Open wagon cargo capacity calculation method and system

Abstract

The invention relates to the technical field of train cargo surveying and mapping, and provides an open wagon cargo capacity calculation method and system, and the method comprises the steps: recording the speed data of a carriage through a speed measurement device; scanning the carriage from the top through a fixed laser scanner and obtaining point cloud data; determining a compartment connection part in the point cloud data and the speed data based on compartment separation characteristics, and performing data segmentation at the position of the compartment connection part in the point cloud data and the speed data; and carrying out three-dimensional point cloud modeling on each carriage by combining the carriage size information and the segmented point cloud data and speed data, and calculating the cargo capacity of the open wagon based on a three-dimensional point cloud model. Compared with the prior art, the method for measuring the cargo capacity of the open wagon in the advancing state is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of train cargo mapping, and in particular to a method and system for calculating the cargo capacity of an open wagon. Background Art

[0002] In the process of transporting goods such as coal and ore, it is crucial to accurately calculate the carrying capacity and the residual volume after unloading. Traditional methods of calculating the carrying capacity usually rely on large-scale mechanical equipment to weigh the entire vehicle, or use other technical means to calculate the volume through post-unloading volume measurement. For example, 3D laser scanning technology is used to obtain point cloud data of the carriage. By scanning the overall structure of the train or truck, it can quickly and accurately obtain 3D data of the vehicle, and then calculate the vehicle's carrying capacity or residual volume.

[0003] Currently, existing technologies have some obvious limitations. First, most methods require the vehicle being measured to be stationary in a fixed position or running at a constant speed, which is not suitable for scanning vehicles during dynamic transportation. Second, the coverage of laser scanners is limited, and scanning data can only be effectively acquired within a specific area. In addition, some solutions require the deployment of expensive and complex weighing equipment on the track, and the high installation and maintenance costs of such equipment also limit the practical application of these solutions. The shortcomings of existing technologies have seriously restricted the deployment flexibility and economy of measurement systems, especially in industrial scenarios that require continuous monitoring of moving trains. Summary of the Invention

[0004] In order to overcome the defect of the prior art that the cargo capacity of a moving train cannot be measured, the present invention provides a method and system for calculating the cargo capacity of an open car.

[0005] In order to achieve the above technical effects, the technical solutions of the present invention are as follows:

[0006] The present invention proposes a method for calculating the cargo capacity of an open wagon, comprising the following steps:

[0007] Use speed measuring equipment to record the speed data of the carriage;

[0008] Scan the carriage from the top using a fixed laser scanner and acquire point cloud data;

[0009] Determining a carriage connection portion in the point cloud data and the speed data based on a carriage separation feature, and performing data segmentation at a position of the carriage connection portion in the point cloud data and the speed data;

[0010] The three-dimensional point cloud modeling of each carriage is performed by combining the carriage size information, segmented point cloud data and speed data, and the cargo capacity of the open car is calculated based on the three-dimensional point cloud model.

[0011] As a preferred solution, the carriage separation features include separation features of speed data and separation features of point cloud data; the separation features of speed data include the part of the speed data where the speed measurement is zero; the segmentation features of point cloud data include the part where the projection of the point cloud on the ground exceeds the carriage size range.

[0012] As a preferred solution, the step of inferring the car connection portion from the speed data based on the car separation characteristics includes:

[0013] Calculating a threshold based on the speed measurement frequency of the speed measurement device and the last speed record in the speed measurement device;

[0014] Calculate the number of sampling times of the speed measurement result with a speed of zero;

[0015] The portion of the speed measurement result in which the number of sampling times is greater than the threshold is used as the carriage connection portion.

[0016] As a preferred solution, the three-dimensional point cloud includes Y-axis data perpendicular to the ground, Z-axis data in the direction of train movement, and X-axis data parallel to the ground and perpendicular to the Z-axis; wherein the point cloud data includes X-axis and Y-axis data; the Z-axis data is calculated based on the speed data.

[0017] As a preferred solution, the method further includes: after the point cloud data is collected, removing the point cloud whose X-axis data is larger than the car body size data.

[0018] As a preferred solution, the step of calculating the cargo capacity of the open wagon based on the three-dimensional point cloud model includes: calculating the cargo capacity of the open wagon by triangulating the three-dimensional point cloud model.

[0019] As a preferred solution, the method further includes: marking each train car based on radio frequency identification, and making a one-to-one correspondence between the marking result and the cargo capacity of the open car.

