Operation control system, method, medium and product for engineering machinery vehicle

Abstract

The invention provides an operation control system and method for an engineering machinery vehicle, a medium and a product. The system comprises an RTK positioning system which comprises an RTK base station used for receiving a signal of a positioning system satellite, calculating differential data according to a measured position of the RTK base station obtained through calculation and a pre-stored actual position of the RTK base station, and transmitting the differential data to an RTK mobile station; the RTK mobile station is used for calculating the position of the RTK mobile station according to the received signal of the positioning system satellite, correcting the position of the RTK mobile station by using the differential data to obtain the corrected position of the RTK mobile station, taking the corrected position as the position of the engineering machinery vehicle, and sending the position of the engineering machinery vehicle to the remote platform; the engineering machinery vehicle is used for sending the state data of the engineering machinery vehicle to the remote platform; and the remote platform is used for automatically allocating operation tasks to the engineering machinery vehicles according to operation requirements and the position and state data of the engineering machinery vehicles.

Description

Technical Field

[0001] The present disclosure relates to the control field of engineering machinery, and in particular to an operation control system, method, medium and product for an engineering machinery vehicle. Background Art

[0002] With the rapid development of modern engineering construction, construction machinery products, as important construction equipment, play a key role in the intelligent construction process of various engineering projects. At the same time, with the rapid development of science and technology, unmanned driving technology has shown great application potential and value in many fields. However, to achieve the precise operation of unmanned engineering machinery vehicles, efficient and reliable positioning technology is the key. Traditional positioning methods, such as GPS (Global Positioning System) positioning alone, although it can provide certain location information, its positioning accuracy often cannot meet the needs of high-precision operations in complex construction environments. Therefore, in related technologies, the positioning accuracy of engineering machinery vehicles is relatively low, which affects the implementation of engineering operations. Summary of the invention

[0003] A technical problem solved by the present disclosure is that in the related art, the positioning accuracy of engineering machinery vehicles is relatively low, which affects the implementation of engineering operations.

[0004] According to one aspect of the present disclosure, there is provided an operation control system for an engineering machinery vehicle, wherein the engineering machinery vehicle is an unmanned engineering machinery vehicle, and the operation control system comprises: an RTK positioning system, comprising: an RTK base station, for receiving signals from positioning system satellites, calculating the measured position of the RTK base station itself through the signals from the positioning system satellites, calculating differential data according to the measured position of the RTK base station itself and the actual position of the RTK base station stored in advance, and transmitting the differential data to an RTK mobile station; the RTK mobile station, installed on the engineering machinery vehicle, for receiving signals from the positioning system satellites, calculating the position of the RTK mobile station according to the received signals from the positioning system satellites, and correcting the position of the RTK mobile station using the differential data to obtain the corrected position of the RTK mobile station as the position of the engineering machinery vehicle, and sending the position of the engineering machinery vehicle to a remote platform; the engineering machinery vehicle, for sending the status data of the engineering machinery vehicle to the remote platform; and the remote platform, for automatically allocating operation tasks to each engineering machinery vehicle according to operation requirements and the position and status data of each engineering machinery vehicle.

[0005] In some embodiments, the RTK mobile station includes: an RTK antenna for receiving signals from the positioning system satellite; and an RTK receiver for receiving the differential data from the RTK base station, and calculating the position of the engineering machinery vehicle based on the signals from the positioning system satellite received by the RTK antenna and the differential data.

[0006] In some embodiments, the RTK antenna includes an RTK positioning antenna and an RTK directional antenna, and the RTK positioning antenna and the RTK directional antenna are installed at the highest position directly above the cab of the engineering machinery vehicle, and the line connecting the phase center of the RTK positioning antenna and the phase center of the RTK directional antenna is consistent with or parallel to the central axis of the engineering machinery vehicle.

[0007] In some embodiments, the engineering machinery vehicle includes: an environmental perception sensor for detecting environmental data around the engineering machinery vehicle; a vehicle parameter sensor for detecting vehicle parameters of the engineering machinery vehicle, and the vehicle parameters include: oil pressure, temperature, speed, position and flow of the actuator in the engineering machinery vehicle.

[0008] In some embodiments, the status data of the engineering machinery vehicle includes: the environmental data, the vehicle parameters and the vehicle operation data of the engineering machinery vehicle, wherein the vehicle operation data includes engine data, chassis and driving data, safety system data and electrical and electronic system data.

[0009] In some embodiments, the engineering machinery vehicle includes: an operation planning module, which is used to receive engineering drawing data and terrain data, and automatically generate the operation path and operation parameters of the engineering machinery vehicle according to the operation task, the engineering drawing data, the terrain data and the position of the engineering machinery vehicle, so that the actuator of the engineering machinery vehicle performs corresponding operations.

[0010] In some embodiments, the engineering machinery vehicle also includes: an unmanned driving control system, which is used to control the actuator of the engineering machinery vehicle to perform corresponding operations according to the status data of the engineering machinery vehicle, the position of the engineering machinery vehicle, and the operating path and operating parameters of the engineering machinery vehicle.

[0011] In some embodiments, the unmanned driving control system is also used to autonomously locate the engineering machinery vehicle using the simultaneous positioning and mapping (SLAM) technology, and to calibrate the autonomously located position using the position of the engineering machinery vehicle obtained by the RTK positioning system.

[0012] In some embodiments, the unmanned driving control system is also used to re-plan the operating path of the engineering machinery vehicle when it is identified that there are obstacles in the operating area of ​​the engineering machinery vehicle based on environmental data around the engineering machinery vehicle, so as to obtain the optimal operating path of the engineering machinery vehicle, and control the actuator of the engineering machinery vehicle to perform corresponding operations according to the optimal operating path.

[0013] In some embodiments, the remote platform is also used to display the real-time location information, operation status, operation progress and environmental images of the engineering machinery vehicles, adjust the operation sequence for each engineering machinery vehicle, send operation instructions to each engineering machinery vehicle, and adjust the operation parameters in the operation task.

