Logistics vehicle loading rate calculation method and device and electronic equipment

Abstract

The invention provides a logistics vehicle loading rate calculation method and device and electronic equipment. The method comprises the steps that 3D point cloud data of all packages are acquired, an edge detection algorithm is adopted to carry out package edge detection processing on the 3D point cloud data of the packages, package contour information is obtained, and the 3D point cloud data are obtained by splicing local 3D point cloud data collected by a plurality of 3D cameras; the type of the package is determined according to the contour information, the volume of the package is determined at least according to the type and the contour information, and the type comprises a regular type and an irregular type; and after the parcel is conveyed to the logistics vehicle, the loading rate data of the logistics vehicle is updated in real time according to the volume of the parcel. The parcel point cloud data is acquired by adopting the 3D measurement technology, the parcel type is determined, and the parcel volume is calculated according to the parcel type, so that the problem of transportation capacity waste of logistics vehicles caused by relatively low parcel volume calculation accuracy in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the technical field of calculation of loading rate of logistics vehicles, and in particular, to a method, device, computer-readable storage medium and electronic device for calculating loading rate of logistics vehicles. Background Art

[0002] With the rapid development of e-commerce and logistics industries, the express delivery industry has developed by leaps and bounds. Express delivery sorting operations are still in the semi-automatic or even manual stage. There are generally uneven distribution of workers at various sorting points and unreasonable deployment of vehicles, which greatly reduces sorting efficiency and increases transportation costs.

[0003] There are usually two ways to calculate vehicle loading rate data, one is to calculate by weight (loading rate data = actual loading weight / vehicle rated loading weight * 100%), and the other is to calculate by volume (loading rate data = actual loading volume / vehicle rated loading volume * 100%). The weight calculation method often results in the vehicle not being fully loaded and wasting space. Combined with the actual situation of the logistics industry, it is more appropriate to use volume measurement. For regular packages, the volume of the package can be calculated or measured relatively accurately. However, in the actual logistics working environment, most packages are special-shaped pieces. The accuracy of the package volume calculated using traditional methods is relatively low. Calculation in regular packaging boxes will result in a measurement result higher than the actual volume, with a large error, which is not conducive to the use of compartment space. The current vehicle loading situation is often only applied to a certain transportation network point, and the data between cross-network points is not interoperable. There is a situation where the vehicle is not full and no longer continues to load. Therefore, there is a waste of transportation resources. Summary of the invention

[0004] The main purpose of the present application is to provide a method, device, computer-readable storage medium and electronic device for calculating the loading rate of a logistics vehicle, so as to at least solve the problem that the accuracy of calculating the package volume in the prior art is relatively low, resulting in waste of transportation capacity of logistics vehicles.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for calculating the loading rate of a logistics vehicle is provided, comprising: acquiring 3D point cloud data of each package, and performing package edge detection processing on the 3D point cloud data of the package using an edge detection algorithm to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras; determining the type of the package according to the contour information, and determining the volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type; after the package is delivered to the logistics vehicle, updating the loading rate data of the logistics vehicle in real time according to the volume of the package.

[0006] Optionally, the volume of the package is determined at least based on the type and the contour information, including: when the type is determined to be the irregular type, voxelizing and dividing the 3D point cloud data of the package according to a preset voxel size to obtain a plurality of voxel grids; and determining the volume of the package at least based on the preset voxel size and the number of voxels, wherein the shape of the voxel grid is a polyhedron.

[0007] Optionally, when it is determined that the type is the irregular type, the 3D point cloud data of the package is voxelized and divided according to a preset voxel size to obtain a plurality of voxel grids, including: constructing a three-dimensional package coordinate system of the package according to the 3D point cloud data, and determining the three-dimensional coordinate center point of the package according to the three-dimensional package coordinate system; and uniformly dividing the three-dimensional package coordinate system space into a plurality of voxel grids, wherein the size of the voxel grid is the preset voxel size.

[0008] Optionally, the volume of the package is determined at least based on the preset voxel size and the number of voxels, wherein the shape of the voxel grid is a polyhedron, including: determining the volume of the package based on a three-dimensional coordinate center point, the number of the voxel grids, and the size of the voxel grids, the three-dimensional coordinate center point being the center point of the package in the three-dimensional coordinate system.

[0009] Optionally, the volume of the package is determined according to the three-dimensional coordinate center point and the number of voxel grids and the size of the voxel grids, including: determining the number of edge voxel grids and non-edge voxel grids according to the three-dimensional coordinate center point and the preset voxel size; determining the volume of the package according to the number of edge voxel grids and the non-edge voxel grids and the preset voxel size.

