Material Sampling Method and Apparatus Based on Point Cloud Data

Abstract

This application provides a material sampling method and apparatus based on point cloud data. It acquires attribute information generated by a transport device and determines the spatial region where the transport device is located based on this attribute information. The spatial region of the transport device is then segmented to determine at least one sampling area. Three-dimensional point cloud data collected by a point cloud generation device for the sampling area is acquired, and the sampling location information of the material to be sampled is determined based on this 3D point cloud data. Finally, the sampling device is triggered to acquire the material based on the sampling location information. Because the spatial region where the transport device is located can be determined first based on the attribute information of the transport device, and then the spatial region can be further subdivided into at least one sampling area, the accurate sampling location information of the material to be sampled can be determined sequentially within the sampling area. This improves the accuracy of determining the sampling location information, thereby improving the accuracy of acquiring the material to be sampled.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a material sampling method and apparatus based on point cloud data. Background Technology

[0002] After a truck loaded with materials enters the factory area, it is necessary to sample and inspect the materials to check for any quality issues. In related technologies, after the truck enters the factory area and parks in a designated location, visual technology is typically used to scan the surrounding area where the truck is parked. Based on the scan results, the location of the material to be sampled is determined, thus enabling sampling.

[0003] However, using the above-mentioned material sampling method to take the surrounding area where the car is parked as the approximate location of the material to be sampled makes the location information of the determined sampling point inaccurate, thereby reducing the accuracy of obtaining the material to be sampled. Summary of the Invention

[0004] This invention provides a material sampling method and apparatus based on point cloud data, which improves the accuracy of obtaining the material to be sampled. Specifically, the embodiments of this application disclose the following technical solutions:

[0005] In a first aspect, embodiments of this application provide a material sampling method based on point cloud data, applied to a terminal device. The terminal device is connected to a transport equipment identification device, a point cloud generation device, and a sampling device. The method includes: after the transport equipment carrying the material to be sampled arrives at its destination, acquiring attribute information generated by the transport equipment identification device based on the transport equipment; determining the spatial region where the transport equipment is located based on the attribute information; segmenting the spatial region of the transport equipment to determine at least one sampling region; acquiring three-dimensional point cloud data collected by the point cloud generation device for the sampling region, and determining the sampling location information of the material to be sampled based on the three-dimensional point cloud data; and triggering the sampling device to acquire the material to be sampled based on the sampling location information.

[0006] In conjunction with the first aspect, in one possible implementation of the first aspect, the transport equipment identification device includes a license plate recognition device and at least two sensor devices disposed at different locations. The attribute information includes the shape information of the transport equipment, the information of the material to be sampled, and the location information of the transport equipment. The acquisition of attribute information generated by the transport equipment identification device based on the transport equipment includes:

[0007] The license plate information of the transport equipment is obtained through a license plate recognition device. Based on the license plate information and the first correspondence, the shape information of the transport equipment and the information of the material to be sampled are obtained. The information of the material to be sampled includes at least the type of material to be sampled. The first correspondence is used to indicate the correspondence between the license plate information of the transport equipment and the shape information and the information of the material to be sampled, respectively. The location information of the transport equipment is obtained by transmitting the distance between the transport equipment and the landmark in the destination through a sensor device.

[0008] In conjunction with the first aspect, in one possible implementation of the first aspect, the spatial area of ​​the transport equipment is segmented to determine at least one sampling area, including:

[0009] Based on the maximum scanning range of the point cloud generation device, the spatial region is segmented to determine at least one sampling region, the size of which is less than or equal to the maximum scanning range of the point cloud generation device.

[0010] In conjunction with the first aspect, in one possible implementation of the first aspect, determining the sampling location information of the material to be sampled based on three-dimensional point cloud data includes:

[0011] Based on the 3D point cloud data, determine whether the sampling area includes the material to be sampled; if it does, determine the sampling location information of the material to be sampled based on the 3D point cloud data.

