manual operation is easily affected by the environment and material distribution, while fixed systems lack flexibility and are difficult to adapt to complex working conditions. Therefore, the research on unmanned silo loading methods has become an urgent problem to be solved currently.

[0003] For traditional loading methods, operators need to monitor the material flow state of the silo and the loading situation of the vehicle in real time, and vehicle drivers need to frequently start and stop the vehicle under the operator's prompt, which damages the vehicle body, reduces the service life of the vehicle and increases energy consumption. Therefore, the vehicle speed is planned through lidar point cloud data and the material flow rate, so that the loading operation can be efficiently completed with only one start and stop.

[0004] When the transport vehicle enters the loading area, the vehicle's central axis is often difficult to be directly opposite to the discharge port. In this case, loading operations will cause uneven vehicle load distribution, offset of the vehicle's center of gravity, affecting the driving stability of the vehicle, and seriously may lead to accidents such as rollover. Summary of the Invention

[0005] In view of the defects of the prior art, the present invention provides an adaptive unmanned silo loading method, device, host computer and medium, which estimate the vehicle size and perform real-time positioning on the vehicle through lidar point cloud data, dynamically plan the vehicle speed based on the real-time state of the material, and perform adaptive adjustment of the discharge port according to the deviation degree of the vehicle, so as to make the vehicle load distribution uniform, improve the stability of the loading work and ensure the safety of the driver.

[0006] The technical solution of the present invention is as follows:

[0007] According to the first aspect of the embodiments of the present invention, an adaptive unmanned silo loading method is provided, including:

[0008] In response to a silo loading request instruction, obtain the hopper size and hopper structure data, and generate a control instruction set based on the hopper structure data. The control instruction set is used to control the transport vehicle to run to a preset vehicle position, and the control instruction set is used to control the discharge direction adjusting device to move to a position corresponding to the hopper;

[0009] Based on the hopper size and the hopper structure data, determine the material level detection area and the material level limit height;

[0010] Obtain the material level data in the truck bed and determine the vehicle running speed;

[0011] Obtain the position of the rear baffle of the truck bed and the position of the discharging opening, and judge whether the vehicle has reached the discharging end position based on the position of the rear baffle of the truck bed and the position of the discharging opening. If so, close the discharging opening and the vehicle drives away.

[0012] Further, in response to the silo loading request instruction, obtain the truck bed size and the truck bed structure data, and generate a control instruction set based on the truck bed structure data, including:

[0013] In response to the silo loading request instruction, obtain the point cloud data collected by the lidar;

[0014] Process the point cloud data to obtain the processed point cloud data;

[0015] Obtain the truck bed structure data, determine the position of the front baffle of the vehicle truck bed and the positions of the left and right baffles of the truck bed, and determine the truck bed size and the truck bed volume based on the processed point cloud data;

[0016] Generate a first control instruction set based on the position of the front baffle of the vehicle truck bed, and the first control instruction set is used to control the transport vehicle to run to a preset vehicle position;

[0017] Generate a second control instruction set based on the positions of the left and right baffles of the truck bed, and the second control instruction set is used to control the discharging direction adjusting device to move to a position corresponding to the truck bed.

[0018] Further, based on the truck bed size and the truck bed structure data, determine the material level detection area and the material level limit height, including:

[0019] Determine the material level monitoring area based on the truck bed size;

[0020] Determine the material level limit height based on the truck bed structure data.

[0021] Further, obtain the material level data in the truck bed and determine the vehicle running speed, including:

[0022] Obtain the material level data in the truck bed, and determine the discharging time and the volume of the material in the truck bed based on the material level data in the truck bed;

[0023] Determine the silo discharging flow rate based on the discharging time and the volume of the material in the truck bed;

[0024] Obtain the material quantity in the preset material loading area to be loaded, and determine the average vehicle speed that the vehicle should travel based on the material quantity in the preset material loading area to be loaded and the silo discharging flow rate;

[0025] Determine the vehicle running speed based on the average vehicle speed that the vehicle should travel.

[0026] According to the second aspect of the embodiments of the present invention, an adaptive unmanned silo loading device is provided, including:

[0027] A data acquisition module, configured to, in response to a silo loading request instruction, acquire hopper size and hopper structure data, generate a control instruction set based on the hopper structure data, the control instruction set being used to control a transport vehicle to run to a preset vehicle position, and the control instruction set being used to control a blanking direction adjusting device to move to a position corresponding to the hopper;

[0028] A material level determination module, configured to determine a material level detection area and a material level limit height based on the hopper size and the hopper structure data;

[0029] A speed determination module, configured to acquire hopper internal material level data and determine the vehicle running speed;

[0030] A position judgment module, configured to acquire the position of the hopper rear baffle and the position of the blanking port, and judge whether the vehicle has traveled to the blanking end position based on the position of the hopper rear baffle and the position of the blanking port. If so, the blanking port is closed and the vehicle drives away.