[0010] Optionally, after updating the loading rate data of the logistics vehicle in real time according to the volume of the package, the method further includes: determining whether the loading rate data is greater than or equal to a preset loading rate of the logistics vehicle; when the loading rate data is greater than or equal to the preset loading rate, generating a prompt message, wherein the prompt message is used to prompt that the logistics vehicle is full.

[0011] Optionally, determining the volume of the package according to the type and the profile information includes: when determining that the type is the regular type, extracting the size information of the package from the profile information, and determining the volume of the package according to the size information; after updating the loading rate data of the logistics vehicle in real time according to the volume of the package, the method also includes: uploading the loading rate data of the entire life cycle of the logistics vehicle to a cloud platform.

[0012] According to another aspect of the present application, a loading rate calculation device for a logistics vehicle is provided, including: an acquisition unit, used to acquire 3D point cloud data of each package, and use an edge detection algorithm to perform package edge detection processing on the 3D point cloud data of the package to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras; determining the type of the package according to the contour information, and determining the volume of the package according to at least the type and the contour information, wherein the type includes a regular type and an irregular type; after the package is delivered to the logistics vehicle, updating the loading rate data of the logistics vehicle in real time according to the volume of the package.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the methods for calculating the loading rate of a logistics vehicle.

[0014] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the loading rate calculation methods for logistics vehicles.

[0015] By applying the technical solution of the present application, the 3D point cloud data of each package is obtained, and the edge detection algorithm is used to perform package edge detection processing on the 3D point cloud data of the package to obtain the package contour information, wherein the 3D point cloud data is obtained by splicing the local 3D point cloud data collected by multiple 3D cameras; the type of the package is determined according to the contour information, and the volume of the package is determined at least according to the type and the contour information, wherein the type includes a regular type and an irregular type; after the package is delivered to the logistics vehicle, the loading rate data of the logistics vehicle is updated in real time according to the volume of the package. By using 3D measurement technology to obtain the package point cloud data, determine the type of the package, and calculate the volume of the package according to the type of the package, the problem of relatively low accuracy in calculating the package volume in the prior art, resulting in waste of transportation capacity of logistics vehicles, is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1A hardware structure block diagram of a mobile terminal for executing a method for calculating a loading rate of a logistics vehicle provided in an embodiment of the present application is shown;

[0018] Figure 2 A schematic diagram of a flow chart of a method for calculating a loading rate of a logistics vehicle provided in accordance with an embodiment of the present application is shown;

[0019] Figure 3 A schematic diagram of the overall framework of a package volume measurement system provided according to an embodiment of the present application is shown;

[0020] Figure 4 A specific structural diagram of the calculation of the loading rate of a logistics vehicle provided in an embodiment of the present application is shown;

[0021] Figure 5 A structural block diagram of a loading rate calculation device for a logistics vehicle provided according to an embodiment of the present application is shown.

[0022] The above drawings include the following reference numerals:

[0023] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0024] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] As introduced in the background technology, the accuracy of calculating the package volume in the prior art is relatively low. In order to solve the problem that the accuracy of calculating the package volume in the prior art is relatively low, resulting in waste of transportation capacity of logistics vehicles, the embodiments of the present application provide a method, device, computer-readable storage medium and electronic device for calculating the loading rate of a logistics vehicle.

[0028] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0029] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for calculating a loading rate of a logistics vehicle according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0030] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for calculating the loading rate of logistics vehicles in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0031] In this embodiment, a method for calculating the loading rate of a logistics vehicle running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] Figure 2 FIG. 1 is a flow chart of a method for calculating a loading rate of a logistics vehicle according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0033] Step S201, obtaining 3D point cloud data of each package, and performing package edge detection processing on the 3D point cloud data of the package using an edge detection algorithm to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras;

[0034] Among them, the 3D point cloud data is collected by a 3D camera. During the package transmission process, multiple 3D cameras installed at different positions on the gantry are used to shoot the package passing through the gantry from different angles. The shooting ranges of all 3D cameras overlap. Each 3D camera has an installation position coordinate. One of the 3D cameras is used as a reference to calculate the relative posture of other cameras relative to the camera. The global 3D point cloud data of the package is obtained by splicing the local 3D point cloud data collected by multiple cameras. Among them, the shooting angle of the 3D camera can be selected in sequence based on the pre-constructed attitude angle matrix to ensure that the camera shooting range fully covers the entire package. Each element in the attitude angle matrix is ​​an attitude angle, the attitude angle difference of adjacent serial numbers is equal, and the element serial number corresponds to the 3D camera one by one. For example, the positions of camera No. 1, camera No. 2... are fixed, but the attitude angles 1, attitude angle 2... used are increased according to the specified step size until the maximum attitude angle allowed by the camera is reached.

