362 results about "Road traffic control" patented technology

The invention provides a distributed AGV system traffic control and scheduling method realized based on ROS2, and the method comprises the steps: building AGV vehicle nodes, building a traffic control module, developing a scheduling module, building a communication network, integrating a sensor data processing module, providing a central control board / man-machine interaction interface, and building a database / log module. According to the method, efficient data interaction and collaborative operation between AGVs are realized by using a distributed communication framework of ROS2, and by constructing a traffic control module and an optimized scheduling algorithm, the position and state of each AGV can be monitored in real time, the running path of each AGV can be dynamically adjusted, and traffic congestion and collision events are avoided. Compared with a traditional centralized control system, the system has the advantages that the expandability and fault-tolerant capability of the system are improved, the system is suitable for a complex and changeable industrial logistics environment, and the efficiency and reliability of logistics transportation are remarkably improved.

The invention discloses a smart city traffic resource optimal configuration method, a platform and a storage medium, and relates to the technical field of traffic optimization. When a road accident is detected, accident information is collected, and a traffic accident position and traffic accident characteristics are obtained; time consumption prediction is carried out according to the traffic accident characteristics, and traffic control duration is obtained; calling real-time traffic data from an intelligent traffic system to obtain associated road data; executing multi-thread traffic flow back-stepping analysis to obtain associated traffic flow prediction information; carrying out shunting performance evaluation; and dynamically updating the signal lamp configuration of the plurality of shunting candidate roads by taking the associated traffic flow prediction information as a constraint. According to the invention, the technical problems of aggravation of traffic jam and low emergency management efficiency caused by lack of scientificity and dynamic adaptability of shunting planning and lagging of signal lamp regulation in a traffic accident scene in the prior art are solved, and the technical effects of optimizing traffic resource configuration, relieving traffic jam and improving traffic emergency management efficiency are achieved.

The invention provides an urban inland inundation risk management method and system based on multi-model coupling, and the method comprises the steps: constructing a digital twin holographic base, constructing an urban rainfall flood analysis model through the deep coupling of a ponding recognition intelligent model and a physical mechanism drive type rainfall flood simulation engine, and carrying out the construction of an urban inland inundation risk management model. A high-precision waterlogging risk thermodynamic diagram and a dynamic forecast threshold value are generated, and the risk situation is broadcasted in real time through a voice interaction module; constructing a rainstorm-runoff-pipe network three-dimensional coupling simulation system by means of a digital twin holographic base, updating a deduction result at a minute-level frequency, and transmitting rainfall space-time distribution data to an urban rainfall flood analysis model in real time; and establishing a multi-target collaborative decision engine, inputting the accumulated water diffusion rate to the multi-target collaborative decision engine, and generating a pump station scheduling scheme, a traffic control strategy and a material delivery path. The system comprises a model construction module, a data analysis module and a collaborative decision module. According to the invention, a complete urban inland inundation risk management system is constructed.

The invention belongs to the technical field of smart tourism management, and relates to an AI-based smart tourism recommendation method and system, and the method comprises the steps: obtaining instantaneous aggregation density data collected by a plurality of hidden monitoring nodes in a scenic spot in real time; generating a dynamic environment grade set according to the instantaneous aggregation density data, the real-time weather elements and the traffic control information; generating a user physical exhaustion level, and generating an interest preference label through an electronic map zooming operation track; identifying space-time synchronization features between the associated mobile terminals to generate a group type identifier, and binding the group type identifier with a strategy recommendation template; performing peak shifting recommendation sorting in combination with the dynamic environment level to form an initial recommendation scheme set; and comparing the matching degree between the initial recommendation scheme and the interest preference label, triggering the secondary optimization of the recommendation content based on the group type identifier when the matching degree is lower than a set range, and injecting an emergency risk avoiding path to complete a closed-loop recommendation decision chain. The problem that in the prior art, due to the fact that the data collection dimension is single, recommendation strategies lack group adaptability is solved.

