An adaptive unmanned silo loading method and device, host computer and medium

Abstract

The application discloses a kind of self-adapting unmanned silo truck loading method, belong to unmanned silo truck loading technical field, response to silo truck loading request instruction, obtain car hopper size and car hopper structure data, control instruction set is generated based on the car hopper structure data, the control instruction set is used to control transport vehicle to run to preset vehicle position, and, the control instruction set is used to control the movement of unloading direction adjusting device to the position corresponding to car hopper;Based on the car hopper size and the car hopper structure data, determine material level detection area and material level limit height;Obtain material level data in car hopper, determine vehicle running speed;Obtain car hopper back baffle position and unloading port position, based on the car hopper back baffle position and unloading port position whether vehicle is driven to unloading termination position, if yes, unloading port is closed and vehicle drives away.The application is for the deviation degree of vehicle, carries out the adaptive adjustment of unloading port, improves the stability of loading work.

Description

Technical Field

[0001] This invention discloses an adaptive unmanned silo loading method, which belongs to the field of unmanned silo loading technology. Background Technology

[0002] Silo loading is a common operation in industrial, agricultural, 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.

[0003] Traditional loading methods require operators to monitor the material flow in the silo and the vehicle's loading status in real time. Drivers, guided by operators, frequently start and stop the vehicle, which damages the vehicle body, reduces its lifespan, and increases energy consumption. Therefore, by using LiDAR point cloud data and material flow rate to plan vehicle speed, loading operations can be completed efficiently with only one start and stop.

[0004] When transport vehicles enter the loading area, it is often difficult for the vehicle's centerline to be aligned with the unloading port. Under such circumstances, loading operations will result in uneven load distribution, a shift in the vehicle's center of gravity, and affect the vehicle's driving stability. In severe cases, it may lead to accidents such as rollover. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes an adaptive unmanned silo loading method, device, host computer, and medium. It uses lidar point cloud data to estimate vehicle size and locate it in real time, dynamically plans vehicle speed based on real-time material status, and adaptively adjusts the discharge port according to the degree of vehicle deviation, thereby ensuring uniform load distribution, improving the stability of loading operations, and guaranteeing driver safety.

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

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

[0008] In response to a silo loading request command, the dimensions and structure data of the silo are obtained, and a control command set is generated based on the silo structure data. The control command set is used to control the transport vehicle to run to a preset vehicle position, and the control command set is used to control the unloading direction adjustment device to move to the position corresponding to the silo.

[0009] Based on the bucket dimensions and bucket structure data, the material level detection area and material level limit height are determined;

[0010] Obtain material level data inside the truck bed to determine vehicle speed;

[0011] The position of the rear baffle of the truck bed and the position of the discharge port are obtained. Based on the position of the rear baffle of the truck bed and the position of the discharge port, it is determined whether the vehicle has driven to the discharge termination position. If so, the discharge port is closed and the vehicle drives away.

[0012] Furthermore, in response to a silo loading request command, the dimensions and structure data of the truck bed are acquired, and a set of control commands is generated based on the truck bed structure data, including:

[0013] In response to the silo loading request command, the point cloud data collected by the lidar is acquired;

[0014] The point cloud data is processed to obtain processed point cloud data;

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

[0016] Based on the position of the front baffle of the vehicle bed, a first set of control instructions is generated. The first set of control instructions is used to control the transport vehicle to run to the preset vehicle position.

[0017] Based on the positions of the left and right baffles of the truck bed, a second set of control instructions is generated. The second set of control instructions is used to control the material feeding direction adjustment device to move to the position corresponding to the truck bed.

[0018] Further, based on the bucket size and the bucket structure data, the material level detection area and the material level limit height are determined, including:

[0019] Based on the dimensions of the truck bed, the material level monitoring area is determined;

[0020] Based on the truck bed structure data, the material level limit height is determined.

[0021] Further, acquiring material level data inside the truck bed and determining the vehicle's operating speed includes:

[0022] Obtain material level data in the truck hopper, and determine the feeding time and material volume in the truck hopper based on the material level data in the truck hopper;

[0023] The silo discharge flow rate is determined based on the discharge time and the volume of material in the hopper;

[0024] Obtain the material quantity in the preset loading area, and determine the average vehicle speed based on the material quantity in the preset loading area and the silo discharge flow rate.