[0012] In conjunction with the first aspect, one possible implementation of the first aspect, determining the sampling location information of the material to be sampled based on three-dimensional point cloud data, further includes:

[0013] Based on 3D point cloud data, determine whether the sampling area includes obstacles; if obstacles are included, determine whether there is space to avoid the obstacles within the sampling area; if so, determine whether the sampling area includes the material to be sampled; if the material is included, determine the sampling location information of the material to be sampled based on 3D point cloud data.

[0014] In conjunction with the first aspect, in one possible implementation of the first aspect, the attribute information also includes the weight of the material, and the sampling location information of the material to be sampled is determined based on three-dimensional point cloud data, including:

[0015] Based on the preset sampling ratio and weight, determine the target sampling weight of the material to be sampled; determine whether the sampling area includes the material to be sampled based on the 3D point cloud data; if it includes the material to be sampled, determine whether the weight of the material to be sampled in the sampling area meets the target sampling weight; if not, select a new sampling area from multiple sampling areas to continue to obtain the material to be sampled until the total weight of the obtained material to be sampled meets the target sampling weight.

[0016] In conjunction with the first aspect, one possible implementation of the first aspect also includes:

[0017] The number of times the sampling location information of the material to be sampled is determined based on 3D point cloud data;

[0018] If the number of attempts exceeds a preset threshold, an early warning message will be generated.

[0019] Secondly, embodiments of this application also provide a material sampling device based on point cloud data, the device comprising:

[0020] The first acquisition module is used to acquire attribute information generated by the transportation equipment identification device based on the transportation equipment after the transportation equipment carrying the material to be sampled arrives at the destination.

[0021] The first determining module is used to determine the spatial area where the transportation equipment is located based on attribute information;

[0022] The generation module is used to segment the spatial area of ​​the transportation equipment and determine at least one sampling area;

[0023] The second determining module is used to acquire the three-dimensional point cloud data collected by the point cloud generation device for the area to be sampled, and to determine the sampling location information of the material to be sampled based on the three-dimensional point cloud data.

[0024] The second acquisition module is used to trigger the sampling device to acquire the material to be sampled based on the sampling location information.

[0025] Thirdly, embodiments of this application provide an electronic device (computer device), including: a processor and a memory; the memory is used to store computer-executable instructions; the processor is used to read instructions from the memory and execute the instructions to implement the aforementioned first aspect and any implementation thereof.

[0026] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer instructions for causing the computer to perform the methods of the first aspect and any implementation thereof.

[0027] In addition, embodiments of this application also provide a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the method in any implementation of the first aspect described above.

[0028] The material sampling method and apparatus based on point cloud data provided in this application involves acquiring attribute information generated by a transport equipment identification device based on the transport equipment after the transport equipment carrying the material to be sampled arrives at its destination, and determining the spatial area where the transport equipment is located based on the attribute information. The spatial area of ​​the transport equipment is then segmented to determine at least one sampling area. Three-dimensional point cloud data collected by a point cloud generation device for the sampling area is acquired, and the sampling location information of the material to be sampled is determined based on the three-dimensional point cloud data. Finally, the sampling equipment is triggered to acquire the material to be sampled based on the sampling location information. Since the spatial area where the transport equipment is located can be determined first based on the attribute information of the transport equipment, and then the spatial area can be further subdivided into at least one sampling area, the accurate sampling location information of the material to be sampled can be determined sequentially within the sampling area, thus improving the accuracy of determining the sampling location information and consequently improving the accuracy of acquiring the material to be sampled. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A schematic diagram illustrating a scenario for a material sampling method based on point cloud data provided in an embodiment of this application;

[0031] Figure 2 A flowchart illustrating a material sampling method based on point cloud data provided in this application embodiment;

[0032] Figure 3 A schematic diagram of a grid model of a transportation device provided in an embodiment of this application;

[0033] Figure 4 A flowchart for determining sampling location information is provided in an embodiment of this application;

[0034] Figure 5 A flowchart illustrating another method for determining sampling location information provided in this application embodiment;

[0035] Figure 6 A flowchart illustrating another method for determining sampling location information provided in this application embodiment;

[0036] Figure 7 A flowchart illustrating an overall process for a material sampling method based on point cloud data, provided in this application embodiment;