The invention relates to the field of real-time visualization of road conditions, in particular to a data simulation visualization rendering method and system for urban traffic road conditions. The method comprises the following steps: extracting a real-time satellite streetscape image based on urban satellite remote sensing scanning, and performing scene pixel-level segmentation to obtain scene texture rendering parameters; scene illumination visual identification is carried out according to the real-time satellite streetscape image, traffic scene background modeling is carried out based on scene texture rendering parameters, and a real-time scene background model is constructed; the method comprises the following steps: acquiring urban-level multi-source traffic monitoring data flow, performing vehicle state sensing, and constructing a multi-dimensional particle feature matrix; road network topological correlation analysis and global traffic network state perception are carried out according to the real-time satellite streetscape images, and a road network state perception model is constructed. According to the invention, a real real-time traffic environment is visualized, scene effects in different traffic states are presented, the current road condition can be rapidly evaluated, and the traffic control decision efficiency is improved.

In a server apparatus of a traffic control system, a pre-processor records a position of a vehicle in a database. A control information generator periodically generates individual control information regarding vehicles by using information in the database, and transmits the individual control information to each vehicle. Each vehicle controls travel of the subject vehicle by using the latest individual control information received from the server apparatus. In the server apparatus, an emergency processor is implemented when received travel information includes information that hinders travel of another vehicle, and generates and transmits information corresponding to the information that hinders the travel of the other vehicle, as the individual control information regarding each of the vehicles.

The invention relates to the technical field of air traffic control and voice recognition, in particular to an air traffic control instruction end-to-end voice recognition method under a high-noise condition, which comprises the following steps: preprocessing to-be-recognized voice, and extracting to obtain original voice frequency characteristics; carrying out noise reduction intensity control, frequency band gain control and fundamental tone tracking processing on the original voice frequency characteristics by adopting a self-adaptive attention noise reduction module to obtain noise-reduced voice; preprocessing the noise-reduced voice, and extracting to obtain noise-reduced voice frequency characteristics; a voice recognition module is adopted to carry out shared encoder coding, connection time sequence classification decoder decoding and attention-based encoder-decoder decoding on the noise reduction voice frequency characteristics, and an output voice recognition transcription text is obtained; according to the invention, the voice recognition precision of the air traffic control instruction under the high-noise condition can be improved.

The invention discloses a traffic control and guidance system and method based on an ant colony algorithm, belongs to the technical field of road traffic control, and solves the problems that a neural network model in an existing method mainly focuses on respective route conditions of a plurality of bifurcation routes of a bifurcation on a one-way driving road, and the information fusion degree between traffic elements is low. The method comprises the following steps: identifying dynamic characteristic information of vehicles in a road network, pre-constructing a road network optimization model based on an ant colony algorithm combined with deep learning, and analyzing a pheromone spread function cluster solution; according to the invention, the road network optimization model based on the ant colony algorithm and the deep learning is pre-constructed, the collaborative optimization of traffic signal control and vehicle induction is realized, the traffic signal optimization control strategy and the vehicle optimization path strategy are output, so that the signal timing and the induction path can be matched with each other, and the control accuracy is improved. Therefore, the operation efficiency of the traffic system is improved and the accuracy and effectiveness of the guidance strategy are ensured.

Systems and methods for detecting anomalies in aviation data communication systems (e.g., air traffic control surveillance systems), include a processor receiving device status information. A variational autoencoder receives and optimizes the device status information and determines whether it qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the device status information prior to optimization.

The invention provides a dynamic route planning method of a first-aid system, which comprises the following steps: acquiring a first-aid request data set including vital signs of a patient, position coordinates and a real-time position of a first-aid vehicle; collecting dynamic traffic data in real time, wherein the dynamic traffic data comprises a passing speed, a traffic control area, an accident point coordinate and a severity level; medical institution state data are obtained, wherein the medical institution state data comprise the reception capacity, the first-aid channel occupation state and the resource matching degree parameter; based on the above data, an initial path set is calculated through an improved dynamic modulation factor, and a three-dimensional demodulation matrix is used to carry out priority ranking on the paths. A final navigation path is generated through multi-level logic judgment, and fuel quantity judgment, accident point influence updating and medical institution state calibration are included. And finally, correcting the dynamic modulation factor according to the path execution time deviation. The first-aid efficiency and the success rate can be improved, and precious treatment time is won for patients.

