Intelligent box falling detection method and device

Abstract

The application discloses an intelligent container falling detection method and device, relates to the technical field of detection, and mainly aims to improve the container falling detection efficiency of railway loading and unloading of a train freight station. The main technical scheme of the application is as follows: first target data and second target data are acquired; wherein the first target data at least includes edge information of a train flat car for storing a to-be-placed container and position information of an F-TR lock installed on the train flat car; the second target data at least includes edge information of the train flat car having stored the to-be-placed container, position information of the F-TR lock not being shielded by the to-be-placed container and edge information of the to-be-placed container; whether a relative position relationship between the to-be-placed container and the train flat car conforms to a preset condition is judged based on the first target data and the second target data; if yes, it is determined that the container falling is successful; if not, it is determined that the container falling fails, and a reminder is given. The application is used for container falling operation of a train station.

Description

Technical Field

[0001] This invention relates to the field of detection technology, and in particular to an intelligent box-dropping detection method and device. Background Technology

[0002] When loading and unloading containers at railway freight stations, specialized gantry cranes are used to lift the containers and transport them to dedicated railway container flatcars. The four corner fittings of the container's bottom are then secured to specialized locks on the flatcar, firmly securing the container. However, due to human error or weather conditions, some corners may not be fully engaged in the locks, resulting in misalignment of one or more locks and the container not being fully secured. Furthermore, the gantry crane operator cannot detect this situation from their cab. Therefore, it is crucial to ensure that such situations can be detected promptly.

[0003] Currently, manual inspection is used to check whether containers have been properly placed into the special locks on the railway container flatcars, which is time-consuming and inefficient. Summary of the Invention

[0004] In view of the above problems, the present invention provides an intelligent container dropping detection method and device, the main purpose of which is to improve the efficiency of container dropping detection during railway loading and unloading at railway freight stations.

[0005] To solve the above-mentioned technical problems, the present invention proposes the following solution:

[0006] In a first aspect, the present invention provides an intelligent box-dropping detection method, the method comprising:

[0007] Acquire first target data and second target data; wherein, the first target data includes at least the edge information of the train flatcar used to store the container to be placed and the position information of the F-TR lock installed on the train flatcar; the second target data includes at least the edge information of the train flatcar that has stored the container to be placed, the position information of the F-TR lock that is not obscured by the container to be placed, and the edge information of the container to be placed.

[0008] Based on the first target data and the second target data, determine whether the relative positional relationship between the container to be placed and the train flatcar meets the preset conditions;

[0009] If so, then the container to be placed has been successfully placed.

[0010] If not, it is determined that the container to be placed has failed to be placed, and a reminder is issued.

[0011] Preferably, determining whether the relative positional relationship between the container to be placed and the train flatcar meets preset conditions based on the first target data and the second target data includes:

[0012] The number of F-TR locks that do not overlap with the container to be placed is obtained based on the first target data and the second target data;

[0013] Determine whether the number of F-TR locks that do not overlap with the container to be placed is not greater than 0.

[0014] Preferably, the step of determining that the container to be placed has failed to be placed if the relative positional relationship between the container to be placed and the flatcar does not meet the preset conditions based on the first target data and the second target data includes:

[0015] If the number of F-TR locks that do not overlap with the container to be placed is greater than 0, then it is determined whether the number of F-TR locks that do not overlap with the container to be placed is an even number;

[0016] If the number of F-TR locks that do not overlap with the container to be placed is even, then it is determined whether the F-TR locks that do not overlap with the container to be placed are located on the same side of the container to be placed.

[0017] If the F-TR lock that does not coincide with the container to be placed is located on the same side of the container to be placed, then the type of failure of the container to be placed is determined to be parallel offset.

[0018] If the F-TR lock that does not coincide with the container to be placed is not located on the same side as the container to be placed, then the type of failure of the container to be placed is determined to be tilting.

[0019] Preferably, after determining whether the number of F-TR locks that do not overlap with the container B to be placed is even, the method further includes:

[0020] If the number of F-TR locks that do not overlap with the container to be placed is not an even number, then the type of failure to place the container is determined to be tilting.

[0021] Preferably, the method further includes:

[0022] If the number of F-TR locks that do not overlap with the container to be placed is not greater than 0, then the distance between the edge of the container to be placed and the edge of the corresponding flatcar is obtained according to the first target data and the second target data.

[0023] Determine whether the distance between the edge of the container to be placed and the edge of the corresponding flatcar is within a preset threshold range;

[0024] If so, then the container to be placed has been successfully placed.

[0025] If not, then the container to be placed has failed to be placed.

[0026] Preferably, the first target data and the second target data are collected by a laser point cloud sensor, processed by a switch, and then stored in a programmable logic controller.

[0027] The acquisition of the first target data and the second target data includes:

[0028] When the height of the spreader that has grabbed the container to be placed reaches the preset height, the programmable logic controller acquires the first target data.

[0029] When the container to be placed is placed on the flatcar of the train, the second target data is acquired using the programmable logic controller or the laser point cloud sensor.

