A logistics vehicle for quartz product turnover and a turnover control system thereof

Abstract

This invention discloses a logistics vehicle and its turnover control system for quartz products, belonging to the field of motor vehicles. Based on collected three-dimensional road surface imaging images, this invention constructs a three-dimensional road surface model. Based on the three-dimensional road surface model, it identifies the specific shapes of road protrusions or depressions and the deformation data of obstacles determined by the vehicle body. It calculates the required lifting displacement value for the bottom wheels and freely adjusts the required lifting displacement value of the bottom wheels according to the shape of the protrusions or depressions, avoiding swaying caused by the bottom wheels passing over protrusions or depressions. Simultaneously, by determining the deformation data of the obstacles, it further accurately determines the required lifting displacement value of the bottom wheels, further avoiding vehicle swaying caused by wheels being suspended in the air when passing over elastic components. This greatly improves the stability of the vehicle and prevents damage to quartz products during movement.

Description

Technical Field

[0001] This invention belongs to the field of motor vehicle technology, specifically a logistics vehicle and its turnover control system for the turnover of quartz products. Background Technology

[0002] Quartz product processing involves multiple departments and processes, including heat treatment, machining, and inspection. Currently, products are stacked on trolleys with corrugated boxes underneath, and then manually pushed to other departments. This manual process involves pushing the trolleys to the next department only when they are full. In urgent cases, products must be carried by hand, which wastes a significant amount of time, as products need to wait a long time after completing one process before moving to the next. Furthermore, quartz products are very fragile and easily scratched or bumped during handling, which can lead to breakage during subsequent processing. Finally, it wastes a lot of manpower; when there are many products, trolleys need to be pushed back and forth between departments multiple times a day, increasing the workload. To solve these problems, an automated transport system is proposed.

[0003] For example, patent CN211765974U discloses an automated factory transport trolley, including a carriage and a base. The carriage is a box structure with an open top. A gravity sensor is fixedly installed on the bottom wall inside the carriage. A first limit switch is installed on the outer wall of one side of the top of the carriage, and a second limit switch is installed on the outer wall of the other side of the top of the carriage. A motor is fixedly installed on one side of the bottom surface of the base. Base columns are symmetrically installed on one side of the top surface of the base. A hydraulic device is installed at the center of the other side of the top surface of the base. A power source is located at the center of the top surface of the base. A controller is located on the top surface of the base next to the power source. Buffer boxes are symmetrically installed on both sides of the base. Traveling wheels are installed at the bottom of each of the four corners of the base. This automated factory transport trolley can achieve automatic reciprocating and unloading, while mitigating collisions between the trolley and the stop block, thus improving the stability of the goods.

[0004] Meanwhile, for example, patent CN205554348U discloses an automatically operating logistics turnover tractor, including a frame. The key technical point is that the lower part of the frame is provided with a drive mechanism for supporting and moving the frame according to instructions, and the end of the frame in the direction of travel is provided with a protective railing to prevent collisions. This utility model provides an automatically operating logistics turnover tractor that can move or turn according to design instructions via the drive mechanism, automatically pulling the turnover vehicle to a designated position. It features a simple design, convenient operation, improved efficiency, and reduced costs.

[0005] All of the above patents suffer from the problems described in this background technology: no matter how advanced the shock absorption system is during transportation, existing vehicle bodies will still sway when on uneven roads. This swaying can easily damage quartz products. Workpieces are often found on factory roads, and when logistics vehicles pass over these workpieces or damaged road surfaces, they will sway, causing damage to quartz products. If the tires are raised to pass over them, it can easily cause the vehicle's center of gravity to become unstable, leading to vehicle deviation. To solve these problems, this application designs a logistics vehicle and its turnover control system for the turnover of quartz products. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a logistics vehicle and its turnover control system for quartz products. Based on collected three-dimensional road surface imaging images, this invention constructs a three-dimensional road surface model. Based on this model, it identifies the specific shapes of road surface protrusions or depressions and the deformation data of obstacles determined by the vehicle's movement. It then calculates the required lifting displacement value for the bottom wheels and freely adjusts the displacement value according to the shape of the protrusions or depressions, preventing swaying caused by the wheels passing over them. Furthermore, by determining the deformation data of the obstacles, it further accurately judges the required lifting displacement value for the bottom wheels, further preventing vehicle swaying caused by wheels being suspended in the air when passing over elastic components. This greatly improves the stability of the vehicle and prevents damage to the quartz products during movement.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A logistics vehicle for the turnover of quartz products includes a vehicle body, the bottom of which is equipped with lifting wheels that can move longitudinally according to the shape of obstacles, a 3D imaging scanner for scanning and imaging obstacles is installed at the front of the vehicle body and at the corresponding position of the lifting wheels, and a controller is installed inside the vehicle body. The controller can identify the deformation of obstacles and control the lifting wheels to move longitudinally according to the deformation shape of the obstacles. The controller operates a turnover control system.

