Method and device for processing load information of unmanned transport vehicle, electronic equipment and storage medium

Abstract

The present disclosure relates to a self-driving transport vehicle load information processing method and device, electronic equipment and storage medium, the method comprising: obtaining the carrying information of a second self-driving transport vehicle by a first loading capacity sensing device of a first self-driving transport vehicle, wherein the carrying information is used to represent the real-time carrying capacity of the second self-driving transport vehicle; and in the case that the real-time carrying capacity does not meet the load condition, sending a prompt information to the excavator of the first self-driving transport vehicle performing the loading operation, so as to make the excavator continue to perform the loading operation, stop the loading operation or adjust the loading capacity of the loading operation, thereby greatly improving the loading efficiency and ensuring the driving safety.

Description

Technical Field

[0001] This disclosure relates to the field of unmanned driving technology, and in particular to methods, devices, electronic devices and storage media for processing load information of unmanned transport vehicles. Background Technology

[0002] In current unmanned mining operations, mining activities mainly include three stages: mining, transportation, and dumping. "Mining" refers to the process of excavating and collecting materials, which requires the cooperation of excavators and transport vehicles; "transportation" and "dumping" refer to the processes of transporting and unloading materials, respectively, which can be completed by transport vehicles alone.

[0003] However, during the loading process of transport vehicles, excavator operators usually need to observe the loading status of the vehicles to determine the amount of minerals loaded. Because excavator operators generally cannot accurately control the amount of minerals loaded, in many cases the transport vehicles are overloaded or underloaded, resulting in low loading efficiency. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for processing load information of unmanned transport vehicles.

[0005] According to a first aspect of this disclosure, a method for processing load information of an unmanned transport vehicle is provided, the method comprising:

[0006] The first loading capacity sensing device of the first unmanned transport vehicle is used to obtain the transport information of the second unmanned transport vehicle, and the transport information is used to represent the real-time transport capacity of the second unmanned transport vehicle.

[0007] At least if the real-time load capacity does not meet the load conditions, a prompt message is sent to the excavator performing the loading operation on the first unmanned transport vehicle, so that the excavator can continue to perform the loading operation, stop the loading operation, or adjust the load capacity of the loading operation.

[0008] Optionally, the loading area includes at least one waiting position and at least one loading position, the second unmanned transport vehicle is located at the loading position, and the at least one first unmanned transport vehicle is located at the waiting position or loading position adjacent to the second unmanned transport vehicle.

[0009] Optionally, the method further includes:

[0010] During the loading process of the second driverless transport vehicle, the real-time carrying capacity of the second driverless transport vehicle is detected;

[0011] If the real-time load is less than a first preset value, a prompt message is generated to remind the excavator that loading is not complete.

[0012] Alternatively, if the real-time load is equal to a first preset value, a notification message is generated to remind the excavator that loading has been completed.

[0013] Alternatively, if the real-time load exceeds a first preset value, a notification message is generated to remind the excavator that its load has been overloaded.

[0014] Optionally, when the second unmanned transport vehicle is in a carrying state, and the at least one first unmanned transport vehicle travels in the opposite direction or in the same direction as the second unmanned transport vehicle, and the at least one first unmanned transport vehicle is located within a preset range of the second unmanned transport vehicle, the method further includes:

[0015] During the operation of the second driverless transport vehicle, the real-time carrying capacity of the second driverless transport vehicle is detected;

[0016] When the real-time carrying capacity exceeds a second preset value, a prompt message is generated to remind the second unmanned transport vehicle, which is at least one of the first unmanned transport vehicles, that it is overloaded.

[0017] Optionally, the method further includes:

[0018] Control the speed of the second driverless transport vehicle to be no greater than a preset speed;

[0019] And / or, control the distance between the at least one first unmanned transport vehicle and the second unmanned transport vehicle to be greater than a preset distance.

[0020] Optionally, a second load sensing device is installed at the target location on the road; the method further includes:

[0021] Acquire the first point cloud data obtained by the first load sensing device;

[0022] During the process of the second unmanned transport vehicle passing through the target location, the second point cloud data of the second unmanned transport vehicle is acquired by the second loading amount sensing device;

[0023] The first point cloud data and the second point cloud data are fused to obtain fused point cloud data, and the real-time carrying capacity of the second unmanned transport vehicle is obtained based on the fused point cloud data.

[0024] Optionally, the first load sensing device and the second load sensing device are used to collect point cloud data, and the first load sensing device and the second load sensing device respectively include LiDAR.

[0025] According to a second aspect of this disclosure, a load information processing device for an unmanned transport vehicle is provided, the device comprising:

[0026] The load acquisition module is used to acquire the load information of the second unmanned transport vehicle using the first load sensing device of the first unmanned transport vehicle. The load information is used to represent the real-time load of the second unmanned transport vehicle.