[0020] The present invention also provides a system for calculating the cargo capacity of an open wagon, comprising:

[0021] Speed measurement module: It is equipped with a speed measurement device to record the speed data of the carriage in motion;

[0022] Laser scanning module: It is equipped with a laser scanner to scan the car from the top and obtain point cloud data;

[0023] Data segmentation module: used for segmenting the point cloud data and the speed data at the position of the carriage connection;

[0024] Cargo capacity calculation module: used to combine the segmented point cloud data, car size information and the speed data to perform three-dimensional point cloud modeling on each car, and calculate the open car cargo capacity based on the three-dimensional point cloud model.

[0025] The present invention also proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for calculating the cargo capacity of an open car as described in the present invention is implemented.

[0026] The present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for calculating the cargo capacity of an open car as described in the present invention is implemented.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention fixes a laser scanner above the track and performs scanning, and uses the high-density point cloud of the laser scanner in combination with a speedometer to record the train's speed in real time. This can effectively synchronize the scanning data with the train's motion status. Regardless of whether the train is running at a uniform speed, the laser point cloud data and speed data can be integrated and analyzed to accurately generate a three-dimensional point cloud model of the entire train, thereby calculating the cargo capacity of the open car. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The figure is a flow chart of a method for calculating the cargo capacity of an open wagon.

[0030] Figure 2 A top view of the open car.

[0031] Figure 3 This is a cross-sectional view of an open car.

[0032] Figure 4 This is an architectural diagram of an open car cargo capacity calculation system. DETAILED DESCRIPTION

[0033] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting the present invention;

[0034] It is understandable to those skilled in the art that some well-known descriptions may be omitted in the drawings.

[0035] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0036] Example 1

[0037] This embodiment proposes a method for calculating the cargo capacity of an open wagon. Figure 1 FIG. 1 is a flow chart of a method for calculating the cargo capacity of an open wagon according to this embodiment.

[0038] The method for calculating the cargo capacity of an open wagon proposed in this embodiment includes the following steps:

[0039] Use speed measuring equipment to record the speed data of the carriage;

[0040] Scan the carriage from the top using a fixed laser scanner and acquire point cloud data;

[0041] Determining a carriage connection portion in the point cloud data and the speed data based on a carriage separation feature, and performing data segmentation at a position of the carriage connection portion in the point cloud data and the speed data;

[0042] The three-dimensional point cloud modeling of each carriage is performed by combining the carriage size information, segmented point cloud data and speed data, and the cargo capacity of the open car is calculated based on the three-dimensional point cloud model.

[0043] In this embodiment, the speed measuring equipment is used to record speed data during train travel, providing information on car movement, while the laser scanner performs fixed scanning, generating high-density point cloud data to capture car information. Based on the compartment separation characteristics, combined with speed data and point cloud data, the car connections are segmented to determine the boundary positions of each car. Based on the segmented point cloud data and speed data, combined with the car dimensions, a three-dimensional point cloud model is created for each car, and the cargo capacity of each car is calculated by building the 3D model. By integrating laser scanning and speed data, this solution improves model accuracy and can calculate cargo capacity regardless of the train's operating status.

[0044] As an exemplary illustration, the laser scanner is a single-line laser scanner fixed on a gantry above the rails.

[0045] In an optional embodiment, the compartment separation feature includes a separation feature of the speed data and a separation feature of the point cloud data; the separation feature of the speed data includes the part of the speed data where the speed measurement is zero; the segmentation feature of the point cloud data includes the part where the projection of the point cloud on the ground exceeds the compartment size range.

[0046] In this embodiment, by incorporating separation features from speed data and point cloud data into the compartment division process, the inference of compartment connections is more accurate. Specifically, zero-speed measurement data and the portion of the point cloud projection on the ground that exceeds the compartment dimensions are both indicators of compartment connections. This approach, combining multiple data sources, effectively improves the accuracy of compartment division and, consequently, the precision of cargo capacity calculations.

[0047] In an optional embodiment, the step of inferring the car connection from the speed data based on the car separation characteristics includes:

[0048] Calculating a threshold based on the speed measurement frequency of the speed measurement device and the last speed record in the speed measurement device;

[0049] Calculate the number of sampling times of the speed measurement result with a speed of zero;

[0050] The portion of the speed measurement result in which the number of sampling times is greater than the threshold is used as the carriage connection portion.

[0051] Specifically, the calculation formula of the threshold is as follows:

[0052] A=L / Vn / f

[0053] Where A is the threshold, L is the length of a single carriage, Vn is the speed of the last speed measurement, and f is the sampling frequency of the speed measurement device.

