A shore power system mounting platform mobile control method, device and electronic equipment
By acquiring the positioning data of the shore power platform and using topology maps and various algorithms to generate the optimal path, the problem of low mobility of existing shore power systems has been solved, and autonomous movement and efficient scheduling of shore power systems have been realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- WUHAN UNIV OF TECH
- Filing Date
- 2024-09-02
- Publication Date
- 2026-08-04
AI Technical Summary
The movement of existing shore power systems mainly relies on manual operation or remote control, resulting in low positioning accuracy and movement efficiency, and an inability to quickly adapt to situations such as ship berthing position deviations.
By acquiring the positioning data of the shore power platform, the optimal path is generated based on the topology map, and theoretical motion state information is calculated using technologies such as extended Kalman filtering, Dijkstra algorithm, and trajectory tracking control, thus enabling the autonomous movement of the shore power platform.
It enables rapid and accurate scheduling of the shore power system, improves port efficiency, reduces manpower burden, and ensures the autonomous movement and positioning accuracy of shore power equipment.
Smart Images
Figure CN119200466B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of mobile power transmission technology, and in particular to a mobile control method, device and electronic device for a shore power system platform. Background Technology
[0002] With the development of green ports, shore power systems are gradually replacing the traditional method of ships using their own power supply systems. A shore power system consists of three parts: the shore power supply system, cable connection equipment, and the ship's power receiving system. The power supply interfaces for shore power systems are mainly fixed junction boxes and mobile vehicle-mounted systems. Current technology allows the shore power supply system to be installed as a complete set on a transport vehicle. When a ship needs to connect to shore power, the transport vehicle delivers the system to the port berth. Currently, the operation and driving of the transport vehicle is still manual, which is inefficient and requires driver training. Autonomous mobile control and remote dispatching of the vehicle-mounted shore power system can improve port efficiency and reduce manpower burden. Therefore, a solution for autonomous mobile control of the vehicle-mounted shore power system is needed.
[0003] Currently, the movement of vehicle-mounted shore power systems relies primarily on manual operation, controlled by a driver or remotely. This mainly involves controlling the system's movement between power supply interfaces at various berths. However, when situations arise, such as ship berthing position deviations, requiring repositioning, this process cannot be performed quickly. By employing multi-positioning information fusion technology to obtain positioning data combined with a vehicle chassis-based movement control algorithm, autonomous movement of the vehicle-mounted shore power equipment can be achieved while maintaining positioning accuracy and movement efficiency, thereby improving terminal operational efficiency. Summary of the Invention
[0004] In view of this, it is necessary to provide a method, device and electronic device for controlling the movement of a shore power system platform, in order to solve the technical problem that the movement of shore power systems in the prior art mainly relies on manual operation, which is carried out by a driver or remote control, resulting in low positioning accuracy and movement efficiency.
[0005] To address the above problems, this invention provides a method for controlling the movement of a shore power system platform, comprising: Obtain the positioning data of the shore power platform; Based on the topology map, an optimal path is generated according to the preset target point location and the positioning data; and based on the total path distance of the optimal path, the target status information of the shore power platform at each time node is calculated, wherein the status information includes location information, driving direction and speed information. Based on the error information between the real-time status information of the shore power platform and the target status information at the corresponding time node, the theoretical motion status information of the shore power platform is determined. Based on the theoretical motion state information, the shore power platform is controlled to move along the optimal path to the preset target point.
[0006] In one possible implementation, acquiring the positioning data of the shore power platform includes: Acquire positioning data based on differential GPS, inertial navigation system data, and drive wheel encoder data; The positioning data from the differential GPS, the inertial navigation system, and the drive wheel encoder are fused to obtain the fused positioning data.
[0007] In one possible implementation, fusing the positioning data from the differential GPS, the inertial navigation system data, and the data from the drive wheel encoder to obtain fused positioning data includes: The vehicle's state information is predicted using a pre-defined extended Kalman filter method based on data from the inertial navigation system and drive wheel encoders to obtain initial positioning data. Based on the preset extended Kalman filter method, the initial positioning data is modified according to the positioning data of differential GPS to obtain the fused positioning data.
