Loading ratio calculation system, loading ratio calculation method, and loading ratio calculation program

Abstract

This makes it possible to easily measure the loading rate of the cargo compartment of mobile vehicles such as trucks. [Solution] The data acquisition unit 111 acquires 3D point cloud data of the cargo bed area obtained by measuring the cargo bed area from the measurement side of the cargo bed. The occupancy rate calculation unit 112 sets each cross-section of the cargo bed area at unit distances in the depth direction, which is the direction away from the measurement side, as a target cross-section, and for each target cross-section, calculates the occupancy rate as the ratio of the area where point data included in the 3D point cloud data exists on the measurement side of the target cross-section to the area of ​​the cargo bed surface perpendicular to the depth direction. The loading rate identification unit 113 identifies the average value of the occupancy rates calculated for each cross-section at unit distances as the loading rate of the cargo bed.

Description

Technical Field

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[0001] The present disclosure relates to a technique for measuring a loading rate, which is the ratio of the amount of cargo loaded on the loading platform of a truck or the like.

Background Art

[0002] From the perspective of environmental load and the like, it is recommended to increase the loading rate of the loading platform of a moving body such as a truck. Many companies engaged in transportation calculate and manage the loading rate by estimation based on the state of the cargo on the loading platform. However, the accuracy of the calculated loading rate is low in estimation, and it also takes time to calculate.

[0003] Patent Document 1 describes a technique for calculating the loading amount by measuring the distance to the cargo using a three-dimensional sensor fixed to the ceiling position of the cargo compartment of a truck and distinguishing between the space where the cargo exists and the empty space. <00000!3>

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the technique described in Patent Document 1, the loading amount cannot be calculated unless a three-dimensional sensor is installed on the ceiling of the cargo compartment of the truck. However, since the installation of the three-dimensional sensor is costly and time-consuming, it may be difficult to install three-dimensional sensors on all trucks. An object of the present disclosure is to enable easy measurement of the loading rate of the cargo compartment of a moving body such as a truck.

Means for Solving the Problems

[0006] The loading rate calculation system according to the present disclosure is A data acquisition unit that acquires 3D point cloud data of the cargo area, which is the area of ​​the cargo bed of a mobile vehicle, by measuring the cargo bed from the measurement side using a measurement terminal, A unit for calculating occupancy rates sets each cross-section of the cargo bed region at unit distances in the depth direction, which is the direction away from the measurement side, as a target cross-section, and calculates the occupancy rate as the ratio of the area where point data included in the 3D point cloud data acquired by the measurement unit exists on the measurement side of the target cross-section, relative to the area of ​​the surface of the cargo bed perpendicular to the depth direction. A loading rate determination unit identifies the average value of the occupancy rate calculated for each cross section at each unit distance by the occupancy rate calculation unit as the loading rate of the cargo bed. It is equipped with. [Effects of the Invention]

[0007] This disclosure allows for the calculation of the loading rate by acquiring 3D point cloud data of the cargo area using a measurement terminal. Therefore, there is no need to install a 3D sensor on the ceiling of the truck's cargo compartment, and the loading rate of the cargo compartment can be measured simply. [Brief explanation of the drawing]

[0008] [Figure 1] A diagram illustrating the configuration of the load factor calculation system 100 according to Embodiment 1. [Figure 2] Configuration diagram of the load factor calculation device 10 according to Embodiment 1. [Figure 3] Configuration diagram of the measurement terminal 20 according to Embodiment 1. [Figure 4] Configuration diagram of the report output terminal 30 according to Embodiment 1. [Figure 5] A flowchart of the processing of the loading rate calculation system 100 according to Embodiment 1. [Figure 6] An explanatory diagram of the method for specifying the surface portion 42 of the cargo bed area 41 according to Embodiment 1. [Figure 7] An explanatory diagram of the method for calculating the surface area S according to Embodiment 1. [Figure 8]An explanatory diagram of a cross-section 43 of the cargo bed area 41 according to Embodiment 1. [Figure 9] An explanatory diagram of the small region 44 according to Embodiment 1. [Figure 10] An explanatory diagram of the region where point data according to Embodiment 1 exists. [Figure 11] Diagram illustrating the report data according to Embodiment 1. [Figure 12] An explanatory diagram of a cross-section 43 of the cargo bed area 41 according to Embodiment 2. [Modes for carrying out the invention]