[0030] Preferably, the method further includes:

[0031] Obtain the real-time height information of the spreader that has grabbed the container to be placed;

[0032] Based on the real-time height information of the spreader that has grabbed the container to be placed, data analysis is performed using a laser point cloud algorithm. The relative heights of the spreader that has grabbed the container to be placed and the flatcar are then displayed on a screen in a side view.

[0033] Based on the first target data and the second target data, data analysis is performed using a laser point cloud algorithm. The relative positions of the container to be placed and the train flatcar obtained from the analysis are displayed on a screen in a top-down view.

[0034] Secondly, the present invention provides an intelligent box-dropping detection device, the device comprising:

[0035] The first acquisition unit is used to acquire first target data and second target data; wherein, the first target data includes at least the edge information of the train flatcar used to store the container to be placed and the position information of the F-TR lock installed on the train flatcar; the second target data includes at least the edge information of the train flatcar that has stored the container to be placed, the position information of the F-TR lock that is not obscured by the container to be placed, and the edge information of the container to be placed.

[0036] The judgment unit is used to determine, based on the first target data and the second target data, whether the relative positional relationship between the container to be placed and the train flatcar meets the preset conditions;

[0037] The first determining unit is used to determine that the container to be placed has been successfully placed if the relative positional relationship between the container to be placed and the flatcar meets the preset conditions.

[0038] The second determining unit is used to determine that the container to be placed has failed to be placed if the relative positional relationship between the container to be placed and the flatcar does not meet the preset conditions, and to issue a reminder.

[0039] Preferably, the determining unit includes:

[0040] The acquisition module is used to acquire the number of F-TR locks that do not overlap with the container to be placed, based on the first target data and the second target data;

[0041] The judgment module is used to determine whether the number of F-TR locks that do not overlap with the container to be placed is not greater than 0.

[0042] Preferably, the second determining unit includes:

[0043] The first judgment module is used to determine whether the number of F-TR locks that do not overlap with the container to be placed is an even number if the number of F-TR locks that do not overlap with the container to be placed is greater than 0.

[0044] The second judgment module is used to determine whether the F-TR locks that do not overlap with the container to be placed are located on the same side of the container to be placed if the number of F-TR locks that do not overlap with the container to be placed is even.

[0045] The first determining module is used to determine that if the F-TR lock that does not overlap with the container to be placed is located on the same side of the container to be placed, the type of failure of the container to be placed is parallel offset.

[0046] The second determining module is used to determine that the failure of the container to be placed is of the type of tilt if the F-TR lock that does not overlap with the container to be placed is not located on the same side of the container to be placed.

[0047] Preferably, the second determining unit further includes:

[0048] The second determining module is further configured to determine that the type of failure of the container to be placed is tilting if the number of F-TR locks that do not overlap with the container to be placed is not an even number.

[0049] Preferably, the first determining unit includes:

[0050] The acquisition module is used to acquire the distance between the edge of the container to be placed and the edge of the corresponding flatcar based on the first target data and the second target data if the number of F-TR locks that do not overlap with the container to be placed is not greater than 0.

[0051] The judgment module is used to determine whether the distance between the edge of the container to be placed and the edge of the corresponding flatcar is within a preset threshold range;

[0052] The first determining module is used to determine that the container to be placed has been successfully placed if the distance between the edge of the container to be placed and the edge of the corresponding flatcar is within a preset threshold range.

[0053] The second determining module is used to determine that the container to be placed has failed to be placed if the distance between the edge of the container to be placed and the edge of the corresponding flatcar is not within a preset threshold range.

[0054] Preferably, the first target data and the second target data are collected by a laser point cloud sensor, processed by a switch, and then stored in a programmable logic controller.

[0055] The first acquisition unit includes:

[0056] The first acquisition module is used to acquire the first target data using the programmable logic controller when the height of the spreader that has grabbed the container to be placed reaches a preset height.

[0057] The second acquisition module is used to acquire the second target data using the programmable logic controller or the laser point cloud sensor when the container to be placed is placed on the flatcar of the train.

[0058] Preferably, the device further includes:

[0059] The second acquisition unit is used to acquire the real-time height information of the spreader that has grabbed the container to be placed;

[0060] The first display unit is used to perform data analysis based on the real-time height information of the spreader that has grabbed the container to be placed, using a laser point cloud algorithm, and to display the height relative position of the spreader that has grabbed the container to be placed and the flatcar on the display screen in a side view.

[0061] The second display unit is used to perform data analysis based on the first target data and the second target data using a laser point cloud algorithm, and to display the relative positions of the container to be placed and the train flatcar obtained from the analysis on a display screen in a top view.

[0062] To achieve the above objectives, according to a third aspect of the present invention, a storage medium is provided, the storage medium including a stored program, wherein, when the program is executed, the device where the storage medium is located is controlled to perform the intelligent box-dropping detection method described in the first aspect.

[0063] To achieve the above objectives, according to a fourth aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, performs all or part of the steps for an intelligent box-dropping detection device as described in the second aspect.