[0014] According to another aspect of the present disclosure, there is provided an operation control method for an engineering machinery vehicle, wherein the engineering machinery vehicle is an unmanned engineering machinery vehicle, and the operation control method comprises: using an RTK base station to receive signals from positioning system satellites, calculating the measured position of the RTK base station itself through the signals from the positioning system satellites, calculating differential data based on the measured position of the RTK base station itself and the actual position of the RTK base station stored in advance, and transmitting the differential data to an RTK mobile station installed on the engineering machinery vehicle; using the RTK mobile station to receive signals from the positioning system satellites, and calculating the position of the RTK mobile station based on the received signals from the positioning system satellites, and correcting the position of the RTK mobile station using the differential data to obtain the corrected position of the RTK mobile station as the position of the engineering machinery vehicle, and sending the position of the engineering machinery vehicle to a remote platform; using the engineering machinery vehicle to send the status data of the engineering machinery vehicle to the remote platform; and using the remote platform to automatically assign operation tasks to each engineering machinery vehicle according to operation requirements and the position and status data of each engineering machinery vehicle.

[0015] According to another aspect of the present disclosure, a job control system is provided, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute the job control method as described above based on instructions stored in the memory.

[0016] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which computer instructions are stored. When the computer instructions are executed by a processor, the job control method as described above is implemented.

[0017] According to another aspect of the present disclosure, a computer program product is provided. The computer program product includes a computer program or instructions. When the computer program or instructions are executed by a processor, the job control method as described above is implemented.

[0018] In the above-mentioned operation control system, RTK positioning technology is used to realize the positioning of engineering machinery vehicles, thereby improving the positioning accuracy of engineering machinery vehicles and assisting the unmanned driving control system to perform high-precision operations, thereby improving the accuracy of engineering machinery vehicle operations, facilitating the implementation of engineering operations, and improving construction efficiency.

[0019] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0021] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0022] Figure 1 is a block diagram schematically illustrating a structure of a job control system according to some embodiments of the present disclosure;

[0023] Figure 2 is a schematic diagram schematically illustrating an engineering machinery vehicle according to some embodiments of the present disclosure;

[0024] Figure 3 is a schematic diagram schematically showing the installation of an RTK positioning antenna and an RTK directional antenna according to some embodiments of the present disclosure;

[0025] Figure 4 is a flowchart illustrating a method for performing positioning by an RTK positioning system according to some embodiments of the present disclosure;

[0026] Figure 5 is a flowchart illustrating a job control method according to some embodiments of the present disclosure;

[0027] Figure 6 is a block diagram schematically showing a structure of a job control system according to other embodiments of the present disclosure;

[0028] Figure 7 is a block diagram schematically showing a structure of a job control system according to other embodiments of the present disclosure. DETAILED DESCRIPTION

[0029] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0030] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0031] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0032] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0033] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0034] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0035] Figure 1 Schematically shows a structural block diagram of an operation control system according to some embodiments of the present disclosure. The operation control system is used for an engineering machinery vehicle, which may be an unmanned engineering machinery vehicle.

[0036] like Figure 1 As shown, the operation control system includes an RTK (Real-Time Kinematic) positioning system 110 , an engineering vehicle 120 , and a remote platform 130 . The RTK positioning system 110 includes an RTK base station 111 and an RTK mobile station 112 .

[0037] The RTK base station 111 is used to receive signals from positioning system satellites (e.g., GPS satellites or other available satellites (such as Beidou satellites, Galileo satellites, etc.)), calculate the measured position of the RTK base station itself through the signals from the positioning system satellites, calculate differential data (or called differential correction values) based on the measured position of the RTK base station itself and the actual position (i.e., the real position) of the RTK base station stored in advance, and transmit the differential data to the RTK mobile station.

[0038] For example, the RTK base station is set up at a fixed location with known precise coordinates, such as a high ground near the working area of ​​the construction machinery vehicle 120 (e.g., an unmanned excavator), and is responsible for receiving GPS satellite signals and sending differential data to the RTK mobile station via wireless communication (e.g., 4G / 5G, Wi-Fi, or a dedicated radio network).

[0039] The RTK mobile station 112 is installed on the engineering machinery vehicle. The RTK mobile station 112 is used to receive the signal of the positioning system satellite, calculate the position of the RTK mobile station according to the received signal of the positioning system satellite, and correct the position of the RTK mobile station using the differential data to obtain the corrected position of the RTK mobile station as the position of the engineering machinery vehicle, and send the position of the engineering machinery vehicle to the remote platform 130. Here, a known method can be used, and the RTK mobile station combines the satellite signal data received by itself with the differential correction value to eliminate the error during positioning.

[0040] For example, a high-precision RTK positioning system can use a multi-frequency GNSS (Global Navigation Satellite System) receiver as the core component of the RTK mobile station and RTK base station to improve the signal capture capability and positioning accuracy of engineering vehicles (e.g., excavators) in bulk earthwork construction scenarios with sidewall material obstruction, so that engineering vehicles can obtain centimeter-level precision position information in real time during operation. In addition, a dedicated wireless data communication module can be used to enable real-time and stable data transmission between the base station and the mobile station, and high-precision positioning can be maintained even in weak signal areas.

[0041] The engineering machinery vehicle 120 is used to send the status data of the engineering machinery vehicle to the remote platform 130. For example, the engineering machinery vehicle 120 may be an engineering machinery vehicle such as an excavator, a loader, or a road roller. For another example, the engineering machinery vehicle 120 may be an unmanned engineering machinery vehicle. For example, the engineering machinery vehicle 120 is an unmanned excavator.

[0042] The remote platform 130 is used to automatically assign work tasks to each engineering machinery vehicle according to work requirements and the location and status data of each engineering machinery vehicle.

[0043] The remote platform 130 can assign work tasks corresponding to the position and state of each engineering machinery vehicle to each engineering machinery vehicle according to the position and state data of each engineering machinery vehicle and in combination with the work needs. For example, if a certain excavator (as an engineering machinery vehicle) is located at the position closest to a certain work area and the excavator is currently not working, the most work tasks can be assigned to the excavator so that the excavator can work.