[0027] The information sending module is used to send a prompt message to the excavator performing loading operations on the first unmanned transport vehicle, at least when the real-time load does not meet the load conditions, so that the excavator can continue to perform loading operations, stop loading operations, or adjust the load of the loading operations.

[0028] Optionally, the loading area includes at least one waiting position and at least one loading position, the second unmanned transport vehicle is located at the loading position, and the at least one first unmanned transport vehicle is located at the waiting position or loading position adjacent to the second unmanned transport vehicle.

[0029] Optionally, the device further includes an information generation module.

[0030] The information generation mold body is used for:

[0031] During the loading process of the excavator onto the second unmanned transport vehicle, the real-time carrying capacity of the second unmanned transport vehicle is detected;

[0032] If the real-time load is not greater than a first preset value, a prompt message is generated to remind the excavator that loading is not complete.

[0033] Alternatively, if the real-time load exceeds a first preset value, a notification message is generated to remind the excavator that loading is complete.

[0034] Optionally, when the second unmanned transport vehicle is in a carrying state, the at least one first unmanned transport vehicle travels in the opposite direction or in the same direction as the second unmanned transport vehicle, and the at least one first unmanned transport vehicle is located within a preset range of the second unmanned transport vehicle; the information generation module is further used for:

[0035] During the operation of the second driverless transport vehicle, the real-time carrying capacity of the second driverless transport vehicle is detected;

[0036] When the real-time carrying capacity exceeds a second preset value, a prompt message is generated to remind the second unmanned transport vehicle, which is at least one of the first unmanned transport vehicles, that it is overloaded.

[0037] Optionally, the device further includes:

[0038] The first control module is used to control the speed of the second driverless transport vehicle to be no greater than a preset speed;

[0039] The second control module is used to control the distance between the at least one first unmanned transport vehicle and the second unmanned transport vehicle to be greater than a preset distance.

[0040] Optionally, a second load sensing device is installed at the target location on the road; the device further includes:

[0041] The first point cloud data acquisition module is used to acquire the first point cloud data obtained by the first loading volume sensing device.

[0042] The second point cloud data acquisition module is used to acquire the second point cloud data of the second unmanned transport vehicle through the second loading amount sensing device during the process of the second unmanned transport vehicle passing through the target location.

[0043] The real-time carrying capacity acquisition module is used to fuse the first point cloud data and the second point cloud data to obtain fused point cloud data, and to obtain the real-time carrying capacity of the second unmanned transport vehicle based on the fused point cloud data.

[0044] Optionally, the first load sensing device and the second load sensing device are used to collect point cloud data, and the first load sensing device and the second load sensing device include LiDAR.

[0045] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0046] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods described above.

[0047] The unmanned transport vehicle load information processing method, apparatus, electronic device and storage medium provided in this disclosure use a first device quantity sensing device of a first unmanned transport vehicle to obtain the transport information of a second unmanned transport vehicle, and at least when the real-time load does not meet the load conditions, issue a prompt message to the excavator performing the loading operation of the first unmanned transport vehicle, so that the excavator can continue to perform the loading operation, stop the loading operation, or adjust the load of the loading operation. This can greatly improve loading efficiency and ensure driving safety. Attached Figure Description

[0048] Further details, features, and advantages of this disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0049] Figure 1 A schematic diagram of a scenario provided for an exemplary embodiment of this disclosure;

[0050] Figure 2 Another scenario illustration provided for an exemplary embodiment of this disclosure;

[0051] Figure 3 Another scenario illustration provided for an exemplary embodiment of this disclosure;

[0052] Figure 4 A flowchart of a method for processing load information of an unmanned transport vehicle provided as an exemplary embodiment of this disclosure;

[0053] Figure 5 A schematic block diagram of the functional modules of an unmanned transport vehicle load information processing device provided as an exemplary embodiment of this disclosure;

[0054] Figure 6 A structural block diagram of an electronic device provided as an exemplary embodiment of this disclosure;

[0055] Figure 7 A block diagram of a computer system provided for an exemplary embodiment of this disclosure. Detailed Implementation

[0056] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0057] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0058] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0059] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0060] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0061] Currently, autonomous driving operations in mining areas mainly include three stages: mining, transportation, and unloading. "Mining" refers to the material loading process, which requires the cooperation of excavators and unmanned transport vehicles. "Transportation" and "unloading" refer to the material transportation and unloading processes, respectively, which are completed by unmanned transport vehicles. In some actual scenarios of autonomous driving operations in mining trucks, material transportation and unloading tasks have achieved a level of automation; however, the material loading process has not yet reached an automation level and requires control by the excavator operator. When the operator observes the unmanned transport vehicle in an empty state stopping at the loading position, they begin loading materials. The operator controls the excavator to load materials into the unmanned transport vehicle's cargo bed. After loading is complete, a loading completion command is sent to the unmanned transport vehicle via a terminal, and the unmanned transport vehicle then begins its material transportation task.