[0009] Embodiment 1. ***Explanation of the structure*** Referring to Figure 1, the configuration of the load factor calculation system 100 according to Embodiment 1 will be described. The load factor calculation system 100 comprises a load factor calculation device 10, a measurement terminal 20, and a report output terminal 30. The load factor calculation device 10 is connected to the measurement terminal 20 and the report output terminal 30 via a transmission line. The transmission line is, in specific examples, the internet. The loading rate calculation device 10 is a computer such as a cloud server that measures the loading rate, which is the percentage of cargo loaded on the cargo bed, using 3D point cloud data of the cargo bed area, which is the area of ​​the cargo bed of a moving object. In Embodiment 1, the moving object is assumed to be a truck. The measurement terminal 20 is a device for measuring 3D point cloud data of the cargo bed area. A specific example of the measurement terminal 20 is a smartphone equipped with a measurement device that collects 3D point cloud data, such as LiDAR. LiDAR stands for Light Detection and Ranging. For example, the measurement terminal 20 is a terminal used by workers loading cargo onto the cargo bed. The report output terminal 30 is a computer such as a PC that displays a report showing the loading rate, etc., measured by the loading rate calculation device 10. PC stands for Personal Computer. For example, the report output terminal 30 is a terminal used by cargo transport managers, etc.

[0010] Referring to FIG. 2, the configuration of the loading rate calculation device 10 according to Embodiment 1 will be described. The loading rate calculation device 10 includes hardware such as a processor 11, a memory 12, a storage 13, and a communication interface 14. The processor 11 is connected to other hardware via signal lines and controls these other hardware.

[0011] As functional components, the loading rate calculation device 10 includes a data acquisition unit 111, an occupancy rate calculation unit 112, a loading rate determination unit 113, and a report generation unit 114. The functions of each functional component of the loading rate calculation device 10 are realized by software. The storage 13 stores a program for realizing the functions of each functional component of the loading rate calculation device 10. This program is read into the memory 12 by the processor 11 and executed by the processor 11. Thereby, the functions of each functional component of the loading rate calculation device 10 are realized.

[0012] Referring to FIG. 3, the configuration of the measurement terminal 20 according to Embodiment 1 will be described. The measurement terminal 20 includes hardware such as a processor 21, a memory 22, a storage 23, a communication interface 24, a camera 25, and a measuring device 26. The processor 21 is connected to other hardware via signal lines and controls these other hardware.

[0013] As functional components, the measurement terminal 20 includes a photographing unit 211, a measurement unit 212, a display unit 213, a reception unit 214, and a communication unit 215. The functions of each functional component of the measurement terminal 20 are realized by software. The storage 23 stores a program for realizing the functions of each functional component of the measurement terminal 20. This program is read into the memory 22 by the processor 21 and executed by the processor 21. Thereby, the functions of each functional component of the measurement terminal 20 are realized.

[0014] Referring to FIG. 4, the configuration of the report output terminal 30 according to Embodiment 1 will be described. The report output terminal 30 comprises hardware including a processor 31, memory 32, storage 33, and a communication interface 34. The processor 31 is connected to and controls the other hardware via signal lines.

[0015] The report output terminal 30 includes a communication unit 311 and a display unit 312 as functional components. The functions of each functional component of the report output terminal 30 are implemented by software. Storage 33 stores programs that implement the functions of each functional component of the report output terminal 30. These programs are loaded into memory 32 by the processor 31 and executed by the processor 31. This enables the implementation of the functions of each functional component of the report output terminal 30.

[0016] Processors 11, 21, and 31 are integrated circuits (ICs) that perform processing. IC stands for Integrated Circuit. Specific examples of processors 11, 21, and 31 include CPUs, DSPs, and GPUs. CPU stands for Central Processing Unit. DSP stands for Digital Signal Processor. GPU stands for Graphics Processing Unit.

[0017] Memory 12, 22, and 32 are memory devices that temporarily store data. Specific examples of memory 12, 22, and 32 include SRAM and DRAM. SRAM stands for Static Random Access Memory. DRAM stands for Dynamic Random Access Memory.

[0018] Storage 13, 23, and 33 are storage devices for storing data. Specific examples of storage 13, 23, and 33 include HDDs and SSDs. HDD stands for Hard Disk Drive. SSD stands for Solid State Drive. Storage 13, 23, and 33 may also be portable recording media such as SD® memory cards, CompactFlash®, NAND flash, flexible disks, optical disks, compact disks, Blu-ray® discs, and DVDs. SD stands for Secure Digital. DVD stands for Digital Versatile Disk.

[0019] Communication interfaces 14, 24, and 34 are interfaces for communicating with external devices. Specific examples of communication interfaces 14, 24, and 34 include Ethernet®, USB, and HDMI® ports. USB stands for Universal Serial Bus. HDMI stands for High-Definition Multimedia Interface.

[0020] Camera 25 is an optical image acquisition device.

[0021] The measurement device 26 is a device that collects 3D point cloud data of a target region by emitting light into the target region and receiving the reflected wave. A specific example of the measurement device 26 is a LiDAR.