[0044] So far, an operation control system according to some embodiments of the present disclosure is provided. The operation control system includes: an RTK positioning system, including: an RTK base station, which is used to receive signals from positioning system satellites, calculate the measured position of the RTK base station itself through the signals from the positioning system satellites, calculate differential data based on the measured position of the RTK base station itself and the actual position of the RTK base station stored in advance, and transmit the differential data to the RTK mobile station; the RTK mobile station is installed on an engineering machinery vehicle, which is used to receive signals from positioning system satellites, calculate the position of the RTK mobile station based on the received signals from the positioning system satellites, and correct the position of the RTK mobile station using the differential data to obtain the corrected position of the RTK mobile station as the position of the engineering machinery vehicle, and send the position of the engineering machinery vehicle to a remote platform; the engineering machinery vehicle is used to send the status data of the engineering machinery vehicle to the remote platform; and the remote platform is used to automatically assign operation tasks to each engineering machinery vehicle according to the operation requirements and the position and status data of each engineering machinery vehicle. In this operation control system, RTK positioning technology is used to realize the positioning of engineering machinery vehicles, thereby improving the positioning accuracy of engineering machinery vehicles and assisting the unmanned driving control system to perform high-precision operations, thereby improving the accuracy of engineering machinery vehicle operations, facilitating the implementation of engineering operations, and improving construction efficiency.

[0045] RTK technology achieves centimeter-level or even millimeter-level positioning accuracy by utilizing the relative position information of the Global Navigation Satellite System (GNSS) and the RTK base station, thus improving the reliability and accuracy of the positioning system. RTK technology can also improve the adaptability of excavators in complex construction environments. In environments with weak signals or many obstructions, RTK technology can use the differential information between the base station and the mobile station to correct positioning errors, maintain stable positioning accuracy, and ensure the normal operation of the excavator.

[0046] In some embodiments, the RTK mobile station includes: an RTK antenna and an RTK receiver. The RTK antenna is used to receive signals from positioning system satellites. The RTK receiver is used to receive differential data from the RTK base station, and calculate the position of the engineering machinery vehicle based on the signals and differential data of the positioning system satellites received by the RTK antenna. Here, known techniques can be used to calculate the precise position of the engineering machinery vehicle based on the signals and differential data of the positioning system satellites. The RTK receiver can receive differential data from the RTK base station through a wireless data communication module. In this embodiment, by setting the RTK antenna and the RTK receiver in the RTK mobile station, the positioning function of the RTK mobile station can be realized, thereby improving the positioning accuracy of the engineering machinery vehicle.

[0047] The RTK mobile station can directly send the location information of the construction machinery vehicle to the industrial computer on the vehicle, which is the hardware processing unit of the unmanned driving control system, through the CAN (Controller Area Network) bus or serial communication. The construction machinery vehicle can send relevant data (including location information) to the remote platform through the wireless communication unit.

[0048] The RTK receiver can be used to communicate with the RTK base station, receive differential data and calculate the high-precision position information, speed and attitude information of the unmanned excavator. Here, the speed and attitude information can be obtained through the high-precision MEMS gyroscope and accelerometer built into the RTK receiver.

[0049] In some embodiments, the RTK antenna includes an RTK positioning antenna and an RTK directional antenna. The RTK positioning antenna and the RTK directional antenna are installed at the highest position directly above the cab of the engineering machinery vehicle, and the line connecting the phase center of the RTK positioning antenna and the phase center of the RTK directional antenna is consistent with or parallel to the central axis direction of the engineering machinery vehicle. Here, the RTK positioning antenna and the RTK directional antenna can be the same or similar antennas, but are respectively connected to different interfaces of the RTK receiver. In this embodiment, by setting the installation position and installation method of the RTK positioning antenna and the RTK directional antenna, a good GNSS signal can be received as much as possible.

[0050] Figure 2 is a schematic diagram schematically illustrating an engineering machinery vehicle according to some embodiments of the present disclosure. Figure 3 It is a schematic diagram schematically showing the installation of an RTK positioning antenna and an RTK directional antenna according to some embodiments of the present disclosure.

[0051] like Figure 2 and Figure 3 As shown, the RTK positioning antenna 201 and the RTK directional antenna 202 are installed at the highest position directly above the cab of the engineering machinery vehicle 120, and the line connecting the phase center of the RTK positioning antenna 201 and the phase center of the RTK directional antenna 202 is consistent with or parallel to the central axis direction of the engineering machinery vehicle 120 (for example, the vehicle forward direction). Here, the phase center of the antenna refers to the physical center position of the antenna, and the data of the phase center is mainly used when measuring parameters during calibration. The central axis of the engineering machinery vehicle refers to a straight line in the front and rear direction of the engineering machinery vehicle and passing through the center of the engineering machinery vehicle.

[0052] By installing the RTK positioning antenna and the RTK directional antenna according to the above-mentioned installation position and installation method, it is possible to receive good GNSS signals as much as possible, thereby improving the positioning accuracy of the construction machinery vehicle.

[0053] In some embodiments, the engineering vehicle 120 includes an environment perception sensor, which is used to detect environmental data around the engineering vehicle.

[0054] For example, environmental perception sensors include cameras, lidar, ultrasonic radar, forward millimeter-wave radar, rotary encoders, inclinometer sensors, etc.

[0055] For example, Figure 2 As shown, a laser radar 212 and a camera 211 are installed above the front of the cab of an engineering machinery vehicle 120 (for example, an excavator), and the number is two or more. A forward millimeter-wave radar 213 is installed in front of the engineering machinery vehicle 120. A rotary encoder 215 is installed on the slewing platform above the slewing bearing of the engineering machinery vehicle 120. Inclination sensors 216 are installed at appropriate positions of the boom, dipper arm, and bucket of the engineering machinery vehicle 120. Ultrasonic radars 214 are installed on the front, back, left, and right sides of the engineering machinery vehicle 120. Cameras are installed on the left, right, and rear of the engineering machinery vehicle 120. The installation positions of the environmental perception sensors are not fully marked in the figure, and the type and number of environmental perception sensors can be adjusted according to different scenarios and needs.

[0056] The camera image and the point cloud data of the LiDAR can accurately identify and classify the target, effectively identify and track pedestrians, vehicles, large rocks and other targets. The point cloud data provided by the LiDAR can realize the functions of autonomous positioning, map construction and path planning of the excavator. The ultrasonic radar is used to monitor the distance information of the target within a short range to prevent unnecessary collisions. The forward millimeter-wave radar can quickly obtain the relative distance, speed, angle and direction of movement of the surrounding objects, and provide certain data information for the unmanned driving control system. The rotary encoder and inclination sensor can obtain the posture information of the construction machinery vehicle, and provide certain data information for the unmanned driving control system.