[0054] In this embodiment, car connections are determined by sampling and analyzing speed data, combining the frequency of the speed measurement equipment and the last speed record. By quantifying the zero-speed portion and calculating the number of speed measurement samples at the connection based on the speed data, the cars are separated, improving the accuracy of car connection inference.

[0055] In an optional embodiment, the three-dimensional point cloud includes Y-axis data perpendicular to the ground, Z-axis data in the direction of train travel, and X-axis data parallel to the ground and perpendicular to the Z-axis; wherein the point cloud data includes X-axis and Y-axis data; the Z-axis data is calculated based on the speed data, and its calculation formula is as follows:

[0056] Z n =Z n-1 +V n *T n

[0057] Among them, Z n is the Z value of the nth point cloud, and Z1=0; V n is the velocity data at the nth point cloud; T n is the scanning time point of the nth point cloud.

[0058] Specifically, if Figure 2 The figure shows a top view of the open car. The coordinates of the X-axis and the Y-axis are calculated as follows:

[0059] X=Len*Sin(Angle)

[0060] Y=Len*Cos(Angle)

[0061] Wherein, Len is the length measured by laser, and Angle is the angle.

[0062] In this embodiment, by calculating the Z-axis data based on the speed data, the relative position of the point cloud in the Z-axis direction can be accurately measured even when the train is in motion or even in a non-uniform motion state, thereby establishing a more accurate three-dimensional model. Specifically, the measurement of cargo capacity is generally chosen before entering the station or after leaving the station. In this case, the train is generally running in a non-uniform state. At this time, the present invention can accurately calculate the change of the Z axis through the speed data and correct the deviation in the point cloud data in real time. Even when the train is accelerating, decelerating or experiencing slight vibrations, it can effectively eliminate the influence of these factors on the measurement results, ensuring high-precision cargo capacity measurement. This method is particularly suitable for the complex motion state of the train before and after entering the station. It can accurately reflect the spatial relationship between the train and the cargo object, thereby providing a reliable basis for subsequent data analysis and processing, and ensuring the efficiency of data acquisition.

[0063] In an optional embodiment, the method further includes: after the point cloud data is collected, removing the point cloud whose X-axis data is larger than the car body size data.

[0064] like Figure 3 The figure shows a cross-section of a gondola. Figure 3 The orange area in the figure is the point cloud data where the X-axis data is larger than the car size data.

[0065] Specifically, the width of the carriage can be used as a known parameter value, thereby filtering out point cloud data outside the carriage or inside or on the top of the carriage, leaving only the point cloud data inside the carriage to be measured.

[0066] In this embodiment, this method effectively eliminates irrelevant data points, ensuring the validity and accuracy of the point cloud data. In practical applications, different car body sizes can cause point cloud data to exceed a certain range. Eliminating point cloud data outside this range can further improve the accuracy of cargo capacity calculations.

[0067] In an optional embodiment, the step of calculating the cargo capacity of the open wagon based on the three-dimensional point cloud model includes: calculating the cargo capacity of the open wagon by triangulating the three-dimensional point cloud model.

[0068] As an exemplary illustration, the point cloud is triangulated using the Delaunay algorithm, and after triangulation, it can be divided into independent cylinders for volume calculation.

[0069] In this embodiment, triangulation of the 3D point cloud model converts complex point cloud data into a mesh model suitable for numerical calculations. This allows for better analysis and processing of the point cloud data and calculation of the cargo capacity of each carriage. Triangulation not only improves computational efficiency but also increases accuracy, enabling this embodiment to maintain efficient and accurate performance even in large-scale carriage data processing.

[0070] In an optional embodiment, the method further includes: marking each train car based on radio frequency identification, and making a one-to-one correspondence between the marking result and the cargo capacity of the open car.

[0071] As an example, an RFID card is installed on each carriage.

[0072] In this embodiment, a 3D model of each carriage's cargo volume is generated based on the carriage model recorded in the RDIF and the point cloud data from the point cloud scan. Triangulating the point cloud allows the cargo volume to be calculated. Each carriage is scanned once upon entering or exiting the station, recording the entry point as M1 and the exit point as M2. The actual cargo volume of each entry can be compared by M1-M2. RFID tags enable a precise relationship between cargo volume calculation and carriage identification.

[0073] Example 2

[0074] This embodiment proposes a system for calculating the cargo capacity of an open wagon, and applies the method for calculating the cargo capacity of an open wagon proposed in Example 1. Figure 2 FIG. 1 is an architecture diagram of an open car cargo capacity calculation system according to an embodiment of the present invention.