The invention relates to the technical field of emergency linkage, and discloses an urban fire emergency linkage command system and method based on digital twinning, and the system comprises a multi-source heterogeneous data collection module which obtains the multi-source heterogeneous data of multiple departments in real time; the dynamic digital twinborn body construction module is used for generating a three-dimensional fire scene twinborn body with a unified space-time reference; the intelligent decision-making module is used for coupling a fire behavior diffusion prediction result of the fluid dynamic equation and a building risk prediction result of the finite element analysis model and generating a fire-fighting power dispatching instruction, a medical aid distribution instruction and a traffic control instruction; and the multi-department linkage execution module is used for sending a fire-fighting power dispatching instruction to the fire-fighting terminal, sending a medical aid distribution instruction to the medical terminal and sending a traffic control instruction to the traffic terminal so as to realize cross-department dynamic collaborative dispatching. According to the method, the emergency response speed and the decision accuracy are improved, efficient cooperation of urban fire emergency rescue is realized, and the rescue efficiency and the capability of coping with complex fire scenes are remarkably enhanced.

The invention provides an intelligent intelligent supervision system for civil aviation VoIP communication equipment, relates to the basic field of civil aviation air traffic control equipment, and solves the limitation problems of non-centralized supervision, imperfect supervision, non-autonomous supervision and the like existing in an existing supervision mode. The system comprises a system architecture module which adopts a layered modular design to construct a double-layer system comprising a top layer management architecture and a bottom layer operation architecture; the function service module comprises an equipment state monitoring unit, a risk assessment engine and an operation and maintenance management unit; the data processing architecture forms a multi-stage processing channel by a data acquisition layer, a data transmission layer, a data storage layer, a data calculation layer and a data application layer which are connected in sequence; the technical support system comprises a micro-service component group, a continuous integration platform and a security authentication module. The intelligent supervision network architecture of the VoIP communication equipment is subjected to brand-new autonomous design, transformation from passive response to active management is completed, and the supervision efficiency and the treatment efficiency are improved.

Systems and methods of automatic correction of map data for autonomous vehicle navigation are disclosed. One or more servers can receive an indication of a traffic condition at an autonomous vehicle hub; upon receiving the indication of the traffic condition at the autonomous vehicle hub, identify an autonomous vehicle traveling a route that includes the autonomous vehicle hub; generate a control command for the autonomous vehicle based on the route and the traffic condition; and transmit the control command to the autonomous vehicle to correct the traffic condition at the autonomous vehicle hub.

The invention provides an abnormal early warning method and system based on driving behaviors, and is applied to the field of data processing. According to the method, multi-level judgment of vehicle driving behaviors is realized by combining individual driving habits of drivers, road types and traffic control information, firstly, real-time driving parameters are detected based on driving identity information pre-recorded by a central controller and are compared with the individual habits, and only behaviors obviously deviating from the habits enter subsequent analysis; the driving behavior data is generated through the prediction model in combination with the road type and the traffic control information, whether the behavior is reasonable or not is further judged, the early warning pertinence is ensured, and finally the abnormal driving behavior is compared by using the group driving behavior baseline, so that the individual difference dynamic correction is realized. Therefore, on the basis of fully considering individual differences of drivers, the false alarm rate is effectively reduced, meanwhile, timely early warning of abnormal behaviors is ensured, and the driving safety and the early warning practicability are improved.

The invention relates to the technical field of intelligent traffic, and discloses a method and device for detecting a thrown object on a road surface, and the method comprises the steps: automatically selecting the wavelength of infrared light according to the environment illumination intensity, and calculating an environment compensation coefficient; the infrared transmitting unit transmits a multi-angle light beam to the infrared receiving unit on the opposite side, and the infrared receiving unit determines the receiving state of the light beam; constructing a fan-shaped infrared beam grid, and determining an area where a suspected target is located; judging whether the suspected target is a thrown object or not, and using an area positioning model for the thrown object to obtain a central position and an effective area; implementing graded alarm; visible light image recognition is replaced by an infrared beam technology, the influence of severe weather on detection precision is effectively overcome through a self-adaptive wavelength selection mechanism, a specific lane where a thrown object is located can be accurately determined in combination with a lane recognition mechanism, and accurate guidance is provided for traffic control and cleaning operation.