[0009] Specifically, a lifting assembly is fixedly installed at the bottom of the vehicle body, and a lifting wheel is installed at the output end of the lifting assembly. The output end of the lifting assembly drives the lifting wheel to move longitudinally. Distance sensors capable of monitoring the distance to nearby vehicles are installed around the vehicle body, and an anti-slip compartment is installed on top of the vehicle body to prevent the quartz product from sliding.

[0010] Specifically, a turnover control system for quartz products is implemented based on the aforementioned logistics vehicle for quartz product turnover. It includes a control module, a road surface 3D model construction module, a data processing module, a signal conversion module, a road surface data acquisition module, a vehicle body position data acquisition module, and a bottom wheel lifting module. The control module controls the operation of these modules. The road surface data acquisition module acquires 3D road surface images using a 3D imaging scanner and extracts images of surface protrusions or depressions. The road surface 3D model construction module constructs a 3D road surface model based on the acquired 3D road surface images. The data processing module identifies the specific shapes of road surface protrusions or depressions and the deformation data of obstacles crushed by the vehicle body based on the 3D road surface model, and calculates the required lifting displacement value for the bottom wheels.

[0011] The 3D imaging scanner here uses laser or ultrasonic transmitters and receivers to determine the level of the road surface. This is a mature, existing technology and will not be described in detail here.

[0012] Specifically, the vehicle body position data acquisition module is used to collect the relative position data between the lifting wheel and the identified road protrusions or depressions; the signal conversion module is used to convert the control signal of the control module into a PLC control signal to control the output of the lifting component to work accurately; and the wheel lifting module is used to drive the lifting wheel to lift or lower by a specified displacement value according to the calculated lifting displacement value required by the lifting wheel.

[0013] Specifically, the data processing module includes a model data extraction unit, an obstacle shape judgment unit, and an obstacle deformation calculation unit. The model data extraction unit is used to extract the boundary data of the obstacle's position. The obstacle shape judgment unit is used to identify and determine the specific attributes of the obstacle based on the boundary data of the obstacle's position, image data, and the shapes and image data of each component in the scene. The obstacle deformation calculation unit is used to calculate the deformation data of the determined obstacle during the vehicle's rolling process, and then calculate the lifting displacement value required for the lifting wheel.

[0014] Specifically, the obstacle model recognition strategy is run in the model data extraction unit, and the obstacle model recognition strategy includes the following specific steps:

[0015] S11. Extract three-dimensional imaging image data of the road surface in real time. Using the road surface as the reference plane, extract the height of the points in the three-dimensional imaging image relative to the reference plane and set it as the three-dimensional imaging height. Extract the three-dimensional imaging height and set the positions where the absolute value of the three-dimensional imaging height is greater than or equal to the set height value as the obstacle positions.

[0016] S12. Extract the boundary data of the obstacle location, identify the outline of the obstacle, and extract the pixel value data of each pixel in the image of the obstacle location.

[0017] It should be noted that the set height value here can be automatically adjusted according to the vehicle's shock resistance.

[0018] Specifically, the obstacle shape determination unit includes an obstacle shape determination strategy, which includes the following specific steps:

[0019] S21. Obtain the contour boundary data of the obstacle position, and simultaneously extract the image contour boundary data of each component in the stored scene. Rotate the image contour boundary data of each component at any angle. Substitute the contour boundary data of the obstacle position and the rotated image contour boundary data of each component into the image contour similarity calculation formula to calculate the image contour similarity. The image contour similarity calculation formula is as follows: Where n is the number of vectors selected in the contour boundary, A i B is the i-th vector in the contour boundary data of the obstacle location. j i Let i be the i-th vector in the contour boundary data of the component rotated by j angles;

[0020] S22. Obtain the pixel values ​​of each pixel in the image of each component after rotation at any angle and the pixel values ​​of each pixel in the image of the obstacle position. Import the pixel values ​​of each pixel in the image of each component after rotation at any angle and the pixel values ​​of each pixel in the image of the obstacle position into the image pixel similarity calculation formula to calculate the image pixel similarity. The image pixel similarity calculation formula is as follows: Where m is the number of image pixels at the obstacle's location, x k Let y be the pixel value of the k-th pixel in the image where the obstacle is located. k j Let be the pixel value of the k-th pixel in the component image rotated by an angle j;

[0021] S23. Substitute the calculated image contour similarity and image pixel similarity into the overall similarity calculation formula to calculate the overall similarity. The overall similarity calculation formula is as follows: Where λ1 is the image contour proportion coefficient, λ2 is the image pixel proportion coefficient, and λ1+λ2=1;

[0022] S24. Obtain the component and its rotation angle corresponding to the maximum overall similarity, set as the determined obstacle and the selected angle, obtain the compression amount of the determined obstacle and the elastic modulus of the material of the determined obstacle, where the formula for calculating the compression amount is: K = K1 - K2, where K1 is the height value of the determined obstacle at the selected angle, and K2 is the three-dimensional imaging height value of the determined obstacle.