[0022] In Figure 2, only one processor 11 was shown. However, there may be multiple processors 11, and multiple processors 11 may work together to execute programs that implement each function. Similarly, in Figure 3, only one processor 21 was shown. However, there may be multiple processors 21, and multiple processors 21 may work together to execute programs that implement each function. Similarly, in Figure 4, only one processor 31 was shown. However, there may be multiple processors 31, and multiple processors 31 may work together to execute programs that implement each function.

[0023] In Embodiment 1, as shown in Figure 1, the load factor calculation system 100 is assumed to have a configuration comprising a load factor calculation device 10, a measurement terminal 20, and a report output terminal 30. However, the load factor calculation system 100 is not limited to this configuration. For example, the load factor calculation system 100 may not include the load factor calculation device 10, and the functions of the load factor calculation device 10 may be assigned to the measurement terminal 20. Also, for example, the load factor calculation system 100 may not include the report output terminal 30, and the functions of the report output terminal 30 may be assigned to the measurement terminal 20.

[0024] ***Explanation of operation*** Referring to Figures 5 to 11, the operation of the load factor calculation system 100 according to Embodiment 1 will be explained. The operating procedure of the load factor calculation system 100 according to Embodiment 1 corresponds to the load factor calculation method according to Embodiment 1. Furthermore, the program that implements the operation of the load factor calculation system 100 according to Embodiment 1 corresponds to the load factor calculation program according to Embodiment 1.

[0025] Referring to Figure 5, the processing flow of the loading rate calculation system 100 according to Embodiment 1 will be explained. (Step S101: Image capture process) The imaging unit 211 of the measurement terminal 20 uses a camera 25 to photograph the truck bed and acquire image data of the truck bed. In Embodiment 1, the imaging unit 211 photographs the truck bed from either side of the truck bed, which is the measurement side, and acquires image data.

[0026] (Step S102: Measurement process) The measurement unit 212 of the measurement terminal 20 measures the cargo bed area 41, which is the area of ​​the truck bed, using the measurement device 26 and acquires 3D point cloud data of the cargo bed area 41. In Embodiment 1, the measurement unit 212 measures the cargo bed area 41 from the measurement side of the side of the cargo bed and acquires 3D point cloud data. In this case, depending on the size of the cargo bed area 41, it may be difficult to obtain 3D point cloud data of the entire cargo bed area 41 in a single measurement. In this case, the user using the measurement terminal 20 can obtain 3D point cloud data of the entire cargo bed area 41 by repeatedly taking measurements while moving as needed.

[0027] Furthermore, the imaging unit 211 and the measurement unit 212 of the measurement terminal 20 can simultaneously acquire image data and 3D point cloud data, thereby enabling the correspondence between pixels of the image data and each point in the 3D point cloud data. Here, it is assumed that pixels of the image data and each point in the 3D point cloud data are already associated.

[0028] (Step S103: Data display processing) The display unit 213 of the measurement terminal 20 displays the image data acquired in step S101 and the 3D point cloud data acquired in step S102. For example, the display unit 213 displays the 3D point cloud data superimposed on the image data.

[0029] (Step S104: Reception Processing) The reception unit 214 of the measurement terminal 20 receives the truck identification information and the specification of the surface portion 42 of the cargo bed area 41 in the data displayed in step S103. The truck identification information is, for example, information that can identify the name of the truck. Once the truck identification information is identified, the length, width, and depth of the cargo bed area 41 can be determined. The surface portion 42 of the cargo bed area 41 is the surface portion of the cargo bed area 41 that is measured, as shown in Figure 6. Specifically, as shown in Figure 6, the reception unit 214 accepts the designation of each of the four corner points on the surface portion 42 of the rectangular cargo bed area 41. Alternatively, the reception unit 214 accepts the designation of two points located diagonally opposite each other on the surface portion 42 of the rectangular cargo bed area 41. Alternatively, the reception unit 214 may accept the designation of any two points at any two corners on the surface portion 42 of the rectangular cargo bed area 41. Since the vertical size of the cargo bed area 41 can be determined from the truck identification information, if the positions of any two points are known, the positions of the remaining two points can also be determined.

[0030] (Step S105: Data transmission process) The communication unit 215 of the measurement terminal 20 transmits the image data acquired in step S101, the 3D point cloud data acquired in step S102, and the truck identification information and information indicating the surface portion 42 of the cargo bed area 41 received in step S104 to the load rate calculation device 10.

[0031] (Step S106: Receiving process) The data acquisition unit 111 of the load rate calculation device 10 acquires image data, 3D point cloud data, and information indicating the surface portion 42 of the cargo bed area 41 that was transmitted in step S105.