The invention provides an air traffic control scene instruction recommendation method and system combined with a knowledge graph, and relates to the technical field of air traffic control. The method comprises the following steps: firstly, establishing a dynamic matching relationship between current air traffic state data (including flight real-time operation parameters, airspace use state parameters and meteorological influence parameters) and an association rule set (including air traffic control rule entries, historical instruction execution constraint conditions and airspace resource allocation criteria), and generating a preliminary instruction set; adjusting the initial instruction set in real time according to real-time air traffic state change data (including flight position change parameters, airspace flow fluctuation parameters and meteorological condition updating parameters) to obtain an adjusted instruction set; and then verifying the compatibility between the adjusted instruction set and airspace use constraint conditions (including airspace capacity limiting parameters, flight interval safety parameters and equipment operation state parameters), outputting a compatible instruction set to cooperate as a target recommendation instruction set, and assisting air traffic control personnel to make a control instruction decision.

The invention relates to the technical field of path planning, in particular to a terminal delivery planning method and system, and the method comprises the following steps: S1, obtaining the basic traffic data, traffic control data and client aging demand data of each road section in a target region in real time, and generating a dynamic road network state matrix; s2, calculating a time-space comprehensive priority weight value of each delivery node according to the dynamic road network state matrix, and generating a priority delivery scheduling sequence; and S3, based on the priority delivery scheduling sequence, adopting a dynamic window algorithm to generate an optimal delivery path. According to the method, distribution resource conflicts are effectively relieved in a high-density delivery scene, the overall service stability and scheduling rationality are improved, rapid path convergence and scheduling self-adaption in a real-time environment are achieved, and compared with a traditional fixed strategy, the comprehensive performance in the aspects of energy consumption control and delivery success rate is remarkably improved.

The invention discloses a road construction dustproof evaluation method, which comprises the following steps: constructing a macroscopic traffic flow model based on a cellular automaton, taking a visibility distribution field as an input condition, simulating behavior responses of drivers under different visibility, including vehicle speed selection and vehicle distance maintenance, and obtaining a traffic flow state evolution process in a construction area; the visibility, the vehicle speed and the vehicle distance serve as state input, a deep reinforcement learning algorithm is adopted to train a microscopic driving behavior model, a driving behavior strategy is optimized through a reward and punishment mechanism, and accurate description and prediction of driving behaviors under different visibility conditions are achieved; and calling a preset traffic control strategy library according to the influence degree of the road construction dust on the traffic safety, and selecting a proper traffic control scheme for dust prevention, including adjustment of a lane speed limit value and release of warning information. According to the invention, the traffic safety risk caused by construction dust is effectively reduced, and the road traffic safety is guaranteed.

The invention discloses an intelligent channel planning and safety supervision system driven by Yangtze River shipping big data, and belongs to the technical field of Yangtze River shipping. Comprising a ship information collection system, a channel information collection system, a meteorological information collection system, a port information collection system, an information processing system, an early warning system and a historical information collection system, the navigation channel information collection system collects landform, water depth and hydrological information of a navigation channel, the weather collection system collects weather information of the navigation channel, the port information collection system collects position, berth number and berth water depth information of a port, and the historical information collection system collects traffic control rules and historical navigation records of a route. The information processing system collects, analyzes and processes information collected by the ship information collection system, the channel information collection system, the weather information collection system, the port information collection system and the historical information collection system.

The invention relates to the technical field of traffic control, in particular to an airspace situation prediction method and system based on a self-adaptive grid, and the method comprises the following steps: collecting the information of all aircrafts in a current airspace, including the number, speed, x coordinate, y coordinate, z coordinate, course, pitch angle and time of the aircrafts; according to the x coordinate, the y coordinate and the z coordinate of the aircraft, mapping the acquired information of the aircraft into grids of a pre-constructed airspace basic grid network, and obtaining an input feature sequence; and inputting the input feature sequence into an airspace grid situation prediction model based on 3D CNN-GCN-Transform, and outputting to obtain an airspace situation prediction result. According to the invention, accurate prediction of airspace situation change can be realized, and technical support is provided for air traffic control.