[0064] By employing the above technical solution, the intelligent container placement detection method and device provided by this invention addresses the issue that during railway container loading and unloading at freight stations, due to human error or weather conditions, some containers may not be fully placed into the locking mechanisms at all four corners. Currently, manual inspection is used to check whether the container has been properly placed into the dedicated locking mechanisms fixed on the railway container flatcar, which is time-consuming and inefficient. Therefore, this invention acquires first target data and second target data. The first target data includes at least the edge information of the train flatcar used to store the container to be placed and the position information of the F-TR lock installed on the train flatcar. The second target data includes at least the edge information of the train flatcar that already stores the container to be placed, the position information of the F-TR lock not obstructed by the container to be placed, and the edge information of the container to be placed. Based on the first and second target data, it is determined whether the relative positional relationship between the container to be placed and the train flatcar meets preset conditions. If yes, the container to be placed is determined to have been successfully placed; if not, the container to be placed has failed to be placed, and a notification is issued. This invention can automatically detect whether a container has been correctly placed, thereby improving the efficiency of container placement detection at railway freight stations.

[0065] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0066] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0067] Figure 1 A flowchart of an intelligent box-dropping detection method provided by an embodiment of the present invention is shown;

[0068] Figure 2 This invention provides a flowchart of another intelligent box-dropping detection method.

[0069] Figure 3 This diagram illustrates a block diagram of an intelligent box-dropping detection device provided in an embodiment of the present invention.

[0070] Figure 4 This diagram illustrates the composition of another intelligent box-dropping detection device provided in an embodiment of the present invention. Detailed Implementation

[0071] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0072] Currently, when loading and unloading containers at railway freight stations, manual inspection is used to check whether the containers have been properly secured in the special locks on the railway container flatcars. This method is time-consuming and inefficient. To address this problem, the inventor devised a solution: by comparing the edge information of a flatcar without a container and the position information of the F-TP locks installed on it with the edge information of the flatcar after the container has been placed and the position information of the F-TP locks not obscured by the container, the relative position of the container and the flatcar can be automatically determined, thereby improving efficiency.

[0073] Therefore, this invention provides an intelligent container drop detection method, which improves the efficiency of container drop detection at railway freight stations. The specific execution steps are as follows: Figure 1 As shown, it includes:

[0074] 101. Obtain the first target data and the second target data.

[0075] The application scenario of this invention is a lifting operation on a railway station suspension bridge, specifically transferring containers placed on the ground to a flatcar using a lifting device. In this scenario, two crossbeam supports, typically 15 meters high, are installed at the railway station. A square frame is movably installed between the two crossbeam supports, and a lifting device is mounted on the lower part of this frame. The lifting device can move up and down relative to the frame and is used to grab containers placed on the ground and transfer them to the flatcar. F-TR locks are installed at the four corners of the flatcar. The F-TR lock operates as follows: when the unit controller detects that a container is installed in a designated position, it controls the lock body to automatically rise and lock; upon receiving an unlocking command from the system, the unit controller automatically unlocks and lowers the lock body into its seat; the unit controller monitors the lock's operating status and the container's load-bearing status in real time, and immediately sends an alarm to the onboard central controller and the cloud system if any abnormality is detected.

[0076] The first target data includes at least the edge information of the train flatcar used to store the container to be placed and the position information of the F-TR lock installed on the train flatcar; the second target data includes at least the edge information of the train flatcar that has stored the container to be placed, the position information of the F-TR lock that is not obscured by the container to be placed, and the edge information of the container to be placed.

[0077] It should be noted that: the first target data is detected when the train flatcar is not carrying the container to be placed, and is used for subsequent comparison; the second target data is detected when the train flatcar has the container to be placed placed on it, in which case some edges of the train flatcar may be obscured by the container. The detection method can be a laser point cloud sensor, and this embodiment does not specifically limit it.

[0078] 102. Based on the first target data and the second target data, determine whether the relative positional relationship between the container to be placed and the train flatcar meets the preset conditions.

[0079] The preset condition is that the container to be placed coincides with all the F-TP locks installed on the train flatcar, and the distance between the edge of the container to be placed and the edge of the train flatcar meets a preset threshold range, such as 10-20cm. This embodiment does not make specific limitations.

[0080] By comparing the "edge information of the train flatcar used to store the container to be placed and the position information of the F-TR locks installed on the train flatcar" obtained from step 101 with "edge information of the train flatcar that has stored the container to be placed, the position information of the F-TR locks that are not obscured by the container to be placed and the edge information of the container to be placed," the number of F-TR locks that overlap with the container to be placed and the distance between the edge of the container to be placed and the edge of the train flatcar can be obtained.

[0081] Based on the number of F-TP locks that overlap with the container to be placed, it can be determined whether all F-TP locks installed on the train flatcar overlap with the container to be placed; further, based on whether the distance between the edge of the container to be placed and the edge of the train flatcar is within a preset threshold range, it can be determined whether the distance between the edge of the container to be placed and the edge of the train flatcar meets the preset threshold range.

[0082] 103. If the relative positional relationship between the container to be placed and the flatcar on the train meets the preset conditions, then the container to be placed is determined to have been successfully placed.