[0035] Step S202, determining the type of the package according to the contour information, and determining the volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type;

[0036] Specifically, an edge detection algorithm is used to perform edge detection on the 3D point cloud data to obtain package contour information, and regular packages and irregular packages are classified according to the contour information.

[0037] Step S203, after the above-mentioned package is delivered to the logistics vehicle, the loading rate data of the logistics vehicle is updated in real time according to the volume of the above-mentioned package.

[0038] Specifically, the vehicle's loading rate data is calculated by accumulating the total volume of packages loaded in a vehicle. A comprehensive analysis system based on the voice big model and the CV big model can also be used to provide customers with simple and convenient user operation methods and vehicle loading files. Through the capabilities of the voice big model, users can initiate a query on the vehicle's real-time loading rate through voice (such as direct voice input of "the loading rate of the vehicle with license plate number XXX").

[0039] Through this embodiment, the 3D point cloud data of each package is obtained, and the edge detection algorithm is used to perform package edge detection processing on the 3D point cloud data of the package to obtain the package contour information, wherein the 3D point cloud data is obtained by splicing the local 3D point cloud data collected by multiple 3D cameras; the type of the package is determined according to the contour information, and the volume of the package is determined at least according to the type and the contour information, wherein the type includes a regular type and an irregular type; after the package is delivered to the logistics vehicle, the loading rate data of the logistics vehicle is updated in real time according to the volume of the package. By using 3D measurement technology to obtain the package point cloud data, determine the type of the package, and calculate the volume of the package according to the type of the package, the problem of relatively low accuracy in calculating the package volume in the prior art, resulting in waste of transportation capacity in logistics vehicles, is solved.

[0040] In the specific implementation process, the volume of the above-mentioned package is determined at least according to the above-mentioned type and the above-mentioned contour information, including: when the above-mentioned type is determined to be the above-mentioned irregular type, the 3D point cloud data of the above-mentioned package is voxelized and divided according to a preset voxel size to obtain a plurality of voxel grids; the volume of the above-mentioned package is determined at least according to the above-mentioned preset voxel size and the number of the above-mentioned voxels, wherein the shape of the above-mentioned voxel grid is a polyhedron.

[0041] The shape of the voxel grid can be set to be a cuboid, a tetrahedron or other polyhedrons.

[0042] Specifically, when it is determined that the above-mentioned type is the above-mentioned irregular type, the 3D point cloud data of the above-mentioned package is voxelized and divided according to the preset voxel size to obtain multiple voxel grids, including: constructing a three-dimensional package coordinate system of the above-mentioned package according to the above-mentioned 3D point cloud data, and determining the three-dimensional coordinate center point of the above-mentioned package according to the above-mentioned three-dimensional package coordinate system; uniformly dividing the space of the above-mentioned three-dimensional package coordinate system into multiple above-mentioned voxel grids, wherein the size of the above-mentioned voxel grids is the above-mentioned preset voxel size.

[0043] The process of voxelizing 3D point cloud data in the method can be as follows: taking the package placement plane as the XOY plane and the vertical upward direction as the Y axis, a three-dimensional package coordinate system is established, and the 3D point cloud data is mapped into three-dimensional package coordinate data wrapped in the three-dimensional package coordinate system; the three-dimensional package coordinate system space is evenly divided into multiple voxel grids, and the voxel grids have preset size parameters; then the three-dimensional package coordinates located in the same voxel grid are calculated corresponding to the average value of the X, Y, and Z axes, and the obtained average value is used as the coordinate value to generate new package coordinate data in the three-dimensional package coordinate system, and the new package coordinates in the new package coordinate data correspond to the voxel grids one by one.

[0044] More specifically, the volume of the package is determined at least based on the preset voxel size and the number of voxels, wherein the shape of the voxel grid is a polyhedron, including: determining the volume of the package based on a three-dimensional coordinate center point, the number of the voxel grids and the size of the voxel grids, the three-dimensional coordinate center point being the center point of the package in the three-dimensional coordinate system.

[0045] The method obtains the center point of a plurality of vertices wrapped in a three-dimensional coordinate system by averaging the coordinates of the vertices wrapped in the three-dimensional coordinate system.