[0025] The vehicle's operating speed is determined based on the average speed at which the vehicle should travel.

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

[0027] The data acquisition module is used to respond to the silo loading request command, acquire the hopper size and hopper structure data, generate a control command set based on the hopper structure data, the control command set is used to control the transport vehicle to run to the preset vehicle position, and the control command set is used to control the material discharge direction adjustment device to move to the position corresponding to the hopper.

[0028] The material level determination module is used to determine the material level detection area and the material level limit height based on the size of the truck bed and the structure data of the truck bed;

[0029] The speed determination module is used to acquire material level data in the truck bed and determine the vehicle's running speed;

[0030] The position determination module is used to obtain the position of the rear baffle of the truck bed and the position of the discharge port. Based on the position of the rear baffle of the truck bed and the position of the discharge port, it determines whether the vehicle has driven to the discharge termination position. If so, the discharge port is closed and the vehicle drives away.

[0031] Furthermore, the data acquisition module is used for:

[0032] In response to the silo loading request command, the point cloud data collected by the lidar is acquired;

[0033] The point cloud data is processed to obtain processed point cloud data;

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

[0035] Based on the position of the front baffle of the vehicle bed, a first set of control instructions is generated. The first set of control instructions is used to control the transport vehicle to run to the preset vehicle position.

[0036] Based on the positions of the left and right baffles of the truck bed, a second set of control instructions is generated. The second set of control instructions is used to control the material feeding direction adjustment device to move to the position corresponding to the truck bed.

[0037] Furthermore, the material level determination module is used for:

[0038] Based on the dimensions of the truck bed, the material level monitoring area is determined;

[0039] Based on the truck bed structure data, the material level limit height is determined.

[0040] Furthermore, the speed determination module is used for:

[0041] Obtain material level data in the truck hopper, and determine the feeding time and material volume in the truck hopper based on the material level data in the truck hopper;

[0042] The silo discharge flow rate is determined based on the discharge time and the volume of material in the hopper;

[0043] Obtain the material quantity in the preset loading area, and determine the average vehicle speed based on the material quantity in the preset loading area and the silo discharge flow rate.

[0044] The vehicle's operating speed is determined based on the average speed at which the vehicle should travel.

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

[0046] One or more processors;

[0047] Memory for storing the one or more processor-executable instructions;

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

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

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

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

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

[0053] This invention provides an adaptive unmanned silo loading method, device, host computer, and medium. It establishes an adaptive material level detection method based on silo size and vehicle position, unlike previous technologies that used fixed material level detection instruments such as ultrasonic sensors. This method can determine suitable material level detection areas and 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 inside the silo, the volume of material after fixed-point unloading is calculated to obtain the material discharge speed. Based on the area of ​​material to be loaded in the silo, the vehicle's driving speed is planned, and the vehicle speed is dynamically adjusted based on real-time material level monitoring, enabling the vehicle to 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 exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

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

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

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

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

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

[0060] in:

[0061] 1. Host computer;

[0062] 2. Lower-level machine;

[0063] 3. Feeding direction adjustment actuator;

[0064] 4. First lidar,

[0065] 5. Feed port opening / closing actuator;

[0066] 6. Sliding hopper;

[0067] 7. Second lidar. Detailed Implementation

[0068] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] In the description of this invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0070] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

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

[0072] Example 1

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

[0074] Step 101: In response to the silo loading request command, obtain the hopper size and hopper structure data, generate a control command set based on the hopper structure data, the control command set is used to control the transport vehicle to run to the preset vehicle position, and the control command set is used to control the material discharge direction adjustment device to move to the position corresponding to the hopper;

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

[0076] By defining the region of interest (ROI) of the point cloud, irrelevant point clouds outside the truck bed are removed. Then, noise reduction algorithms are applied to the point cloud data to remove noise points and improve the quality of the point cloud data. A point cloud clustering algorithm based on connected components is used to segment the point cloud data according to the target region, extracting the point cloud data of the transport vehicle. A plane fitting algorithm is used to segment the side and bottom point cloud data of the truck bed, and the plane equations of five planes are calculated. The segmented side and bottom point cloud data are downsampled to reduce the amount of computation and retain key feature points, finally obtaining the processed point cloud data.