[0083] According to step 102, when the number of F-TP locks overlapping with the container to be placed is 4, it is determined that the container to be placed overlaps with all the F-TP locks installed on the train flatcar; when the distance between the edge of the container to be placed and the edge of the train flatcar is within a preset threshold range, it is determined that the distance between the edge of the container to be placed and the edge of the train flatcar meets the preset threshold range; when the above two conditions are met, it is finally determined that the container to be placed has been successfully placed.

[0084] 104. If the relative position of the container to be placed and the flatcar does not meet the preset conditions, the container to be placed is determined to have failed to be placed, and a reminder is issued.

[0085] According to step 102, if the number of F-TP locks that overlap with the container to be placed is less than 4, it is determined that the container to be placed does not overlap with all the F-TP locks installed on the train flatcar; then it is directly determined that the container to be placed has failed to be placed, and a failure reminder is given through a pop-up window or sound.

[0086] When the number of F-TP locks overlapping with the container to be placed is 4, but the distance between the edge of the container to be placed and the edge of the train flatcar is not within the preset threshold range, it is determined that the distance between the edge of the container to be placed and the edge of the train flatcar meets the preset threshold range, and the container to be placed is determined to have failed to be placed. Then, a reminder of the failure to place the container is given through a pop-up window or sound.

[0087] Based on the above Figure 1 As can be seen from the implementation of the embodiments, the present invention provides an intelligent container placement detection method. The present invention acquires first target data and second target data. The first target data includes at least the edge information of the train flatcar used to store the container to be placed and the position information of the F-TR lock installed on the train flatcar. The second target data includes at least the edge information of the train flatcar that already stores the container to be placed, the position information of the F-TR lock not obscured by the container to be placed, and the edge information of the container to be placed. Based on the first target data and the second target data, the present invention determines whether the relative positional relationship between the container to be placed and the train flatcar meets preset conditions. If yes, the container to be placed is determined to have been successfully placed; if not, the container to be placed has failed to be placed, and a reminder is issued. The present invention can automatically detect whether a container has been correctly placed, thereby improving the efficiency of container placement detection at railway freight stations.

[0088] Furthermore, as a response to Figure 1 Further refinement and extension of the illustrated embodiment, this invention also provides another intelligent box-dropping detection method, such as... Figure 2 As shown, the specific steps are as follows:

[0089] 201. Obtain the first target data and the second target data.

[0090] This step combines the description of step 101 in the above method, and the same content will not be repeated here.

[0091] The first target data and the second target data are collected by a laser point cloud sensor, processed by a switch, and stored in a programmable logic controller (PLC). When the height of the spreader that has grabbed the container to be placed reaches a preset height, the PLC is used to obtain the first target data. When the container to be placed is placed on the flatcar, the PLC or the laser point cloud sensor is used to obtain the second target data.

[0092] The hardware device of this invention includes a laser point cloud sensor, a programmable logic controller (PLC), a switch, a detection and analysis instrument, a display screen, and an audible alarm.

[0093] Two laser point cloud sensors are installed on opposite sides of the square frame. The PLC is mounted on the square frame. The switch, the detection analyzer, the display screen, and the audible alarm are all located in the driver's cab. The laser point cloud sensors are connected to the PLC and the detection analyzer via the switch. The PLC is also connected to the detection analyzer via the switch. The detection analyzer displays the analysis results in real time on the display screen in top and side views. The audible alarm is used to indicate the analysis results, such as successful or failed box placement. This embodiment does not impose specific limitations.

[0094] The laser point cloud sensor, utilizing multi-line eye-safe laser beams and time-of-flight ranging principles, possesses superior 3D perception capabilities and high robustness. It also boasts advantages such as a wide detection field of view, ultra-long detection distance, high precision, and high resolution, making it suitable for lifting operations on railway station suspension bridges. Furthermore, driven by advanced sensing software, the laser point cloud sensor can trigger corresponding actions based on real-time scene analysis results. The PLC, a microprocessor-based digital computing controller for automated control, can load control commands into memory for storage and execution. The switch is a device that performs information exchange in a communication system. The detection and analysis instrument uses a laser point cloud algorithm for data analysis and displays the analysis results on the display screen.

[0095] For example:

[0096] Assume there are two crossbeam supports at the train station, with a movable frame positioned between them. A lifting device is installed under the frame, and laser point cloud sensors are installed on both sides of the frame. There is an idle flatcar A and a container B to be placed on the train. F-TR locks are installed at the four corners of flatcar A. The laser point cloud sensors collect real-time data on the height of the lifting device, the position and edge information of flatcar A, the position information of the four F-TR locks, and the position and edge information of container B. A PLC is mounted on the frame and receives and stores the data collected by the laser point cloud sensors after processing by a switch.

[0097] When the operation begins, the driver controls the spreader to grab the container B to be placed and move it to directly above the flatcar A of the train, and then continues to descend. When the spreader reaches a height of 10 meters above the ground, the PLC controls the detection and analysis instrument to start detection. The detection and analysis instrument first obtains the first target data from the PLC. The first target data includes the edge information of the flatcar A of the train and the position information of the four F-TR locks.