The invention relates to a multi-mode large model and light-weight small model collaborative road surface ice condensation state prediction method, and belongs to the field of road traffic safety monitoring and prediction. The method aims at solving the problem that real-time early warning and accurate prevention and control are difficult in the prior art. According to the invention, a multi-modal data coding system is constructed, road surface monitoring images, meteorological time sequence data and historical ice condensation text cases are integrated, and feature fusion is realized by adopting visual-physical feature joint coding, meteorological time sequence feature enhancement and text semantic mining; a pseudo label is generated through large model zero sample reasoning, and a lightweight small model is trained through cross-modal knowledge distillation; and finally, on the basis of a dynamic trigger type double-model reasoning framework, calling a cloud large model for fine judgment when the small model is low in confidence coefficient or high in scene complexity, and outputting the icing starting moment and thickness through confidence coefficient weighted fusion. According to the method, the prediction accuracy and real-time performance are improved, the model generalization ability is enhanced, and reliable support is provided for road traffic control in winter.

In a server apparatus of a traffic control system, a pre-processor records a position of a vehicle in a database. A control information generator periodically generates individual control information regarding vehicles by using information in the database, and transmits the individual control information to each vehicle. Each vehicle controls travel of the subject vehicle by using the individual control information received from the server apparatus. In the server apparatus, an emergency processor is implemented when received travel information includes information that hinders travel of another vehicle, identifies the position of the vehicle related to the travel information, and records passage regulation regions and in the database. The control information generator generates and transmits the individual control information for deceleration or a stop for a vehicle that is likely to travel in the passage regulation regions and in the database.

The invention provides an AGV scheduling method and related equipment integrating path planning and traffic control, the method comprises the steps of obtaining task information, vehicle dynamic factors and topological map data of an AGV vehicle, the vehicle dynamic factors comprising a cargo state, a storage location state, spinning prohibition and point location prohibition; based on the task information, the vehicle dynamic factors and the topological map data, a depth deterministic strategy gradient network is adopted to optimize the weight, and an improved A star algorithm formed by the sum of basic cost and real-time congestion cost is adopted to plan the driving path of the AGV; continuously monitoring the running state of the AGV cluster in the process that the AGV runs based on the planned path; and conflict prediction is carried out based on the operation state of the AGV cluster, and traffic control is carried out on the AGV cluster by using a preset conflict prevention mechanism and a heuristic deadlock removing strategy. According to the method, vehicle dynamic factors are considered, an A star algorithm is optimized, traffic control is configured, and the AGV scheduling efficiency is improved.

The invention discloses a highway inspection unmanned aerial vehicle task planning monitoring system based on a knowledge graph, particularly relates to the technical field of unmanned aerial vehicle intelligent inspection, and is used for solving the problem that an existing system cannot generate a feasible decision scheme when an airspace control rule conflicts with an emergency timeliness requirement. The method comprises the following steps: constructing a knowledge graph integrating geographic no-fly zone space coordinates, an aging constraint time threshold and a traffic flow thermodynamic map layer, triggering a rule conflict state when detecting that an overlapping region exists between an aging constraint target position and a no-fly zone, and further fusing a real-time traffic control instruction and unmanned aerial vehicle maneuvering characteristic parameters to generate an elastic space domain; and after verification of a high-flow area overlapping rate threshold value or approval of a forced passing instruction, creating a temporary channel entity which covers the overlapping area coordinates and is associated with a time threshold value, and finally generating an inspection route which penetrates through a conflict area and meets a passing time limit based on the entity, thereby realizing coordination of dynamic rule conflict resolution and safety emergency inspection.

The invention discloses a regional air pollutant traceability monitoring and forecasting model and a regulation and control method. Multi-source data such as meteorology, pollutant concentration and pollution source emission in an area are collected and preprocessed, then a model is constructed and trained based on a machine learning or deep learning algorithm, and performance is improved through feature engineering, model selection and training optimization. The trained model is utilized to forecast pollutant concentration, traceability analysis is carried out, results are visualized, emission reduction targets and regulation and control measures are formulated for a target area according to traceability and forecast simulation data, for example, industrial structure optimization and traffic control measures are implemented for different pollution sources, and the implementation effect is evaluated. Meanwhile, the model is evaluated regularly, the model is updated according to a result, parameters are adjusted, data are increased or an algorithm is improved, so that the accuracy and reliability of the model are continuously improved, accurate monitoring, effective source tracing, accurate forecasting and scientific regulation and control of regional air pollutants are achieved, and air quality improvement is assisted.