[0008] In one possible implementation, generating the optimal path based on the topology map, according to the preset target point location and the positioning data, includes: Establish a topology map that matches GPS positioning information in the working port environment; Based on the preset target point location and the fused positioning data, the preset Dijkstra algorithm is used to generate the optimal path.
[0009] In one possible implementation, calculating the target state information of the shore power platform at each time node based on the total path distance of the optimal path includes: Based on the total path distance and the principle of smooth driving, the functions of speed versus time and distance versus time are determined. Based on the functions of velocity and distance over time, the target state information of the shore power platform at each time node is determined using the inverse solution method.
[0010] In one possible implementation, determining the theoretical motion state information of the shore power platform based on the error information between the real-time state information of the shore power platform and the target state information at the corresponding time node includes: Obtain the real-time orientation information of the shore power platform and determine the orientation error between the real-time orientation information and the target orientation information; A left-handed coordinate system is established with the center of the shore power platform as the origin and the direction of travel as the x-axis; and the real-time position information and orientation error are converted into the left-handed coordinate system. Using x-axis error, y-axis error, angle error, theoretical velocity, and theoretical angular velocity as inputs, a preset controller is used to obtain theoretical motion state information.
[0011] One possible implementation also includes: Based on obstacle information monitored and fed back by lidar, the shore power platform is controlled to stop operating.
[0012] Secondly, the present invention also provides a shore power system platform mobility control device, comprising: The acquisition module is used to acquire the positioning data of the shore power platform. The target status information determination module is used to generate an optimal path based on a topology map, according to the preset target point location and the positioning data; and to calculate the target status information of the shore power platform at each time node based on the total path distance of the optimal path, wherein the status information includes location information, driving direction and speed information. The theoretical motion state information determination module is used to determine the theoretical motion state information of the shore power platform based on the error information between the real-time state information of the shore power platform and the target state information at the corresponding time node. The motion module is used to control the shore power platform to move along the optimal path to the preset target point position based on the theoretical motion state information.
[0013] Thirdly, the present invention also provides an electronic device, comprising: a processor and a memory; The memory stores a computer-readable program that can be executed by the processor; When the processor executes the computer-readable program, it implements the steps in the shore power system mounted platform mobility control method as described above.
[0014] Fourthly, the present invention also provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the shore power system mounted platform mobility control method as described above.
[0015] The beneficial effects of this invention are as follows: First, the positioning data of the shore power platform is acquired; then, based on the topology map of the shore power system's working port, an optimal path is generated according to the preset target point location and the positioning data; and based on the total distance of the optimal path, the target status information of the shore power platform at each time node is calculated, where the status information includes position information, driving direction, and speed information; thus, based on the error information between the real-time status information of the shore power platform and the target status information at the corresponding time node, the theoretical motion status information of the shore power platform is determined; finally, based on the theoretical motion status information, the shore power platform is controlled to move along the optimal path to the preset target point location. This achieves rapid and accurate processing and scheduling of mobile shore power equipment, ensuring smooth operation, and eliminating the inefficiency caused by the need for manual driving or remote operation. Attached Figure Description
[0016] Figure 1 A flowchart illustrating an embodiment of the shore power system platform mobility control method provided by the present invention; Figure 2 A schematic diagram of the structure of the shore power system mounting platform in the shore power system mounting platform movement control method provided by the present invention; Figure 3 A flowchart of an embodiment of step S102 in the shore power system platform mobility control method provided by the present invention; Figure 4 A flowchart of an embodiment of step S103 in the shore power system platform mobility control method provided by the present invention; Figure 5 This is a schematic diagram of an embodiment of the shore power system platform movement control device provided by the present invention; Figure 6 This is a schematic diagram of the operating environment of an embodiment of the electronic device provided by the present invention. Detailed Implementation
[0017] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.