[0031] Further, the data acquisition module is configured to:

[0032] In response to a silo loading request instruction, acquire point cloud data collected by a lidar;

[0033] Process the point cloud data to obtain processed point cloud data;

[0034] Acquire hopper structure data, determine the position of the hopper front baffle and the positions of the left and right hopper baffles of the vehicle, and determine the hopper size and hopper volume based on the processed point cloud data;

[0035] Generate a first control instruction set based on the position of the hopper front baffle of the vehicle, the first control instruction set being used to control a transport vehicle to run to a preset vehicle position;

[0036] Generate a second control instruction set based on the positions of the left and right hopper baffles, the second control instruction set being used to control a blanking direction adjusting device to move to a position corresponding to the hopper.

[0037] Further, the material level determination module is configured to:

[0038] Determine the material level monitoring area based on the hopper size;

[0039] Determine the material level limit height based on the hopper structure data.

[0040] Further, the speed determination module is configured to:

[0041] Obtain the material level data in the truck bed, and determine the discharging time and the volume of the material in the truck bed based on the material level data in the truck bed;

[0042] Based on the discharging time and the volume of the material in the truck bed, determine the silo discharging flow rate;

[0043] Obtain the material quantity in the preset material loading area to be loaded, and determine the average vehicle speed that the vehicle should travel based on the material quantity in the preset material loading area to be loaded and the silo discharging flow rate;

[0044] Based on the average vehicle speed that the vehicle should travel, determine the operating speed of the vehicle.

[0045] According to the third aspect of the embodiments of the present invention, there is provided a host computer, including:

[0046] One or more processors;

[0047] A memory for storing executable instructions of the one or more processors;

[0048] Wherein, the one or more processors are configured to:

[0049] Execute the method described in the first aspect of the embodiments of the present invention.

[0050] According to the fourth aspect of the embodiments of the present invention, there is provided a non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by a processor of a host computer, enabling the host computer to execute the method described in the first aspect of the embodiments of the present invention.

[0051] According to the fifth aspect of the embodiments of the present invention, there is provided an application program product, when the application program product is running on a host computer, enabling the host computer to execute the method described in the first aspect of the embodiments of the present invention.

[0052] The beneficial effects of the present invention are as follows:

[0053] The present invention provides an adaptive unmanned silo loading method, device, host computer and medium. The method for adaptive material level detection based on the truck bed size and vehicle position is established, which is different from the fixed material level detection instruments such as ultrasonic waves used in the prior art. This method can determine suitable material level detection areas and material level limits for different vehicle models and vehicle offset situations, thereby improving the accuracy of material level detection. By establishing a vehicle speed planning model based on the material state in the truck bed, the volume of the material after fixed-point discharging is calculated to obtain the material discharging speed, and the driving speed of the vehicle is planned according to the material loading area of the truck bed, and the vehicle speed is dynamically adjusted according to the real-time material level monitoring, so that the vehicle can successfully complete the silo loading task with only one start and stop.

[0054] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a schematic structural diagram of an adaptive unmanned silo loading system shown according to an exemplary embodiment.

[0056] Figure 2 is a flowchart of an adaptive unmanned silo loading method shown according to an exemplary embodiment.

[0057] Figure 3 is a flowchart of an adaptive unmanned silo loading method shown according to an exemplary embodiment.

[0058] Figure 4 is a schematic block diagram of the structure of an adaptive unmanned silo loading device shown according to an exemplary embodiment.

[0059] Figure 5 is a schematic block diagram of the structure of a host computer shown according to an exemplary embodiment.

[0060] Wherein:

[0061] 1. Host computer;

[0062] 2. Lower computer;

[0063] 3. Feeding direction adjustment actuator;

[0064] 4. First lidar,

[0065] 5. Feeding port opening and closing actuator;

[0066] 6. Sliding hopper;

[0067] 7. Second lidar. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0069] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0070] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0071] The embodiment of the present invention provides an adaptive unmanned silo loading method, which is implemented by an adaptive unmanned silo loading system. As Figure 1 shown, the adaptive unmanned silo loading system includes a host computer 1 and a slave computer 2 arranged on a feeding support. A feeding direction adjusting device is installed at the feeding port. The feeding direction adjusting device includes a feeding port opening and closing actuator 5 installed at the feeding port and a feeding direction adjusting actuator 3 with one end installed on the feeding support. The execution end of the feeding direction adjusting actuator 3 is connected to the side of a sliding hopper 6 installed below the feeding port. A first lidar 4 and a second lidar 7 are respectively installed on the feeding support adjacent to the sliding hopper 6. The first lidar 4 and the second lidar 7 are used to collect the point cloud data of the transport vehicle and the material. The host computer 1 is electrically connected to the slave computer 2, the first lidar 4, and the second lidar 7 respectively, and the slave computer 2 is electrically connected to the feeding port opening and closing actuator 5 and the feeding direction adjusting actuator 3 respectively.

[0072] Embodiment 1

[0073] Figure 2 and Figure 3 is a flowchart of an adaptive unmanned silo loading method shown according to an exemplary embodiment. This method is used in the host computer and includes the following steps:

[0074] Step 101, in response to the silo loading request instruction, obtain the hopper size and hopper structure data, and generate a control instruction set based on the hopper structure data. The control instruction set is used to control the transport vehicle to run to a preset vehicle position, and the control instruction set is used to control the feeding direction adjusting device to move to a position corresponding to the hopper.