[0046] Furthermore, the volume of the package is determined according to the three-dimensional coordinate center point and the number of the voxel grids and the size of the voxel grids, including: determining the number of edge voxel grids and non-edge voxel grids according to the three-dimensional coordinate center point and the preset voxel size; determining the volume of the package according to the number of edge voxel grids and the non-edge voxel grids and the preset voxel size.

[0047] The method counts the maximum and minimum values ​​of the three-dimensional coordinate center point corresponding to the wrapped vertex in the three-dimensional coordinate system X, Y, and Z axes to determine the coordinate range of the three-dimensional coordinate center point, and determines the edge voxel grid and non-edge voxel grid according to the coordinate range and the size of the voxel grid;

[0048] For edge voxel grids: determine the adjacent voxel grids of each edge voxel grid, and calculate the absolute value of the coordinate difference between the center of the adjacent voxel grid corresponding to each edge voxel grid and the new package coordinates in the edge voxel grid. If the absolute value of the coordinate difference is greater than the difference between the maximum coordinate and the minimum coordinate of a single voxel grid, the edge voxel grid is counted as a single voxel grid. If the absolute value of the coordinate difference is not greater than the difference between the maximum coordinate and the minimum coordinate of a single voxel grid, the edge voxel grid is counted as a half voxel grid.

[0049] For non-edge voxel grids: count the number of each non-edge voxel grid as a single voxel grid.

[0050] The number counting results of all edge voxel grids are added to the number counting results of all non-edge voxel grids to obtain the number of voxels in the package, so as to calculate the volume of each package = the volume of a single voxel grid * the number of voxels.

[0051] Furthermore, after updating the loading rate data of the logistics vehicle in real time according to the volume of the above-mentioned package, the above-mentioned method also includes: determining whether the above-mentioned loading rate data is greater than or equal to the preset loading rate of the above-mentioned logistics vehicle; when the above-mentioned loading rate data is greater than or equal to the above-mentioned preset loading rate, generating a prompt information, wherein the above-mentioned prompt information is used to prompt that the above-mentioned logistics vehicle is full.

[0052] Among them, the preset loading rate can be set to 99%, 98%, 95%, etc., and can be set according to the specific loading requirements of the vehicle.

[0053] The method determines whether the logistics vehicle is full by setting a preset loading rate. If the vehicle is full, the user can be prompted by voice, which can improve the efficiency of loading packages on the vehicle.

[0054] Specifically, the volume of the above-mentioned package is determined according to the above-mentioned type and the above-mentioned contour information, including: when it is determined that the above-mentioned type is the above-mentioned rule type, extracting the size information of the above-mentioned package from the above-mentioned contour information, and determining the volume of the above-mentioned package according to the above-mentioned size information; after updating the loading rate data of the logistics vehicle in real time according to the volume of the above-mentioned package, the above-mentioned method also includes: uploading the above-mentioned loading rate data of the entire life cycle of the above-mentioned logistics vehicle to the cloud platform.

[0055] Among them, for regular packages, the size parameters of the regular shape corresponding to the contour information are directly extracted to calculate the package volume.

[0056] This method uses a comprehensive analysis system based on a voice big model and a CV big model to provide customers with simple and convenient user operation methods and vehicle loading files. Through the capabilities of the voice big model, users can initiate a query on the real-time loading rate of the vehicle by voice (such as directly inputting "the loading rate of the vehicle with license plate number XXX"). Integrating the capabilities of the CV big model, the loading rate data of the vehicle's entire life cycle can be uploaded to the cloud platform in the form of an archive through the license plate recognition algorithm and the ability to search for everything (such as directly inputting photos containing license plates for license plate recognition), and can be searched and queried on the user interface. In this way, a full life cycle archive can be generated for each transport vehicle, all video data can be connected, and the license plate number can be searched. The real-time loading rate of the vehicle can be queried at any time. The real-time loading rate of the vehicle can also be used for overall scheduling and optimization of transportation resources.

[0057] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the method for calculating the loading rate of the logistics vehicle of the present application will be described in detail below in combination with specific embodiments.