[0077] The following steps will acquire the vehicle bed structure data to determine the positions of the front and left / right side panels of the vehicle bed. Based on the processed point cloud data, the dimensions and volume of the vehicle bed will be determined. Details are as follows:

[0078] The distance between the front and rear baffle planes is the length L of the truck bed. The plane equations of the front and rear baffles are as follows:

[0079]

[0080] The formula for calculating the length L of the truck bed is:

[0081]

[0082] In the formula, A1, B1, and C1 are the coefficients of the plane normal vector, and D1 and D2 are the constant terms of the plane equations of the front and rear baffles.

[0083] The distance between the two flat surfaces of the left and right baffles is the width W of the truck bed. The equations of the left and right baffles are as follows:

[0084]

[0085] The formula for calculating the length L of the truck bed is:

[0086]

[0087] In the formula, A2, B2, and C2 are the coefficients of the plane normal vector, and D3 and D4 are the constant terms of the plane equations of the left and right baffles.

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

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

[0090] Truck bed volume calculation: Based on the length, width, and height of the truck bed, calculate the truck bed volume V as follows:

[0091] V=L×H×W (8)

[0092] Next, based on the position of the front fender of the vehicle bed, a first control command set will be generated. This first control command set is used to control the transport vehicle to move to the preset vehicle position. The specific content is as follows:

[0093] As the transport vehicle moves forward, the host computer performs real-time positioning based on the position of the front baffle of the truck bed and the position of the discharge port. When the vehicle reaches the preset parking position, a stop signal is sent to the transport vehicle. By real-time detection of the position of the front baffle of the truck bed, combined with the previously measured position of the center of the discharge port, the vehicle's position is determined in real time. The vehicle's parking position is determined based on the components of the front baffle and the discharge port in the y-axis direction.

[0094] The center coordinate of the front baffle of the truck bed is P f (x f y f , z f The center coordinate of the rear tailgate of the truck bed is P. r (x r y r , z r The coordinates of the center position of the discharge port are C(x). c y c , z c ).

[0095] When the distance between the front baffle of the truck bed and the center of the discharge port meets the requirements of formula (9), the vehicle stops; otherwise, the vehicle position continues to be adjusted.

[0096] A+ε1>y c -y f >A-ε1 (9)

[0097] A is the preset stopping distance between the front baffle and the center of the discharge port; ε1 is the stopping reaction distance.

[0098] Finally, based on the positions of the left and right baffles of the truck bed, a second set of control instructions is generated. This second set of control instructions is used to control the material feeding direction adjustment device to move to the position corresponding to the truck bed. The specific content is as follows:

[0099] The lateral deviation between the transport vehicle and the center line of the discharge port is calculated based on the positions of the left and right baffles of the hopper. This data is then sent to the lower-level machine 2. The lower-level machine 2 controls the discharge direction adjustment actuator 3, which is fixed to 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 has a limited sliding range, which is restricted to [-x] at the center position of the discharge port. 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 slide down 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 truck bed along the x-axis, denoted as x and x respectively. max and x min Calculate the coordinate x of the centerline of the truck bed in the x-axis direction. c1 :

[0102]

[0103] The offset distance Δx is calculated as follows:

[0104] Δx p =x c1 -x c2 (11)

[0105] In the formula, x c2 This represents the coordinates of the centerline of the feed inlet in the x-axis direction.

[0106] Step 102: Based on the dimensions and structure data of the truck bed, determine the material level detection area and the material level limit height. The specific steps are as follows:

[0107] First, based on the dimensions of the truck bed, the material level monitoring area is determined, as detailed below:

[0108] The material level monitoring area consists of two material level monitoring points (x) c1 y c1 ) and (x c2 y c2 A circular region with radius R centered at ( ). In the y-axis direction, during the feeding process, the material accumulates highest on both sides level with the center of the feeding port. Therefore, two monitoring points are set at the same level as the feeding port, i.e.:

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

[0110] In the x-axis direction, two monitoring points are set on the left and right sides of the truck bed respectively to prevent material from piling up too high on one side and causing leakage.