[0075] In step 101, first, in response to the silo loading request instruction, obtain the point cloud data collected by the lidar, process the point cloud data to obtain the processed point cloud data, and the specific steps are as follows:

[0076] By setting the region of interest of the point cloud, remove the irrelevant point cloud outside the carriage. Then, use algorithms such as denoising the point cloud data to remove noise points and improve the quality of the point cloud data. Adopt a point cloud clustering algorithm based on connected components to segment the point cloud data according to the target area, extract the point cloud data of the transport vehicle, use a plane fitting algorithm to segment the side and bottom point cloud data of the carriage, and calculate the plane equations of five planes. Downsample the segmented side and bottom point cloud data to reduce the calculation amount and retain key feature points, and finally obtain the processed point cloud data.

[0077] Next, the carriage structure data will be obtained, the positions of the front baffle and the left and right baffles of the vehicle carriage will be determined, and based on the processed point cloud data, the dimensions and volume of the carriage will be determined. The specific content is as follows:

[0078] The distance between the planes of the front and rear baffles is the length L of the carriage. The plane equations of the front and rear baffles are respectively:

[0079]

[0080] Then the calculation formula for the carriage length L is:

[0081]

[0082] In the formula, A 1 、B 1 、C 1 are the coefficients of the plane normal vector, and D 1 、D 2 are the constant terms of the front and rear baffle plane equations.

[0083] The distance between the planes of the left and right baffles is the width W of the carriage. The plane equations of the left and right baffles are respectively:

[0084]

[0085] Then the calculation formula for the carriage length L is:

[0086]

[0087] In the formula, A 2 、B 2 、C 2 are the coefficients of the plane normal vector, and D 3 、D 4 are the constant terms of the left and right baffle plane equations.

[0088] Calculate the maximum value z of the side point cloud in the z-axis direction max and the minimum value z min , the height H of the hopper is:

[0089] H = |z max - z min | (7)

[0090] Hopper volume calculation: According to the length, width and height of the hopper, calculate the hopper volume V as:

[0091] V = L × H × W (8)

[0092] Next, a first control instruction set will be generated based on the position of the front baffle of the vehicle hopper. The first control instruction set is used to control the transport vehicle to run to a preset vehicle position. The specific content is as follows:

[0093] During the forward movement of the transport vehicle, the upper computer performs real-time positioning on the transport vehicle according to the position of the front baffle of the hopper and the position of the discharging port. When the vehicle travels to the preset parking position, a stop signal is sent to the transport vehicle. By continuously detecting the position of the front baffle of the vehicle hopper and coordinating with the position of the center of the discharging port of the prior quantity, the vehicle position is determined in real time. The parking position of the vehicle is judged according to the component of the front baffle and the discharging port in the y-axis direction.

[0094] The center coordinates of the front baffle of the hopper are P f (x f , y f , z f ), the center coordinates of the rear baffle of the hopper are P r (x r , y r , z r ), and the coordinates of the center position of the discharging port are C(x c , y c , z c ).

[0095] When the distance between the front baffle of the hopper and the center position of the discharging port satisfies formula (9), the vehicle stops; otherwise, the vehicle position is continuously adjusted;

[0096] A + ε 1 > y c - y f > A - ε 1 (9)

[0097] A is the preset parking distance between the preset front baffle and the center of the discharging port; ε 1 is the parking reaction distance.

[0098] Finally, based on the positions of the left and right baffle plates of the hopper, a second control instruction set is generated. The second control instruction set is used to control the feeding direction adjustment device to move to a position corresponding to the hopper. The specific content is as follows:

[0099] Calculate the lateral deviation between the transport vehicle and the center line of the feeding port according to the positions of the left and right baffle plates of the hopper, and send the data to the lower computer 2. The lower computer 2 controls the feeding direction adjustment actuator 3. The feeding direction adjustment actuator 3 is fixed on the middle layer plate of the silo. The sliding hopper 6 can slide along the slide rail on the middle layer plate. The slide rail is provided with a sliding range limit, and the sliding range of the sliding hopper is limited by the center position of the feeding port as [-x h ,x h .

[0100] The feeding direction adjustment actuator 3 pushes the sliding hopper 6 to change the downward direction of the material, so that the material can descend to the middle of the hopper, and the sliding distance is equal to the offset distance of the vehicle.

[0101] First, detect the left and right boundaries of the hopper in the x-axis direction, denoted as x max and x min . Calculate the coordinate x c1 of the central axis of the hopper in the x-axis direction:

[0102]

[0103] The offset distance Δx is calculated as:

[0104] Δx p =x c1 -x c2 (11)

[0105] In the formula, x c2 is the coordinate of the central axis of the feeding port in the x-axis direction.