[0057] In some embodiments, the engineering vehicle further includes a vehicle parameter sensor. The vehicle parameter sensor is used to detect vehicle parameters of the engineering vehicle, and the vehicle parameters include: oil pressure, temperature, rotation speed, position and flow rate of the actuator in the engineering vehicle.

[0058] For example, vehicle parameter sensors include but are not limited to: oil pressure sensors, temperature sensors, speed sensors, position sensors, flow sensors, etc. Vehicle parameter sensors can monitor the operating status and parameters of construction machinery vehicles (e.g., excavators) in real time. Vehicle sensor data provides key information for the unmanned driving control system of the vehicle, enabling unmanned construction machinery vehicles to work more accurately, efficiently, and safely.

[0059] In some embodiments, the state data of the engineering machinery vehicle includes: environmental data, vehicle parameters and vehicle operation data of the engineering machinery vehicle. The vehicle operation data includes engine data, chassis and driving data, safety system data and electrical and electronic system data.

[0060] For example, engine data includes: engine start and stop time, speed, temperature, throttle opening, fuel consumption rate, etc.; chassis and driving data includes: vehicle speed, acceleration, braking status, steering angle, etc.; safety system data includes: airbag status, seat belt usage, collision detection data, etc.; electrical and electronic system data includes: battery voltage, ignition system status, lighting system status, etc. These data provide real-time feedback on vehicle performance, operating status, and external environment, which is of great significance for fault diagnosis and safety monitoring of unmanned construction machinery vehicles (for example, unmanned excavators).

[0061] In some embodiments, the engineering machinery vehicle includes an operation planning module. The operation planning module is used to receive engineering drawing data and terrain data, and automatically generate the operation path and operation parameters (for example, parameters such as excavation depth) of the engineering machinery vehicle according to the operation task, engineering drawing data, terrain data and the position of the engineering machinery vehicle, so that the actuator of the engineering machinery vehicle performs the corresponding operation. In this embodiment, since the position of the engineering machinery vehicle is a high-precision position obtained by RTK positioning technology, a more accurate operation path and operation parameters can be generated, thereby improving the operation efficiency.

[0062] For example, the operation planning module can obtain the operating area and the environmental conditions around the operating area based on the engineering drawing data and terrain data. In this way, the operation planning module can calculate the operating path and operating parameters suitable for the engineering machinery vehicle based on the operating task, the operating area and the environmental conditions around the operating area, and the current position of the engineering machinery vehicle, thereby generating such an operating path and operating parameters.

[0063] For example, the operation planning module can also be called an intelligent operation planning software module. The operation planning module is developed based on edge computing technology, and supports users to upload engineering drawings, terrain data and other materials. The operation planning module can automatically generate parameters such as the operation path and excavation depth of engineering machinery vehicles (for example, unmanned excavators) based on engineering drawings, terrain data or actual site conditions.

[0064] Furthermore, the operation planning module can also introduce machine learning or deep learning algorithms to continuously optimize the operation plan based on historical operation data and current environmental information to improve operation efficiency and accuracy. After the training is completed, the operation planning module can use the current environmental information and the current operation plan to output the results in the current environment.

[0065] In addition, the operation planning module can also provide a visual interface, allowing users to monitor the operation progress in real time, adjust the operation parameters, and receive the operation effect evaluation report fed back by the intelligent operation system. The operation planning module is integrated into the construction machinery vehicle (for example, the unmanned excavator), and its visual interface is remotely synchronized with the remote platform.

[0066] In some embodiments, the engineering machinery vehicle may further include an unmanned driving control system. The unmanned driving control system is used to control the actuator of the engineering machinery vehicle to perform corresponding operations according to the state data of the engineering machinery vehicle, the position of the engineering machinery vehicle, the operation path and operation parameters of the engineering machinery vehicle. In this way, the automatic operation of the engineering machinery vehicle is realized, and the operation efficiency is improved.

[0067] For example, the unmanned driving control system obtains the situation of the engineering machinery vehicle based on the status data and the position of the engineering machinery vehicle, and then combines the situation of the engineering machinery vehicle to make the engineering machinery vehicle travel according to the operating path of the engineering machinery vehicle and make the engineering machinery vehicle perform operating operations (such as excavation operations, etc.) according to the operating parameters.

[0068] For example, the unmanned driving control system uses machine learning or deep learning algorithms, combined with machine vision technology, to design an unmanned driving control system based on artificial intelligence and machine vision. The system can receive the location information provided by the RTK positioning system, and combine the environmental perception sensor data, other vehicle sensor data and vehicle operation data to accurately control the excavator's travel and operation.

[0069] In some embodiments, the unmanned driving control system is also used to autonomously locate the engineering machinery vehicle using SLAM (Simultaneous Localization and Mapping) technology, and calibrate the autonomously located position using the position of the engineering machinery vehicle obtained by the RTK positioning system. Here, SLAM technology is a known technology. In this embodiment, autonomous positioning is performed using SLAM technology, and the position determined by RTK technology is used to calibrate the autonomously located position to improve positioning accuracy.

[0070] For example, the unmanned driving control system can obtain environmental information based on sensors such as laser radar and IMU (Inertial Measurement Unit) to perform SLAM mapping to achieve autonomous positioning of construction machinery vehicles (for example, unmanned excavators). When the RTK satellite signal disappears in a construction scene blocked by side wall materials, accurate positioning information can be provided through SLAM mapping technology. Therefore, high-precision RTK positioning system and SLAM mapping technology can be used to provide high-precision positioning information for construction machinery vehicles (for example, unmanned excavators), and high-precision RTK positioning system can be used to assist SLAM mapping and calibrate its mapping positioning. For example, laser radar can provide point cloud data, IMU provides speed, angle, acceleration and other data, RTK provides positioning data, and SLAM mapping is performed based on these data. Therefore, SLAM mapping technology can provide positioning information, and SLAM fusion mapping can be performed based on laser radar point cloud data and IMU data, thereby providing positioning information. In general, when there is an RTK signal, RTK can provide positioning information.