[0111]

[0112] In the formula, W is the width of the truck bed; Δx is the vehicle offset distance; and Δx1 is the distance from the material level detection point to the edge of the vehicle.

[0113] The following describes how to determine the material level limit height based on the truck bed structure data. Details are as follows:

[0114] To ensure that the material does not exceed the maximum load-bearing height of the truck bed, a material level height limit h is set based on the dimensions of the truck bed. max The specific calculation method is as follows:

[0115] h max =k1·H+k2·W (15)

[0116] In the formula, H is the height of the bucket; W is the width of the bucket; k1 is the material level height ratio factor, which is most related to the height of the bucket; k2 is the material level width ratio factor, which is affected by the width of the bucket. The wider the bucket, the higher the material level limit will be.

[0117] Step 103: Obtain material level data in the truck bed and determine the vehicle's operating speed. The specific steps are as follows:

[0118] First, obtain the material level data inside the truck hopper. Based on the material level data, determine the feeding time and the volume of material inside the truck hopper. The specific details are as follows:

[0119] Let the point cloud data within the material level detection area of ​​the truck bed be P = {p1, p2, ..., p n}, where p i =(x i ,y i ,z i Let be the coordinates of the i-th point in the point cloud, and n be the total number of points within the detection area. The real-time material level h in the truck hopper... r Calculated using the following formula:

[0120]

[0121] In the formula: n is the number of points within the detection area of ​​the truck bed that meet the conditions; z i It is point p i The z-coordinate represents the height of that point; h r This indicates the average height of the material in the truck bed, i.e., the real-time material level.

[0122] The host computer calculates the silo discharge velocity based on the discharge time and the volume of material in the hopper. The material flow velocity at the discharge port is calculated using a method based on the discharge time and the point cloud volume of material in the hopper. The calculation process is as follows:

[0123] Calculation of material feeding time:

[0124] During the silo unloading process, the host computer records the material unloading start time t at the unloading port. start and the time t from the first material feeding limit end Calculate the total material feeding time T.down :

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

[0126] Extraction of point cloud from material pile inside the truck bed:

[0127] After filtering out the fitted side and bottom point clouds, and removing excess point clouds based on the positions of the front and rear baffles of the truck bed, the remaining point clouds are the material point clouds inside the truck bed.

[0128] Material volume calculation: The point cloud volume of the material inside the truck bed is calculated using the point cloud volume integration method based on the average height of points within the grid. The calculation process is as follows:

[0129] First, fit a reference plane at the bottom of the truck bed as a height reference:

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

[0131] Then, a 2D grid is generated, and the point cloud is projected onto the XY plane, with each grid representing a small area at the bottom of the truck bed. For each point (x, y, z), its grid index is calculated based on its x and y coordinates, and all points are grouped according to their grid indices:

[0132]

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

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

[0135]

[0136] Calculate the average height h of points within the grid. avg :

[0137]

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

[0139] A grid =Δx·Δy (22)

[0140] Calculate the grid volume v based on the average height. i :

[0141] v i =h avg ·A grid (twenty three)

[0142] All the small volumes v of the grid i By summing the results, we obtain the total volume V of the material:

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

[0144] Secondly, based on the feeding time and the volume of material in the hopper, the silo feeding flow rate is determined, as detailed below:

[0145] The calculation of the silo discharge velocity Q is shown in formula (25):

[0146]

[0147] Then, the material quantity in the preset loading area is obtained. Based on the material quantity in the preset loading area and the silo discharge flow rate, the average vehicle speed that should be traveled is determined. The specific details are as follows:

[0148] Using the bottom of the truck bed as the reference plane, the material point cloud is transformed into the truck bed coordinate system. The material surface is then reconstructed by meshing the material point cloud data. For each mesh, the material height is... The loading area inside the truck bed is:

[0149]

[0150] In the formula, the threshold height of the material in the truck bed is h. max