[0032] (Step S107: Surface area calculation process) The occupancy rate calculation unit 112 of the loading rate calculation device 10 uses the 3D point cloud data acquired in step S106 and information indicating the surface portion 42 of the loading area 41 to calculate the surface area S, which is the area of ​​the surface portion 42 of the loading area 41. Three-dimensional point cloud data indicates the three-dimensional position of each point. Therefore, by identifying the point data of the four corners of the surface portion 42 of the loading area 41, the surface area S can be calculated. If the surface portion 42 is a distorted rectangle because the identified point data does not lie on the same plane, the surface area S can be easily calculated by dividing the rectangle into two triangles, as shown in Figure 7.

[0033] (Step S108: Occupancy rate calculation process) As shown in Figure 8, the occupancy rate calculation unit 112 of the load rate calculation device 10 sets each of the cross-sections 43 of the cargo bed area 41 at unit distances in the depth direction as the target cross-section 43. As shown in Figure 8, the depth direction is the direction away from the measurement side. For example, each of the n cross-sections 43 is set as the target cross-section 43. n is an integer of 2 or more. Since the size of the cargo bed in the depth direction is determined from the truck identification information, the number n of cross-sections 43 can also be determined. One way to calculate unit distance is to use the following method, for example: The maximum error in the loading rate is calculated as: Surface area × unit distance / cargo bed volume. • Allowable load capacity error × cargo bed volume / surface area = Allowable load capacity error × size in the depth direction Based on these points, The unit distance should be set so that it is less than or equal to the error of the allowable load factor multiplied by the size in the depth direction.

[0034] The occupancy rate calculation unit 112 calculates the occupancy rate O as the ratio of the area where point data included in the 3D point cloud data exists on the measurement side of the target cross-section 43, relative to the area of ​​the cargo bed surface perpendicular to the depth direction. Here, the area of ​​the cargo bed surface perpendicular to the depth direction is the surface area S calculated in step S107. Here, "perpendicular" includes not only the case of being perfectly perpendicular, but also the case of being approximately perpendicular with a certain error. In Embodiment 1, as shown in Figure 9, the occupancy rate calculation unit 112 divides the target cross-section 43 into a plurality of small regions 44. In Figure 9, the target cross-section 43 is divided into four small regions 44 in both the vertical and horizontal directions. The occupancy rate calculation unit 112 prefers to define the small regions 44 as rectangles whose sides are smaller than the maximum measurement error of the measuring device 26 of the measurement terminal 20. By making the small region 44 a rectangle with sides smaller than the maximum measurement error of the measuring device 26, it is expected that misjudgments will be reduced when determining the region in which point data is identified. The maximum value of the measurement error may be determined from the specifications of the measuring device 26, or it may be determined by prior trial use of the measuring device 26. The occupancy rate calculation unit 112 identifies the target cross-section 43 and the small regions 44 on the measurement side of the target cross-section 43 where the number of point data included in the 3D point cloud data is greater than or equal to a certain threshold as regions where point data included in the 3D point cloud data exists. The threshold number can be set arbitrarily and can be dynamically changed, for example, by increasing the threshold number when the density of point data divided into the small regions 44 is high, or conversely, decreasing the threshold number when the density of point data divided into the small regions 44 is low. Let's explain this in detail with reference to Figure 10. Assume that section 43X is the section 43 in question. Also, assume that the reference number is 2. There are three point data points in sub-region 44A in section 43X. Therefore, sub-region 44A is identified as a region where point data points exist. In addition, there is no point data points in sub-region 44B in section 43X, but there are two point data points in sub-region 44B in section 43Y, which is on the measurement side of section 43X. Sub-region 44B in section 43X is identified as a region where point data points exist. Schematically, the two point data points in sub-region 44B in section 43Y are duplicated in sub-region 44B in section 43X. In this way, point data existing in the target section 43X is processed as also existing in section 43, which is located in the depth direction from section 43X. The occupancy rate calculation unit 112 calculates the occupancy rate O by dividing the total area of ​​the small regions 44 identified as containing point data by the surface area S.

[0035] Identifying a small region 44 where point data exists on the measurement side of the target cross-section 43 as a region containing point data included in the 3D point cloud data is based on the assumption that there is cargo further back in the area where cargo is located on the measurement side. In other words, since cargo is loaded from the back of the truck bed, it is assumed that if there is cargo in the front, there is also cargo in the back.

[0036] (Step S109: Loading rate determination process) The loading rate determination unit 113 of the loading rate calculation device 10 determines the average value of the occupancy rate O calculated for each cross section at each unit distance in step S108 as the loading rate L of the cargo bed. In step S108, point data existing in the target section 43X is treated as also existing in section 43 located in the depth direction from section 43X. Therefore, the occupancy rate O tends to be higher for section 43 that is closer to the measurement terminal 20 in the depth direction. Accordingly, in step S109, the average value of the occupancy rate O is taken to calculate the loading rate L of the cargo bed.