[0058] This embodiment relates to a specific method for calculating the loading rate of logistics vehicles. In view of the current situation of inaccurate package volume measurement, this system uses large models, AI and 3D measurement technology to accurately measure the volume of regular packages and irregular packages, thereby accurately calculating the vehicle loading rate. Combined with the large model capability, the vehicle information is identified through the license plate recognition algorithm, and the real-time status of the vehicle can be easily queried through natural language. By mounting the professional knowledge base of logistics transportation item information and outlet information, based on the route planning model, the vehicle transportation route is recommended to achieve the purpose of maximizing the utilization of transportation resources. Specifically including the following contents:

[0059] This embodiment includes a package volume measurement system, a three-dimensional laser imaging measurement system, and a comprehensive analysis system based on a large speech model and a large CV model.

[0060] The package volume measurement system is used to measure the volume of the packages loaded on the vehicle. By accumulating the total volume of the packages loaded on a vehicle, the vehicle loading rate is calculated: vehicle loading rate = total volume of the packages loaded on the vehicle / vehicle volume. The overall framework diagram of the package volume measurement system is as follows Figure 3 shown.

[0061] The package volume measurement system includes: 3D camera, industrial computer, gantry. The 3D camera is installed on the gantry and set up above the conveyor belt. Figure 4 As shown, the workflow is:

[0062] During the package transmission process, multiple 3D cameras installed at different positions on the gantry are used to shoot the package passing through the gantry from different angles. The shooting ranges of all 3D cameras overlap. Each 3D camera has the installation position coordinates. One of the cameras is used as a reference to calculate the relative posture of other cameras relative to the camera. The global 3D point cloud data of the package is obtained by splicing the local 3D point cloud data collected by multiple cameras.

[0063] Among them, the shooting angle of the 3D camera can be selected in sequence based on the pre-built attitude angle matrix to ensure that the camera shooting range fully covers the entire package. Each element in the attitude angle matrix is ​​an attitude angle, the difference between the attitude angles of adjacent numbers is equal, and the element numbers correspond to the 3D cameras one by one. For example, the positions of camera No. 1, camera No. 2, etc. are fixed, but the attitude angles 1, attitude angle 2, etc. used are increased according to the specified step size until the maximum attitude angle allowed by the camera is reached.

[0064] The edge detection algorithm is used to perform edge detection on the 3D point cloud data to obtain the package contour information, and regular packages and irregular packages are classified according to the contour information.

[0065] For regular packages, the size parameters of the regular shape corresponding to the contour information are directly extracted to calculate the package volume.

[0066] For irregular parcels, the volume is calculated by voxelizing the 3D point cloud data: the global 3D point cloud data of each parcel is divided into multiple voxels according to the custom voxel size, and the volume of each parcel is calculated = the volume of a single voxel * the number of voxels. The shape of the voxel can be a cuboid, tetrahedron, etc.

[0067] The process of voxelizing 3D point cloud data can be as follows: taking the package placement plane as the XOY plane and the vertical upward direction as the Y axis, a three-dimensional package coordinate system is established, and the 3D point cloud data is mapped into three-dimensional package coordinate data wrapped in the three-dimensional package coordinate system; the three-dimensional package coordinate system space is evenly divided into multiple voxel grids, and the voxel grids have preset size parameters; then the three-dimensional package coordinates located in the same voxel grid are calculated corresponding to the average value of the X, Y, and Z axes, and the obtained average value is used as the coordinate value to generate new package coordinate data in the three-dimensional package coordinate system, and the new package coordinates in the new package coordinate data correspond to the voxel grids one by one.

[0068] The package volume calculation process can be:

[0069] Count the maximum and minimum values ​​of the new package coordinates corresponding to the X, Y, and Z axes to determine the coordinate range of the new package coordinate data, and determine the edge voxel grid and the non-edge voxel grid according to the coordinate range;

[0070] For edge voxel grids: determine the adjacent voxel grids of each edge voxel grid, and calculate the absolute value of the coordinate difference between the center of the adjacent voxel grid corresponding to each edge voxel grid and the new package coordinates in the edge voxel grid. If the absolute value of the coordinate difference is greater than the difference between the maximum coordinate and the minimum coordinate of a single voxel grid, the edge voxel grid is counted as a single voxel grid. If the absolute value of the coordinate difference is not greater than the difference between the maximum coordinate and the minimum coordinate of a single voxel grid, the edge voxel grid is counted as a half voxel grid.

[0071] For non-edge voxel grids: count the number of each non-edge voxel grid as a single voxel grid.

[0072] The number counting results of all edge voxel grids are added to the number counting results of all non-edge voxel grids to obtain the number of voxels in the package, so as to calculate the volume of each package = the volume of a single voxel grid * the number of voxels.

[0073] When the vehicle arrives at the loading dock, it is empty and the rated volume of the vehicle can be directly used for loading rate calculation.