[0071] In some embodiments, the unmanned driving control system is also used to re-plan the operation path of the engineering machinery vehicle when it is identified that there are obstacles in the operation area of ​​the engineering machinery vehicle based on the environmental data around the engineering machinery vehicle, so as to obtain the optimal operation path of the engineering machinery vehicle, and control the actuator of the engineering machinery vehicle to perform corresponding operations according to the optimal operation path. In this way, when there are obstacles, the optimal operation path that bypasses the obstacles can be obtained, thereby improving the safety of the engineering machinery vehicle.

[0072] For example, the unmanned driving control system can identify obstacles such as large rocks, boundary lines, terrain features, etc. in the working area, and automatically re-plan the optimal working path based on this (for example, the optimal working path can be the shortest path to bypass obstacles), and introduce the mechanical arm dynamics model and motion control algorithm in known technologies to achieve precise control of the excavator arm and bucket, and automatically adjust parameters such as the arm angle and bucket depth according to the working planning instructions and the positioning information provided by RTK.

[0073] In some embodiments, the engineering machinery vehicle may further include a control instruction execution unit. The control instruction execution unit is used to execute corresponding actions according to the instructions issued by the programmable logic controller of the unmanned driving control system, and is responsible for controlling various actuators (e.g., bucket, dipper arm, boom, motor, oil pump, brake, etc.) of the engineering machinery vehicle (e.g., unmanned excavator) to achieve control of the vehicle, for example, to achieve control of various functions of the vehicle such as starting, accelerating, decelerating, steering, loading, unloading, etc.

[0074] The combined use of RTK technology and other environmental perception sensor technologies, such as lidar, cameras, etc., further enhances the perception and decision-making capabilities of the intelligent operation system of unmanned engineering machinery vehicles. The combination of these sensors and RTK technology can achieve all-round perception and real-time analysis of the working environment, provide more accurate operation guidance and obstacle avoidance strategies for excavators, and thus improve operating efficiency and safety. Therefore, the above-mentioned embodiments of the present disclosure implement an intelligent operation system for engineering machinery vehicles based on RTK positioning technology, and provide a strong guarantee for the precise operation of unmanned engineering machinery vehicles in complex construction environments by realizing high-precision and high-reliability positioning services.

[0075] In some embodiments, the construction machinery vehicle may further include a human-machine interface. The human-machine interface is used to display various status information of the construction machinery vehicle (e.g., unmanned excavator) itself and related sensor data information, such as working mode, current position, battery power, system status, etc. Through the human-machine interface, the operator can switch the construction machinery vehicle (e.g., unmanned excavator) to the local manned driving mode to realize manned control of the excavator.

[0076] In some embodiments, the construction machinery vehicle may further include an alarm device. For example, the alarm device may include an alarm light and a buzzer. When the excavator encounters an abnormal situation, such as low battery, system failure, obstacle blocking, etc., the alarm device may immediately send out an audible and visual alarm signal to alert the operator, and the alarm device signal is remotely synchronized to the remote platform.

[0077] In some embodiments, the remote platform is also used to display the real-time location information, operation status, operation progress and environmental images of the engineering machinery vehicles, adjust the operation sequence for each engineering machinery vehicle, send operation instructions to each engineering machinery vehicle, and adjust the operation parameters in the operation task. In this way, the remote platform can control the operation of the engineering machinery vehicles and improve the operation efficiency.

[0078] For example, the remote platform can adjust the operating order of each engineering vehicle and send operating instructions to each engineering vehicle according to the real-time status and position of each engineering vehicle; the remote platform can also automatically adjust the operating parameters (such as excavation depth) in the operating tasks of each engineering vehicle according to the operating requirements and the real-time status and position of each engineering vehicle, or adjust the operating parameters in the operating tasks of each engineering vehicle according to the operating parameters input by the user.

[0079] The remote platform can also be called a remote monitoring and dispatching platform, which is mainly used to realize the remote monitoring and dispatching management functions of construction machinery vehicles (for example, unmanned excavators), including a remote monitoring display system and a dispatching management system.

[0080] The remote monitoring and display system is equipped with a large-screen display system to display the real-time location information, operation status, operation progress, environmental images and other data information of the construction machinery vehicle. For example, the construction machinery vehicle transmits the real-time status to the remote monitoring and display system through the wireless communication unit, which can be displayed on the screen interface, such as: longitude and latitude location information, whether the operation status is digging or walking, which step or link of the operation progress is currently in the planned operation progress, and the camera's photo / video data. All of these data can be displayed.

[0081] The dispatch management system can automatically assign work tasks and adjust the work order according to the work requirements and the status of the construction machinery and vehicles, and monitor the operation status of the construction machinery and vehicles in real time, and support remote sending of work instructions and adjustment of work parameters. For example, the dispatch management system can assign a work task to a certain construction machinery and vehicle after comprehensive analysis based on the priority of the work requirements and the current status of the construction machinery and vehicle (for example, an unmanned excavator). Here, work tasks can be assigned to multiple construction machinery and vehicles separately. Once the work tasks are assigned, the work order of the entire system will also be adjusted accordingly.

[0082] In some embodiments, the operation control system may also include a wireless communication unit. The wireless communication unit is a high-reliability remote wireless communication network unit that is responsible for sending and receiving real-time data and video monitoring data between the engineering machinery vehicle (e.g., an unmanned excavator) and the remote monitoring and scheduling platform. For example, the wireless communication unit can send the vehicle's status data to the remote platform, and play the role of transmitting data between the vehicle and the remote platform, and of course also includes sending the vehicle's location information to the remote platform. The status data of the engineering machinery vehicle includes: environmental data, vehicle parameters, and vehicle operation data of the engineering machinery vehicle. The wireless communication unit ensures that the data can be accurately, efficiently, and reliably transmitted between the engineering machinery vehicle and the remote monitoring and scheduling platform as much as possible by processing the transmission, reception, protocol, and synchronization of the data.