[0023] Specifically, the obstacle deformation calculation unit includes an obstacle deformation calculation strategy, which includes the following specific steps:

[0024] S31. Extract the overall weight data of the quartz product on the vehicle body and the vehicle body itself. Substitute the overall weight data and the elastic modulus of the obstacle material into the maximum compression calculation formula to calculate the maximum compression of the obstacle when the vehicle body is traveling on the obstacle surface. The maximum compression calculation formula is: Where m1 is the overall weight data of the quartz product on the vehicle body and the vehicle body, g is the gravitational acceleration, and γ is the elastic modulus of the material used to determine the obstacle.

[0025] S32. Compare the maximum compression amount with the compressed amount. If the obtained maximum compression amount is greater than or equal to the compressed amount, the maximum compression amount is used as the deformation data to determine the obstacle. If the obtained maximum compression amount is less than the compressed amount, the compressed amount is used as the deformation data to determine the obstacle.

[0026] S33. Obtain the deformation data of the determined obstacle, and use the height value of the determined obstacle at the selected angle minus the deformation data of the determined obstacle as the lifting displacement value required for the lifting wheel.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] This invention uses a 3D imaging scanner to acquire three-dimensional images of the road surface and extract images of surface protrusions or depressions. Based on the acquired three-dimensional images, a three-dimensional model of the road surface is constructed. Based on the three-dimensional model, the specific shapes of the protrusions or depressions and the deformation data of the obstacles determined by vehicle rolling are identified. The required lifting displacement value of the bottom wheel is calculated. The required lifting displacement value of the bottom wheel can be freely adjusted according to the shape of the protrusion or depression, avoiding the shaking caused by the bottom wheel passing over the shape of the protrusion or depression. At the same time, by determining the deformation data of the obstacle, the required lifting displacement value of the bottom wheel is further accurately determined, further avoiding the vehicle body shaking caused by the wheel being suspended in the air when passing over the elastic component. This greatly improves the stability of the vehicle body and avoids damage to the quartz product during movement. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of a turnover control system architecture for quartz product turnover according to the present invention;

[0030] Figure 2 This is a schematic diagram of the data processing module of a turnover control system for quartz product turnover according to the present invention.

[0031] Figure 3 This is a schematic diagram of the overall structure of a logistics vehicle for the turnover of quartz products according to the present invention;

[0032] Figure 4 This is a schematic cross-sectional view of the bottom of a logistics vehicle for the turnover of quartz products according to the present invention.

[0033] In the diagram: 1. Vehicle body; 2. Controller; 3. Lifting wheels; 4. 3D imaging scanner; 5. Anti-slip carriage; 6. Distance sensor; 7. Lifting assembly. Detailed Implementation

[0034] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0035] Example 1

[0036] Please see Figures 3-4 The present invention provides an embodiment of a logistics vehicle for the turnover of quartz products, which includes a vehicle body 1. The bottom of the vehicle body 1 is equipped with lifting wheels 3 that can move longitudinally according to the shape of obstacles. A 3D imaging scanner 4 for scanning and imaging obstacles is installed at the front of the vehicle body 1 and at the corresponding position of the lifting wheels 3. A controller 2 is installed inside the vehicle body 1. The controller 2 can identify the deformation of obstacles and control the lifting wheels 3 to move longitudinally according to the deformation shape of obstacles. The controller 2 operates a turnover control system.

[0037] In this embodiment, a lifting assembly 7 is fixedly installed at the bottom of the vehicle body 1, and a lifting wheel 3 is installed at the output end of the lifting assembly 7. The output end of the lifting assembly 7 drives the lifting wheel 3 to move longitudinally. A distance sensor 6 that can monitor the distance of nearby vehicle bodies 1 is installed around the vehicle body 1, and an anti-slip carriage 5 that prevents the quartz product from sliding is installed on the top of the vehicle body 1.

[0038] The usage method of this embodiment is as follows: First, the product is placed in the anti-slip compartment 5 by the operator. Then, the transportation station to be delivered is entered. The start button is pressed and the trolley runs to the station. The operator takes away the required materials. The starting point is entered again, and the quartz product is placed inside the anti-slip compartment 5. The vehicle body 1 moves the quartz product to the destination. The 3D imaging scanner 4 located in front of the vehicle body 1 scans and images the obstacles on the road. Then, the controller 2 can identify the deformation of the obstacles and control the output end of the lifting component 7 to drive the lifting bottom wheel 3 to move longitudinally according to the deformation shape of the obstacle.