[0062] However, in the current scenario, unmanned transport vehicles often experience overloading or insufficient loading. When unmanned transport vehicles are overloaded, there are safety hazards; when unmanned transport vehicles are not fully loaded (i.e., the loading is insufficient), it will affect the efficiency of operation. The main reason for the above situations is that human operation of excavators cannot accurately control the loading amount each time.

[0063] Therefore, this disclosure provides a method, device, electronic equipment, and storage medium for processing the load information of unmanned transport vehicles, which can accurately calculate the load of each transport task of the unmanned transport vehicle and generate prompt information when the load does not meet the loading conditions, thereby ensuring the transport efficiency and operational safety of the unmanned transport vehicle.

[0064] In the embodiments provided in this disclosure, such as Figure 1 As shown, the front of the excavator 10 is the loading area 20, which is the area where the excavator 10 loads materials. The loading area 20 may include at least one waiting position and at least one loading position. Figure 1 The following description uses a loading area 20, including loading positions 23 and 24, and waiting positions 21 and 22, as an example. Unmanned transport vehicles can park in the waiting positions and / or loading positions of the loading area 20, and load the unmanned transport vehicles parked in loading positions 23 and / or 24. Unmanned transport vehicles can also park in waiting positions 21 and / or 22. An excavator 10 can perform loading operations on the unmanned transport vehicles parked in loading positions 23 and / or 24. After loading is completed, the unmanned transport vehicles will leave the loading positions, and the first unmanned transport vehicle parked in a waiting position will move into an empty loading position. Other vehicles will also move into empty waiting positions. In this embodiment, the number of unmanned transport vehicles parked in the loading area 20 can be controlled as needed. In this embodiment, the unmanned transport vehicle can specifically be an unmanned wide-body vehicle, but the embodiment is not limited to this.

[0065] The unmanned transport vehicle parked in loading area 23 can be designated as the second unmanned transport vehicle, and other vehicles parked near the second unmanned transport vehicle can be designated as the first unmanned transport vehicle. At least one first unmanned transport vehicle is parked near the second unmanned transport vehicle, and this at least one first unmanned transport vehicle is equipped with a point cloud data acquisition device. This point cloud data acquisition device is used to collect point cloud data of the target first unmanned transport vehicle. The first unmanned transport vehicles near the target first unmanned transport vehicle can be, in addition to the first unmanned transport vehicle parked in loading area 20, first unmanned transport vehicles parked in other adjacent loading areas. These first unmanned transport vehicles can be unmanned. Alternatively, the first unmanned transport vehicle can also be an excavator, which can be equipped with a point cloud data acquisition device. In this embodiment, the excavator can be unmanned, remotely controlled, or manned.

[0066] In this way, at least one unmanned transport vehicle near the target unmanned transport vehicle can acquire point cloud data for that target vehicle. Each unmanned transport vehicle can upload the collected point cloud data to a cloud server. The server can extract the point cloud data of the target unmanned transport vehicle's cargo bed from the point cloud data and use it to create a 3D model, obtaining a 3D model of the material loaded in the cargo bed of the second unmanned transport vehicle. Then, based on this 3D model, the volume of the material loaded in the cargo bed of the second unmanned transport vehicle can be calculated, and the weight of the material can be determined by combining the material type. For example, the density of the material can be determined based on its type, and the mass or weight of the material can be obtained based on the density and volume. Alternatively, a direct correspondence between the weight or mass of different material types at corresponding volumes can be established; the embodiments are not limited to these.

[0067] In this embodiment, the second unmanned transport vehicle is observed from different angles by multiple first unmanned transport vehicles to obtain the carrying capacity information, such as point cloud data. This allows for a more accurate determination of the volume of materials loaded on the second unmanned transport vehicle, and thus a more accurate determination of the weight or mass of the materials. In this embodiment, the weight or mass of the materials can be represented by the loading capacity.

[0068] In the embodiments provided in this disclosure, during the loading process of the second unmanned transport vehicle, the excavator 20 obtains the current loading capacity of the second unmanned transport vehicle by using the carrying capacity information of at least one first unmanned transport vehicle nearby. If the loading capacity does not meet the load conditions, a prompt message can be generated. For example, when the loading of the second unmanned transport vehicle is not complete, a prompt message can be sent to the excavator to indicate that loading is not finished and needs to continue. When the loading of the second unmanned transport vehicle is complete, a prompt message can be sent to the excavator to indicate that loading is complete and needs to be stopped to avoid overloading. Since the excavator's bucket has a certain loading capacity, it may be overloaded before the previous loading is completed. Therefore, based on the loading capacity of the second unmanned transport vehicle, it is still necessary to send a prompt message to the excavator during operation to indicate that it is overloaded and needs to stop loading.