[0098] The driver controls the spreader to continue descending until the container B to be placed lands on the train flatcar A. At this time, the detection and analysis instrument obtains the second target data from the PLC. The second target data includes the edge information of the train flatcar A that is not obscured by the container B to be placed, the position information of the F-TR lock that is not obscured by the container B to be placed, and the position information and edge information of the container B to be placed.

[0099] 202. Based on the first target data and the second target data, determine whether the relative positional relationship between the container to be placed and the train flatcar meets the preset conditions.

[0100] This step combines the description of step 102 in the above method, and the same content will not be repeated here.

[0101] The number of F-TR locks that do not overlap with the container to be placed is obtained based on the first target data and the second target data; it is then determined whether the number of F-TR locks that do not overlap with the container to be placed is not greater than 0.

[0102] Let's continue with the example of 201:

[0103] The relative position data between the container B to be placed and the train flatcar A includes the relative position data between the edge of the container B to be placed and the F-TR lock on the train flatcar A, and the relative position data between the edge of the container B to be placed and the edge of the train flatcar A. The detection analyzer determines whether the container B to be placed has been successfully placed on the train flatcar A based on the relative position data between the container B to be placed and the train flatcar A, and displays the determination result in a pop-up window on the display screen. An audio reminder can also be set at the same time.

[0104] 203. If the relative positional relationship between the container to be placed and the flatcar on the train meets the preset conditions, then the container to be placed is determined to have been successfully placed.

[0105] This step combines the description of step 103 in the above method, and the same content will not be repeated here.

[0106] If the number of F-TR locks that do not overlap with the container to be placed is not greater than 0, then the distance between the edge of the container to be placed and the edge of the corresponding flatcar is obtained according to the first target data and the second target data; it is determined whether the distance between the edge of the container to be placed and the edge of the corresponding flatcar is within a preset threshold range; if so, it is determined that the container to be placed has been successfully placed.

[0107] Let's continue with the example of 202:

[0108] The specific judgment method is as follows:

[0109] When the number of F-TR locks that do not overlap with the container B to be placed is 0, the distance between the edge of the container B to be placed and the edge of the corresponding train flatcar A is further obtained based on the relative position data of the edge of the container B to be placed and the edge of the train flatcar A; when the distance between the edge of the container B to be placed and the edge of the corresponding train flatcar A is within a preset threshold range, it is determined that the container B to be placed has been successfully placed; the display screen shows a green warning message "Placing container normally".

[0110] 204. If the relative position of the container to be placed and the flatcar does not meet the preset conditions, the container to be placed is determined to have failed to be placed, and a reminder is issued.

[0111] This step combines the description of step 104 in the above method, and the same content will not be repeated here.

[0112] First scenario: If the number of F-TR locks that do not overlap with the container to be placed is not greater than 0, then the distance between the edge of the container to be placed and the edge of the corresponding train flatcar is obtained according to the first target data and the second target data; it is determined whether the distance between the edge of the container to be placed and the edge of the corresponding train flatcar is within a preset threshold range; if not, it is determined that the container to be placed has failed to be placed.

[0113] The second scenario: If the number of F-TR locks not overlapping with the container to be placed is greater than 0, then it is determined whether the number of F-TR locks not overlapping with the container to be placed is even. If the number of F-TR locks not overlapping with the container to be placed is even, then it is determined whether the F-TR locks not overlapping with the container to be placed are located on the same side of the container to be placed. If the F-TR locks not overlapping with the container to be placed are located on the same side of the container to be placed, then the type of container placement failure is determined to be parallel offset. If the F-TR locks not overlapping with the container to be placed are not located on the same side of the container to be placed, then the type of container placement failure is determined to be tilt. If the number of F-TR locks not overlapping with the container to be placed is not even, then the type of container placement failure is determined to be tilt.

[0114] Let's continue with example 203:

[0115] The specific judgment method is as follows:

[0116] The number of F-TR locks that do not overlap with the container B to be placed is obtained based on the relative position data of the edge of the container B to be placed and the F-TR lock on the flatcar A.

[0117] When the number of F-TR locks that do not coincide with the container B to be placed is greater than 0, it is determined that the container B to be placed has failed to be placed; the display screen shows a red warning message "Placing abnormal" and an alarm sound is emitted at the same time.

[0118] It should be noted that there are two situations in which the container B to be placed fails to be placed: one is that the container B to be placed is tilted when placed; the other is that the container B to be placed is offset horizontally when placed.

[0119] Therefore, it can be further determined whether the number of F-TR locks that do not overlap with the container B to be placed is even and whether they are located on the same side as the container B to be placed; if so, it is determined that the container B to be placed is parallel offset when it is placed; if not, the container B to be placed is tilted when it is placed.