[0018] The power supply interfaces of onshore power supply systems are mainly fixed junction boxes and mobile vehicle-mounted types. Current technology allows onshore power supply systems to be installed as a complete set on a transport vehicle. The onshore power supply system is moved by a mobile platform, which can be a transport vehicle, container, self-propelled platform, trailer frame, platform vehicle, driverless vehicle, or mobile generator vehicle, etc.
[0019] A specific embodiment of the present invention discloses a method for controlling the movement of a shore power system platform. Please refer to [link to relevant documentation]. Figure 1,include: S101. Obtain the positioning data of the shore power platform; S102. Based on the topology map of the working port of the shore power system, generate the optimal path according to the preset target point location and the positioning data; and calculate the target status information of the shore power platform at each time node according to the total path distance of the optimal path, wherein the status information includes location information, driving direction and speed information. S103. Based on the error information between the real-time status information of the shore power platform and the target status information at the corresponding time node, determine the theoretical motion status information of the shore power platform. S104. Based on the theoretical motion state information, control the shore power platform to move along the optimal path to the preset target point.
[0020] In this embodiment, the positioning data of the shore power platform is first acquired. Then, based on the topology map of the shore power system's working port, an optimal path is generated according to the preset target point location and the positioning data. Based on the total distance of the optimal path, the target status information of the shore power platform at each time node is calculated, including location information, driving direction, and speed information. Thus, based on the error information between the real-time status information of the shore power platform and the target status information at the corresponding time node, the theoretical motion status information of the shore power platform is determined. Finally, based on the theoretical motion status information, the shore power platform is controlled to move along the optimal path to the preset target point. This achieves rapid and accurate processing and scheduling of mobile shore power equipment, ensuring smooth operation and eliminating the inefficiency caused by requiring manual driving or remote operation.
[0021] In step S101, the positioning data of the shore power platform is fused data, including positioning data based on differential GPS, inertial navigation system data, and data from the drive wheel encoder. Specifically, as shown... Figure 2 As shown, a 5G communication module is first installed on the top of the container to communicate with the dispatching platform. A differential GPS and inertial measurement unit are installed, combined with the vehicle drive wheel encoders to form a fusion positioning module. An industrial control computer is used as the vehicle control module to receive positioning and communication data and control the vehicle chassis. LiDAR is installed at the front and rear of the vehicle as an obstacle avoidance module.
[0022] Furthermore, positioning data can be fused using methods such as Kalman filtering, particle filtering, weighted averaging, or deep learning. Since Kalman filtering is suitable for real-time fusion of dynamic systems and is particularly well-suited for updating the real-time position of shore power platforms, this embodiment selects Kalman filtering. Based on data from the inertial navigation system and drive wheel encoders, the vehicle's state information is predicted to obtain initial positioning data. Then, based on a preset extended Kalman filter, the initial positioning data is refined using differential GPS positioning data to obtain the fused positioning data. Extended Kalman filtering is a data fusion method used to combine sensor data from different sources. It leverages the short-term high accuracy of inertial navigation and drive wheel encoders and the long-term stability of differential GPS to provide more accurate positioning.
[0023] Specifically, the system state equations are established based on the vehicle inertial navigation and positioning model, and then a Taylor expansion of the state equations is performed, discarding higher-order terms, to obtain the system state equations:
[0024] ; ; ; .
[0025] In the formula This is the predicted value of the system at time k; This represents the optimal estimate of the system state at time k −1; These represent the vehicle's x-coordinate, y-coordinate, heading angle, velocity, angular velocity, and acceleration at time k, respectively; T represents the time interval. , , and This is an intermediate parameter.
[0026] Using GPS positioning data as observation data, the latitude and longitude information returned by GPS is first converted to planar coordinates in the Gaussian plane coordinate system through Gaussian projection, and then converted to the local navigation plane coordinate system. The system's observation equations are then established based on the GPS positioning information.