[0037] (Step S110: Report generation process) As shown in Figure 11, the report generation unit 114 of the load rate calculation device 10 generates report data that includes the load rate L identified in step S109, drawing data representing the area of ​​the cargo bed and the cargo loaded on the cargo bed, and truck identification information received in step S104. Figure 11 shows the state in which the report data is displayed on the measurement terminal 20. The report generation unit 114 writes the report data to the storage 13. In Figure 11, the plotting data shows a two-dimensional representation of the cargo bed and cargo area. However, the report generation unit 114 may also generate plotting data to create a three-dimensional model representing the cargo bed and cargo area in three dimensions. This makes it easier to understand the available space on the near side.

[0038] (Step S111: Report submission process) The report generation unit 114 of the load rate calculation device 10 transmits the report data generated in step S110 to the measurement terminal 20.

[0039] (Step S112: Report display process) The communication unit 215 of the measurement terminal 20 acquires the report data transmitted in step S111. The display unit 213 of the measurement terminal 20 displays the acquired report data.

[0040] Furthermore, when instructed by the user, the communication unit 311 of the report output terminal 30 reads and acquires the report data generated in step S110 from the storage 13. The display unit 312 of the report output terminal 30 displays the acquired report data.

[0041] In the above description, it is assumed that the position of the surface portion 42 of the cargo bed area 41 is specified. However, the surface portion 42 of the cargo bed area 41 may be recognized from image data. It is also possible to recognize the surface portion 42 of the cargo bed area 41 using existing image recognition technology. The process of recognizing the surface portion 42 of the cargo bed area 41 may be performed by the measurement terminal 20 or by the load rate calculation device 10.

[0042] Furthermore, in the above explanation, the size of the cargo bed in the depth direction was determined by specifying the truck's identification information. However, it is also possible to measure the size of the cargo bed in the depth direction by acquiring 3D point cloud data from the rear side of the cargo bed using the measuring device 26 of the measuring terminal 20.

[0043] In the processing flow shown in Figure 5, image data is acquired in step S101, the pixels of the image data are associated with each point in the 3D point cloud data in step S102, and the image data and the 3D point cloud data acquired in step S102 are displayed in step S103, so that the specification of the surface portion 42 of the cargo bed area 41 is accepted in step S104. However, it is also possible to configure the system so that only the 3D point cloud data is displayed in step S103, and the specification of the surface portion 42 is accepted in step S104. In that case, the acquisition of image data in step S101 is unnecessary, and the process of associating the pixels of the image data with each point in the 3D point cloud data in step S102 is not performed.

[0044] ***Effects of Embodiment 1*** As described above, the loading rate calculation system 100 according to Embodiment 1 can calculate the loading rate by acquiring 3D point cloud data of the cargo bed area using the measurement terminal 20. Therefore, there is no need to install a 3D sensor on the ceiling of the truck's cargo compartment, and the loading rate of the cargo compartment can be measured easily.

[0045] Embodiment 2. Embodiment 2 differs from Embodiment 1 in that it obtains 3D point cloud data by measuring the cargo bed area 41 from both sides of the cargo bed's side. Embodiment 2 will explain this difference, and the same points will not be explained.

[0046] ***Explanation of operation*** Referring to Figure 5, the processing flow of the loading rate calculation system 100 according to Embodiment 1 will be explained. The processes from step S105 to step S107 and from step S109 to step S112 are the same as in Embodiment 1.

[0047] (Step S101: Image capture process) The imaging unit 211 of the measurement terminal 20 takes images of the cargo bed from both sides, using each side as the measurement side, and acquires image data. Here, image data taken from the right side is called the right image, and image data taken from the left side is called the left image. For example, the right side is the side on the driver's side, and the left side is the side on the passenger side.

[0048] (Step S102: Measurement process) The measurement unit 212 of the measurement terminal 20 measures the cargo bed area 41 from both sides, using each side as the measurement side, and acquires 3D point cloud data. Here, the 3D point cloud data acquired by measuring from the right side is called the right point cloud data, and the 3D point cloud data acquired by measuring from the left side is called the left point cloud data.

[0049] (Step S103: Data display processing) The display unit 213 of the measurement terminal 20 displays image data and 3D point cloud data. For example, the display unit 213 displays the right point cloud data superimposed on the right image, and the left point cloud data superimposed on the left image.

[0050] (Step S104: Reception Processing) The reception unit 214 of the measurement terminal 20 receives the truck identification information and the specification of the surface portion 42 of the cargo bed area 41 in the data displayed in step S103. Since the cargo bed area 41 is assumed to be a rectangular parallelepiped, the surface portion 42 only needs to be specified for one of the sides.