[0106] Step 102, based on the hopper size and the hopper structure data, determine the material level detection area and the material level limit height. The specific steps are as follows:

[0107] First, based on the hopper size, determine the material level monitoring area. The specific content is as follows:

[0108] The material level monitoring area is two circular areas with the material level monitoring points (x c1 , y c1 ) and (x c2 , y c2 ) as the centers and a radius of R. In the y-axis direction, during the feeding process, the material accumulates the highest on both sides flush with the center of the feeding port. Therefore, the two monitoring points are set at the position flush with the feeding port, that is:

[0109] y c1 =y c2 =yc (12)

[0110] In the x-axis direction, two monitoring points are respectively arranged on the left and right sides of the hopper to prevent the material from piling up too high on one side and leaking, that is:

[0111]

[0112] In the formula, W is the width of the hopper; Δx is the vehicle offset distance; Δx 1 is the distance from the material level detection point to the vehicle edge.

[0113] The following introduces the determination of the material level limit height based on the hopper structure data, and the specific content is as follows:

[0114] To ensure that the material does not exceed the maximum load height of the hopper, the material level height limit h max is set according to the size information of the hopper. The specific calculation method is:

[0115] h max = k 1 ·H + k 2 ·W (15)

[0116] In the formula, H is the height of the hopper; W is the width of the hopper; k 1 is the material level height proportionality factor, and the material level height limit is most related to the height of the hopper; k 2 is the material level width proportionality factor. The material level limit is affected by the width of the hopper. The wider the hopper, the slightly higher the material level limit will be.

[0117] Step 103, obtain the material level data in the hopper and determine the vehicle running speed. The specific steps are as follows:

[0118] First, obtain the material level data in the hopper and determine the discharging time and the volume of the material in the hopper. The specific content is as follows:

[0119] Set the point cloud data in the hopper material level detection area as P = {p 1 , p 2 ,..., p n}, where p i = (x i , y i , z i ) is the coordinate of the i-th point in the point cloud, and n is the total number of points in the detection area. The real-time material level h r of the hopper is calculated by the following formula:

[0120]

[0121] In the formula: n is the number of qualified points in the hopper detection area; z i is the point pi The z - coordinate, representing the height value of the point; h r represents the average height of the material in the hopper, that is, the real - time material level.

[0122] The host computer calculates the silo discharging flow rate according to the discharging time and the volume of the material in the hopper. The material flow rate at the discharging port adopts a calculation method based on the discharging time and the point - cloud volume of the material in the hopper. The calculation process is as follows:

[0123] Calculation of the discharging time:

[0124] During the silo discharging process, the host computer records the starting time t of the material discharging at the discharging port start and the time t when it first reaches the discharging limit end , and calculates the total discharging time T down :

[0125] T down = t end - t start (17)

[0126] Extraction of the point cloud of the material pile in the hopper:

[0127] Filter out the side and bottom point clouds fitted above, and remove the redundant point clouds according to the positions of the front and rear baffles of the hopper. The remaining point clouds are the point clouds of the material in the hopper.

[0128] Calculation of the material volume: The calculation of the point - cloud volume of the material in the hopper adopts a point - cloud volume integration method based on the average height of the points in the grid. The calculation process is as follows:

[0129] First, fit the reference plane at the bottom of the hopper for height reference:

[0130] ax + by + cz + d = 0 (18)

[0131] Then generate a two - dimensional grid, project the point cloud onto the XY plane, and each grid represents a small area at the bottom of the hopper. For each point (x, y, z), calculate its grid index according to its x and y coordinates and group all points according to their grid indices:

[0132]

[0133] where Δx and Δy are the widths of a single grid along the x - axis direction and the y - axis direction respectively.

[0134] The height h of each point from the reference plane i :

[0135]

[0136] Calculate the average height h of the points in the gridavg :

[0137]

[0138] Calculate the area A of the grid grid :

[0139] A grid = Δx·Δy (22)

[0140] Calculate the grid volume v according to the average height i :

[0141] v i = h avg ·A grid (23)

[0142] Accumulate the small volumes v of all grids i to obtain the total volume V of the material:

[0143] V = ∑ i v i = ∑ i h avg ·A grid (24)

[0144] Secondly, based on the discharging time and the volume of the material in the hopper, determine the discharging flow rate of the silo. The specific content is as follows:

[0145] The calculation of the discharging flow rate Q of the silo is shown in formula (25):

[0146]

[0147] Then, obtain the amount of material in the preset area to be loaded. Based on the amount of material in the preset area to be loaded and the discharging flow rate of the silo, determine the average vehicle speed that the vehicle should travel. The specific content is as follows:

[0148] Taking the bottom of the hopper as the reference plane, transform the material point cloud into the hopper coordinate system, and perform grid reconstruction on the material surface of the material point cloud data. For each grid, the material height is Then the area to be loaded in the hopper is:

[0149]

[0150] where the threshold of the material height in the hopper is h max

[0151] Establish a distribution model of the area to be loaded: Divide the length direction of the hopper into n segments, and the length of each segment is The amount of material to be loaded in each section of the area:

[0152]

[0153] The blanking time for each section of the truck bed is During the blanking process of each section of the truck bed, the average vehicle speed that the vehicle should travel:

[0154]

[0155] Finally, based on the average vehicle speed that the vehicle should travel, determine the vehicle operating speed, the specific content is as follows:

[0156] Smooth the vehicle speed and use the interpolation method to generate a continuous vehicle speed curve:

[0157]

[0158] In the formula, N = 2K + 1 is the size of the smoothing window.