[0069] In this embodiment, the loading status (incomplete, completed, or overloaded) can be determined by comparing the current loading capacity of the first unmanned transport vehicle with a preset loading capacity. For the same type of second unmanned transport vehicle and the same type of material, and for the same excavator, the number of loading operations can be recorded, or the second unmanned transport vehicle can detect the excavator's loading operations to obtain the average number of loading operations at completion. This allows for advance warning messages to be issued when loading is about to be completed, enabling the excavator or excavator operator to prepare. For example, if the average number of loading operations is 36, a reminder can be issued when loading reaches 33 operations. If loading has reached the average of 36 operations but is not yet complete, the excavator can be prompted to reduce the next bucket load to avoid overloading. This avoids overloading caused by inaccurate load information or when only a small amount of material is needed to complete the loading.

[0070] For example, the server extracts point cloud data of the truck bed from point cloud data, uses the truck bed point cloud data to perform 3D modeling, obtains a 3D model of the cargo carried by the first unmanned transport vehicle, calculates the volume of the cargo, and calculates the weight of the cargo based on the volume. If the weight of the cargo is lower than the first preset carrying capacity, it is determined that the loading is too small (or the number of times it is not fully loaded exceeds a certain number), and a prompt message is sent to the corresponding excavator terminal to remind the driver to fill the truck bed when loading materials. If the weight of the cargo exceeds the second preset carrying capacity (or the number of times it is overloaded exceeds a certain number), it is determined that it is overloaded, and a reminder is issued. A prompt message is sent to the corresponding excavator terminal to remind the driver to appropriately reduce the loading capacity when loading materials.

[0071] In the embodiments provided in this disclosure, such as Figure 2 As shown, when the second unmanned transport vehicle 30 is driving on the road, there may be at least one other first unmanned transport vehicle equipped with a point cloud data acquisition device 40 nearby, for example... Figure 2The first unmanned transport vehicles 31, 32, and 33, equipped with point cloud data acquisition devices, can collect point cloud data from the second unmanned transport vehicle 30 from different angles and send it to a cloud server. The server fuses these point cloud data from different angles to obtain fused point cloud data. Based on this point cloud data, a 3D model of the second unmanned transport vehicle 30 can be obtained. Then, through model segmentation and other methods, a 3D model of the material loaded in the truck bed is segmented to obtain the volume of the material loaded in the truck bed. Combined with the type or density of the material, the loading amount of the second unmanned transport vehicle 30 is determined. In this embodiment, the material can specifically be minerals, such as coal or stone. In this embodiment, the second unmanned transport vehicle 30 can also be equipped with a point cloud data acquisition device 40 for collecting point cloud data from other first unmanned transport vehicles.

[0072] In the embodiments provided in this disclosure, such as Figure 3As shown, point cloud data acquisition equipment can be installed on the road where the second unmanned transport vehicle 30 travels. Specifically, a left radar 31, a front radar 32, and a right radar 33 can be installed to simultaneously acquire point cloud data as the second unmanned transport vehicle 30 passes through the point cloud data acquisition equipment. Specifically, the left radar 31 and right radar 33 can collect point cloud data from multiple slices of the second unmanned transport vehicle 30, and the thickness of the three-dimensional model of the second unmanned transport vehicle 30 can be determined through the point cloud data obtained by the front radar 32. Through the system's software algorithm, the point cloud slice data is synthesized and calculated to obtain the length, width, and height data of the vehicle's cargo bed outline, thereby determining the volume of material loaded by the second unmanned transport vehicle 30. The loading amount of material, such as the weight or mass of the material, is determined by combining the volume and the type of material in the above manner. This allows for the determination of overloading by monitoring the material load of the second unmanned transport vehicle 30. If overloaded, the vehicle's speed can be controlled within a certain range. Furthermore, a warning message can be sent to the nearby first unmanned transport vehicles 31, 32, and 33, indicating that the second vehicle is overloaded. Upon receiving this warning, these vehicles will maintain a safe distance from the second vehicle, ensuring safe operation. Alternatively, a cloud server can directly control the nearby first unmanned transport vehicles to maintain a safe distance from the second vehicle.

[0073] In the embodiments provided in this disclosure, it is also possible to Figure 3 The point cloud data acquired by the point cloud data acquisition device in the middle and Figure 1 and / or Figure 2 The point cloud data obtained in the above method is fused to obtain fused point cloud data. In this way, the loading amount of materials carried by the second unmanned transport vehicle can be more accurately determined.