[0037] Figure 8A schematic diagram of a material sampling device based on point cloud data provided in this application embodiment;

[0038] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0039] To enable those skilled in the art to better understand the technical solutions in the embodiments of this application, and to make the above-mentioned objectives, features and advantages of the embodiments of this application more apparent and understandable, the technical solutions in the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0040] In existing technologies, after a truck loaded with materials enters the factory area, sampling and inspection of the materials are necessary to check for quality issues. Related technologies typically use visual technology to scan the surrounding area after the truck enters the factory and parks at a designated location. The location of the material to be sampled is then determined based on the scan results. However, this method uses the surrounding area of ​​the parked truck as a general reference for the material's location, resulting in inaccurate sampling point information and reduced accuracy in obtaining the sampled material.

[0041] In view of this, this application proposes a material sampling method and apparatus based on point cloud data. After the transport equipment carrying the material to be sampled arrives at its destination, attribute information generated by a transport equipment identification device based on the transport equipment is acquired, and the spatial area where the transport equipment is located is determined based on the attribute information. The spatial area of ​​the transport equipment is then segmented to determine at least one sampling area. Three-dimensional point cloud data collected by a point cloud generation device for the sampling area is acquired, and the sampling location information of the material to be sampled is determined based on the three-dimensional point cloud data. Finally, the sampling device is triggered to acquire the material to be sampled based on the sampling location information. Since the spatial area where the transport equipment is located can be determined first based on the attribute information of the transport equipment, and then the spatial area can be further subdivided into at least one sampling area, the accurate sampling location information of the material to be sampled can be determined sequentially within the sampling area, improving the accuracy of determining the sampling location information and thus improving the accuracy of acquiring the material to be sampled.

[0042] The material sampling method based on point cloud data provided in this application is applied to a terminal device. The terminal device is connected to a transportation equipment identification device, a point cloud generation device, and a sampling device. The transportation equipment identification device is a device for identifying information related to transportation equipment. For example, it can be a license plate recognition device to identify the license plate information of the transportation equipment; or it can be a sensor device to identify the distance between the transportation equipment and landmarks in the destination, obtaining the location information of the transportation equipment. The point cloud generation device is a device capable of generating point cloud data. For example, the point cloud generation device may include a 3D detection device; of course, the point cloud generation device may also include other devices, such as lidar, etc., which are not limited in this application embodiment. The sampling device may include a gantry robotic arm system. Figure 1 As shown, Figure 1 This application provides a schematic diagram of a scenario for a material sampling method based on point cloud data, as illustrated in the embodiments of this application. Figure 1 In the diagram, A represents the license plate recognition device, B represents the sensor device, C represents the terminal equipment, D represents the gantry robotic arm system, and E represents the 3D inspection equipment.

[0043] The technical solutions provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings. Figure 2 A flowchart of a material sampling method based on point cloud data provided in this application embodiment is included, the method comprising the following steps:

[0044] Step 202: After the transport equipment carrying the material to be sampled arrives at the destination, obtain the attribute information generated by the transport equipment identification device based on the transport equipment.

[0045] The transportation equipment identification device may include a license plate recognition device and at least two sensor devices located at different positions. The attribute information may include the shape information of the transportation equipment, the information of the material to be sampled, and the location information of the transportation equipment. The transportation equipment identification device is communicatively connected to a terminal device, and the attribute information acquired by the transportation equipment identification device can be sent to the terminal device for processing.

[0046] In some alternative methods, when generating attribute information for transport equipment, the transport equipment identification device can obtain the license plate information of the transport equipment through a license plate recognition device, and based on the license plate information and a first correspondence relationship, obtain the shape information and the information of the material to be sampled from the transport equipment. The information of the material to be sampled includes at least the type of material to be sampled, and the first correspondence relationship indicates the correspondence between the license plate information of the transport equipment and the shape information and the information of the material to be sampled. Therefore, after obtaining the license plate information of the transport equipment, the corresponding shape information and the information of the material to be sampled can be quickly and conveniently obtained based on the first correspondence relationship.