[0074] The current vehicle loading rate is promptly fed back by calculating the continuously accumulated package volume and the car compartment volume obtained in advance (i.e. the rated volume of the vehicle).

[0075] The comprehensive analysis system based on the voice big model and the CV big model is used to provide customers with simple and convenient user operation methods and vehicle loading files. Through the capabilities of the voice big model, users can initiate a query of the vehicle's real-time loading rate through voice (such as directly inputting "the loading rate of the vehicle with license plate number XXX"). Integrating the capabilities of the CV big model, the loading rate data of the vehicle's entire life cycle can be archived through the license plate recognition algorithm and the all-things search capability (such as directly inputting photos containing license plates for license plate recognition), and can be searched and queried on the user interface.

[0076] This embodiment uses 3D point cloud edge detection to determine whether the package shape is regular, and 3D point cloud voxelization to calculate the volume of irregular packages to accumulate the volume of loaded packages in real time, making the vehicle loading rate measurement more accurate; and adopts a fusion semantic big model to provide simple and convenient interaction for enterprise users; in addition, it integrates the visual big model and generates a full life cycle file for each transport vehicle based on the big model's ability to search for everything. By connecting all video data and searching for license plates, you can check the real-time loading rate of the vehicle at any time. The real-time loading rate of the vehicle can also be used for overall scheduling and optimization of transportation resources.

[0077] The embodiment of the present application also provides a loading rate calculation device for a logistics vehicle. It should be noted that the loading rate calculation device for a logistics vehicle in the embodiment of the present application can be used to execute the loading rate calculation method for a logistics vehicle provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0078] The following is an introduction to the loading rate calculation device for logistics vehicles provided in an embodiment of the present application.

[0079] Figure 5 Schematic diagram of a loading rate calculation device for a logistics vehicle according to an embodiment of the present application. Figure 5 As shown, the device comprises:

[0080] An acquisition unit 51 is used to acquire 3D point cloud data of each package, and perform package edge detection processing on the 3D point cloud data of the package using an edge detection algorithm to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras;

[0081] A first determining unit 52 is used to determine the type of the package according to the contour information, and determine the volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type;

[0082] The updating unit 53 is used to update the loading rate data of the logistics vehicle in real time according to the volume of the above-mentioned parcel after the above-mentioned parcel is delivered to the logistics vehicle.

[0083] In this embodiment, the acquisition unit is used to acquire the 3D point cloud data of each package, and use the edge detection algorithm to perform package edge detection processing on the 3D point cloud data of the package to obtain the package contour information, wherein the 3D point cloud data is obtained by splicing the local 3D point cloud data collected by multiple 3D cameras; the first determination unit is used to determine the type of the package according to the contour information, and determine the volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type; the update unit is used to update the loading rate data of the logistics vehicle in real time according to the volume of the package after the package is delivered to the logistics vehicle. By using 3D measurement technology to acquire the package point cloud data, determine the type of the package, and calculate the volume of the package according to the type of the package, the problem of relatively low accuracy in calculating the package volume in the prior art, resulting in waste of transportation capacity of the logistics vehicle, is solved.

[0084] As an optional scheme, the first determination unit includes a division processing module and a first determination module; the division processing module is used to, when determining that the above-mentioned type is the above-mentioned irregular type, perform voxel division processing on the 3D point cloud data of the above-mentioned package according to a preset voxel size to obtain multiple voxel grids; the first determination module is used to determine the volume of the above-mentioned package based on at least the above-mentioned preset voxel size and the number of the above-mentioned voxels, wherein the shape of the above-mentioned voxel grid is a polyhedron.

[0085] An optional solution, the division processing module includes a first determination submodule and a division submodule; the first determination submodule is used to construct a three-dimensional package coordinate system of the above-mentioned package according to the above-mentioned 3D point cloud data, and determine the three-dimensional coordinate center point of the above-mentioned package according to the above-mentioned three-dimensional package coordinate system; the division submodule is used to evenly divide the above-mentioned three-dimensional package coordinate system space into multiple above-mentioned voxel grids, wherein the size of the above-mentioned voxel grid is the above-mentioned preset voxel size.

[0086] In an optional solution, the first determination module includes a second determination submodule, which is used to determine the volume of the above-mentioned package based on the three-dimensional coordinate center point and the number of the above-mentioned voxel grids and the size of the above-mentioned voxel grids. The above-mentioned three-dimensional coordinate center point is the center point of the above-mentioned package in the three-dimensional coordinate system.