[0074] Based on the above embodiments, in another embodiment provided in this disclosure, a method for processing the load information of an unmanned transport vehicle is provided, such as... Figure 4 As shown, the method may include the following steps:

[0075] In step S410, the first loading capacity sensing device of the first unmanned transport vehicle is used to acquire the transport information of the second unmanned transport vehicle. The transport information represents the real-time load capacity of the second unmanned transport vehicle.

[0076] In step S420, at least when the real-time load does not meet the load conditions, a prompt message is sent to the excavator performing the loading operation of the first unmanned transport vehicle, so that the excavator can continue to perform the loading operation, stop the loading operation, or adjust the load of the loading operation.

[0077] In this embodiment, the second driverless transport vehicle may be in a loaded state, for example, as described above. Figure 1 In a corresponding embodiment, the second unmanned transport vehicle is parked at the loading position, and the excavator loads materials onto the second unmanned transport vehicle. At least one unmanned transport vehicle is parked nearby, either at the loading position or the waiting position, or it could be an excavator. In this embodiment, the first unmanned transport vehicle or excavator is equipped with a first loading capacity sensing device, which can specifically be a point cloud data acquisition device. The first unmanned transport vehicle can specifically be an unmanned wide-body transport vehicle.

[0078] In the embodiments provided in this disclosure, such as Figure 1 As shown, the loading area includes at least one waiting position and at least one loading position, with a second unmanned transport vehicle located at the loading position, and at least one first unmanned transport vehicle located at the waiting position or loading position adjacent to the second unmanned transport vehicle.

[0079] The second driverless transport vehicle can also be in motion, for example... Figure 2 In a corresponding embodiment, at least one first unmanned transport vehicle is traveling near the second unmanned transport vehicle. The first unmanned transport vehicle is equipped with a load sensing device, which may specifically be a point cloud data acquisition device.

[0080] In this embodiment, the carrying capacity information of the second unmanned transport vehicle can be obtained from at least one first unmanned transport vehicle near the carrying capacity sensing device. This carrying capacity information can specifically be point cloud data, or image data captured by a camera, such as image data captured by a camera or infrared imaging data.

[0081] In this embodiment, when the second unmanned transport vehicle is in a loading state, the load information of the second unmanned transport vehicle can be obtained through the above-described method, and the real-time load of the second unmanned transport vehicle can be obtained based on this load information. The load information includes point cloud data. A first unmanned transport vehicle near the second unmanned transport vehicle is equipped with a point cloud data acquisition device, which is used to acquire point cloud data. The volume of the material loaded by the second unmanned transport vehicle can be obtained based on the point cloud data, and the real-time load can be determined based on the type and volume of the material. Therefore, the real-time load of the second unmanned transport vehicle can be detected during the loading process.

[0082] In this way, if the real-time load is less than the first preset value, a prompt message is generated to remind the excavator that the loading is not completed. Upon receiving this prompt message, the excavator may continue to load materials onto the second unmanned transport vehicle.

[0083] Alternatively, if the real-time load is equal to a first preset value, a notification message is generated to remind the excavator that loading is complete. Upon receiving this notification message, the excavator will stop loading materials onto the second unmanned transport vehicle.

[0084] Alternatively, if the real-time load exceeds a first preset value, a notification message is generated to remind the excavator that the load is overloaded. Upon receiving this notification message, the excavator will stop loading materials onto the second unmanned transport vehicle. Materials can also be unloaded from the second unmanned transport vehicle as needed, for example, by using a bucket to remove some materials from the second unmanned transport vehicle, until the real-time load does not exceed the first preset value.

[0085] The unmanned transport vehicle load information processing method provided in this embodiment uses a first device quantity sensing device of the first unmanned transport vehicle to obtain the transport information of the second unmanned transport vehicle, and at least when the real-time load does not meet the load conditions, sends a prompt message to the excavator performing the loading operation of the first unmanned transport vehicle, so that the excavator can continue to perform the loading operation, stop the loading operation, or adjust the load of the loading operation. This can greatly improve loading efficiency and ensure driving safety.

[0086] In conjunction with the above embodiments, in another embodiment provided in this disclosure, when the second unmanned transport vehicle is in a carrying state, at least one first unmanned transport vehicle travels towards or in the same direction as the second unmanned transport vehicle, and at least one first unmanned transport vehicle is located within a preset range of the second unmanned transport vehicle, the method may further include the following steps:

[0087] S431, during the operation of the second unmanned transport vehicle, the real-time load capacity of the second unmanned transport vehicle is detected.