[0159] Set the acceleration limit. The vehicle speed change should meet the acceleration limit of the truck to avoid sudden acceleration or deceleration, that is, the vehicle speed change should meet:

[0160]

[0161] In the formula, a max is the maximum acceleration of the truck.

[0162] Dynamic vehicle speed adjustment method based on dual material level monitoring: This method adjusts the vehicle speed by monitoring the material accumulation height h 0 , the material level rising speed and the distance y from the blanking port d the distance to the material accumulation height h of the rear material 1 , the material level rising speed These four data to adjust the vehicle speed.

[0163] The vehicle speed adjustment formula:

[0164]

[0165] In the formula, v 0 is the reference speed of the vehicle; k 3 is the adjustment coefficient of the material level deviation, used to quickly respond to the material level deviation; k 4 is the adjustment coefficient of the material level rising speed, used to predict the material level change in advance to avoid lag in vehicle speed adjustment, and v is the vehicle operating speed;

[0166] Step 104, obtain the position of the truck bed rear baffle and the position of the blanking port, and judge whether the vehicle has traveled to the blanking termination position based on the position of the truck bed rear baffle and the position of the blanking port. If so, close the blanking port and the vehicle drives away. The specific steps are as follows:

[0167] Judge whether the vehicle has reached the preset unloading end position according to the position of the rear baffle of the truck bed and the position of the unloading port. If it reaches the end position, send a signal to stop unloading to the lower computer and a signal to leave to the transport vehicle. This loading operation is completed, and wait for the next transport vehicle to enter the radar monitoring range to enter the next operation cycle; the upper computer judges whether it has traveled to the unloading end position by detecting the position of the rear baffle of the vehicle's truck bed in real time and cooperating with the position of the center of the prior measurement unloading port.

[0168] The central coordinate of the rear baffle of the truck bed is P r (x r , y r , z r ), and the coordinate of the center position of the unloading port is C(x c , y c , z c ).

[0169] When the distance between the rear baffle of the truck bed and the center position of the unloading port meets the following conditions, the vehicle stops; otherwise, continue to adjust the position of the vehicle.

[0170] B + ε 2 > y c -y r > B - ε 2 (32)

[0171] In the formula, B is the preset parking distance between the set rear baffle and the center of the unloading port; ε 2 is the reaction distance for the completion of unloading.

[0172] In this embodiment, the established method for adaptive material level detection based on the truck bed size and vehicle position is different from the fixed material level detection instruments such as ultrasonic waves used in the prior art. This method can determine the suitable material level detection area and material level limit for different vehicle models and vehicle offset situations, thereby improving the accuracy of material level detection. By establishing a vehicle speed planning model based on the material state in the truck bed, calculate the volume of the material after the fixed-point unloading is completed to obtain the material unloading speed, plan the driving speed of the vehicle according to the material loading area of the truck bed, and dynamically adjust the vehicle speed according to the real-time material level monitoring, so that the vehicle can successfully complete the silo loading task with only one start and stop.

[0173] Embodiment 2

[0174] Figure 4 is a structural schematic diagram of an adaptive unmanned silo loading device shown according to an exemplary embodiment. The device includes:

[0175] The data acquisition module 210 is configured to, in response to a silo loading request instruction, acquire the hopper size and hopper structure data, generate a control instruction set based on the hopper structure data, the control instruction set being used to control the transport vehicle to run to a preset vehicle position, and the control instruction set being used to control the blanking direction adjustment device to move to a position corresponding to the hopper;

[0176] The material level determination module 220 is configured to determine a material level detection area and a material level limit height based on the hopper size and the hopper structure data;

[0177] The speed determination module 230 is configured to acquire the material level data in the hopper and determine the vehicle running speed;

[0178] The position judgment module 240 is configured to acquire the position of the hopper rear baffle and the position of the blanking port, and judge whether the vehicle has traveled to the blanking end position based on the position of the hopper rear baffle and the position of the blanking port. If so, the blanking port is closed and the vehicle drives away.

[0179] Further, the data acquisition module 210 is configured to:

[0180] In response to a silo loading request instruction, acquire the point cloud data collected by the lidar;

[0181] Process the point cloud data to obtain the processed point cloud data;

[0182] Acquire the hopper structure data, determine the position of the hopper front baffle and the positions of the left and right hopper baffles of the vehicle, and determine the hopper size and hopper volume based on the processed point cloud data;

[0183] Generate a first control instruction set based on the position of the hopper front baffle of the vehicle, the first control instruction set being used to control the transport vehicle to run to a preset vehicle position;

[0184] Generate a second control instruction set based on the positions of the left and right hopper baffles of the hopper, the second control instruction set being used to control the blanking direction adjustment device to move to a position corresponding to the hopper.