[0047] In addition, the transportation equipment identification device may also include at least two sensor devices installed at different locations. The location information of the transportation equipment is obtained by transmitting the distances between the transportation equipment and landmarks at the destination through the sensor devices at different locations. Optionally, the sensor devices can be photoelectric sensors, and the landmarks at the destination can be factory buildings. By installing four sets of photoelectric sensors inside the factory building, four types of distance information can be obtained after the transportation equipment arrives at the destination and stops: the distance from the rear of the transportation equipment to the factory building, the distance from the front of the transportation equipment to the factory building, the distance from the left side of the transportation equipment to the factory building, and the distance from the right side of the transportation equipment to the factory building. Based on these four types of distance information, the location information of the transportation equipment can be obtained.

[0048] Step 204: Determine the spatial area where the transportation equipment is located based on the attribute information.

[0049] Based on the shape information of the transport equipment, the information of the material to be sampled, and the location information of the transport equipment, the spatial area where the transport equipment is located can be determined.

[0050] Step 206: Divide the spatial area of ​​the transportation equipment and determine at least one sampling area.

[0051] Specifically, the spatial region can be segmented based on the maximum scanning range of the point cloud generation device to determine at least one sampling region. The size of the sampling region is less than or equal to the maximum scanning range of the point cloud generation device. For example... Figure 3 As shown, Figure 3 This is a schematic diagram of a grid model of a transportation equipment provided in an embodiment of this application. After the spatial area of ​​the transportation equipment is divided into grids, each grid can be used as a sampling area, thereby obtaining at least one sampling area.

[0052] in, Figure 3 In the diagram, 1 represents the main body of the transport equipment, 2 represents the lower right of the spatial area, 3 represents the upper right column, 4 represents the upper right column, 5 represents the upper left column, 6 represents the upper left column, 7 represents the lower left column, 8 represents the segmented spatial area, and 9 represents the cell width, which is the maximum scanning range of the point cloud generation device.

[0053] Step 208: Obtain the three-dimensional point cloud data collected by the point cloud generation device for the area to be sampled, and determine the sampling location information of the material to be sampled based on the three-dimensional point cloud data.

[0054] Since there are at least one sampling region, when multiple sampling regions exist, one of them can be selected each time to collect 3D point cloud data according to a preset rule. The preset rule can be a random selection rule, or a rule that selects sampling regions in a fixed order. Of course, other rules can also be used to select sampling regions, and this application embodiment does not specifically limit this.

[0055] In some embodiments, such as Figure 4 As shown, Figure 4 A flowchart for determining sampling location information provided in this application embodiment includes the following steps:

[0056] Step 402: Based on the three-dimensional point cloud data, determine whether the sampling area includes the material to be sampled.

[0057] Step 404: If the material to be sampled is included, determine the sampling location information of the material to be sampled based on the 3D point cloud data.

[0058] This can be achieved by processing the 3D point cloud data to determine whether the sampling area includes the material to be sampled. Optionally, the relevant feature information of the collected 3D point cloud data can be compared with the feature information of the material to be sampled to determine whether the sampling area includes the material to be sampled.

[0059] If the sampled material is included, the sampling location information can be obtained by calculating the collected 3D point cloud data. This sampling location information can include both coordinate and angle information. Therefore, the sampled material can be obtained based on the specific coordinate and angle information. Compared to traditional manual sampling processes, which are limited by various obstacles, the point cloud-based material sampling method provided in this application has higher sampling efficiency and accuracy.

[0060] In other embodiments, such as Figure 5 As shown, Figure 5 Another flowchart for determining sampling location information provided in this application embodiment includes the following steps:

[0061] Step 502: If obstacles are included, determine whether there is any avoidance space for the obstacles within the sampling area based on the 3D point cloud data.

[0062] Step 504: If it exists, determine whether the area to be sampled includes the material to be sampled.

[0063] Step 506: If the material to be sampled is included, determine the sampling location information of the material to be sampled based on the 3D point cloud data.