[0088] S432, when the real-time carrying capacity exceeds the second preset value, generates a prompt message to remind at least one of the first unmanned transport vehicles that the second unmanned transport vehicle is overloaded.

[0089] In this embodiment, the second preset value is greater than the first preset value. When the real-time carrying capacity is greater than the second preset value, it indicates that the second unmanned transport vehicle is overloaded. This means that the second unmanned transport vehicle needs to pay attention to safety while driving on the road. Therefore, a prompt message can be generated and sent to at least one first unmanned transport vehicle near the second unmanned transport vehicle to indicate that the at least one first unmanned transport vehicle is overloaded, ensuring that a safe distance is maintained between the two vehicles.

[0090] In this embodiment, the speed of the second unmanned transport vehicle is controlled to be no greater than a preset speed, and / or the distance between at least one first unmanned transport vehicle and the second unmanned transport vehicle is controlled to be greater than a preset distance. Specifically, the speed of the second unmanned transport vehicle can be controlled to remain within a reasonable speed range, i.e., the speed should not be too high to ensure driving safety. Simultaneously, other first unmanned transport vehicles near the second unmanned transport vehicle can also be controlled to maintain a safe distance from it to ensure driving safety.

[0091] In the embodiments provided in this disclosure, first point cloud data obtained by a first loading capacity sensing device can be acquired, and second point cloud data of the second unmanned transport vehicle can be acquired by a second loading capacity sensing device as the second unmanned transport vehicle passes through the target location. Thus, by fusing the first and second point cloud data, fused point cloud data is obtained, and the real-time carrying capacity of the second unmanned transport vehicle is obtained based on the fused point cloud data. For details regarding point cloud fusion, please refer to the above embodiments, which will not be repeated here. The first and second loading capacity sensing devices are used to collect point cloud data, and both include LiDAR.

[0092] Based on the above embodiments, in another embodiment provided in this disclosure, a load sensing device is installed at the target location on the road, and step S430 may specifically include:

[0093] S431, during the process of the second unmanned transport vehicle passing the target location, the second material loading of the second unmanned transport vehicle is detected by the loading amount sensing device.

[0094] S432, based on the real-time carrying capacity and the second material loading capacity, determines the average loading capacity of the second unmanned transport vehicle, and generates a prompt message if the average loading capacity does not meet the load conditions.

[0095] During the passage of the second unmanned transport vehicle to the target location, a loading sensor located at the target location on the road can detect the second material loading amount of the second unmanned transport vehicle. This second material loading amount can be combined with the previously obtained real-time transport volume to obtain the average loading amount. This average loading amount is used to determine whether the load conditions are met, such as whether it is overloaded. If not, a warning message is generated. This warning message can be sent to vehicles near the second unmanned transport vehicle to remind them to maintain a safe distance from the target first unmanned transport vehicle. See the above for details. Figure 3 The corresponding implementation examples are not described in detail here.

[0096] By dividing each function into corresponding functional modules, this disclosure provides an unmanned transport vehicle load information processing device, which can be a server or a chip applied to a server. Figure 5 This is a schematic block diagram of the functional modules of an unmanned transport vehicle load information processing device provided as an exemplary embodiment of this disclosure. Figure 5 As shown, the unmanned transport vehicle load information processing device includes:

[0097] The carrying capacity acquisition module 11 is used to acquire the carrying information of the second unmanned transport vehicle using the first loading capacity sensing device of the first unmanned transport vehicle, and the carrying information is used to represent the real-time carrying capacity of the second unmanned transport vehicle.

[0098] The information sending module 12 is used to send a prompt message to the excavator performing loading operations on the first unmanned transport vehicle, at least when the real-time load does not meet the load conditions, so that the excavator can continue to perform loading operations, stop loading operations, or adjust the load of loading operations.

[0099] In another embodiment provided in this disclosure, the loading area includes at least one waiting position and at least one loading position, the second unmanned transport vehicle is located at the loading position, and the at least one first unmanned transport vehicle is located at the waiting position or loading position adjacent to the second unmanned transport vehicle.

[0100] In yet another embodiment provided in this disclosure, the apparatus further includes an information generation module.

[0101] The information generation mold body is used for:

[0102] During the loading process of the excavator onto the second unmanned transport vehicle, the real-time carrying capacity of the second unmanned transport vehicle is detected;

[0103] If the real-time load is not greater than a first preset value, a prompt message is generated to remind the excavator that loading is not complete.

[0104] Alternatively, if the real-time load exceeds a first preset value, a notification message is generated to remind the excavator that loading is complete.