[0120] When the distance between the edge of the container B to be placed and the edge of the corresponding flatcar A is not within the preset threshold range, it is determined that the container B to be placed has failed to be placed; for example, the preset threshold range is 10-20cm, which is not specifically limited in this embodiment; the display screen shows a red warning message "Container Placement Abnormal" and at the same time emits an alarm sound.

[0121] 205. Based on the first target data and the second target data, data analysis is performed using a laser point cloud algorithm. The relative positions of the container to be placed and the train flatcar obtained from the analysis are displayed on a screen in a top-down view.

[0122] Let's continue with the example of 204:

[0123] The detection and analysis instrument uses a laser point cloud algorithm to analyze the data based on the first and second target data to obtain the relative position data of the container B to be placed and the train flatcar A. The detection and analysis instrument sends the relative position data to the display screen in real time for display in a top-down view. The driver can intuitively see the relative position relationship between the train flatcar A and the container B to be placed from the image displayed on the screen, and it can also prompt the driver on how to adjust next time, thereby reducing the communication time between the dispatcher below the gantry crane and the gantry crane driver, and improving the efficiency of placing containers on the train flatcar at the freight train yard.

[0124] 206. Obtain the real-time height information of the spreader that has grabbed the container to be placed.

[0125] Let's continue with the example of 201:

[0126] The detection analyzer of the present invention also obtains the height information of the lifting device and the container B to be placed from the PLC in real time after the detection begins.

[0127] 207. Based on the real-time height information of the spreader that has grabbed the container to be placed, the data is analyzed using a laser point cloud algorithm. The relative height of the spreader that has grabbed the container to be placed and the flatcar are then displayed on a side view screen.

[0128] Let's continue with the example of 206:

[0129] Based on the position information of the train flatcar A and the height information of the spreader and the container B to be placed, the laser point cloud algorithm is used to perform data analysis to obtain the height position data of the spreader and the container B to be placed relative to the train flatcar A. The detection and analysis instrument sends the height position data to the display screen in real time for display in a side view. The driver can intuitively see from the image displayed on the screen whether the container B to be placed has been placed on the train flatcar A.

[0130] Based on the above Figure 2 As can be seen from the implementation method, this invention provides an intelligent container placement detection method. This invention allows the driver to visually observe the container placement status below through a display screen in the driver's cab while operating the container placement mechanism. This provides the driver with a basis for adjusting the container placement position, eliminating the need for manual intercom, saving time and improving work efficiency. This invention uses laser point cloud sensors installed on both sides of the trolley frame, which is easy to deploy and install without affecting the normal operation of the frame and spreader. This invention is easily productized, requiring no on-site data collection or algorithm training; only a small amount of on-site testing and verification, and adjustment of the point cloud analysis data thresholds, are needed to complete the data analysis and display functions and system debugging. This invention utilizes laser point cloud sensors for point cloud data analysis, unlike video analysis systems, enabling it to adapt to various harsh environments and meet the application scenarios of different freight yards.

[0131] Furthermore, as a response to the above Figure 1 In addition to the implementation of the method shown, this embodiment of the invention also provides an intelligent box-dropping detection device for detecting the above-mentioned... Figure 1 The method shown is implemented accordingly. This device embodiment corresponds to the foregoing method embodiment. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiment. Figure 3 As shown, the device includes:

[0132] The first acquisition unit 31 is used to acquire first target data and second target data; wherein, the first target data includes at least the edge information of the train flatcar used to store the container to be placed and the position information of the F-TR lock installed on the train flatcar; the second target data includes at least the edge information of the train flatcar that has stored the container to be placed, the position information of the F-TR lock that is not covered by the container to be placed, and the edge information of the container to be placed.

[0133] The judgment unit 32 is used to determine whether the relative positional relationship between the container to be placed and the flatcar meets the preset conditions based on the first target data and the second target data obtained from the first acquisition unit 31.

[0134] The first determining unit 33 is used to determine that the container to be placed has been successfully placed if the relative positional relationship between the container to be placed and the flatcar obtained from the judging unit 32 meets the preset conditions.

[0135] The second determining unit 34 is used to determine that the container to be placed has failed to be placed if the relative positional relationship between the container to be placed and the flatcar obtained from the determining unit 32 does not meet the preset conditions, and to issue a reminder.

[0136] Furthermore, as a response to the above Figure 2 In addition to the method shown, this embodiment of the invention also provides another intelligent box-dropping detection device for detecting the above-mentioned... Figure 2 The method shown is implemented accordingly. This device embodiment corresponds to the foregoing method embodiment. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiment. Figure 4 As shown, the device includes:

[0137] The first acquisition unit 31 is used to acquire first target data and second target data; wherein, the first target data includes at least the edge information of the train flatcar used to store the container to be placed and the position information of the F-TR lock installed on the train flatcar; the second target data includes at least the edge information of the train flatcar that has stored the container to be placed, the position information of the F-TR lock that is not covered by the container to be placed, and the edge information of the container to be placed.

[0138] The judgment unit 32 is used to determine whether the relative positional relationship between the container to be placed and the flatcar meets the preset conditions based on the first target data and the second target data obtained from the first acquisition unit 31.