[0151] Establish a model for the distribution of the loading area: Divide the length of the truck bed into n segments, each segment having a length of... The amount of material to be loaded in each section:

[0152]

[0153] The unloading time for each section of the truck bed is The average speed the vehicle should travel during each section of the unloading process in the truck bed is:

[0154]

[0155] Finally, based on the average speed the vehicle should travel, the vehicle's operating speed is determined, as detailed below:

[0156] The vehicle speed is smoothed by using interpolation to generate a continuous speed curve:

[0157]

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

[0159] Set acceleration limits; changes in vehicle speed should meet the acceleration limits for trucks to avoid sudden acceleration or deceleration. In other words, speed changes should meet the following requirements:

[0160]

[0161] In the formula, a max This is the truck's maximum acceleration.

[0162] Dynamic speed adjustment method based on dual material level monitoring: This method monitors the material accumulation height h0 at the discharge port and the material level rise rate. and distance from the discharge port y d Distance from the material accumulation height h1, material level rise rate Four data points are used to adjust the vehicle speed.

[0163] Formula for adjusting vehicle speed:

[0164]

[0165] In the formula, v0 is the vehicle's base speed; k3 is the adjustment coefficient for material level deviation, used to quickly respond to material level deviation; k4 is the adjustment coefficient for material level rise rate, used to predict material level changes in advance and avoid vehicle speed adjustment lag; and v is the vehicle's running speed.

[0166] Step 104: Obtain the position of the rear baffle of the truck bed and the position of the discharge port. Based on the positions of the rear baffle and the discharge port, determine whether the vehicle has reached the discharge termination position. If so, close the discharge port and drive away. The specific steps are as follows:

[0167] The system determines whether the vehicle has reached the preset material discharge termination position based on the position of the rear baffle of the truck bed and the position of the discharge port. If the termination position is reached, a stop material discharge signal is sent to the lower-level computer, and a departure signal is sent to the transport vehicle. The loading operation is completed, and the system waits for the next transport vehicle to enter the radar monitoring range to start the next operation cycle. The upper-level computer determines whether the vehicle has reached the material discharge termination position by real-time detection of the rear baffle position of the truck bed and the position of the center of the discharge port as previously measured.

[0168] The center coordinate of the rear tailgate of the truck bed is P. r (x r y r , z r The coordinates of the center position of the discharge port are C(x). c y c , z c ).

[0169] The vehicle stops when the rear baffle of the truck bed meets the following conditions in relation to the center of the discharge port; otherwise, the vehicle position is adjusted.

[0170] B+ε2>y c -y r >B-ε2 (32)

[0171] In the formula, B is the preset stopping distance between the rear baffle and the center of the discharge port; ε2 is the reaction distance after the discharge is completed.

[0172] In this embodiment, the adaptive material level detection method based on the size of the hopper and the vehicle position differs from the fixed material level detection instruments such as ultrasonic sensors used in previous technologies. 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, the volume of the material after fixed-point unloading is calculated to obtain the material unloading speed. Based on the area of ​​the material to be loaded in the hopper, the vehicle's driving speed is planned, and the vehicle speed is dynamically adjusted based on real-time material level monitoring, so that the vehicle can successfully complete the silo loading task with only one start and stop.

[0173] Example 2

[0174] Figure 4 This is a schematic block diagram illustrating the structure of an adaptive unmanned silo loading device according to an exemplary embodiment. The device includes:

[0175] The data acquisition module 210 is used to respond to the silo loading request command, acquire the hopper size and hopper structure data, generate a control command set based on the hopper structure data, the control command set is used to control the transport vehicle to run to a preset vehicle position, and the control command set is used to control the material discharge direction adjustment device to move to the position corresponding to the hopper.

[0176] The material level determination module 220 is used to determine the material level detection area and the material level limit height based on the size of the truck bed and the structure data of the truck bed;

[0177] The speed determination module 230 is used to acquire material level data in the truck bed and determine the vehicle's running speed;

[0178] The position determination module 240 is used to obtain the position of the rear baffle of the truck bed and the position of the discharge port, and determine whether the vehicle has driven to the discharge termination position based on the position of the rear baffle of the truck bed and the position of the discharge port. If so, the discharge port is closed and the vehicle drives away.