[0083] In the embodiments of the present disclosure, in view of the technical difficulties in the related art that engineering machinery vehicles have high labor costs, difficult operations, and limited operation accuracy when operating in complex or dangerous environments, the embodiments of the present disclosure propose an intelligent operation system for unmanned excavators based on RTK positioning technology. The system combines high-precision RTK positioning technology with intelligent control of unmanned driving technology, and realizes the precise operation of engineering machinery vehicles (for example, unmanned excavators) in bulk earthwork construction scenes with side wall material obstruction. Through the intelligent operation planning module and the remote monitoring and scheduling platform, the operation efficiency and management level of the unmanned excavator are improved, and the system reduces the risks and costs of manual operation and improves the safety of operation.

[0084] The operation control system can include high-precision RTK positioning system design, engineering machinery vehicles, and remote monitoring and scheduling platform design. Among them, engineering machinery vehicles include unmanned driving control system design and intelligent operation planning module design.

[0085] (1) Design of high-precision RTK positioning system

[0086] For example, a multi-frequency GNSS receiver can be selected as the core component of the RTK mobile station and the RTK base station to improve the signal capture capability and positioning accuracy of construction machinery vehicles (e.g., excavators) in bulk earthwork construction scenarios with sidewall material obstruction, ensuring that construction machinery vehicles can obtain centimeter-level accurate position information in real time during operation. A dedicated data communication module is designed to ensure real-time and stable data transmission between the RTK base station and the RTK mobile station, and to maintain high-precision positioning even in weak signal areas.

[0087] (2) Design of unmanned driving control system

[0088] Machine learning / deep learning algorithms can be used in combination with machine vision technology to design an unmanned driving control system based on artificial intelligence and machine vision. The system can receive the location information provided by the RTK positioning system and combine the data of environmental perception sensors (such as lidar, cameras, etc.) to accurately control the driving and operation of construction machinery vehicles (such as excavators). The system can identify obstacles, boundary lines, terrain features, etc. in the working area, and automatically plan the optimal working path based on this, and introduce the mechanical arm dynamics model and motion control algorithm to achieve precise control of the boom and bucket of the construction machinery vehicle (such as an excavator), and automatically adjust the boom angle, bucket depth and other parameters according to the positioning information and operation planning instructions provided by RTK.

[0089] (3) Design of intelligent operation planning module

[0090] Based on edge computing technology, an intelligent operation planning software module can be developed to support users to upload engineering drawings, terrain data, etc. The software module can automatically generate parameters such as the operation path and excavation depth of engineering machinery vehicles (for example, excavators) according to the engineering drawings, terrain data or the actual situation of the site environment. In addition, machine learning / deep learning algorithms are introduced to continuously optimize the operation plan to improve operation efficiency and accuracy based on historical operation data and current environmental information. A visual interface is provided to allow users to monitor the progress of operations in real time, adjust operation parameters, and receive operation effect evaluation reports fed back by the system.

[0091] (4) Design of remote monitoring and dispatching platform

[0092] A remote monitoring and dispatching platform is established to realize remote monitoring and dispatching management of construction machinery vehicles (for example, unmanned excavators), and a highly reliable remote communication network is established to support real-time data transmission and video monitoring between unmanned excavators and remote monitoring centers. The remote monitoring center is equipped with a large-screen display system to display the real-time location information, operation status, operation progress, environmental images and other information of the excavator. The remote monitoring and dispatching platform has the ability to automatically assign operation tasks and adjust the operation sequence according to the operation requirements and the status of the construction machinery vehicles, and monitor the operation status of the construction machinery vehicles in real time, and support remote sending of operation instructions and adjustment of operation parameters.

[0093] The embodiments of the present disclosure can provide an intelligent operation system for engineering machinery vehicles based on RTK positioning technology. By adopting high-precision RTK positioning technology and SLAM technology, the signal capture capability and positioning accuracy of engineering machinery vehicles (for example, excavators) in bulk earthwork construction scenes with side wall materials blocking are realized, thereby improving operation accuracy and efficiency. Through the unmanned driving control system, unmanned driving and unmanned operation control of engineering machinery vehicles are realized, the optimal operation path is automatically optimized, and the parameters such as the boom angle and bucket depth of the engineering machinery vehicles are automatically adjusted, thereby reducing labor intensity and improving operation safety. Through the remote monitoring and scheduling platform, remote monitoring and operation scheduling of engineering machinery vehicles are realized, improving management efficiency and operation flexibility. Based on edge computing technology, an intelligent operation planning software module is developed to automatically generate the operation path and excavation depth of engineering machinery vehicles, and continuously optimize the operation plan based on historical operation data and current environmental information, thereby improving operation efficiency and resource utilization.

[0094] Figure 4 is a flow chart showing a method for performing positioning by an RTK positioning system according to some embodiments of the present disclosure. Figure 4 As shown, the method includes steps S401 to S406.

[0095] In step S401, the RTK base station and the RTK mobile station receive satellite signals. The RTK base station and the RTK mobile station can receive signals from multiple GNSS satellites, and these signals contain the position and time information of the satellites.

[0096] In step S402, the RTK base station performs data processing. The RTK base station processes the received satellite signals and calculates its own precise position. At the same time, the RTK base station also records the observed satellite signal data (including pseudorange, carrier phase, etc.).

[0097] In step S403, the differential correction value (ie, the differential data described above) is calculated. The RTK base station compares its own position information with the known precise position and calculates the differential correction value, which reflects the positioning error caused by factors such as atmospheric delay and satellite clock error.

[0098] In step S404, data transmission is performed. The RTK base station transmits the differential correction value to the RTK mobile station in real time through a dedicated data communication module.

[0099] In step S405, the RTK mobile station performs data processing. In addition to receiving satellite signals, the RTK mobile station also receives differential correction values ​​from the RTK base station; the RTK mobile station combines the satellite signal data it receives with the differential correction values ​​to eliminate positioning errors.

[0100] The RTK mobile station uses differential corrections to correct the delays caused by the atmosphere (including the ionosphere and troposphere), satellite clock errors, etc. on satellite signal propagation, which can significantly improve positioning accuracy. The basic principle of differential GPS is based on the spatial correlation between the ground base station and the mobile station. GPS satellites are distributed in space about 20,000 kilometers above the ground, and the distance between the ground base station and the mobile station is generally between a few kilometers and a few hundred kilometers, which is negligible relative to the distance between the satellite and the base station. Therefore, it can be considered that the spatial environment around the base station and the mobile station has an equivalent impact on the navigation positioning of the two receivers.