[0039] Example 2

[0040] like Figure 1 and Figure 2 As shown, a turnover control system for quartz products is implemented based on the aforementioned logistics vehicle for quartz product turnover. Specifically, it includes a control module, a road surface 3D model construction module, a data processing module, a signal conversion module, a road surface data acquisition module, a vehicle body position data acquisition module, and a bottom wheel lifting module. The control module controls the operation of these modules. The road surface data acquisition module acquires 3D road surface images using a 3D imaging scanner 4 and extracts images of surface protrusions or depressions. The road surface 3D model construction module constructs a 3D road surface model based on the acquired 3D road surface images. The data processing module identifies the specific shapes of road surface protrusions or depressions and the deformation data of obstacles crushed by the vehicle body 1 based on the 3D road surface model, and calculates the required lifting displacement value for the bottom wheel 3.

[0041] The 3D imaging scanner 4 here uses a laser or ultrasonic transmitter and a receiver to determine the level of the road surface. This is a mature existing technology and will not be described in detail here.

[0042] In this embodiment, the vehicle position data acquisition module is used to acquire the relative position data between the lifting wheel 3 and the identified road protrusions or depressions. The signal conversion module is used to convert the control signal of the control module into a PLC control signal to control the output of the lifting assembly 7 to work accurately. The wheel lifting module is used to drive the lifting wheel 3 to lift or lower by a specified displacement value according to the calculated lifting displacement value required by the lifting wheel 3.

[0043] In this embodiment, the data processing module includes a model data extraction unit, an obstacle shape judgment unit, and an obstacle deformation calculation unit. The model data extraction unit is used to extract the boundary data of the obstacle position. The obstacle shape judgment unit is used to identify and determine the specific attributes of the obstacle based on the boundary data of the obstacle position, image data, and the shape and image data of each component in the scene. The obstacle deformation calculation unit is used to calculate the deformation data of the determined obstacle during the vehicle rolling process, and then calculate the lifting displacement value required by the lifting wheel 3.

[0044] In this embodiment, the obstacle model recognition strategy is run in the model data extraction unit. The obstacle model recognition strategy includes the following specific steps:

[0045] S11. Extract three-dimensional imaging image data of the road surface in real time. Using the road surface as the reference plane, extract the height of the points in the three-dimensional imaging image relative to the reference plane and set it as the three-dimensional imaging height. Extract the three-dimensional imaging height and set the positions where the absolute value of the three-dimensional imaging height is greater than or equal to the set height value as the obstacle positions.

[0046] S12. Extract the boundary data of the obstacle location, identify the outline of the obstacle, and extract the pixel value data of each pixel in the image of the obstacle location.

[0047] It should be noted that the set height value here can be automatically adjusted according to the vehicle's shock resistance.

[0048] In this embodiment, the obstacle shape determination unit includes an obstacle shape determination strategy, which includes the following specific steps:

[0049] S21. Obtain the contour boundary data of the obstacle position, and simultaneously extract the image contour boundary data of each component in the stored scene. Rotate the image contour boundary data of each component at any angle. Substitute the contour boundary data of the obstacle position and the rotated image contour boundary data of each component into the image contour similarity calculation formula to calculate the image contour similarity. The image contour similarity calculation formula is as follows: Where n is the number of vectors selected in the contour boundary, A i B is the i-th vector in the contour boundary data of the obstacle location. j i Let i be the i-th vector in the contour boundary data of the component rotated by j angles;

[0050] S22. Obtain the pixel values ​​of each pixel in the image of each component after rotation at any angle and the pixel values ​​of each pixel in the image of the obstacle position. Import the pixel values ​​of each pixel in the image of each component after rotation at any angle and the pixel values ​​of each pixel in the image of the obstacle position into the image pixel similarity calculation formula to calculate the image pixel similarity. The image pixel similarity calculation formula is as follows: Where m is the number of image pixels at the obstacle's location, x k Let y be the pixel value of the k-th pixel in the image where the obstacle is located. k j Let be the pixel value of the k-th pixel in the component image rotated by an angle j;

[0051] S23. Substitute the calculated image contour similarity and image pixel similarity into the overall similarity calculation formula to calculate the overall similarity. The overall similarity calculation formula is as follows: Where λ1 is the image contour proportion coefficient, λ2 is the image pixel proportion coefficient, and λ1+λ2=1;

[0052] S24. Obtain the component and its rotation angle corresponding to the maximum overall similarity, set as the determined obstacle and the selected angle, obtain the compression amount of the determined obstacle and the elastic modulus of the material of the determined obstacle, where the formula for calculating the compression amount is: K = K1 - K2, where K1 is the height value of the determined obstacle at the selected angle, and K2 is the three-dimensional imaging height value of the determined obstacle.