[0139] The first determining unit 33 is used to determine that the container to be placed has been successfully placed if the relative positional relationship between the container to be placed and the flatcar obtained from the judging unit 32 meets the preset conditions.

[0140] The second determining unit 34 is used to determine that the container to be placed has failed to be placed if the relative positional relationship between the container to be placed and the flatcar obtained from the determining unit 32 does not meet the preset conditions, and to issue a reminder.

[0141] The second acquisition unit 35 is used to acquire the real-time height information of the spreader that has grabbed the container to be placed;

[0142] The first display unit 36 ​​is used to perform data analysis using a laser point cloud algorithm based on the real-time height information of the spreader that has grabbed the container to be placed, obtained from the second acquisition unit 35, and to display the height relative position of the spreader that has grabbed the container to be placed and the flatcar of the train through a display screen in a side view.

[0143] The second display unit 37 is used to perform data analysis based on the first target data and the second target data obtained from the first acquisition unit 31 using a laser point cloud algorithm, and to display the relative positions of the container to be placed and the train flatcar obtained from the analysis on a display screen in a top view.

[0144] Furthermore, the determination unit 32 includes:

[0145] The acquisition module 321 is used to acquire the number of F-TR locks that do not overlap with the container to be placed, based on the first target data and the second target data.

[0146] The judgment module 322 is used to determine whether the number of F-TR locks that do not overlap with the container to be placed, obtained from the acquisition module 321, is not greater than 0.

[0147] Furthermore, the second determining unit 34 includes:

[0148] The first judgment module 341 is used to determine whether the number of F-TR locks that do not overlap with the container to be placed is an even number if the number of F-TR locks that do not overlap with the container to be placed is greater than 0.

[0149] The second judgment module 342 is used to determine whether the F-TR locks that do not overlap with the container to be placed are located on the same side of the container to be placed if the number of F-TR locks that do not overlap with the container to be placed obtained from the first judgment module 341 is an even number.

[0150] The first determining module 343 is used to determine that the type of failure of the container to be placed is parallel offset if the F-TR lock that does not overlap with the container to be placed, as obtained from the second determining module 342, is located on the same side of the container to be placed.

[0151] The second determining module 344 is used to determine that the type of failure of the container to be placed is tilting if the F-TR lock that does not overlap with the container to be placed, as obtained from the second determining module 342, is not located on the same side of the container to be placed.

[0152] Furthermore, the second determining unit 34 also includes:

[0153] The second determining module 344 is further configured to determine that the type of failure of the container to be placed is tilting if the number of F-TR locks that do not overlap with the container to be placed is not an even number, as determined by the first determining module 341.

[0154] Furthermore, the first determining unit 33 includes:

[0155] The acquisition module 331 is used to acquire the distance between the edge of the container to be placed and the edge of the corresponding flatcar based on the first target data and the second target data if the number of F-TR locks that do not overlap with the container to be placed is not greater than 0.

[0156] The judgment module 332 is used to determine whether the distance between the edge of the container to be placed and the edge of the corresponding flatcar obtained from the acquisition module 331 is within a preset threshold range;

[0157] The first determining module 333 is used to determine that the container to be placed has been successfully placed if the distance between the edge of the container to be placed and the edge of the corresponding flatcar obtained from the determining module 332 is within a preset threshold range.

[0158] The second determining module 334 is used to determine that the container to be placed has failed to be placed if the distance between the edge of the container to be placed and the edge of the corresponding flatcar obtained from the determining module 332 is not within a preset threshold range.

[0159] Furthermore, the first target data and the second target data are collected by a laser point cloud sensor, processed by a switch, and then stored in a programmable logic controller.

[0160] The first acquisition unit 31 includes:

[0161] The first acquisition module 311 is used to acquire the first target data using the programmable logic controller when the height position of the spreader that has grabbed the container to be placed reaches a preset height.

[0162] The second acquisition module 312 is used to acquire the second target data using the programmable logic controller or the laser point cloud sensor when the container to be placed is placed on the flatcar of the train.

[0163] Furthermore, embodiments of the present invention also provide a processor for running a program, wherein the program executes the above-described... Figure 1-2 The intelligent box-dropping detection method described in the article.

[0164] Furthermore, embodiments of the present invention also provide a storage medium for storing a computer program, wherein the computer program, when running, controls the device where the storage medium is located to execute the above-described... Figure 1-2 The intelligent box-dropping detection method described in the article.

[0165] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0166] It is understood that the relevant features in the above methods and apparatus can be referenced interchangeably. Furthermore, the terms "first," "second," etc., in the above embodiments are used to distinguish between embodiments and do not represent the superiority or inferiority of any particular embodiment.

[0167] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0168] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0169] In addition, the memory may include non-permanent memory in computer-readable media, such as 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.