[0087] An optional solution is that the second determination submodule includes a third determination submodule and a fourth determination submodule; the third determination submodule is used to determine the number of edge voxel grids and non-edge voxel grids based on the above-mentioned three-dimensional coordinate center point and the above-mentioned preset voxel size; the fourth determination submodule is used to determine the volume of the above-mentioned package based on the number of the above-mentioned edge voxel grids and the above-mentioned non-edge voxel grids and the above-mentioned preset voxel size.

[0088] An optional solution, the device also includes a second determination unit and a generation unit; the second determination unit is used to determine whether the loading rate data is greater than or equal to the preset loading rate of the above-mentioned logistics vehicle after the loading rate data of the logistics vehicle is updated in real time according to the volume of the above-mentioned package; the generation unit is used to generate a prompt message when the loading rate data is greater than or equal to the above-mentioned preset loading rate, wherein the above-mentioned prompt message is used to prompt that the above-mentioned logistics vehicle is full.

[0089] An optional solution, the first determination unit includes a second determination module, which is used to extract the size information of the above-mentioned package from the above-mentioned contour information when it is determined that the above-mentioned type is the above-mentioned rule type, and determine the volume of the above-mentioned package based on the above-mentioned size information; the device also includes an uploading unit, which is used to upload the above-mentioned loading rate data of the above-mentioned logistics vehicle throughout its life cycle to the cloud platform after updating the loading rate data of the logistics vehicle in real time according to the volume of the above-mentioned package.

[0090] The above-mentioned loading rate calculation device for logistics vehicles includes a processor and a memory, and the above-mentioned acquisition unit, the first determination unit and the update unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in any combination.

[0091] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to solve the problem that the accuracy of calculating the volume of packages in the existing technology is relatively low, resulting in waste of transportation capacity in logistics vehicles.

[0092] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0093] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the loading rate calculation method of the logistics vehicle.

[0094] Specifically, the method for calculating the loading rate of logistics vehicles includes:

[0095] Step S201, obtaining 3D point cloud data of each package, and performing package edge detection processing on the 3D point cloud data of the package using an edge detection algorithm to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras;

[0096] Step S202, determining the type of the package according to the contour information, and determining the volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type;

[0097] Step S203, after the above-mentioned package is delivered to the logistics vehicle, the loading rate data of the logistics vehicle is updated in real time according to the volume of the above-mentioned package.

[0098] An embodiment of the present invention provides a processor, which is used to run a program, wherein the loading rate calculation method of the logistics vehicle is executed when the program is running.

[0099] Specifically, the method for calculating the loading rate of logistics vehicles includes:

[0100] Step S201, obtaining 3D point cloud data of each package, and performing package edge detection processing on the 3D point cloud data of the package using an edge detection algorithm to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras;

[0101] Step S202, determining the type of the package according to the contour information, and determining the volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type;

[0102] Step S203, after the above-mentioned package is delivered to the logistics vehicle, the loading rate data of the logistics vehicle is updated in real time according to the volume of the above-mentioned package.

[0103] An embodiment of the present invention provides an electronic device, the device comprising a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0104] Step S201, obtaining 3D point cloud data of each package, and performing package edge detection processing on the 3D point cloud data of the package using an edge detection algorithm to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras;

[0105] Step S202, determining the type of the package according to the contour information, and determining the volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type;

[0106] Step S203, after the above-mentioned package is delivered to the logistics vehicle, the loading rate data of the logistics vehicle is updated in real time according to the volume of the above-mentioned package.

[0107] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0108] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0109] Step S201, obtaining 3D point cloud data of each package, and performing package edge detection processing on the 3D point cloud data of the package using an edge detection algorithm to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras;

[0110] Step S202, determining the type of the package according to the contour information, and determining the volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type;

[0111] Step S203, after the above-mentioned package is delivered to the logistics vehicle, the loading rate data of the logistics vehicle is updated in real time according to the volume of the above-mentioned package.

[0112] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0113] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0114] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0115] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0117] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0118] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0119] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0120] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0121] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0122] 1) A method for calculating the loading rate of a logistics vehicle of the present application includes: obtaining 3D point cloud data of each package, and using an edge detection algorithm to perform package edge detection processing on the 3D point cloud data of the package to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras; determining the type of the package according to the contour information, and determining the volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type; after the package is delivered to the logistics vehicle, updating the loading rate data of the logistics vehicle in real time according to the volume of the package. By using 3D measurement technology to obtain package point cloud data, determine the type of the package, and calculate the volume of the package according to the type of the package, the problem of relatively low accuracy in calculating the volume of the package in the prior art, which leads to waste of transportation capacity in logistics vehicles, is solved.