[0053] In this embodiment, the obstacle deformation calculation unit includes an obstacle deformation calculation strategy, which includes the following specific steps:

[0054] S31. Extract the quartz product on vehicle body 1 and the overall weight data of vehicle body 1. Substitute the overall weight data and the elastic modulus of the obstacle material into the maximum compression calculation formula to calculate the maximum compression of the obstacle when vehicle body 1 is traveling on the obstacle surface. The maximum compression calculation formula is: Where m1 is the weight data of the quartz product on vehicle body 1 and the overall weight of vehicle body 1, g is the gravitational acceleration, and γ is the elastic modulus of the material used to determine the obstacle.

[0055] S32. Compare the maximum compression amount with the compressed amount. If the obtained maximum compression amount is greater than or equal to the compressed amount, the maximum compression amount is used as the deformation data to determine the obstacle. If the obtained maximum compression amount is less than the compressed amount, the compressed amount is used as the deformation data to determine the obstacle.

[0056] S33. Obtain the deformation data of the determined obstacle, and use the height value of the determined obstacle at the selected angle minus the deformation data of the determined obstacle as the lifting displacement value required by the lifting wheel 3.

[0057] In this embodiment, a 3D imaging scanner 4 is used to acquire three-dimensional images of the road surface and extract images of surface protrusions or depressions. A three-dimensional model of the road surface is constructed based on the acquired three-dimensional images. Based on the three-dimensional model, the specific shapes of the road surface protrusions or depressions and the deformation data of the obstacles determined by the vehicle body 1 are identified. The required lifting displacement value of the lifting wheel 3 is calculated. The required lifting displacement value of the wheel is freely adjusted according to the shape of the protrusions or depressions to avoid the shaking caused by the wheel passing over the shape of the protrusions or depressions. At the same time, by determining the deformation data of the obstacles, the required lifting displacement value of the wheel is further accurately determined, which further avoids the shaking of the vehicle body 1 caused by the wheel being suspended when passing over the elastic component. This greatly improves the stability of the vehicle body 1 and avoids damage to the quartz product during movement.

[0058] Example 3

[0059] This embodiment combines a turnover control system for quartz product handling with a logistics vehicle, such as... Figures 1-4As shown, a logistics vehicle for the turnover of quartz products includes a vehicle body 1. The bottom of the vehicle body 1 is equipped with lifting wheels 3 that can move longitudinally according to the shape of obstacles. A 3D imaging scanner 4 for scanning and imaging obstacles is installed at the front of the vehicle body 1 and at the corresponding position of the lifting wheels 3. A controller 2 is installed inside the vehicle body 1. The controller 2 can identify the deformation of obstacles and control the lifting wheels 3 to move longitudinally according to the deformation shape of the obstacles. The controller 2 operates the turnover control system.

[0060] In this embodiment, a lifting assembly 7 is fixedly installed at the bottom of the vehicle body 1, and a lifting wheel 3 is installed at the output end of the lifting assembly 7. The output end of the lifting assembly 7 drives the lifting wheel 3 to move longitudinally. A distance sensor 6 that can monitor the distance of nearby vehicle bodies 1 is installed around the vehicle body 1, and an anti-slip carriage 5 that prevents the quartz product from sliding is installed on the top of the vehicle body 1.

[0061] The turnover control system includes a control module, a road surface 3D model construction module, a data processing module, a signal conversion module, a road surface data acquisition module, a vehicle body position data acquisition module, and a bottom wheel lifting module. The control module controls the operation of the road surface 3D model construction module, the data processing module, the signal conversion module, the road surface data acquisition module, the vehicle body position data acquisition module, and the bottom wheel lifting module. The road surface data acquisition module is used to acquire 3D imaging images of the road surface through the 3D imaging scanner 4 and extract images of surface protrusions or depressions. The road surface 3D model construction module is used to construct a 3D model of the road surface based on the acquired 3D imaging images. The data processing module is used to identify the specific shape of road surface protrusions or depressions and the deformation data of obstacles determined by the vehicle body 1 based on the 3D model of the road surface, and calculate the lifting displacement value required for the bottom wheel 3.

[0062] The 3D imaging scanner 4 here uses a laser or ultrasonic transmitter and a receiver to determine the level of the road surface. This is a mature existing technology and will not be described in detail here.

[0063] In this embodiment, the vehicle position data acquisition module is used to acquire the relative position data between the lifting wheel 3 and the identified road protrusions or depressions. The signal conversion module is used to convert the control signal of the control module into a PLC control signal to control the output of the lifting assembly 7 to work accurately. The wheel lifting module is used to drive the lifting wheel 3 to lift or lower by a specified displacement value according to the calculated lifting displacement value required by the lifting wheel 3.

[0064] In this embodiment, the data processing module includes a model data extraction unit, an obstacle shape judgment unit, and an obstacle deformation calculation unit. The model data extraction unit is used to extract the boundary data of the obstacle position. The obstacle shape judgment unit is used to identify and determine the specific attributes of the obstacle based on the boundary data of the obstacle position, image data, and the shape and image data of each component in the scene. The obstacle deformation calculation unit is used to calculate the deformation data of the determined obstacle during the vehicle rolling process, and then calculate the lifting displacement value required by the lifting wheel 3.