[0170] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0171] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0172] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0173] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0174] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0175] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0176] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0177] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0178] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0179] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for intelligent box dropping detection, characterized in that, The method includes: Acquire first target data and second target data; wherein, the first target data includes at least the edge information of the train flatcar used to store the container to be placed and the position information of the F-TR lock installed on the train flatcar; the second target data includes at least the edge information of the train flatcar that has stored the container to be placed, the position information of the F-TR lock that is not obscured by the container to be placed, and the edge information of the container to be placed. Based on the first target data and the second target data, determine whether the relative positional relationship between the container to be placed and the train flatcar meets the preset conditions; If so, then the container to be placed has been successfully placed. If not, it is determined that the container to be placed has failed to be placed, and a reminder is issued; The step of determining whether the relative positional relationship between the container to be placed and the train flatcar meets preset conditions based on the first target data and the second target data includes: The number of F-TR locks that do not overlap with the container to be placed is obtained based on the first target data and the second target data; Determine whether the number of F-TR locks that do not overlap with the container to be placed is not greater than 0; The method further includes: if the number of F-TR locks that do not overlap with the container to be placed is not greater than 0, then the distance between the edge of the container to be placed and the edge of the corresponding flatcar is obtained according to the first target data and the second target data; Determine whether the distance between the edge of the container to be placed and the edge of the corresponding flatcar is within a preset threshold range; If so, then the container to be placed has been successfully placed. If not, then the container to be placed has failed to be placed.

2. The method according to claim 1, characterized in that, If the relative positional relationship between the container to be placed and the train flatcar, determined based on the first target data and the second target data, does not meet the preset conditions, then the container placement is determined to have failed, including: If the number of F-TR locks that do not overlap with the container to be placed is greater than 0, then it is determined whether the number of F-TR locks that do not overlap with the container to be placed is an even number; If the number of F-TR locks that do not overlap with the container to be placed is even, then it is determined whether the F-TR locks that do not overlap with the container to be placed are located on the same side of the container to be placed. If the F-TR lock that does not coincide with the container to be placed is located on the same side of the container to be placed, then the type of failure of the container to be placed is determined to be parallel offset. If the F-TR lock that does not coincide with the container to be placed is not located on the same side as the container to be placed, then the type of failure of the container to be placed is determined to be tilting.

3. The method according to claim 2, characterized in that, After determining whether the number of F-TR locks that do not overlap with the container to be placed is even, the method further includes: If the number of F-TR locks that do not overlap with the container to be placed is not an even number, then the type of failure to place the container is determined to be tilting.

4. The method according to any one of claims 1-3, characterized in that, The first target data and the second target data are collected by a laser point cloud sensor, processed by a switch, and then stored in a programmable logic controller. The acquisition of the first target data and the second target data includes: When the height of the spreader that has grabbed the container to be placed reaches the preset height, the programmable logic controller acquires the first target data. When the container to be placed is placed on the flatcar of the train, the second target data is acquired using the programmable logic controller or the laser point cloud sensor.

5. The method according to claim 4, characterized in that, The method further includes: Obtain the real-time height information of the spreader that has grabbed the container to be placed; Based on the real-time height information of the spreader that has grabbed the container to be placed, data analysis is performed using a laser point cloud algorithm. The relative heights of the spreader that has grabbed the container to be placed and the flatcar are then displayed on a screen in a side view. Based on the first target data and the second target data, data analysis is performed using a laser point cloud algorithm. The relative positions of the container to be placed and the train flatcar obtained from the analysis are displayed on a screen in a top-down view.

6. A device for intelligent box dropping detection, characterized in that, include: The first acquisition unit is used to acquire first target data and second target data; wherein, the first target data includes at least the edge information of the train flatcar used to store the container to be placed and the position information of the F-TR lock installed on the train flatcar; the second target data includes at least the edge information of the train flatcar that has stored the container to be placed, the position information of the F-TR lock that is not obscured by the container to be placed, and the edge information of the container to be placed. The judgment unit is used to determine, based on the first target data and the second target data, whether the relative positional relationship between the container to be placed and the train flatcar meets the preset conditions; The first determining unit is used to determine that the container to be placed has been successfully placed if the relative positional relationship between the container to be placed and the flatcar meets the preset conditions. The second determining unit is configured to determine that the container to be placed has failed to be placed if the relative positional relationship between the container to be placed and the train flatcar does not meet preset conditions, and to issue a reminder; the step of determining whether the relative positional relationship between the container to be placed and the train flatcar meets preset conditions based on the first target data and the second target data includes: The number of F-TR locks that do not overlap with the container to be placed is obtained based on the first target data and the second target data; Determine whether the number of F-TR locks that do not overlap with the container to be placed is not greater than 0; The device further includes: if the number of F-TR locks that do not overlap with the container to be placed is not greater than 0, then the distance between the edge of the container to be placed and the edge of the corresponding flatcar is obtained according to the first target data and the second target data; Determine whether the distance between the edge of the container to be placed and the edge of the corresponding flatcar is within a preset threshold range; If so, then the container to be placed has been successfully placed. If not, then the container to be placed has failed to be placed.

7. A storage medium comprising a stored program, characterized in that, When the program is running, it controls the device containing the storage medium to execute the intelligent box-drop detection method according to any one of claims 1 to 5.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the intelligent box-dropping detection method as described in any one of claims 1 to 5.

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