[0185] Further, the material level determination module 220 is configured to:

[0186] Determine the material level monitoring area based on the hopper size;

[0187] Determine the material level limit height based on the hopper structure data.

[0188] Further, the speed determination module 230 is configured to:

[0189] Acquire the material level data in the hopper and determine the blanking time and the volume of the material in the hopper based on the material level data in the hopper;

[0190] Determine the silo discharging flow rate based on the discharging time and the volume of materials in the hopper.

[0191] Obtain the amount of materials in the preset area to be loaded, and determine the average vehicle speed that the vehicle should travel based on the amount of materials in the preset area to be loaded and the silo discharging flow rate.

[0192] Determine the vehicle operating speed based on the average vehicle speed that the vehicle should travel.

[0193] In this embodiment, the method for adaptive material level detection based on the hopper size and vehicle position is different from the fixed material level detection instruments such as ultrasonic waves used in the prior art. This method can determine the appropriate material level detection area and material level limit for different vehicle models and vehicle offset situations, thereby improving the accuracy of material level detection. By establishing a vehicle speed planning model based on the material state in the hopper, calculate the volume of materials after the fixed-point discharging is completed to obtain the material discharging speed, plan the driving speed of the vehicle according to the area of materials to be loaded in the hopper, and dynamically adjust the vehicle speed according to the real-time material level monitoring, so that the vehicle can successfully complete the silo loading task with only one start and stop.

[0194] Embodiment III

[0195] Figure 5 It is a structural block diagram of a host computer provided by an embodiment of the present application. The host computer can be the host computer in the above embodiment. The host computer 300 can be a portable mobile host computer, such as: a smart phone, a tablet computer. The host computer 300 may also be called other names such as user equipment, portable host computer, etc.

[0196] Generally, the host computer 300 includes: a processor 301 and a memory 302.

[0197] The processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 301 may further include an AI (Artificial Intelligence) processor, and the AI processor is used to process computational operations related to machine learning.

[0198] The memory 302 may include one or more computer-readable storage media, and the computer-readable storage media may be tangible and non-transitory. The memory 302 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage media in the memory 302 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 301 to implement an adaptive unmanned silo loading method provided in this application.

[0199] In some embodiments, the host computer 300 may further optionally include: a peripheral device interface 303 and at least one peripheral device. Specifically, the peripheral device includes at least one of a radio frequency circuit 304, a touch display screen 305, a camera 306, an audio circuit 307, a positioning component 308, and a power supply 309.

[0200] The peripheral device interface 303 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the peripheral device interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the peripheral device interface 303 may be implemented on a separate chip or circuit board, and this embodiment does not limit this.

[0201] The radio frequency circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 304 converts electrical signals into electromagnetic signals for transmission, or converts the received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 304 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and so on. The radio frequency circuit 304 can communicate with other host computers through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: the World Wide Web, metropolitan area network, intranet, generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area network, and / or WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 304 may further include a circuit related to NFC (Near Field Communication), which is not limited in this application.

[0202] The touch display screen 305 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. The touch display screen 305 also has the ability to collect touch signals on or above the surface of the touch display screen 305. The touch signals can be input to the processor 301 as control signals for processing. The touch display screen 305 is used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, there may be one touch display screen 305, which is set on the front panel of the host computer 300; in other embodiments, there may be at least two touch display screens 305, which are respectively set on different surfaces of the host computer 300 or in a folding design; in still other embodiments, the touch display screen 305 may be a flexible display screen, which is set on the curved surface or folding surface of the host computer 300. Even, the touch display screen 305 can also be set as an irregular non-rectangular shape, that is, a special-shaped screen. The touch display screen 305 can be prepared from materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0203] The camera module 306 is used to capture images or videos. Optionally, the camera module 306 includes a front camera and a rear camera. Generally, the front camera is used for video calls or selfies, and the rear camera is used for taking photos or videos. In some embodiments, there are at least two rear cameras, which can be any one of a main camera, a depth camera, and a wide-angle camera, so as to realize the background blurring function by fusing the main camera and the depth camera, and realize panoramic shooting and VR (Virtual Reality) shooting functions by fusing the main camera and the wide-angle camera. In some embodiments, the camera module 306 may further include a flash. The flash can be a single-color-temperature flash or a two-color-temperature flash. The two-color-temperature flash refers to the combination of a warm-light flash and a cold-light flash, which can be used for light compensation under different color temperatures.

[0204] The audio circuit 307 is used to provide an audio interface between the user and the host computer 300. The audio circuit 307 may include a microphone and a speaker. The microphone is used to collect sound waves of the user and the environment, and convert the sound waves into electrical signals and input them to the processor 301 for processing, or input them to the radio frequency circuit 304 to achieve voice communication. For the purpose of stereo collection or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the host computer 300. The microphone can also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert the electrical signals from the processor 301 or the radio frequency circuit 304 into sound waves. The speaker can be a traditional thin-film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert electrical signals into audible sound waves for humans, but also convert electrical signals into inaudible sound waves for humans for uses such as ranging. In some embodiments, the audio circuit 307 may further include a headphone jack.