[0101] In step S406, the RTK mobile station performs real-time positioning. After differential correction, the RTK mobile station can obtain high-precision position information in real time. The real-time positioning accuracy can reach centimeter level or even millimeter level, which is far higher than the accuracy of ordinary GPS positioning.

[0102] So far, the working principle of the high-precision RTK positioning system is provided, which realizes the high-precision positioning of engineering machinery vehicles.

[0103] Figure 5 is a flow chart showing a method for controlling a job according to some embodiments of the present disclosure. Figure 5 As shown, the job control method includes steps S502 to S508.

[0104] In step S502, the RTK base station is used to receive signals from the positioning system satellites, and the measurement position of the RTK base station itself is calculated through the signals from the positioning system satellites. The differential data is calculated based on the measurement position of the RTK base station itself and the actual position of the RTK base station stored in advance, and the differential data is transmitted to the RTK mobile station installed on the engineering machinery vehicle.

[0105] In step S504, the RTK mobile station is used to receive signals from the positioning system satellite, and the position of the RTK mobile station is calculated based on the received signals from the positioning system satellite. The position of the RTK mobile station is corrected using differential data to obtain the corrected position of the RTK mobile station as the position of the engineering machinery vehicle, and the position of the engineering machinery vehicle is sent to the remote platform.

[0106] In step S506, the state data of the engineering machinery vehicle is sent to the remote platform using the engineering machinery vehicle.

[0107] In step S508, the remote platform is used to automatically assign work tasks to each engineering machinery vehicle according to work requirements and the location and status data of each engineering machinery vehicle.

[0108] Thus, a work control method according to some embodiments of the present disclosure is provided. In the work control method, the RTK positioning technology is used to realize the positioning of the engineering machinery vehicle, so that the positioning accuracy of the engineering machinery vehicle can be improved, and then the work tasks can be assigned to each engineering machinery vehicle more accurately, which facilitates the implementation of the engineering work and improves the construction efficiency.

[0109] In some embodiments, the above step S504 includes: using an RTK antenna to receive signals from positioning system satellites; and using an RTK receiver to receive differential data from an RTK base station, and calculating the position of the engineering machinery vehicle based on the signals from the positioning system satellites received by the RTK antenna and the differential data.

[0110] In some embodiments, the operation control method may also include: using the operation planning module to receive engineering drawing data and terrain data, and automatically generating the operation path and operation parameters of the engineering machinery vehicle according to the operation task, engineering drawing data, terrain data and the position of the engineering machinery vehicle, so that the actuator of the engineering machinery vehicle performs corresponding operations.

[0111] In some embodiments, the operation control method may also include: using an unmanned driving control system to control the actuator of the engineering machinery vehicle to perform corresponding operations according to the status data of the engineering machinery vehicle, the position of the engineering machinery vehicle, the operation path and operation parameters of the engineering machinery vehicle.

[0112] In some embodiments, the operation control method may further include: using the unmanned driving control system to autonomously locate the engineering machinery vehicle using SLAM technology, and calibrating the autonomously located position of the engineering machinery vehicle using the position of the engineering machinery vehicle obtained by the RTK positioning system.

[0113] In some embodiments, the operation control method may also include: using an unmanned driving control system to identify the presence of obstacles in the operation area of ​​the engineering machinery vehicle based on environmental data around the engineering machinery vehicle, replanning the operation path of the engineering machinery vehicle to obtain the optimal operation path of the engineering machinery vehicle, and controlling the actuator of the engineering machinery vehicle to perform corresponding operations according to the optimal operation path.

[0114] In some embodiments, the operation control method may also include: using a remote platform to display the real-time location information, operation status, operation progress and environmental images of the engineering machinery vehicles, adjusting the operation sequence for each engineering machinery vehicle, sending operation instructions to each engineering machinery vehicle, and adjusting the operation parameters in the operation task.

[0115] Figure 6 6 is a block diagram schematically showing a structure of a job control system according to some other embodiments of the present disclosure. The job control system includes a memory 610 and a processor 620. Among them:

[0116] The memory 610 can be a disk, a flash memory, or any other non-volatile storage medium. The memory is used to store Figure 4 and / or Figure 5 The instructions in the corresponding embodiment.

[0117] The processor 620 is coupled to the memory 610 and can be implemented as one or more integrated circuits, such as a microprocessor or a microcontroller. The processor 620 is used to execute instructions stored in the memory, and use RTK positioning technology to realize the positioning of the engineering machinery vehicle, thereby improving the positioning accuracy of the engineering machinery vehicle, and then more accurately assigning work tasks to each engineering machinery vehicle, facilitating the implementation of engineering operations and improving construction efficiency.

[0118] For example, the job control system may include multiple memories 610 and multiple processors 620 , which may be matched and allocated in different systems, devices, or platforms.

[0119] In one embodiment, it is also possible to Figure 7 As shown, the operation control system 700 includes a memory 710 and a processor 720. The processor 720 is coupled to the memory 710 via a BUS 730. The operation control system 700 can also be connected to an external storage device 750 via a storage interface 740 to call external data, and can also be connected to a network or another computer system (not shown) via a network interface 760, which will not be described in detail here.

[0120] In this embodiment, data instructions are stored in a memory, and the processor processes the instructions, and the RTK positioning technology is used to realize the positioning of the engineering machinery vehicle, thereby improving the positioning accuracy of the engineering machinery vehicle, and then more accurately allocating work tasks to each engineering machinery vehicle, facilitating the implementation of engineering operations and improving construction efficiency.

[0121] For example, the job control system may include multiple memories 710, multiple processors 720, multiple BUS buses 730, multiple storage interfaces 740, multiple external storage devices 750 and multiple network interfaces 760. The multiple memories 710, multiple processors 720, multiple BUS buses 730, multiple storage interfaces 740, multiple external storage devices 750 and multiple network interfaces 760 can be matched and distributed in different systems, devices or platforms.