[0064] Specifically, by processing the 3D point cloud data, it is possible to determine whether the sampling area contains obstacles. Optionally, the relevant feature information of the acquired 3D point cloud data can be compared with the feature information of obstacles to determine whether the sampling area contains obstacles based on the comparison result.

[0065] If obstacles are included, it can be determined whether there is space to avoid them within the sampling area based on 3D point cloud data. In other words, it can be determined whether the sampling device has sufficient space to obtain the material to be sampled, even with obstacles present. Optionally, the size of the space occupied by the obstacles within the sampling area can be calculated, and then the existence of space to avoid them can be determined based on the remaining space, the size of the sampling device, and the size of the material to be sampled.

[0066] In some other embodiments, the attribute information may also include the weight of the material, such as Figure 6 As shown, Figure 6 A flowchart for determining sampling location information provided in this application embodiment, the method includes the following steps:

[0067] Step 602: Determine the target sampling weight of the material to be sampled based on the preset sampling ratio and weight.

[0068] Step 604: Determine whether the sampling area includes the material to be sampled based on the 3D point cloud data.

[0069] Step 606: If the sampled material is included, determine whether the weight of the sampled material in the sampled area meets the target sampling weight.

[0070] Step 608: If the target sampling weight is not met, select a new sampling area from the multiple sampling areas to continue obtaining the sampled material until the total weight of the obtained sampled material meets the target sampling weight.

[0071] The preset sampling ratio can be pre-set according to user needs, for example, it can be set to 30% of the material weight, thus determining the target sampling weight of the material to be sampled. Furthermore, a sampling area can be selected using preset rules, and 3D point cloud data can be collected. Based on the 3D point cloud data, it can be determined whether the sampling area includes the material to be sampled. If it is determined that the material to be sampled is included, it can be judged whether the weight of the currently acquired material meets the target sampling weight. If not, a new sampling area needs to be selected using the preset rules, and the above process is repeated until the material to be sampled that meets the target sampling weight is obtained.

[0072] In some embodiments, the final sampling location information can be determined by combining the different processes described above for determining sampling location information. For example, it can be first determined whether the sampling area includes obstacles based on 3D point cloud data; if obstacles are included, it can be determined whether there is space to avoid the obstacles in the sampling area based on 3D point cloud data; if so, the target sampling weight of the material to be sampled can be determined according to a preset sampling ratio and weight; it can then be determined whether the sampling area includes the material to be sampled based on 3D point cloud data; if the material to be sampled is included, it can be determined whether the weight of the material to be sampled in the sampling area meets the target sampling weight; if not, a new sampling area can be selected from multiple sampling areas to continue obtaining the material to be sampled until the total weight of the obtained material meets the target sampling weight.

[0073] For example, one can first determine whether the sampling area includes the material to be sampled based on the 3D point cloud data. If the material is included, the target sampling weight can be determined according to the preset sampling ratio and weight. Then, the system can determine whether the sampling area includes the material to be sampled based on the 3D point cloud data. If the material is included, it can determine whether the weight of the material in the sampling area meets the target sampling weight. If not, a new sampling area can be selected from multiple sampling areas to continue obtaining the material until the total weight of the obtained material meets the target sampling weight.

[0074] In some embodiments, during the process of selecting new sampling areas multiple times, the sampling location information of the material to be sampled can be determined by acquiring the number of times based on 3D point cloud data. If the number of times exceeds a preset threshold, an early warning message is generated. The number of times the sampling location information of the material to be sampled is determined is also the number of times a new sampling area is selected. The preset threshold can be pre-set based on human experience. When the number of times a new sampling area is selected exceeds the preset threshold, a corresponding alarm message can be generated. Based on this alarm message, subsequent manual intervention can be carried out to check for problems, or the transport equipment can be allowed to proceed directly while recording any abnormalities.

[0075] Step 210: Trigger the sampling device to obtain the material to be sampled based on the sampling location information.

[0076] Once the target sampling location information is determined, the sampling device can be mounted on the robot, which can then send the target sampling location information to the robot. The robot will then drive the sampling device to obtain the material to be sampled based on the sampling location information.