[0179] Furthermore, the data acquisition module 210 is used for:

[0180] In response to the silo loading request command, the point cloud data collected by the lidar is acquired;

[0181] The point cloud data is processed to obtain processed point cloud data;

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

[0183] Based on the position of the front baffle of the vehicle bed, a first set of control instructions is generated. The first set of control instructions is used to control the transport vehicle to run to the preset vehicle position.

[0184] Based on the positions of the left and right baffles of the truck bed, a second set of control instructions is generated. The second set of control instructions is used to control the material feeding direction adjustment device to move to the position corresponding to the truck bed.

[0185] Furthermore, the material level determination module 220 is used for:

[0186] Based on the dimensions of the truck bed, the material level monitoring area is determined;

[0187] Based on the truck bed structure data, the material level limit height is determined.

[0188] Furthermore, the speed determination module 230 is used for:

[0189] Obtain material level data in the truck hopper, and determine the feeding time and material volume in the truck hopper based on the material level data in the truck hopper;

[0190] The silo discharge flow rate is determined based on the discharge time and the volume of material in the hopper;

[0191] Obtain the material quantity in the preset loading area, and determine the average vehicle speed based on the material quantity in the preset loading area and the silo discharge flow rate.

[0192] The vehicle's operating speed is determined based on the average speed at which the vehicle should travel.

[0193] In this embodiment, the adaptive material level detection method based on the size of the hopper and the vehicle position differs from the fixed material level detection instruments such as ultrasonic sensors used in previous technologies. 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, the volume of the material after fixed-point unloading is calculated to obtain the material unloading speed. Based on the area of ​​the material to be loaded in the hopper, the vehicle's driving speed is planned, and the vehicle speed is dynamically adjusted based on real-time material level monitoring, so that the vehicle can successfully complete the silo loading task with only one start and stop.

[0194] Example 3

[0195] Figure 5 This is a structural block diagram of a host computer provided in an embodiment of this application. The host computer can be the host computer in the above embodiments. The host computer 300 can be a portable mobile host computer, such as a smartphone or tablet computer. The host computer 300 may also be referred to as user equipment, portable host computer, or other names.

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

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

[0198] The memory 302 may include one or more computer-readable storage media, which 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 or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 302 are used to store at least one instruction, which is 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 also 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 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 301 and the memory 302. In some embodiments, the processor 301, memory 302, and 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, memory 302, and peripheral device interface 303 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0201] The radio frequency (RF) circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 304 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 304 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF 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, etc. The RF circuit 304 can communicate with other host computers via at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 304 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0202] The touch display screen 305 is used to display a UI (User Interface). This 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 its surface. These touch signals can be input as control signals to the processor 301 for processing. The touch display screen 305 is used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one touch display screen 305, positioned on the front panel of the host computer 300; in other embodiments, there may be at least two touch display screens, respectively positioned on different surfaces of the host computer 300 or in a folded design; in still other embodiments, the touch display screen 305 may be a flexible display screen, positioned on a curved or folded surface of the host computer 300. Furthermore, the touch display screen 305 may be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The touch display screen 305 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0203] Camera assembly 306 is used to acquire images or videos. Optionally, camera assembly 306 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is used for video calls or selfies, and the rear-facing camera is used for taking photos or videos. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, and a wide-angle camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, and panoramic shooting and VR (Virtual Reality) shooting by fusion of the main camera and the wide-angle camera. In some embodiments, camera assembly 306 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash is a combination of a warm light flash and a cool light flash, which can be used for light compensation at different color temperatures.

[0204] Audio circuit 307 provides an audio interface between the user and host computer 300. Audio circuit 307 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to processor 301 for processing, or input to radio frequency circuit 304 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different location on the host computer 300. The microphone may also be an array microphone or an omnidirectional microphone. The speaker converts the electrical signals from processor 301 or radio frequency circuit 304 into sound waves. The speaker may be a traditional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, audio circuit 307 may also include a headphone jack.