[0065] In this embodiment, the obstacle model recognition strategy is run in the model data extraction unit. The obstacle model recognition strategy includes the following specific steps:

[0066] S11. Extract three-dimensional imaging image data of the road surface in real time. Using the road surface as the reference plane, extract the height of the points in the three-dimensional imaging image relative to the reference plane and set it as the three-dimensional imaging height. Extract the three-dimensional imaging height and set the positions where the absolute value of the three-dimensional imaging height is greater than or equal to the set height value as the obstacle positions.

[0067] S12. Extract the boundary data of the obstacle location, identify the outline of the obstacle, and extract the pixel value data of each pixel in the image of the obstacle location.

[0068] It should be noted that the set height value here can be automatically adjusted according to the vehicle's shock resistance.

[0069] In this embodiment, the obstacle shape determination unit includes an obstacle shape determination strategy, which includes the following specific steps:

[0070] S21. Obtain the contour boundary data of the obstacle position, and simultaneously extract the image contour boundary data of each component in the stored scene. Rotate the image contour boundary data of each component at any angle. Substitute the contour boundary data of the obstacle position and the rotated image contour boundary data of each component into the image contour similarity calculation formula to calculate the image contour similarity. The image contour similarity calculation formula is as follows: Where n is the number of vectors selected in the contour boundary, A i B is the i-th vector in the contour boundary data of the obstacle location. j i Let i be the i-th vector in the contour boundary data of the component rotated by j angles;

[0071] S22. Obtain the pixel values ​​of each pixel in the image of each component after rotation at any angle and the pixel values ​​of each pixel in the image of the obstacle position. Import the pixel values ​​of each pixel in the image of each component after rotation at any angle and the pixel values ​​of each pixel in the image of the obstacle position into the image pixel similarity calculation formula to calculate the image pixel similarity. The image pixel similarity calculation formula is as follows: Where m is the number of image pixels at the obstacle's location, x k Let y be the pixel value of the k-th pixel in the image where the obstacle is located. k j Let be the pixel value of the k-th pixel in the component image rotated by an angle j;

[0072] S23. Substitute the calculated image contour similarity and image pixel similarity into the overall similarity calculation formula to calculate the overall similarity. The overall similarity calculation formula is as follows: Where λ1 is the image contour proportion coefficient, λ2 is the image pixel proportion coefficient, and λ1+λ2=1;

[0073] S24. Obtain the component and its rotation angle corresponding to the maximum overall similarity, set as the determined obstacle and the selected angle, obtain the compression amount of the determined obstacle and the elastic modulus of the material of the determined obstacle, where the formula for calculating the compression amount is: K = K1 - K2, where K1 is the height value of the determined obstacle at the selected angle, and K2 is the three-dimensional imaging height value of the determined obstacle.

[0074] In this embodiment, the obstacle deformation calculation unit includes an obstacle deformation calculation strategy, which includes the following specific steps:

[0075] S31. Extract the quartz product on vehicle body 1 and the overall weight data of vehicle body 1. Substitute the overall weight data and the elastic modulus of the obstacle material into the maximum compression calculation formula to calculate the maximum compression of the obstacle when vehicle body 1 is traveling on the obstacle surface. The maximum compression calculation formula is: Where m1 is the weight data of the quartz product on vehicle body 1 and the overall weight of vehicle body 1, g is the gravitational acceleration, and γ is the elastic modulus of the material used to determine the obstacle.

[0076] S32. Compare the maximum compression amount with the compressed amount. If the obtained maximum compression amount is greater than or equal to the compressed amount, the maximum compression amount is used as the deformation data to determine the obstacle. If the obtained maximum compression amount is less than the compressed amount, the compressed amount is used as the deformation data to determine the obstacle.

[0077] S33. Obtain the deformation data of the determined obstacle, and use the height value of the determined obstacle at the selected angle minus the deformation data of the determined obstacle as the lifting displacement value required by the lifting wheel 3.