[0077] In some embodiments, such as Figure 7 As shown, Figure 7A flowchart illustrating a material sampling method based on point cloud data provided in this application embodiment is shown. The method includes the following steps:

[0078] Step 701: After the transport equipment carrying the material to be sampled arrives at the destination, obtain the attribute information generated by the transport equipment identification device based on the transport equipment.

[0079] Specifically, the license plate information of the transport equipment is obtained through the license plate recognition device, and the shape information of the transport equipment and the information of the material to be sampled are obtained based on the license plate information and the first correspondence relationship; the location information of the transport equipment is obtained through the distance between the transport equipment and the landmark in the destination transmitted by the sensor device.

[0080] Step 702: Determine the spatial area where the transportation equipment is located based on the attribute information.

[0081] Step 703: Based on the maximum scanning range of the point cloud generation device, the spatial region is segmented to determine at least one sampling region.

[0082] Step 704: Determine whether the sampling area includes obstacles based on the 3D point cloud data.

[0083] Step 705: If obstacles are included, determine whether there is any avoidance space for the obstacles within the sampling area based on the 3D point cloud data.

[0084] Step 706: If it exists, determine whether the area to be sampled includes the material to be sampled based on the three-dimensional point cloud data.

[0085] Step 707: If the material to be sampled is included, determine the sampling location information of the material to be sampled based on the 3D point cloud data.

[0086] Step 708: If the material to be sampled is not included, select a new sampling area from multiple sampling areas to continue obtaining the material to be sampled.

[0087] In this embodiment, after the transport equipment carrying the material to be sampled arrives at its destination, the attribute information generated by the transport equipment identification device based on the transport equipment is obtained, and the spatial area where the transport equipment is located is determined based on the attribute information. The spatial area of ​​the transport equipment is then segmented to determine at least one sampling area. Three-dimensional point cloud data collected by the point cloud generation device for the sampling area is obtained, and the sampling location information of the material to be sampled is determined based on the three-dimensional point cloud data. Finally, the sampling equipment is triggered to obtain the material to be sampled based on the sampling location information. Since the spatial area where the transport equipment is located can be determined first based on the attribute information of the transport equipment, and then the spatial area can be further subdivided into at least one sampling area, the accurate sampling location information of the material to be sampled can be determined sequentially within the sampling area, thus improving the accuracy of determining the sampling location information and consequently improving the accuracy of obtaining the material to be sampled. In addition, by subdividing the spatial region into at least one sampling region, the contour information of the material and obstacle information can be obtained more accurately. Especially for bagged materials with various shapes, the material sampling method based on point cloud data provided in this application embodiment is more flexible and has a higher material recognition capability, thereby further improving the accuracy of obtaining the physical sample to be sampled.

[0088] The following describes an apparatus embodiment corresponding to the aforementioned method embodiments.

[0089] This application also provides a material sampling device 800 based on point cloud data, used to execute the material sampling method based on point cloud data in the foregoing embodiments.

[0090] Specifically, such as Figure 8 As shown, the device includes: a first acquisition module 801, a first determination module 802, a generation module 803, a second determination module 804, and a second acquisition module 805. Furthermore, the device may also include other units / modules, such as a storage unit, a transmission unit, etc.

[0091] The first acquisition module 801 is used to acquire attribute information generated by the transportation equipment identification device based on the transportation equipment after the transportation equipment carrying the material to be sampled arrives at the destination.

[0092] The first determining module 802 is used to determine the spatial area where the transportation equipment is located based on the attribute information;

[0093] The generation module 803 is used to segment the spatial area of ​​the transportation equipment and determine at least one sampling area;

[0094] The second determining module 804 is used to acquire the three-dimensional point cloud data collected by the point cloud generation device for the area to be sampled, and to determine the sampling location information of the material to be sampled based on the three-dimensional point cloud data.

[0095] The second acquisition module 805 is used to trigger the sampling device to acquire the material to be sampled based on the sampling location information.