[0051] (Step S108: Occupancy rate calculation process) As shown in Figure 12, the occupancy rate calculation unit 112 of the loading rate calculation device 10 sets the cross section 43 that is included in the range from the right side, which is one side of the side, to the reference position 45 in the depth direction as the target cross section 43. The occupancy rate calculation unit 112 calculates the occupancy rate O of the target cross section 43 based on 3D point cloud data obtained by measurement from the right side. At this time, the occupancy rate calculation unit 112 divides the target cross section 43 into a plurality of small regions 44, and identifies the small region 44 to the right of the target cross section 43 where there are a reference number or more of point data included in the 3D point cloud data as the region where point data included in the 3D point cloud data exists. Furthermore, the occupancy rate calculation unit 112 sets the cross section 43 that is included in the range from the left side, which is the other side of the side, to the reference position 45 in the depth direction as the target cross section 43. The occupancy rate calculation unit 112 calculates the occupancy rate O of the target cross section 43 based on the 3D point cloud data obtained by measurement from the left side. At this time, the occupancy rate calculation unit 112 divides the target cross section 43 into a plurality of sub-regions 44, and identifies the sub-regions 44 to the left of the target cross section 43 that have a reference number or more of point data included in the 3D point cloud data as regions where point data included in the 3D point cloud data exists. The reference position 45 is, for example, the center position in the depth direction. Therefore, for the right half, the occupancy rate O is calculated based on 3D point cloud data obtained from measurements taken from the right side. Similarly, for the left half, the occupancy rate O is calculated based on 3D point cloud data obtained from measurements taken from the left side.

[0052] As for how to set the reference position 45, for example, the occupancy rate calculation unit 112 may identify the position with the highest cargo height by comparing the heights of point data present in the cross-section 43, and set the vicinity of that position as the reference position 45. Alternatively, the system may be configured to accept the setting of the reference position 45 from an operations manager or the like who has visually confirmed the peak height. When the reference position 45 is set to an arbitrary position in this way, the loading rate determination process in step S109 calculates the loading rate L as follows. Loading ratio L = Distance from the right side to the reference position 45 / Size in the depth direction × Occupancy ratio of the right side O + Distance from the left side to the reference position 45 / Size in the depth direction × Occupancy ratio of the left side O

[0053] ***Effects of Embodiment 2*** As described above, the loading rate calculation system 100 according to Embodiment 2 measures the cargo bed area 41 from both sides of the cargo bed and acquires 3D point cloud data. The loading rate calculation system 100 then calculates the occupancy rate O for the cross section 43 from the right side to the reference position 45 based on the right point cloud data, and for the cross section 43 from the left side to the reference position 45 based on the left point cloud data. This makes it possible to determine the loading rate L more accurately. In Embodiment 1, it was assumed that there was cargo behind the measurement side where cargo was located. When cargo is loaded from one side of the loading platform, the cargo is packed in from the back, so the above assumption is easily met. However, when cargo is loaded from both sides of the loading platform, the cargo may be piled up to the highest point in the center of the platform. In such cases, the above assumption no longer holds true. However, as in Embodiment 2, by calculating the occupancy rate O of each cross section 43, it becomes possible to appropriately determine the loading rate L.

[0054] ***Other configurations*** <Example 1> In Embodiment 1, each functional component was implemented in software. However, in Modification 1, each functional component may be implemented in hardware. The differences between this Modification 1 and Embodiment 1 will be explained below.

[0055] When each functional component is implemented in hardware, the loading rate calculation device 10 includes an electronic circuit 15 instead of the processor 11, memory 12, and storage 13. The electronic circuit 15 is a dedicated circuit that implements the functions of each functional component of the loading rate calculation device 10, as well as the functions of the memory 12 and storage 13.

[0056] When each functional component is implemented in hardware, the measurement terminal 20 includes an electronic circuit 27 instead of a processor 21, memory 22, and storage 23. The electronic circuit 27 is a dedicated circuit that implements the functions of each functional component of the measurement terminal 20, as well as the functions of the memory 22 and storage 23.

[0057] When each functional component is implemented in hardware, the report output terminal 30 includes an electronic circuit 35 instead of a processor 31, memory 32, and storage 33. The electronic circuit 35 is a dedicated circuit that implements the functions of each functional component of the report output terminal 30, as well as the functions of the memory 32 and storage 33.

[0058] Electronic circuits 15, 27, and 35 are assumed to include single circuits, complex circuits, programmed processors, parallel programmed processors, logic ICs, GAs, ASICs, and FPGAs. GA stands for Gate Array. ASIC stands for Application Specific Integrated Circuit. FPGA stands for Field-Programmable Gate Array. Each functional component may be implemented in a single electronic circuit 15, 27, or 35, or each functional component may be implemented by distributing them across multiple electronic circuits 15, 27, and 35.