[0105] In another embodiment provided in this disclosure, when the second unmanned transport vehicle is in a carrying state, the at least one first unmanned transport vehicle travels towards or in the same direction as the second unmanned transport vehicle, and the at least one first unmanned transport vehicle is located within a preset range of the second unmanned transport vehicle; the information generation module is further specifically used for:

[0106] During the operation of the second driverless transport vehicle, the real-time carrying capacity of the second driverless transport vehicle is detected;

[0107] When the real-time carrying capacity exceeds a second preset value, a prompt message is generated to remind the second unmanned transport vehicle, which is at least one of the first unmanned transport vehicles, that it is overloaded.

[0108] In yet another embodiment provided in this disclosure, the apparatus further includes:

[0109] The first control module is used to control the speed of the second driverless transport vehicle to be no greater than a preset speed;

[0110] The second control module is used to control the distance between the at least one first unmanned transport vehicle and the second unmanned transport vehicle to be greater than a preset distance.

[0111] In another embodiment provided in this disclosure, a second load sensing device is provided at the target location on the road; the device further includes:

[0112] The first point cloud data acquisition module is used to acquire the first point cloud data obtained by the first loading volume sensing device.

[0113] The second point cloud data acquisition module is used to acquire the second point cloud data of the second unmanned transport vehicle through the second loading amount sensing device during the process of the second unmanned transport vehicle passing through the target location.

[0114] The real-time carrying capacity acquisition module is used to fuse the first point cloud data and the second point cloud data to obtain fused point cloud data, and to obtain the real-time carrying capacity of the second unmanned transport vehicle based on the fused point cloud data.

[0115] In another embodiment provided in this disclosure, the first load sensing device and the second load sensing device are used to collect point cloud data, and the first load sensing device and the second load sensing device include LiDAR.

[0116] For details, please refer to the corresponding implementation examples of the above methods, which will not be repeated here.

[0117] The unmanned transport vehicle load information processing device provided in this embodiment utilizes a first device quantity sensing device of the first unmanned transport vehicle to acquire transport information of the second unmanned transport vehicle. At least when the real-time load does not meet the load conditions, it sends a prompt message to the excavator performing loading operations on the first unmanned transport vehicle, prompting the excavator to continue loading operations, stop loading operations, or adjust the load amount. By detecting the material load in the second unmanned transport vehicle and generating prompt messages, the device can guide the excavator to load accurately during the loading process. Furthermore, during the second unmanned transport vehicle's operation, the prompt messages can control the vehicle speed and remind other vehicles to maintain a safe distance, significantly improving loading efficiency and ensuring driving safety.

[0118] This disclosure also provides an electronic device, including: at least one processor; a memory for storing processor-executable instructions; wherein the at least one processor is configured to execute the instructions to implement the methods disclosed in this disclosure.

[0119] Figure 6 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this disclosure. For example... Figure 6 As shown, the electronic device 1800 includes at least one processor 1801 and a memory 1802 coupled to the processor 1801. The processor 1801 can perform the corresponding steps in the methods disclosed in the embodiments of this disclosure.

[0120] The processor 1801 described above can also be called a central processing unit (CPU), which can be an integrated circuit chip with signal processing capabilities. Each step in the method disclosed in this embodiment can be implemented by the integrated logic circuitry in the processor 1801 or by software instructions. The processor 1801 can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this embodiment can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can be located in the memory 1802, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor 1801 reads information from the memory 1802 and, in conjunction with its hardware, completes the steps of the method described above.

[0121] Furthermore, various operations / processes according to this disclosure, implemented via software and / or firmware, can be transmitted from a storage medium or network to a computer system with a dedicated hardware architecture, such as... Figure 7 The computer system 1900 shown is equipped with the programs that constitute the software. When various programs are installed, the computer system is able to perform various functions, including those described above. Figure 7 A block diagram of a computer system provided for an exemplary embodiment of this disclosure.

[0122] Computer System 1900 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0123] like Figure 7As shown, the computer system 1900 includes a computing unit 1901, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 1902 or a computer program loaded from a storage unit 1908 into a random access memory (RAM) 1903. The RAM 1903 may also store various programs and data required for the operation of the computer system 1900. The computing unit 1901, ROM 1902, and RAM 1903 are interconnected via a bus 1904. An input / output (I / O) interface 1905 is also connected to the bus 1904.

[0124] Multiple components in computer system 1900 are connected to I / O interface 1905, including: input unit 1906, output unit 1907, storage unit 1908, and communication unit 1909. Input unit 1906 can be any type of device capable of inputting information into computer system 1900. Input unit 1906 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 1907 can be any type of device capable of presenting information and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 1908 may include, but is not limited to, hard disks and optical disks. Communication unit 1909 allows computer system 1900 to exchange information / data with other devices via a network such as the Internet, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0125] The computing unit 1901 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1901 performs the various methods and processes described above. For example, in some embodiments, the methods disclosed in this disclosure can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1908. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1900 via ROM 1902 and / or communication unit 1909. In some embodiments, the computing unit 1901 can be configured to perform the methods disclosed in this disclosure by any other suitable means (e.g., by means of firmware).