[0205] The positioning module 308 is used to locate the current geographical location of the host computer 300 to achieve navigation or LBS (Location-Based Service). The positioning module 308 can be a positioning module based on the US GPS (Global Positioning System), the Chinese Beidou system, or the Russian Galileo system.

[0206] The power supply 309 is used to supply power to each component in the host computer 300. The power supply 309 can be alternating current, direct current, a disposable battery, or a rechargeable battery. When the power supply 309 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. The wired rechargeable battery is a battery charged through a wired line, and the wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0207] In some embodiments, the host computer 300 further includes one or more sensors 310. The one or more sensors 310 include, but are not limited to: an acceleration sensor 311, a gyroscope sensor 312, a pressure sensor 313, a fingerprint sensor 314, an optical sensor 315, and a proximity sensor 316.

[0208] The acceleration sensor 311 can detect the magnitudes of accelerations on the three coordinate axes of the coordinate system established with the host computer 300. For example, the acceleration sensor 311 can be used to detect the components of the gravitational acceleration on the three coordinate axes. The processor 301 can control the touch display screen 305 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 311. The acceleration sensor 311 can also be used for collecting game or user movement data.

[0209] The gyroscope sensor 312 can detect the body direction and rotation angle of the host computer 300. The gyroscope sensor 312 can cooperate with the acceleration sensor 311 to collect the 3D (3 Dimensions) actions of the user on the host computer 300. According to the data collected by the gyroscope sensor 312, the processor 301 can implement the following functions: motion sensing (such as changing the UI according to the user's tilting operation), image stabilization during shooting, game control, and inertial navigation.

[0210] The pressure sensor 313 can be disposed on the side frame of the host computer 300 and / or the lower layer of the touch display screen 305. When the pressure sensor 313 is disposed on the side frame of the host computer 300, it can detect the holding signal of the user on the host computer 300, and perform left / right hand identification or shortcut operations according to the holding signal. When the pressure sensor 313 is disposed on the lower layer of the touch display screen 305, it can control the operable controls on the UI interface according to the pressure operation of the user on the touch display screen 305. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0211] The fingerprint sensor 314 is used to collect the fingerprint of the user to identify the user's identity according to the collected fingerprint. When the identity of the user is identified as a trusted identity, the processor 301 authorizes the user to perform relevant sensitive operations, and the sensitive operations include unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings, etc. The fingerprint sensor 314 can be disposed on the front, back, or side of the host computer 300. When there are physical buttons or a manufacturer logo on the host computer 300, the fingerprint sensor 314 can be integrated with the physical buttons or the manufacturer logo.

[0212] The optical sensor 315 is used to collect the ambient light intensity. In one embodiment, the processor 301 can control the display brightness of the touch display screen 305 according to the ambient light intensity collected by the optical sensor 315. Specifically, when the ambient light intensity is high, the display brightness of the touch display screen 305 is increased; when the ambient light intensity is low, the display brightness of the touch display screen 305 is decreased. In another embodiment, the processor 301 can also dynamically adjust the shooting parameters of the camera assembly 306 according to the ambient light intensity collected by the optical sensor 315.

[0213] The proximity sensor 316, also known as the distance sensor, is usually disposed on the front surface of the host computer 300. The proximity sensor 316 is used to collect the distance between the user and the front surface of the host computer 300. In one embodiment, when the proximity sensor 316 detects that the distance between the user and the front surface of the host computer 300 is gradually decreasing, the processor 301 controls the touch display screen 305 to switch from the lit state to the off state; when the proximity sensor 316 detects that the distance between the user and the front surface of the host computer 300 is gradually increasing, the processor 301 controls the touch display screen 305 to switch from the off state to the lit state.

[0214] Those skilled in the art can understand that Figure 3 the structure shown in does not constitute a limitation on the host computer 300, and may include more or fewer components than shown in the figure, or combine certain components, or adopt different component arrangements.

[0215] Embodiment 4

[0216] In an exemplary embodiment, a computer-readable storage medium is further provided, on which a computer program is stored, and when the program is executed by a processor, it implements an adaptive unmanned silo loading method provided by all the inventive embodiments of the present application.

[0217] Any combination of one or more computer-readable media can be adopted. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, apparatus, or device.

[0218] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take many forms, including - but not limited to - electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0219] The program code contained on a computer-readable medium may be transmitted using any appropriate medium, including - but not limited to - wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0220] Computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0221] Example Five

[0222] In an exemplary embodiment, there is also provided an application program product, including one or more instructions that can be executed by the processor 301 of the above device to complete the above-mentioned adaptive unmanned silo loading method.

[0223] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and embodiments. It can be fully applied to various fields suitable for the present invention. For those skilled in the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the equivalent scope, the present invention is not limited to the specific details and the illustrated and described examples here.