The invention provides a voice instruction intelligent analysis method and system applied to an air traffic control scene, relates to the technical field of air traffic control, and aims at the characteristics of voice instruction interaction between a controller and a flight crew in the air traffic control scene to construct an instruction interaction expectation model. The instruction interaction expectation model comprises an instruction sequence association rule and a semantic constraint condition set which are generated based on historical controlled call data, and an interaction feature set of speaker identity features, instruction component time sequence features, voice intonation fluctuation features and the like in a real-time capture voice interaction process; inputting an instruction interaction expectation model for dynamic matching and deviation identification, generating an interaction deviation analysis result, triggering an intention clarification and instruction confirmation process based on the analysis result, obtaining a complete instruction component set, generating an instruction analysis result meeting an air traffic control data exchange standard after structured conversion processing, and pushing the instruction analysis result to an air traffic control automation system. The accuracy and efficiency of air traffic control voice instruction analysis are effectively improved.

The invention provides a robot control method, a robot control platform, a robot control device and a computer storage medium. The robot control method comprises the steps that all path point locations in a target real-time lattice locking path are traversed in sequence, and whether the current path point location conflicts with other robots or not is judged; under the condition that the current path point location does not conflict with other robots, continuing to detect the next path point location until the conflict path point location is determined or all the path point locations are traversed; when the conflict path point location exists, generating a control real-time lattice locking path according to a starting path point location of the target real-time lattice locking path and a previous path point location of the conflict path point location; and the control real-time lattice locking path is issued to the target robot. According to the robot control method, complex front and back sequence task control is replaced with simple traffic control logic, loop vehicle sequence control is achieved, higher robustness is achieved, and deadlock caused by queue jumping can be effectively avoided.

The invention discloses an AGV traffic control system and method based on collision detection and arrival time. The system comprises a collision management module used for testing possible collision between AGVs according to a map; the time table management module is used for determining a time table when the AGV arrives at each point location and adjusting the time table when the time of occupying the road between the AGV tasks has conflict; the collision area management module is used for analyzing whether the AGV tasks to be distributed collide or not and analyzing whether the road occupation time among the AGV tasks has conflicts or not according to the time table, the collision area and the AGV tasks; the collision detection module is used for analyzing whether the path of the to-be-allocated AGV task is in the collision area or not and analyzing whether the path of the to-be-allocated AGV task can enter the collision area or not; and the task allocation determination module is used for determining whether the to-be-allocated AGV task is an allocated task or not. According to the invention, the AGV task execution efficiency and the path utilization rate are improved, and automatic and stable operation can be realized.

The invention relates to a multi-model AGV (Automatic Guided Vehicle) mixed traffic control method based on edge calculation, which comprises the following steps of: S1, arranging a visual camera and an edge calculation platform above a traffic control area, acquiring image information of the traffic control area, and delimiting a plurality of virtual boundary lines in a video image so as to define a monitored area boundary; determining coordinates of a traffic management area, calculating a coordinate function, and establishing a mapping relation from an image to an actual environment; s2, collecting a feature data set of a target object needing traffic control in advance and storing the feature data set into a scheduling system; s3, presetting a traffic rule: if the target object is identified and the target object and the newly-added AGV are about to enter the traffic control area at the same time, the scheduling system sends a traffic control signal to the newly-added AGV to pass through, and the newly-added AGV can pass through after the target object completely passes through the control area; according to the invention, the traffic control problem of newly added AGVs and original AGVs during production line technical transformation is fundamentally solved, and smooth implementation of new projects is facilitated.

The invention provides an AGV forklift layering and design method based on hierarchical design, and the method comprises the steps that an AGV forklift is divided into a scheduling system, a navigation control system and an electromechanical-hydraulic drive-by-wire system according to system functions from the top layer to the bottom layer, the scheduling system is used for task scheduling, path planning and traffic control, the navigation control system is used for navigation control, and the electromechanical-hydraulic drive-by-wire system is used for navigation control. The dispatching system and the navigation control system are connected through a communication interface, a communication protocol is determined, the navigation control system is used for butt joint of the dispatching system and the electromechanical-hydraulic drive-by-wire system and detection of the operation state of the AGV forklift, and the electromechanical-hydraulic drive-by-wire system is used for operation action execution.