[0027] In the formula The system's observation noise is represented by the noise in the x-direction, y-direction, heading angle, and velocity directions in the GPS returned information, respectively. For system status, For system observations.
[0028] After establishing the system's state and observation equations, the vehicle's operating state can be predicted based on the extended Kalman filter's state prediction and observation update steps. The fusion positioning system processes the data using the extended Kalman filter algorithm to obtain the location of the onboard shore power unit, which is then used for vehicle motion control. When the vehicle has no scheduling tasks, only low-frequency differential GPS positioning data is acquired and sent to the scheduling platform for system location monitoring.
[0029] In step S102, the optimal path is generated based on the preset target point location and positioning data, which can employ Dijkstra's algorithm, A* algorithm, genetic algorithm, or reinforcement learning algorithm. In a specific embodiment of step S102, the optimal path is generated based on the topology map of the shore power system's working port, according to the preset target point location and the positioning data. Please refer to [link to relevant documentation]. Figure 3 ,include: S301. Establish a topology map that matches GPS positioning information in the working port environment; S302. Based on the preset target point location and the fused positioning data, the preset Dijkstra algorithm is used to generate the optimal path.
[0030] In this embodiment, a topological map of the working port environment is established and matched with the port's GPS positioning information. Key nodes such as main passages, intersections, ship berthing areas, and parking areas are defined on the topological map. After obtaining target point information from the scheduling platform via a 5G communication module, the vehicle-mounted shore power device uses Dijkstra's algorithm to generate the optimal path passing through the key points. Based on the total path distance, an S-shaped speed curve is generated, and the vehicle's travel distance and speed over time are calculated. Through reverse engineering, the vehicle's position, orientation, and speed information at each time point in the entire movement process are planned.
[0031] Inverse solving refers to working backward from the target state (such as target position and velocity) to the initial state to determine the vehicle's position, orientation, and velocity information at each time point. By integrating the velocity function and calculating the position function, the vehicle's position changes and orientation along the entire path are determined.
[0032] In some embodiments, the theoretical motion state information of the shore power platform is determined based on the error information between the real-time state information of the shore power platform and the target state information at the corresponding time node. Please refer to [link to relevant documentation]. Figure 4 ,include: S401. Obtain the real-time orientation information of the shore power platform and determine the orientation error between the real-time orientation information and the target orientation information; S402. Establish a left-handed coordinate system with the center of the shore power platform as the origin and the direction of travel as the x-axis; and convert the real-time position information and orientation error into the left-handed coordinate system. S403: Using x-axis error, y-axis error, angle error, theoretical velocity, and theoretical angular velocity as inputs, a preset controller is used to obtain theoretical motion state information.
[0033] In this embodiment, trajectory tracking control is a control method designed to enable a vehicle to follow a predetermined path. The controller reduces this error by calculating the error between the current position and the target position in real time and adjusting the control inputs (such as acceleration and steering angle), thereby allowing the vehicle to travel along an ideal trajectory. Error feedback (position and angle errors) is used to adjust control signals (such as speed and angular velocity) so that the vehicle can accurately follow the predetermined trajectory.
[0034] Model Predictive Control (MPC) is an optimization-based control method that uses a dynamic model of the system to predict future states and minimizes prediction errors by optimizing the control input. MPC takes into account the system's constraints and objectives to generate optimal control inputs.
[0035] PID control (Proportional-Integral-Derivative Control) is a classic feedback control method that uses proportional, integral, and derivative terms to adjust the control input to reduce errors.
[0036] In a specific embodiment, backstepping control is used as the trajectory control method. Backstepping is a control method based on Lyapunov stability and feedback control theory. Its basic principle is to decompose and simplify a complex high-order system into multiple simple subsystems. Then, a virtual control variable is selected for each subsystem, and an appropriate Lyapunov function is chosen based on the virtual control variable and the corresponding subsystem. Finally, stability analysis is performed on the Lyapunov functions of each subsystem. By ensuring the asymptotic stability of each subsystem, the asymptotic stability of the entire system is guaranteed. The control law of the system is designed based on the asymptotic stability conditions of each subsystem.