[0096] Optionally, in one specific implementation of this application embodiment, the transportation equipment identification device includes a license plate recognition device and at least two sensor devices disposed at different locations. The attribute information includes the shape information of the transportation equipment, the information of the material to be sampled, and the location information of the transportation equipment. The aforementioned first acquisition module 801 is specifically used to acquire the license plate information of the transportation equipment through the license plate recognition device, and based on the license plate information and a first correspondence relationship, acquire the shape information of the transportation equipment and the information of the material to be sampled. The information of the material to be sampled includes at least the type of the material to be sampled. The first correspondence relationship is used to indicate the correspondence between the license plate information of the transportation equipment and the shape information and the information of the material to be sampled, respectively. The location information of the transportation equipment is acquired by transmitting the distance between the transportation equipment and the landmark in the destination through the sensor devices.

[0097] Optionally, in a specific implementation of this application embodiment, the generation module 803 is specifically used to segment the spatial region based on the maximum scanning range of the point cloud generation device, and determine at least one sampling region, wherein the size of the sampling region is less than or equal to the maximum scanning range of the point cloud generation device.

[0098] Optionally, in a specific implementation of this application embodiment, the second determining module 804 is specifically used to determine whether the area to be sampled includes the material to be sampled based on the three-dimensional point cloud data; if the material to be sampled is included, the sampling location information of the material to be sampled is determined based on the three-dimensional point cloud data.

[0099] Optionally, in a specific implementation of this application embodiment, the second determining module 804 is further configured to determine whether the sampling area includes obstacles based on the three-dimensional point cloud data; if obstacles are included, determine whether there is an obstacle avoidance space in the sampling area based on the three-dimensional point cloud data; if there is, determine whether the sampling area includes the material to be sampled; if the material to be sampled is included, determine the sampling location information of the material to be sampled based on the three-dimensional point cloud data.

[0100] Optionally, in a specific implementation of this application embodiment, the attribute information further includes the weight of the material. The second determining module 804 is further configured to determine the target sampling weight of the material to be sampled based on the preset sampling ratio and weight; determine whether the sampling area includes the material to be sampled based on the three-dimensional point cloud data; if the material to be sampled is included, determine whether the weight of the material to be sampled in the sampling area meets the target sampling weight; if not, select a new sampling area from multiple sampling areas to continue obtaining the material to be sampled until the total weight of the obtained material to be sampled meets the target sampling weight.

[0101] Optionally, in a specific implementation of this application embodiment, the above-mentioned material sampling device based on point cloud data is further used to obtain the number of times the sampling location information of the material to be sampled is determined based on the three-dimensional point cloud data; if the number exceeds a preset number threshold, an early warning information is generated.

[0102] In a specific implementation, this application also provides an electronic device, which may be the server in the foregoing embodiments, used to implement all or part of the steps in the aforementioned material sampling method based on point cloud data.

[0103] like Figure 9 The diagram shown is a structural schematic of an electronic device according to an embodiment of this application. It includes at least one processor, a memory, and at least one interface. Additionally, it may include a communication bus for connecting these components.

[0104] At least one processor may be a CPU or a processing chip, used to read and execute computer program instructions stored in memory, so that at least one processor can execute the method flow in the foregoing embodiments.

[0105] The memory can be non-transitory memory, which may include volatile memory, such as high-speed random access memory (RAM), or non-volatile memory, such as at least one disk storage device.

[0106] At least one interface includes an input / output interface and a communication interface, which can be wired or wireless, thereby enabling communication connections between the electronic device and other devices. The input / output interface can be used to connect peripherals, such as displays, keyboards, etc.

[0107] In some implementations, the memory stores computer-readable program instructions, which, when read and executed by the processor, enable a material sampling method based on point cloud data as described in the foregoing embodiments.

[0108] In addition, this application also provides a computer program product for storing computer-readable program instructions, which, when executed by a processor, can implement a material sampling method based on point cloud data as described in the foregoing embodiments.

[0109] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0110] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0111] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0112] For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0113] More specific examples (a non-exhaustive list) of computer-readable media include the following: electrical connections having one or more wires (electronic devices), portable computer disks (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM).