[0078] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0079] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0080] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0081] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0082] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0083] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0084] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0085] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0086] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0087] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A logistics vehicle for the turnover of quartz products, comprising a vehicle body, characterized in that, The bottom of the vehicle body is equipped with lifting wheels that can move longitudinally according to the shape of the obstacle. A 3D imaging scanner for scanning the obstacle is installed at the front of the vehicle body and at the corresponding position of the lifting wheels. A controller is installed inside the vehicle body. The controller can identify the deformation of the obstacle and control the lifting wheels to move longitudinally according to the deformation shape of the obstacle. The controller operates a turnover control system. The controller's ability to recognize obstacle deformation and control the lifting wheels to move longitudinally according to the obstacle's deformation shape is as follows: S11. Extract three-dimensional imaging image data of the road surface in real time. Using the road surface as the reference plane, extract the height of the points in the three-dimensional imaging image relative to the reference plane and set it as the three-dimensional imaging height. Extract the three-dimensional imaging height and set the positions where the absolute value of the three-dimensional imaging height is greater than or equal to the set height value as the obstacle positions. S12. Extract the boundary data of the obstacle location, identify the outline of the obstacle, and extract the pixel value data of each pixel in the image of the obstacle location. S21. Obtain the contour boundary data of the obstacle position, and simultaneously extract the image contour boundary data of each component in the stored scene. Rotate the image contour boundary data of each component at any angle. Substitute the contour boundary data of the obstacle position and the rotated image contour boundary data of each component into the image contour similarity calculation formula to calculate the image contour similarity. The image contour similarity calculation formula is as follows: Where n is the number of vectors selected from the contour boundary. Let i be the i-th vector in the contour boundary data of the obstacle's location. Let i be the i-th vector in the contour boundary data of the component rotated by j angles; S22. Obtain the pixel values ​​of each pixel in the image of each component after rotation at any angle and the pixel values ​​of each pixel in the image of the obstacle position. Import the pixel values ​​of each pixel in the image of each component after rotation at any angle and the pixel values ​​of each pixel in the image of the obstacle position into the image pixel similarity calculation formula to calculate the image pixel similarity. The image pixel similarity calculation formula is as follows: Where m is the number of image pixels at the obstacle's location. Let k be the pixel value of the k-th pixel in the image where the obstacle is located. Let be the pixel value of the k-th pixel in the component image rotated by an angle j; S23. Substitute the calculated image contour similarity and image pixel similarity into the overall similarity calculation formula to calculate the overall similarity. The overall similarity calculation formula is as follows: ,in, This is the image contour proportion coefficient. This is the image pixel ratio coefficient. ; S24. Obtain the component and its rotation angle corresponding to the highest overall similarity, set as the determined obstacle and the selected angle. Obtain the compression amount of the determined obstacle and the elastic modulus of the material of the determined obstacle. The formula for calculating the compression amount is: ,in, To determine the height of the obstacle at the selected angle, To determine the 3D imaging height value of the obstacle; S31. Extract the overall weight data of the quartz product on the vehicle body and the vehicle body itself. Substitute the overall weight data and the elastic modulus of the obstacle material into the maximum compression calculation formula to calculate the maximum compression of the obstacle when the vehicle body is traveling on the obstacle surface. The maximum compression calculation formula is: ,in, The data represents the weight of the quartz products on the vehicle body and the overall weight of the vehicle body, where g is the acceleration due to gravity. To determine the elastic modulus of the obstacle material; S32. Compare the maximum compression amount with the compressed amount. If the obtained maximum compression amount is greater than or equal to the compressed amount, the maximum compression amount is used as the deformation data to determine the obstacle. If the obtained maximum compression amount is less than the compressed amount, the compressed amount is used as the deformation data to determine the obstacle. S33. Obtain the deformation data of the determined obstacle, and use the height value of the determined obstacle at the selected angle minus the deformation data of the determined obstacle as the lifting displacement value required for the lifting wheel.

2. A logistics vehicle for the turnover of quartz products as described in claim 1, characterized in that, A lifting assembly is fixedly installed at the bottom of the vehicle body. A lifting wheel is installed at the output end of the lifting assembly. The output end of the lifting assembly drives the lifting wheel to move longitudinally. Distance sensors that can monitor the distance to nearby vehicles are installed around the vehicle body. An anti-slip compartment is installed on top of the vehicle body to prevent the quartz product from sliding.

3. A turnover control system for quartz product turnover, implemented based on a logistics vehicle for quartz product turnover as described in any one of claims 1-2, characterized in that, Specifically, it includes a control module, a road surface 3D model construction module, a data processing module, a signal conversion module, a road surface data acquisition module, a vehicle body position data acquisition module, and a bottom wheel lifting module. The control module is used to control the operation of the road surface 3D model construction module, the data processing module, the signal conversion module, the road surface data acquisition module, the vehicle body position data acquisition module, and the bottom wheel lifting module. The road surface data acquisition module is used to acquire 3D imaging images of the road surface using a 3D imaging scanner and extract images of surface protrusions or depressions. The road surface 3D model construction module is used to construct a 3D model of the road surface based on the acquired 3D imaging images. The data processing module is used to identify the specific shape of road surface protrusions or depressions and the deformation data of obstacles determined by vehicle rolling based on the 3D model of the road surface, and calculate the lifting displacement value required for lifting the bottom wheel.