[0037] Specifically, the x-axis error, y-axis error, angle error, ideal velocity, and ideal angular velocity are input into the controller, and the specific control law is as follows:
[0038] In the formula, v is the output speed control value, and w is the output angular velocity control value. For the adjustable control variables of the system, For coordinate error and angle error, For ideal velocity and ideal angular velocity.
[0039] The speed of the vehicle's drive wheels and the angle of the steering wheels are obtained by solving the equations. These speed and angle control values are then sent to the vehicle's chassis actuators to achieve trajectory tracking control of the vehicle.
[0040] Furthermore, during the vehicle's movement, it will use lidar deployed along its direction of travel to detect non-preset fixed obstacles in its path, and promptly control the vehicle to stop based on the set braking distance to prevent damage to the vehicle and port facilities.
[0041] Based on the above-described shore power system platform mobility control method, this invention also provides a shore power system platform mobility control device. Please refer to [link to relevant documentation]. Figure 5 ,include: The acquisition module 510 is used to acquire the positioning data of the shore power platform. The target status information determination module 520 is used to generate an optimal path based on the topology map of the working port of the shore power system, according to the preset target point location and the positioning data; and to calculate the target status information of the shore power platform at each time node based on the total path distance of the optimal path, wherein the status information includes location information, driving direction and speed information. The theoretical motion state information determination module 530 is used to determine the theoretical motion state information of the shore power platform based on the error information between the real-time state information of the shore power platform and the target state information at the corresponding time node. The motion module 540 is used to control the shore power platform to move along the optimal path to the preset target point position based on the theoretical motion state information.
[0042] like Figure 6 As shown, based on the aforementioned shore power system platform mobility control method, this invention also provides an electronic device, which can be a mobile terminal, desktop computer, laptop, handheld computer, server, or other computing electronic device. The electronic device includes a processor 610, a memory 620, and a display 630. Figure 6 Only some components of the electronic device are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.
[0043] In some embodiments, memory 620 may be an internal storage unit of the electronic device, such as a hard disk or memory. In other embodiments, memory 620 may be an external storage device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Furthermore, memory 620 may include both internal and external storage devices. Memory 620 is used to store application software and various types of data installed on the electronic device, such as program code installed on the electronic device. Memory 620 may also be used to temporarily store data that has been output or will be output. In one embodiment, memory 620 stores a shore power system platform mobility control program 640, which can be executed by processor 610 to implement the shore power system platform mobility control method of the various embodiments of this application.
[0044] In some embodiments, processor 610 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 620 or process data, such as executing a shore power system platform mobility control method.
[0045] In some embodiments, display 630 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 630 is used to display information from the motion control electronics on the shore power system platform and to display a visual user interface. Components 610-630 of the electronic equipment communicate with each other via a system bus.
[0046] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.
[0047] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for controlling the movement of a platform mounted on a shore power system, characterized in that, include: Obtain the positioning data of the shore power platform; Based on the topology map, an optimal path is generated according to the preset target point location and the positioning data; Based on the total path distance of the optimal path, the target status information of the shore power platform at each time node is calculated, including position information, driving orientation and speed information. Based on the error information between the real-time status information of the shore power platform and the target status information at the corresponding time node, the theoretical motion status information of the shore power platform is determined, including: acquiring the real-time orientation information of the shore power platform and determining the orientation error between the real-time orientation information and the target orientation information; establishing a left-handed coordinate system with the center of the shore power platform as the origin and the orientation of the forward direction as the x-axis; and converting the real-time position information and orientation error into the left-handed coordinate system; using the x-axis error, y-axis error, angle error, theoretical velocity, and theoretical angular velocity as inputs, and employing a preset controller, the theoretical motion status information is obtained. Based on the theoretical motion state information, the shore power platform is controlled to move along the optimal path to the preset target point. The x-axis error, y-axis error, angle error, ideal velocity, and ideal angular velocity are input into the controller, and the specific control law is as follows: In the formula, v is the output speed control value, and w is the output angular velocity control value. For the adjustable control variables of the system, For coordinate error and angle error, For ideal velocity and ideal angular velocity; The speed of the vehicle's drive wheels and the angle of the steering wheels are obtained by solving the problem. The speed and angle control values are then sent to the vehicle's chassis actuators to achieve trajectory tracking control of the vehicle.