[0122] In another embodiment, the present disclosure further provides a computer-readable storage medium (eg, a non-transitory computer-readable storage medium) having computer program instructions stored thereon, which are executed by a processor to implement Figure 4 and / or Figure 5 The steps of the method in the corresponding embodiment. Those skilled in the art should understand that the embodiments of the present disclosure can be provided as methods, devices, or computer program products. Therefore, the present disclosure can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0126] In some embodiments of the present disclosure, a computer program product is further provided. The computer program product includes a computer program or instructions. When the computer program or instructions are executed by a processor, the job control method as described above is implemented.

[0127] In some embodiments of the present disclosure, a computer program is further provided, comprising: instructions, which, when executed by a processor, cause the processor to execute the job control method as described above.

[0128] So far, the present disclosure has been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0129] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. An operation control system for an engineering vehicle, wherein: The engineering machinery vehicle is an unmanned engineering machinery vehicle, and the operation control system includes: Real-time dynamic differential RTK positioning system, including: An RTK base station is used to receive signals from positioning system satellites, calculate a measured position of the RTK base station itself through the signals from the positioning system satellites, calculate differential data based on the measured position of the RTK base station itself and a pre-stored actual position of the RTK base station, and transmit the differential data to an RTK mobile station; The RTK mobile station is installed on the engineering machinery vehicle, and is used to receive the signal of the positioning system satellite, and calculate the position of the RTK mobile station according to the received signal of the positioning system satellite, and use the differential data to correct the position of the RTK mobile station to obtain the corrected position of the RTK mobile station as the position of the engineering machinery vehicle, and send the position of the engineering machinery vehicle to the remote platform; The engineering machinery vehicle is used to send status data of the engineering machinery vehicle to the remote platform; and The remote platform is used to automatically assign work tasks to each engineering machinery vehicle according to work requirements and the location and status data of each engineering machinery vehicle.

2. The operation control system according to claim 1, wherein: The RTK mobile station comprises: An RTK antenna for receiving signals from the positioning system satellites; and The RTK receiver is used to receive the differential data from the RTK base station and calculate the position of the engineering machinery vehicle based on the signal of the positioning system satellite received by the RTK antenna and the differential data.

3. The operation control system according to claim 2, wherein: The RTK antenna includes an RTK positioning antenna and an RTK directional antenna, which are installed at the highest position directly above the cab of the engineering machinery vehicle, and a line connecting the phase center of the RTK positioning antenna and the phase center of the RTK directional antenna is consistent with or parallel to the central axis of the engineering machinery vehicle.

4. The operation control system according to claim 1, wherein: The engineering machinery vehicle comprises: An environmental perception sensor, used to detect environmental data around the engineering machinery vehicle; The vehicle parameter sensor is used to detect vehicle parameters of the engineering machinery vehicle, and the vehicle parameters include: oil pressure, temperature, rotation speed, position and flow rate of the actuator in the engineering machinery vehicle.

5. The operation control system according to claim 4, wherein: The state data of the engineering machinery vehicle includes: the environmental data, the vehicle parameters and the vehicle operation data of the engineering machinery vehicle, wherein the vehicle operation data includes engine data, chassis and driving data, safety system data and electrical and electronic system data.

6. The operation control system according to any one of claims 1 to 5, wherein: The engineering machinery vehicle comprises: The operation planning module is used to receive engineering drawing data and terrain data, and automatically generate the operation path and operation parameters of the engineering machinery vehicle according to the operation task, the engineering drawing data, the terrain data and the position of the engineering machinery vehicle, so that the actuator of the engineering machinery vehicle performs corresponding operations.

7. The operation control system according to claim 6, wherein: The engineering machinery vehicle further comprises: The unmanned driving control system is used to control the actuator of the engineering machinery vehicle to perform corresponding operations according to the status data of the engineering machinery vehicle, the position of the engineering machinery vehicle, the operation path and operation parameters of the engineering machinery vehicle.

8. The operation control system according to claim 7, wherein: The unmanned driving control system is also used to autonomously locate the engineering machinery vehicle using the simultaneous positioning and mapping (SLAM) technology, and to calibrate the autonomously located position using the position of the engineering machinery vehicle obtained by the RTK positioning system.

9. The operation control system according to claim 8, wherein: The unmanned driving control system is also used to re-plan the operating path of the engineering machinery vehicle when it is identified that there are obstacles in the operating area of ​​the engineering machinery vehicle based on the environmental data around the engineering machinery vehicle, so as to obtain the optimal operating path of the engineering machinery vehicle, and control the actuator of the engineering machinery vehicle to perform corresponding operations according to the optimal operating path.

10. The operation control system according to claim 1, wherein: The remote platform is also used to display the real-time location information, operation status, operation progress and environmental images of the engineering machinery vehicles, adjust the operation sequence for each engineering machinery vehicle, send operation instructions to each engineering machinery vehicle, and adjust the operation parameters in the operation task.

11. A method for controlling an operation of an engineering vehicle, wherein: The engineering machinery vehicle is an unmanned engineering machinery vehicle, and the operation control method includes: Using an RTK base station to receive signals from positioning system satellites, calculating the measured position of the RTK base station itself through the signals from the positioning system satellites, calculating differential data based on the measured position of the RTK base station itself and the actual position of the RTK base station stored in advance, and transmitting the differential data to an RTK mobile station installed on an engineering machinery vehicle; Using the RTK mobile station to receive the signal of the positioning system satellite, and calculating the position of the RTK mobile station according to the received signal of the positioning system satellite, and using the differential data to correct the position of the RTK mobile station to obtain the corrected position of the RTK mobile station as the position of the engineering machinery vehicle, and sending the position of the engineering machinery vehicle to a remote platform; Using the engineering machinery vehicle to send the state data of the engineering machinery vehicle to the remote platform; and The remote platform is used to automatically assign work tasks to each engineering machinery vehicle according to work requirements and the location and status data of each engineering machinery vehicle.

12. An operation control system for an engineering machinery vehicle, comprising: Memory; as well as A processor is coupled to the memory, and the processor is configured to execute the job control method according to claim 11 based on instructions stored in the memory.

13. A computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, implement the job control method according to claim 11.

14. A computer program product, comprising a computer program or instructions, which implement the job control method according to claim 11 when executed by a processor.

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