[0205] The positioning component 308 is used to determine the current geographical location of the host computer 300 in order to enable navigation or LBS (Location Based Service). The positioning component 308 can be a positioning component based on the US GPS (Global Positioning System), China's BeiDou system, or Russia's Galileo system.

[0206] Power supply 309 is used to power the various components in host computer 300. Power supply 309 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 309 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via 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 accelerometer 311, a gyroscope 312, a pressure sensor 313, a fingerprint sensor 314, an optical sensor 315, and a proximity sensor 316.

[0208] Accelerometer 311 can detect the magnitude of acceleration on the three coordinate axes of a coordinate system established by host computer 300. For example, accelerometer 311 can be used to detect the components of gravitational acceleration on the three coordinate axes. Processor 301 can control touch screen 305 to display the user interface in landscape or portrait view based on the gravitational acceleration signal collected by accelerometer 311. Accelerometer 311 can also be used for games or to collect user motion data.

[0209] The gyroscope sensor 312 can detect the orientation and rotation angle of the host computer 300. The gyroscope sensor 312, in conjunction with the accelerometer sensor 311, can collect the user's 3D (3D) movements on the host computer 300. Based on the data collected by the gyroscope sensor 312, the processor 301 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0210] The pressure sensor 313 can be disposed on the side bezel 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 bezel of the host computer 300, it can detect the user's grip signal on the host computer 300 and perform left / right hand recognition or quick operation based on the grip 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 based on the user's pressure operation 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 a user's fingerprint to identify the user's identity. When the user's identity is identified as trusted, the processor 301 authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 314 can be located on the front, back, or side of the host computer 300. When the host computer 300 has physical buttons or a manufacturer's logo, the fingerprint sensor 314 can be integrated with the physical buttons or manufacturer's logo.

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

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

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

[0215] Example 4

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

[0217] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A 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 thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0218] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs 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 suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0220] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0221] Example 5

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

[0223] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Other modifications can be readily made by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and examples shown and described herein.

Claims

1. An adaptive unmanned silo loading method, characterized in that, include: In response to a silo loading request command, the dimensions and structure data of the silo are obtained, and a control command set is generated based on the silo structure data. The control command set is used to control the transport vehicle to run to a preset vehicle position, and the control command set is used to control the unloading direction adjustment device to move to the position corresponding to the silo. Based on the bucket dimensions and bucket structure data, the material level detection area and material level limit height are determined; Obtain material level data inside the truck bed to determine vehicle speed; The position of the rear baffle of the truck bed and the position of the discharge port are obtained. Based on the position of the rear baffle of the truck bed and the position of the discharge port, it is determined whether the vehicle has driven to the discharge termination position. If so, the discharge port is closed and the vehicle drives away. In response to a silo loading request command, the system acquires truck bed dimensions and structure data, and generates a control command set based on the truck bed structure data, including: In response to the silo loading request command, the point cloud data collected by the lidar is acquired; The point cloud data is processed to obtain processed point cloud data; Acquire the vehicle bed structure data, determine the position of the front baffle and the positions of the left and right baffles of the vehicle bed, and determine the dimensions and volume of the vehicle bed based on the processed point cloud data; The distance between the front and rear baffle planes is the length L of the truck bed. The plane equations of the front and rear baffles are as follows: The formula for calculating the length L of the truck bed is: （3） In the formula, A1, B1, and C1 are the coefficients of the plane normal vector, and D1 and D2 are the constant terms of the plane equations of the front and rear baffles. The distance between the two flat surfaces of the left and right baffles is the width W of the truck bed. The equations of the left and right baffles are as follows: The formula for calculating the length L of the truck bed is: （6） In the formula, A2, B2, C2 are the coefficients of the normal vector of the plane, D3, D4 are constant terms of the plane equation of the left and right baffles, and the maximum value z of the side point cloud in the z-axis direction is calculated max and the minimum value z min , and the height H of the car body is: （7） Truck bed volume calculation: Based on the length, width, and height of the truck bed, calculate the truck bed volume V as follows: （8） Based on the position of the front baffle of the vehicle bed, a first set of control instructions is generated. The first set of control instructions is used to control the transport vehicle to run to the preset vehicle position. As the transport vehicle moves forward, the host computer performs real-time positioning of the transport vehicle based on the position of the front baffle of the truck bed and the position of the discharge port. When the vehicle reaches the preset parking position, a parking signal is sent to the transport vehicle. The vehicle position is determined in real time by detecting the position of the front baffle of the truck bed in real time and cooperating with the position of the center of the discharge port in advance. Based on the positions of the left and right baffles of the truck bed, a second set of control instructions is generated. The second set of control instructions is used to control the material feeding direction adjustment device to move to the position corresponding to the truck bed.