2. The shore power system platform movement control method according to claim 1, characterized in that, The acquisition of the location data of the shore power platform includes: Acquire positioning data based on differential GPS, inertial navigation system data, and drive wheel encoder data; The positioning data from the differential GPS, the inertial navigation system, and the drive wheel encoder are fused to obtain the fused positioning data.
3. The shore power system platform movement control method according to claim 2, characterized in that, The process of fusing the positioning data from the differential GPS, the inertial navigation system, and the drive wheel encoder to obtain fused positioning data includes: The vehicle's state information is predicted using a pre-defined extended Kalman filter method based on data from the inertial navigation system and drive wheel encoders to obtain initial positioning data. Based on the preset extended Kalman filter method, the initial positioning data is modified according to the positioning data of differential GPS to obtain the fused positioning data.
4. The shore power system platform movement control method according to claim 2, characterized in that, The step of generating an optimal path based on a topology map, according to the preset target point location and the positioning data, includes: Establish a topology map that matches GPS positioning information in the working port environment; Based on the preset target point location and the fused positioning data, the preset Dijkstra algorithm is used to generate the optimal path.
5. The shore power system platform movement control method according to claim 2, characterized in that, The calculation of the target status information of the shore power platform at each time node based on the total path distance of the optimal path includes: Based on the total path distance and the principle of smooth driving, the functions of speed versus time and distance versus time are determined. Based on the functions of velocity and distance over time, the target state information of the shore power platform at each time node is determined using the inverse solution method.
6. The shore power system platform movement control method according to claim 1, characterized in that, Also includes: Based on obstacle information monitored and fed back by lidar, the shore power platform is controlled to stop operating.
7. A platform movement control device for a shore power system, characterized in that, include: The acquisition module is used to acquire the positioning data of the shore power platform. The target status information determination module is used to generate an optimal path based on a topology map, according to the preset target point location and the positioning data; Based on the total path distance of the optimal path, the target status information of the shore power platform at each time node is calculated, including position information, driving orientation and speed information. The theoretical motion state information determination module is used to determine the theoretical motion state information of the shore power platform based on the error information between the real-time state information of the shore power platform and the target state information at the corresponding time node. This includes: acquiring the real-time orientation information of the shore power platform and determining the orientation error between the real-time orientation information and the target orientation information; establishing a left-handed coordinate system with the center of the shore power platform as the origin and the orientation of the forward direction as the x-axis; converting the real-time position information and orientation error into the left-handed coordinate system; and obtaining the theoretical motion state information using a preset controller with the x-axis error, y-axis error, angle error, theoretical velocity, and theoretical angular velocity as inputs. The motion module is used to control the shore power platform to move along the optimal path to the preset target point position based on the theoretical motion state information. The x-axis error, y-axis error, angle error, ideal velocity, and ideal angular velocity are input into the controller, and the specific control law is as follows: In the formula, v is the output speed control value, and w is the output angular velocity control value. For the adjustable control variables of the system, For coordinate error and angle error, For ideal velocity and ideal angular velocity; The speed of the vehicle's drive wheels and the angle of the steering wheels are obtained by solving the problem. The speed and angle control values are then sent to the vehicle's chassis actuators to achieve trajectory tracking control of the vehicle.
8. An electronic device, characterized in that, include: Processor and memory; The memory stores a computer-readable program that can be executed by the processor; When the processor executes the computer-readable program, it implements the steps of the shore power system platform mobility control method as described in any one of claims 1-6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, which can be executed by one or more processors to implement the steps in the shore power system platform mobility control method as described in any one of claims 1-6.