[0114] Furthermore, the computer-readable medium can even be paper or other suitable media on which programs can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory. It should be understood that various parts of the invention can be implemented in hardware, software, firmware, or a combination thereof.

[0115] In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0116] The above embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A method of sampling material, characterized by, The application is applied to a terminal device connected with a transport equipment identification device, a point cloud generation device and a sampling device, and comprises: After a transport equipment carrying a material to be sampled arrives at a destination, attribute information of the transport equipment generated by the transport equipment identification device is acquired; the attribute information comprises a weight of the material; According to the attribute information, a space region where the transport equipment is located is determined; The space region of the transport equipment is segmented to determine at least one region to be sampled; Three-dimensional point cloud data collected by the point cloud generation device for the region to be sampled is acquired, and sampling position information of the material to be sampled is determined based on the three-dimensional point cloud data; The sampling device is triggered to acquire the material to be sampled based on the sampling position information; The method further comprises determining whether an obstacle is included in the region to be sampled based on the three-dimensional point cloud data; If the obstacle is included, it is determined based on the three-dimensional point cloud data whether an avoiding space exists for the obstacle in the region to be sampled; If the avoiding space exists, a target sampling weight of the material to be sampled is determined according to a preset sampling ratio and the weight; It is determined based on the three-dimensional point cloud data whether the material to be sampled is included in the region to be sampled; If the material to be sampled is included, sampling position information of the material to be sampled is determined based on the three-dimensional point cloud data, and it is determined whether a weight of the material to be sampled in the region to be sampled meets the target sampling weight; If not, a new region to be sampled is selected from a plurality of regions to be sampled to continue to acquire the material to be sampled until a total weight of the acquired material to be sampled meets the target sampling weight.

2. The method of claim 1, wherein The transport equipment identification device comprises a license plate recognition device and at least two sensor devices arranged at different positions, the attribute information comprises shape information of the transport equipment, information of the material to be sampled and position information of the transport equipment, and the attribute information generated by the transport equipment identification device comprises: The license plate information of the transport equipment is acquired by the license plate recognition device, and the shape information of the transport equipment and the information of the material to be sampled are acquired based on the license plate information and a first corresponding relationship, the information of the material to be sampled at least comprising a type of the material to be sampled, and the first corresponding relationship being used to indicate a corresponding relationship between the license plate information of the transport equipment and the shape information of the transport equipment and the information of the material to be sampled respectively; The position information of the transport equipment is acquired based on a distance between the transport equipment and a marker in the destination transmitted by the sensor device.

3. The method of claim 2, wherein The space region of the transport equipment is segmented to determine at least one region to be sampled, comprising: The space region is segmented based on a maximum scanning range of the point cloud generation device to determine the at least one region to be sampled, and a size of the region to be sampled is less than or equal to the maximum scanning range of the point cloud generation device.

4. The method of claim 1, wherein Further comprising: A number of times of determining the sampling position information of the material to be sampled based on the three-dimensional point cloud data is acquired; If the number of times exceeds a preset number threshold, a warning information is generated.

5. A material sampling device, characterized by, The device is configured with the method of claim 1, and the device comprises: The first acquisition module is configured to acquire attribute information generated by a transportation equipment identification device based on the transportation equipment after the transportation equipment carrying the material to be sampled reaches a destination; The first determination module is configured to determine a spatial region where the transportation equipment is located according to the attribute information; The generation module is configured to segment the spatial region of the transportation equipment and determine at least one region to be sampled; The second determination module is configured to acquire three-dimensional point cloud data collected by a point cloud generation device for the region to be sampled, and determine sampling position information of the material to be sampled based on the three-dimensional point cloud data; The second acquisition module is configured to trigger a sampling device to acquire the material to be sampled based on the sampling position information.

6. A computer device comprising: A processor and a memory, characterized in that, The memory is configured to store computer executable instructions; The processor is configured to read the instructions from the memory and execute the instructions to implement the method according to any one of claims 1 to 4.

7. A computer readable storage medium characterized by The storage medium stores computer program instructions, When the computer reads the instructions, the method according to any one of claims 1 to 4 is executed.

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