[0123] 2) A loading rate calculation device for a logistics vehicle of the present application includes: an acquisition unit, used to acquire 3D point cloud data of each package, and use an edge detection algorithm to perform package edge detection processing on the 3D point cloud data of the package to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras; a first determination unit, used to determine the type of the package according to the contour information, and determine the volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type; an update unit, used to update the loading rate data of the logistics vehicle in real time according to the volume of the package after the package is delivered to the logistics vehicle. By using 3D measurement technology to acquire package point cloud data, determine the type of the package, and calculate the volume of the package according to the type of the package, the problem of relatively low accuracy in calculating the volume of the package in the prior art, which leads to waste of transportation capacity in logistics vehicles, is solved.

[0124] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for calculating the loading rate of a logistics vehicle, characterized in that: include: Acquire 3D point cloud data of each package, and use an edge detection algorithm to perform package edge detection processing on the 3D point cloud data of the package to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras; Determining the type of the package according to the contour information, and determining the volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type; After the parcel is delivered to the logistics vehicle, the loading rate data of the logistics vehicle is updated in real time according to the volume of the parcel.

2. The method according to claim 1, characterized in that Determining the volume of the package at least according to the type and the profile information, comprising: When it is determined that the type is the irregular type, the wrapped 3D point cloud data is subjected to voxel division processing according to a preset voxel size to obtain a plurality of voxel grids; The volume of the package is determined at least according to the preset voxel size and the number of the voxels, wherein the shape of the voxel grid is a polyhedron.

3. The method according to claim 2, characterized in that When it is determined that the type is the irregular type, the wrapped 3D point cloud data is subjected to voxel division processing according to a preset voxel size to obtain a plurality of voxel grids, including: Constructing a three-dimensional package coordinate system of the package according to the 3D point cloud data, and determining a three-dimensional coordinate center point of the package according to the three-dimensional package coordinate system; The three-dimensional wrapping coordinate system space is evenly divided into a plurality of voxel grids, wherein the size of the voxel grid is the preset voxel size.

4. The method according to claim 2, characterized in that: The volume of the package is determined at least according to the preset voxel size and the number of voxels, wherein the shape of the voxel grid is a polyhedron, including: The volume of the package is determined according to a three-dimensional coordinate center point, the number of the voxel grids, and the size of the voxel grids. The three-dimensional coordinate center point is the center point of the package in the three-dimensional coordinate system.

5. The method according to claim 4, characterized in that Determining the volume of the package according to the three-dimensional coordinate center point and the number of the voxel grids and the size of the voxel grids includes: Determining the number of edge voxel grids and non-edge voxel grids according to the three-dimensional coordinate center point and the preset voxel size; The volume of the package is determined according to the number of the edge voxel grids and the non-edge voxel grids and the preset voxel size.

6. The method according to claim 1, characterized in that After updating the loading rate data of the logistics vehicle in real time according to the volume of the package, the method further includes: Determining whether the loading rate data is greater than or equal to a preset loading rate of the logistics vehicle; When the loading rate data is greater than or equal to the preset loading rate, a prompt message is generated, wherein the prompt message is used to prompt that the logistics vehicle is full.

7. The method according to claim 1, characterized in that Determining the volume of the package according to the type and the profile information includes: In the case where it is determined that the type is the regular type, extracting the size information of the package from the contour information, and determining the volume of the package according to the size information; After updating the loading rate data of the logistics vehicle in real time according to the volume of the package, the method further includes: The loading rate data of the entire life cycle of the logistics vehicle is uploaded to the cloud platform.

8. A loading rate calculation device for a logistics vehicle, characterized in that: include: an acquisition unit, used to acquire 3D point cloud data of each package, and perform package edge detection processing on the 3D point cloud data of the package using an edge detection algorithm to obtain package contour information, wherein the 3D point cloud data is obtained by splicing local 3D point cloud data collected by multiple 3D cameras; a first determining unit, configured to determine a type of the package according to the contour information, and determine a volume of the package at least according to the type and the contour information, wherein the type includes a regular type and an irregular type; An updating unit is used to update the loading rate data of the logistics vehicle in real time according to the volume of the parcel after the parcel is delivered to the logistics vehicle.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for calculating the loading rate of a logistics vehicle according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for calculating the loading rate of a logistics vehicle according to any one of claims 1 to 7.

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