[0059] <Modification 2> As a second variation, some of the functional components may be implemented in hardware, while others may be implemented in software.

[0060] The processor 11, memory 12, storage 13, and electronic circuit 15 are collectively referred to as the processing circuit. In other words, the function of each functional component is realized by the processing circuit.

[0061] Furthermore, the term "part" in the above explanation may be replaced with "circuit," "process," "procedure," "processing," or "processing circuit."

[0062] The various aspects of this disclosure are summarized below as an appendix. (Note 1) A data acquisition unit that acquires 3D point cloud data of the cargo area, which is the area of ​​the cargo bed of a mobile vehicle, by measuring the cargo bed from the measurement side using a measurement terminal, A unit for calculating occupancy rates sets each cross-section of the cargo bed region at unit distances in the depth direction, which is the direction away from the measurement side, as a target cross-section, and calculates the occupancy rate as the ratio of the area where point data included in the 3D point cloud data acquired by the data acquisition unit exists on the measurement side of the target cross-section relative to the area of ​​the surface of the cargo bed perpendicular to the depth direction, A loading rate determination unit identifies the average value of the occupancy rate calculated for each cross section at each unit distance by the occupancy rate calculation unit as the loading rate of the cargo bed. A load factor calculation system equipped with the following features. (Note 2) The data acquisition unit acquires the 3D point cloud data obtained with each of the two sides of the moving body being the measurement side, The occupancy rate calculation unit calculates the occupancy rate based on 3D point cloud data obtained by measuring from one side, using the cross section included in the range from one side of the side to the reference position in the depth direction as the target cross section, and also calculates the occupancy rate based on 3D point cloud data obtained by measuring from the other side, using the cross section included in the range from the other side of the side to the reference position in the depth direction as the target cross section. The load factor calculation system described in Appendix 1. (Note 3) The occupancy rate calculation unit divides the cross-section of the target into a plurality of sub-regions, and calculates the occupancy rate for any sub-region on the measurement side of the cross-section of the target in which there are a standard number or more of point data included in the 3D point cloud data, considering the region in which the point data included in the 3D point cloud data exists as the region in which the point data included in the 3D point cloud data exists. The load factor calculation system described in Appendix 1 or 2. (Note 4) Each of the aforementioned sub-regions is a rectangle whose sides are smaller than the maximum measurement error of the measurement terminal. The load factor calculation system described in Appendix 3. (Note 5) The aforementioned load factor calculation system further, A display unit that displays the loading rate determined by the loading rate determination unit and drawing data representing the area of ​​the cargo bed and the cargo loaded on the cargo bed. A load factor calculation system as described in any one of the appendices 1 to 4, comprising the above. (Note 6) The display unit displays a three-dimensional model as drawing data, representing the area of ​​the cargo bed and the cargo loaded on the cargo bed. Loading rate calculation system as described in Appendix 5. (Note 7) A computer obtains 3D point cloud data of the cargo area, which is the area of ​​the cargo bed of a mobile object, by measuring it from the measurement side of the cargo bed using a measurement terminal. The computer sets each cross-section of the cargo bed area at unit distances in the depth direction, which is the direction away from the measurement side, as a target cross-section, and for each target cross-section, it calculates the occupancy rate as the ratio of the area where point data included in the 3D point cloud data exists on the measurement side of the target cross-section to the area of ​​the surface of the cargo bed perpendicular to the depth direction. A method for calculating the loading rate, wherein a computer identifies the average value of the occupancy rate calculated for each cross section at each unit distance as the loading rate of the cargo bed. (Note 8) A data acquisition process that obtains 3D point cloud data of the cargo area, which is the area of ​​the cargo bed of a mobile vehicle, by measuring the cargo bed from the measurement side using a measurement terminal, and A occupancy rate calculation process is performed to set each cross-section of the cargo bed region at unit distances toward the depth direction, which is the direction away from the measurement side, as a target cross-section, and for each target cross-section, the occupancy rate is calculated as the ratio of the area where point data included in the 3D point cloud data acquired by the data acquisition process exists toward the measurement side of the target cross-section, relative to the area of ​​the surface of the cargo bed perpendicular to the depth direction, A loading rate determination process is performed to determine the average value of the occupancy rate calculated for each cross section at each unit distance by the occupancy rate calculation process as the loading rate of the cargo bed. A load factor calculation program that uses a computer to function as a load factor calculation system.