2. The adaptive unmanned silo loading method according to claim 1, characterized in that, Based on the bucket size and bucket structure data, the material level detection area and the material level limit height are determined, including: The material level detection area is determined based on the dimensions of the truck bed; Based on the truck bed structure data, the material level limit height is determined.

3. The adaptive unmanned silo loading method according to claim 1, characterized in that, Acquiring material level data in the truck bed and determining the vehicle's operating speed includes: Obtain material level data in the truck hopper, and determine the feeding time and material volume in the truck hopper based on the material level data in the truck hopper; The silo discharge flow rate is determined based on the discharge time and the volume of material in the hopper; Obtain the material quantity in the preset loading area, and determine the average vehicle speed based on the material quantity in the preset loading area and the silo discharge flow rate. The vehicle's operating speed is determined based on the average speed at which the vehicle should travel.

4. An adaptive unmanned silo loading device, applied to the adaptive unmanned silo loading method according to any one of claims 1-3, characterized in that, include: The data acquisition module is used to respond to the silo loading request command, acquire the hopper size and hopper structure data, generate a control command set based on the hopper structure data, the control command set is used to control the transport vehicle to run to the preset vehicle position, and the control command set is used to control the material discharge direction adjustment device to move to the position corresponding to the hopper. The material level determination module is used to determine the material level detection area and the material level limit height based on the size of the truck bed and the structure data of the truck bed; The speed determination module is used to acquire material level data in the truck bed and determine the vehicle's running speed; The position determination module is used to obtain the position of the rear baffle of the truck bed and the position of the discharge port. Based on the position of the rear baffle of the truck bed and the position of the discharge port, it determines whether the vehicle has driven to the discharge termination position. If so, the discharge port is closed and the vehicle drives away.

5. The adaptive unmanned silo loading device according to claim 4, characterized in that, The data acquisition module is used for: In response to the silo loading request command, the point cloud data collected by the lidar is acquired; The point cloud data is processed to obtain processed point cloud data; Acquire the vehicle bed structure data, determine the position of the front baffle and the positions of the left and right baffles of the vehicle bed, and determine the dimensions and volume of the vehicle bed based on the processed point cloud data; Based on the position of the front baffle of the vehicle bed, a first set of control instructions is generated. The first set of control instructions is used to control the transport vehicle to run to the preset vehicle position. Based on the positions of the left and right baffles of the truck bed, a second set of control instructions is generated. The second set of control instructions is used to control the material feeding direction adjustment device to move to the position corresponding to the truck bed.

6. The adaptive unmanned silo loading device according to claim 4, characterized in that, The material level determination module is used for: The material level detection area is determined based on the dimensions of the truck bed; Based on the truck bed structure data, the material level limit height is determined.

7. The adaptive unmanned silo loading device according to claim 4, characterized in that, The speed determination module is used for: Obtain material level data in the truck hopper, and determine the feeding time and material volume in the truck hopper based on the material level data in the truck hopper; The silo discharge flow rate is determined based on the discharge time and the volume of material in the hopper; Obtain the material quantity in the preset loading area, and determine the average vehicle speed based on the material quantity in the preset loading area and the silo discharge flow rate. The vehicle's operating speed is determined based on the average speed at which the vehicle should travel.

8. A host computer, characterized in that, include: One or more processors; Memory for storing the one or more processor-executable instructions; Wherein, the one or more processors are configured as follows: Perform the adaptive unmanned silo loading method as described in any one of claims 1 to 3.

9. 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 is able to execute the adaptive unmanned silo loading method as described in any one of claims 1 to 3.

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