Claims

1. An adaptive unmanned silo loading method, characterized in that: include: In response to a silo loading request instruction, obtain bucket size and bucket structure data, generate a control instruction set based on the bucket structure data, the control instruction set is used to control the transport vehicle to move to a preset vehicle position, and the control instruction set is used to control the unloading direction adjustment device to move to a position corresponding to the bucket; Determine a material level detection area and a material level limit height based on the bucket size and the bucket structure data; Obtain material level data in the truck bed and determine the vehicle running speed; The position of the tailgate of the truck bed and the position of the unloading port are obtained, and based on the position of the tailgate of the truck bed and the position of the unloading port, it is determined whether the vehicle has reached the unloading termination position. If so, the unloading port is closed and the vehicle drives away.

2. The adaptive unmanned silo loading method according to claim 1, characterized in that: In response to a silo loading request instruction, a bucket size and bucket structure data are obtained, and a control instruction set is generated based on the bucket structure data, including: In response to a silo loading request instruction, obtaining point cloud data collected by a laser radar; Processing the point cloud data to obtain processed point cloud data; Acquire the bucket structure data, determine the position of the front baffle of the vehicle bucket and the positions of the left and right baffles of the vehicle bucket, and determine the bucket size and bucket volume based on the processed point cloud data; Based on the position of the front fender of the vehicle bucket, a first control instruction set is generated, wherein the first control instruction set is used to control the transport vehicle to run to a preset vehicle position; Based on the position of the left and right baffles of the truck bucket, a second control instruction set is generated, and the second control instruction set is used to control the unloading direction adjustment device to move to a position corresponding to the truck bucket.

3. The adaptive unmanned silo loading method according to claim 1, characterized in that: Determining the material level detection area and the material level limit height based on the bucket size and the bucket structure data includes: Based on the bucket size, determining the material level monitoring area; Based on the bucket structure data, the material level limit height is determined.

4. The adaptive unmanned silo loading method according to claim 1, characterized in that: Obtaining material level data in the bucket and determining the running speed of the vehicle includes: Acquire material level data in the bucket, and determine the material unloading time and the material volume in the bucket based on the material level data in the bucket; Determine the silo unloading flow rate based on the unloading time and the volume of the material in the bucket; Obtaining the amount of material in a preset area to be loaded, and determining an average speed at which the vehicle should travel based on the amount of material in the preset area to be loaded and the material discharge flow rate of the silo; The vehicle running speed is determined based on an average vehicle speed at which the vehicle should travel.

5. An adaptive unmanned silo loading device, characterized in that: include: A data acquisition module, for acquiring bucket size and bucket structure data in response to a silo loading request instruction, and generating a control instruction set based on the bucket structure data, wherein the control instruction set is used to control the transport vehicle to move to a preset vehicle position, and the control instruction set is used to control the unloading direction adjustment device to move to a position corresponding to the bucket; A material level determination module, used to determine a material level detection area and a material level limit height based on the bucket size and the bucket structure data; The speed determination module is used to obtain the material level data in the bucket and determine the vehicle running speed; The position judgment module is used to obtain the position of the rear tailgate of the truck bed and the position of the unloading port, and judge whether the vehicle has reached the unloading end position based on the position of the rear tailgate of the truck bed and the position of the unloading port. If so, the unloading port is closed and the vehicle drives away.

6. The adaptive unmanned silo loading device according to claim 5, characterized in that: The data acquisition module is used to: In response to a silo loading request instruction, obtaining point cloud data collected by a laser radar; Processing the point cloud data to obtain processed point cloud data; Acquire the bucket structure data, determine the position of the front baffle of the vehicle bucket and the positions of the left and right baffles of the vehicle bucket, and determine the bucket size and bucket volume based on the processed point cloud data; Based on the position of the front fender of the vehicle bucket, a first control instruction set is generated, wherein the first control instruction set is used to control the transport vehicle to run to a preset vehicle position; Based on the position of the left and right baffles of the truck bucket, a second control instruction set is generated, and the second control instruction set is used to control the unloading direction adjustment device to move to a position corresponding to the truck bucket.

7. The adaptive unmanned silo loading device according to claim 5, characterized in that: The material level determination module is used to: Based on the bucket size, determining the material level monitoring area; Based on the bucket structure data, the material level limit height is determined.

8. The adaptive unmanned silo loading device according to claim 5, characterized in that: The speed determination module is used to: Acquire material level data in the bucket, and determine the material unloading time and the material volume in the bucket based on the material level data in the bucket; Determine the silo unloading flow rate based on the unloading time and the volume of the material in the bucket; Obtaining the amount of material in a preset area to be loaded, and determining an average speed at which the vehicle should travel based on the amount of material in the preset area to be loaded and the material discharge flow rate of the silo; The vehicle running speed is determined based on an average vehicle speed at which the vehicle should travel.

9. A host computer, characterized in that: include: one or more processors; a memory for storing the one or more processor-executable instructions; Wherein, the one or more processors are configured to: Execute the adaptive unmanned silo loading method as described in any one of claims 1 to 4.

10. A non-transitory computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by the processor of the host computer, the host computer can execute the adaptive unmanned silo loading method as described in any one of claims 1 to 4.

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