[0075] This embodiment provides a system for calculating the cargo capacity of an open wagon, including:

[0076] Speed measurement module: It is equipped with a speed measurement device to record the speed data of the carriage in motion;

[0077] Laser scanning module: It is equipped with a laser scanner to scan the car from the top and obtain point cloud data;

[0078] Data segmentation module: used for segmenting the point cloud data and the speed data at the carriage connection part;

[0079] Cargo capacity calculation module: used to combine the segmented point cloud data, car size information and the speed data to perform three-dimensional point cloud modeling on each car, and calculate the open car cargo capacity based on the three-dimensional point cloud model.

[0080] It can be understood that the system of this embodiment corresponds to the method of the above-mentioned embodiment 1, and the options in the above-mentioned embodiment 1 are also applicable to this embodiment, so they will not be described again here.

[0081] Example 3

[0082] This embodiment proposes a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, the processor executes the steps of a method for calculating the cargo capacity of an open car proposed in Example 1.

[0083] Example 4

[0084] This embodiment proposes a storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement the steps of a method for calculating the cargo capacity of an open car proposed in Example 1.

[0085] The terms in the drawings are for illustrative purposes only and should not be construed as limiting this patent.

[0086] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating the cargo capacity of an open wagon, characterized in that: The following steps are involved: Use speed measuring equipment to record the speed data of the carriage; Scan the carriage from the top using a fixed laser scanner and acquire point cloud data; Determining a carriage connection portion in the point cloud data and the speed data based on a carriage separation feature, and performing data segmentation at a position of the carriage connection portion in the point cloud data and the speed data; The three-dimensional point cloud modeling of each carriage is performed by combining the carriage size information, segmented point cloud data and speed data, and the cargo capacity of the open car is calculated based on the three-dimensional point cloud model.

2. A method for calculating cargo capacity of an open wagon according to claim 1, characterized in that: The compartment separation feature includes the separation feature of the speed data and the separation feature of the point cloud data; the separation feature of the speed data includes the part of the speed data where the speed measurement is zero; the segmentation feature of the point cloud data includes the part where the projection of the point cloud on the ground exceeds the compartment size range.

3. The method for calculating the cargo capacity of an open wagon according to claim 2, characterized in that: The steps of inferring the car connection from the speed data based on the car separation characteristics include: Calculating a threshold based on the speed measurement frequency of the speed measurement device and the last speed record in the speed measurement device; Calculate the number of sampling times of the speed measurement result with a speed of zero; The portion of the speed measurement result in which the number of sampling times is greater than the threshold is used as the carriage connection portion.

4. A method for calculating cargo capacity of an open wagon according to claim 1, characterized in that: The three-dimensional point cloud includes Y-axis data perpendicular to the ground, Z-axis data in the direction of the train's travel, and X-axis data parallel to the ground and perpendicular to the Z-axis. The point cloud data includes X-axis and Y-axis data. The Z-axis data is calculated based on the speed data, and its calculation formula is as follows: Z n =Z n-1 +V n *T n Among them, Z n is the Z value of the nth point cloud, and Z1=0; V n is the velocity data at the nth point cloud; T n is the scanning time point of the nth point cloud.

5. A method for calculating cargo capacity of an open wagon according to claim 4, characterized in that: The method further includes: after the point cloud data is collected, removing the point cloud whose X-axis data is larger than the car body size data.

6. A method for calculating cargo capacity of an open wagon according to claim 1, characterized in that: The step of calculating the cargo capacity of the open wagon based on the three-dimensional point cloud model includes: calculating the cargo capacity of the open wagon by triangulating the three-dimensional point cloud model.

7. A method for calculating cargo capacity of an open wagon according to any one of claims 1 to 6, characterized in that: The method further includes marking each train car based on radio frequency identification, and making a one-to-one correspondence between the marking result and the cargo capacity of the open car.

8. A system for calculating the cargo capacity of an open wagon, applied to a method for calculating the cargo capacity of an open wagon according to any one of claims 1 to 7, characterized in that: The system comprises: Speed measurement module: It is equipped with a speed measurement device to record the speed data of the carriage in motion; Laser scanning module: It is equipped with a laser scanner to scan the car from the top and obtain point cloud data; Data segmentation module: used for segmenting the point cloud data and the speed data at the position of the carriage connection; Cargo capacity calculation module: used to combine the segmented point cloud data, car size information and the speed data to perform three-dimensional point cloud modeling on each car, and calculate the open car cargo capacity based on the three-dimensional point cloud model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, a method for calculating the cargo capacity of an open car according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a method for calculating the cargo capacity of an open wagon is implemented as described in any one of claims 1 to 7.

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