[0126] This disclosure also provides a computer-readable storage medium, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is able to perform the methods disclosed in this disclosure.

[0127] The computer-readable storage medium in this disclosure can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The aforementioned computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specifically, the aforementioned computer-readable storage medium may include electrical connections based on one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0128] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0129] This disclosure also provides a computer program product, including a computer program, wherein the computer program, when executed by a processor, implements the methods disclosed in the embodiments of this disclosure.

[0130] In embodiments of this disclosure, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or it can be connected to an external computer.

[0131] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0132] The modules, components, or units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the modules, components, or units do not necessarily constitute a limitation on the module, component, or unit itself.

[0133] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that can be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0134] The above description is merely an embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0135] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A method for processing load information of an unmanned transport vehicle, characterized in that, The method includes: Using the first loading capacity sensing devices of multiple first unmanned transport vehicles, point cloud data of the second unmanned transport vehicle is acquired from different angles. The point cloud data from different angles are fused and processed to obtain the transport information of the second unmanned transport vehicle. The transport information is used to represent the real-time transport capacity of the second unmanned transport vehicle. At least if the real-time load capacity does not meet the load conditions, a prompt message is sent to the excavator performing the loading operation on the first unmanned transport vehicle, so that the excavator can continue to perform the loading operation, stop the loading operation, or adjust the load capacity of the loading operation.

2. The method according to claim 1, characterized in that, When the second driverless transport vehicle is in a loading state, the second driverless transport vehicle is located in the loading position of the loading area, and the first driverless transport vehicle is located in the waiting position or loading position adjacent to the second driverless transport vehicle.

3. The method according to claim 2, characterized in that, The method further includes: During the loading process of the second driverless transport vehicle, the real-time carrying capacity of the second driverless transport vehicle is detected; If the real-time load is less than a first preset value, a prompt message is generated to remind the excavator that loading is not complete. Alternatively, if the real-time load is equal to a first preset value, a notification message is generated to remind the excavator that loading has been completed. Alternatively, if the real-time load exceeds a first preset value, a notification message is generated to remind the excavator that its load has been overloaded.

4. The method according to claim 1, characterized in that, When the second driverless transport vehicle is in a carrying state, the plurality of first driverless transport vehicles travel in the opposite direction or in the same direction as the second driverless transport vehicle, and the plurality of first driverless transport vehicles are located within a preset range of the second driverless transport vehicle. The method further includes: During the operation of the second driverless transport vehicle, the real-time carrying capacity of the second driverless transport vehicle is detected; When the real-time carrying capacity exceeds a second preset value, a prompt message is generated to remind the second unmanned transport vehicle, which is at least one of the first unmanned transport vehicles, that it is overloaded.

5. The method according to claim 4, characterized in that, The method further includes: Control the speed of the second driverless transport vehicle to be no greater than a preset speed; And / or, control the distance between the plurality of first unmanned transport vehicles and the second unmanned transport vehicle to be greater than a preset distance.

6. The method according to claim 4, characterized in that, A second load sensing device is installed at the target location on the road; the method further includes: Acquire the first point cloud data obtained by the first load sensing device; During the process of the second unmanned transport vehicle passing the target location, the second point cloud data of the second unmanned transport vehicle is acquired by the second loading volume sensing device; The first point cloud data and the second point cloud data are fused to obtain fused point cloud data, and the real-time carrying capacity of the second unmanned transport vehicle is obtained based on the fused point cloud data.

7. The method according to claim 6, characterized in that, The first load sensing device and the second load sensing device are used to collect point cloud data, and the first load sensing device and the second load sensing device each include a lidar.

8. A load information processing device for an unmanned transport vehicle, characterized in that, The device includes: The vehicle acquisition module is used to acquire multiple unmanned transport vehicles near the second unmanned transport vehicle. The loading capacity information acquisition module is used to acquire the loading capacity sensing devices of the multiple first unmanned transport vehicles, acquire point cloud data of the second unmanned transport vehicle from different angles, fuse and process the point cloud data from different angles to obtain the loading capacity information of the second unmanned transport vehicle, and the loading capacity information is used to obtain the material loading capacity of the second unmanned transport vehicle. The real-time carrying capacity acquisition module is used to obtain the real-time carrying capacity of the second unmanned transport vehicle based on the carrying capacity information. The information generation module is used to generate a prompt message at least when the real-time carrying capacity does not meet the load conditions.

9. An electronic device, characterized in that, include: At least one processor; Memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1-7.

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