[0063] The embodiments and variations of this disclosure have been described above. Some of these embodiments and variations may be implemented in combination. Alternatively, some or all of them may be implemented in part. However, this disclosure is not limited to the embodiments and variations described above, and various modifications are possible as needed. [Explanation of Symbols]

[0064] 100 Loading rate calculation system, 10 Loading rate calculation device, 11 Processor, 12 Memory, 13 Storage, 14 Communication interface, 111 Data acquisition unit, 112 Occupancy rate calculation unit, 113 Loading rate identification unit, 114 Report generation unit, 20 Measurement terminal, 21 Processor, 22 Memory, 23 Storage, 24 Communication interface, 25 Camera, 26 Measurement device, 211 Shooting unit, 212 Measurement unit, 213 Display unit, 214 Reception unit, 215 Communication unit, 30 Report output terminal, 31 Processor, 32 Memory, 33 Storage, 34 Communication interface, 311 Communication unit, 312 Display unit, 41 Loading bed area, 42 Surface area, 43 Cross-section, 44 Small area, 45 Reference position.

Claims

1. A data acquisition unit that acquires three-dimensional point cloud data of the cargo area, which is the area of ​​the cargo bed of a mobile vehicle, by measuring the cargo area from the measurement side of the cargo bed using a measurement terminal, A unit occupancy calculation unit sets each cross-section of the cargo bed area at unit distances toward the depth direction, which is the direction away from the measurement side, as a target cross-section, and calculates as an occupancy rate the ratio of the area in which point data included in the 3D point cloud data acquired by the data acquisition unit exists with respect to the area of ​​the surface of the cargo bed perpendicular to the depth direction, and the area in which point data included in the 3D point cloud data acquired by the data acquisition unit exists toward the measurement side of the target cross-section, A loading rate determination unit identifies the average value of the occupancy rate calculated for each cross section of each unit distance by the occupancy rate calculation unit as the loading rate of the cargo bed. A load factor calculation system equipped with the following features.

2. The data acquisition unit acquires the three-dimensional point cloud data obtained with each of the two sides of the moving body as the measurement side, The occupancy rate calculation unit calculates the occupancy rate based on 3D point cloud data obtained by measuring from one side, using the cross section included in the range from one side of the side to the reference position in the depth direction as the target cross section, and also calculates the occupancy rate based on 3D point cloud data obtained by measuring from the other side, using the cross section included in the range from the other side of the side to the reference position in the depth direction as the target cross section. The load factor calculation system according to claim 1.

3. The occupancy rate calculation unit divides the cross-section of the target into a plurality of sub-regions, and calculates the occupancy rate by considering the cross-section of the target and the sub-regions on the measurement side of the cross-section of the target, where there are a standard number or more of point data included in the 3D point cloud data, as regions where point data included in the 3D point cloud data exists. A load factor calculation system according to claim 1 or 2.

4. Each of the aforementioned sub-regions is a rectangle whose sides are smaller than the maximum measurement error of the measurement terminal. The load factor calculation system according to claim 3.

5. The aforementioned load factor calculation system further, A display unit that displays the loading rate determined by the loading rate determination unit and drawing data representing the area of ​​the cargo bed and the cargo loaded on the cargo bed. The load factor calculation system according to claim 1, comprising:

6. The display unit displays a three-dimensional model as drawing data, representing the area of ​​the cargo bed and the cargo loaded on the cargo bed. The load factor calculation system according to claim 5.

7. The computer obtains three-dimensional point cloud data of the cargo area, which is the area of ​​the cargo bed of the mobile object, by measuring it from the measurement side of the cargo bed using a measurement terminal. The computer sets each cross-section of the cargo bed area at unit distances in the depth direction, which is the direction away from the measurement side, as a target cross-section, and for each target cross-section, it calculates the occupancy rate as the ratio of the area where point data included in the 3D point cloud data exists on the measurement side of the target cross-section relative to the area of ​​the cargo bed surface perpendicular to the depth direction. A method for calculating the loading rate, wherein a computer identifies the average value of the occupancy rate calculated for each cross section at each unit distance as the loading rate of the cargo bed.

8. A data acquisition process that obtains 3D point cloud data of the cargo area, which is the area of ​​the cargo bed of a mobile vehicle, by measuring the cargo bed from the measurement side using a measurement terminal, and A occupancy rate calculation process is performed to set each cross-section of the cargo bed region at unit distances toward the depth direction, which is the direction away from the measurement side, as a target cross-section, and for each target cross-section, the occupancy rate is calculated as the ratio of the area where point data included in the 3D point cloud data acquired by the data acquisition process exists toward the measurement side of the target cross-section, relative to the area of ​​the surface of the cargo bed perpendicular to the depth direction, A loading rate determination process is performed to determine the average value of the occupancy rate calculated for each cross section at each unit distance by the occupancy rate calculation process as the loading rate of the cargo bed. A load factor calculation program that uses a computer to function as a load factor calculation system.

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