4. A turnover control system for quartz product turnover as described in claim 3, characterized in that, The vehicle position data acquisition module is used to collect the relative position data between the lifting wheel and the identified road protrusions or depressions. The signal conversion module is used to convert the control signal of the control module into a PLC control signal to control the output of the lifting component to work accurately. The wheel lifting module is used to drive the lifting wheel to lift or lower by a specified displacement value according to the calculated lifting displacement value required by the lifting wheel.

5. A turnover control system for quartz product turnover as described in claim 4, characterized in that, The data processing module includes a model data extraction unit, an obstacle shape judgment unit, and an obstacle deformation calculation unit. The model data extraction unit is used to extract the boundary data of the obstacle's position. The obstacle shape judgment unit is used to identify and determine the specific attributes of the obstacle based on the boundary data of the obstacle's position, image data, and the shapes and image data of each component in the scene. The obstacle deformation calculation unit is used to calculate the deformation data of the determined obstacle during the vehicle's rolling process, and then calculate the lifting displacement value required for the lifting wheel.

6. A turnover control system for quartz product turnover as described in claim 5, characterized in that, The obstacle model recognition strategy is run in the model data extraction unit, and the obstacle model recognition strategy includes the following specific steps: S11. Extract three-dimensional imaging image data of the road surface in real time. Using the road surface as the reference plane, extract the height of the points in the three-dimensional imaging image relative to the reference plane and set it as the three-dimensional imaging height. Extract the three-dimensional imaging height and set the positions where the absolute value of the three-dimensional imaging height is greater than or equal to the set height value as the obstacle positions. S12. Extract the boundary data of the obstacle location, identify the outline of the obstacle, and extract the pixel value data of each pixel in the image of the obstacle location.

7. A turnover control system for quartz product turnover as described in claim 6, characterized in that, The obstacle shape determination unit includes an obstacle shape determination strategy, which includes the following specific steps: S21. Obtain the contour boundary data of the obstacle position, and simultaneously extract the image contour boundary data of each component in the stored scene. Rotate the image contour boundary data of each component at any angle. Substitute the contour boundary data of the obstacle position and the rotated image contour boundary data of each component into the image contour similarity calculation formula to calculate the image contour similarity. The image contour similarity calculation formula is as follows: Where n is the number of vectors selected from the contour boundary. Let i be the i-th vector in the contour boundary data of the obstacle's location. Let i be the i-th vector in the contour boundary data of the component rotated by j angles.

8. A turnover control system for quartz product turnover as described in claim 7, characterized in that, The obstacle shape determination strategy also includes the following specific steps: S22. Obtain the pixel values ​​of each pixel in the image of each component after rotation at any angle and the pixel values ​​of each pixel in the image of the obstacle position. Import the pixel values ​​of each pixel in the image of each component after rotation at any angle and the pixel values ​​of each pixel in the image of the obstacle position into the image pixel similarity calculation formula to calculate the image pixel similarity. The image pixel similarity calculation formula is as follows: Where m is the number of image pixels at the obstacle's location. Let k be the pixel value of the k-th pixel in the image where the obstacle is located. Let be the pixel value of the k-th pixel in the component image rotated by an angle j; S23. Substitute the calculated image contour similarity and image pixel similarity into the overall similarity calculation formula to calculate the overall similarity. The overall similarity calculation formula is as follows: ,in, This is the image contour proportion coefficient. This is the image pixel ratio coefficient. .

9. A turnover control system for quartz product turnover as described in claim 8, characterized in that, The obstacle shape determination strategy further includes the following specific steps: S24, obtaining the component corresponding to the highest overall similarity and its rotation angle, setting it as the determined obstacle and the selected angle, obtaining the compressed amount of the determined obstacle and the elastic modulus of the determined obstacle material, wherein the formula for calculating the compressed amount is: ,in, To determine the height of the obstacle at the selected angle, To determine the 3D imaging height value of the obstacle.

10. A turnover control system for quartz product turnover as described in claim 9, characterized in that, The obstacle deformation calculation unit includes an obstacle deformation calculation strategy, which includes the following specific steps: S31. Extract the overall weight data of the quartz product on the vehicle body and the vehicle body itself. Substitute the overall weight data and the elastic modulus of the obstacle material into the maximum compression calculation formula to calculate the maximum compression of the obstacle when the vehicle body is traveling on the obstacle surface. The maximum compression calculation formula is: ,in, The data represents the weight of the quartz products on the vehicle body and the overall weight of the vehicle body, where g is the acceleration due to gravity. To determine the elastic modulus of the obstacle material; S32. Compare the maximum compression amount with the compressed amount. If the obtained maximum compression amount is greater than or equal to the compressed amount, the maximum compression amount is used as the deformation data to determine the obstacle. If the obtained maximum compression amount is less than the compressed amount, the compressed amount is used as the deformation data to determine the obstacle. S33. Obtain the deformation data of the determined obstacle, and use the height value of the determined obstacle at the selected angle minus the deformation data of the determined obstacle as the lifting displacement value required for the lifting wheel.

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