276 results about "Road traffic control" patented technology

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.

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.

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.

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 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.

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 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 relates to a traffic signal control system and method, and relates to the technical field of traffic control, and the method comprises the steps: collecting traffic flow data through multi-source sensing equipment and V2X communication, and carrying out the preprocessing to construct a traffic feature matrix; a hybrid modeling framework is adopted, a queue theory, a cellular automaton and deep reinforcement learning are integrated, a traffic state is evaluated, and a signal control strategy is optimized. Through a double-closed-loop feedback mechanism, the inner loop uses a PID controller to finely adjust the phase, and the outer loop learns online to update reinforcement learning proxy parameters. And finally, an optimization strategy is executed, the variable lane control equipment is linked, and a signal state and a vehicle speed guide instruction are pushed to the vehicle-mounted terminal, so that remarkable beneficial effects can be generated in the aspects of improving traffic efficiency, reducing congestion, improving safety and the like.

The embodiment of the invention discloses a flow refined prediction method driven by air traffic flow pattern recognition, and relates to the technical field of air traffic situation awareness and flow management. According to the method, spatial-temporal characteristics in a flight path are efficiently extracted, a local flow mode is accurately identified, a graph model is constructed based on a representative flow mode, and fine-grained prediction of traffic flow in an airspace is realized, so that the fineness and dynamic response capability of local region regulation and control are greatly improved. The method is especially suitable for deep analysis of flight path data in an airspace sector, and provides accurate auxiliary decision support for air traffic control, route planning and airspace design.

The invention provides an arterial traffic coordination control method and system, and belongs to the field of road traffic control, and the method comprises the steps: introducing a graph neural network to carry out the fusion of global information of traffic, and carrying out the coding of a gating time sequence convolution network; global traffic prediction data is obtained according to the coding state and the fusion sum features, and traffic state grades are mapped according to the prediction data; obtaining traffic flow and vehicle speed of local traffic, calculating a fuzzy membership degree, calculating a weighted relation of traffic flow among nodes by using a dynamic traffic flow weighted model, and adjusting local green light timing; establishing a global-local joint objective function, inputting the traffic state grade and the local green light timing into the objective function, and outputting a global-local objective function value; and selecting a control strategy with the minimum global-local objective function value, and outputting the green light control duration. A globally coordinated signal timing scheme is established, the traffic efficiency of the whole arterial traffic is improved, and traffic flow changes and emergencies can be quickly responded.

The invention discloses an aircraft recovery scheduling method and device based on a self-adaptive Lagrange multiplier, and relates to the technical field of aviation traffic control automation, and the method comprises the steps: building a constrained Markov decision process model of an aircraft recovery scheduling problem; introducing and initializing a Lagrangian multiplier by using a Lagrangian relaxation technology, and converting the constrained Markov decision process model into an unconstrained optimization problem to obtain a Lagrangian function; and performing main dual collaborative iterative optimization based on a Carlo demander-Kuhn-Tucker condition, and adaptively adjusting the Lagrange multiplier until the Lagrange multiplier converges to obtain an optimal strategy. According to the method, the constraint weight is dynamically adjusted in the main dual collaborative iteration mechanism, so that the rationality and accuracy of large-scale cluster recovery scheduling are improved.

The invention discloses an urban multi-layer traffic network modeling and toughness analysis method under the influence of rainstorm, and relates to the technical field of traffic control, a rainstorm ponding model is built based on historical rainfall information, and a runoff depth and a ponding depth are simulated based on the rainstorm ponding model; building a multi-mode traffic model based on SUMO software; a rainstorm ponding model and a multi-mode traffic model are integrated to construct a coupling model, ponding information is matched to a corresponding road through a road ID, failure mechanisms of different traffic modes under rainstorm ponding are obtained, ponding influences are considered for road traffic and rail traffic respectively, and corresponding influence parameters are set. Adjusting the road speed limit in real time and updating the route in real time; the SUMO simulation output result is utilized, the toughness index change in different scenes is calculated, the comprehensive traffic network toughness in different rainstorm scenes is analyzed from the four dimensions of topology, convenience, service and safety, the comprehensive traffic network toughness is evaluated, the dynamic capture of traffic characteristics is realized, and the analysis depth and breadth are improved.

The present invention discloses a dynamic capability-graded traffic management method for heterogeneous robots, including: S1 establishing a unified dispatching platform to receive capability registration information from heterogeneous robots; S2 defining the capability type of the robot, which is determined by a combination of a basic capability feature set and a non-empty subset of an extended capability feature set; S3 the unified dispatching platform dynamically generates a decision plan based on the robot's registered capability type and path conflicts; S4 monitoring queues and issuing queue positions to non-read-only robots, which then queue up in turn at a certain distance and speed; S5 detecting and identifying faulty robots, demarcating fault isolation zones, and executing avoidance based on capability type; S6 monitoring deadlocks and executing deadlock resolution strategies based on capability type; S7 fusing robot sensor data, with the platform and local terminals collaboratively executing avoidance. Through dynamic capability grading and targeted dispatching strategies, the operational efficiency and safety of heterogeneous robots and the overall system performance are effectively improved.

The invention discloses a method for generating a signal lamp signal according to a phase name, and relates to the technical field of road traffic control, and the method specifically comprises the steps: obtaining a direction keyword array and a turning keyword array according to a set phase name, reading an array of a half-crossing pedestrian lamp in each direction of an artificially set intersection, and determining whether the intersection is a half-crossing pedestrian lamp or not; the method comprises the following steps of: calculating a vehicle driving lamp state, assigning a value to the lamp color array, obtaining a driving lamp array, calculating a pedestrian lamp state according to the driving lamp array, obtaining a pedestrian lamp array, and controlling the driving lamp and the pedestrian lamp according to the driving lamp array and the pedestrian lamp array. According to the invention, the standard signal lamp color comparison table is automatically generated according to the phase name, the complicated calculation of manual setting is reduced, and the error probability is reduced.

A computer-implemented method comprising the following steps: obtaining (600) an image timestamp indicating the time of acquisition, by a traffic control radar, of an image showing a vehicle's license plate, the image timestamp having been provided by a clock on the traffic control radar; obtaining (602) time synchronization data, said time synchronization data having been received by the traffic control radar during a predefined time interval including the image timestamp to temporally synchronize the clock of the traffic control radar with a time server; and consistency testing (604) of the image timestamp with the time synchronization data, the consistency test producing a test result indicating whether the image timestamp is valid or invalid. Figure for the abstract: Fig. 4

The invention discloses a road passage control method and system based on visual detection, and relates to the technical field of road passage control methods, and the method comprises the steps: determining a release lane passage road according to a multi-phase passage road, and determining the congestion condition of each driving road, according to the congestion condition of each driving road, the plurality of traffic routes and the real-time image of the multi-phase passing road, the traffic guidance of the traffic signal to each driving road is determined, and the accuracy of the traffic guidance of the traffic signal to each driving road is improved. Determining an overall congestion combination according to the abnormal congestion event, the daily congestion event and the traffic plan project of each driving road; according to the traffic combination content of each overall congestion combination, the sub-traffic route of each driving road and the corresponding road peak driving flow, the driving load level corresponding to each driving road is determined, the accuracy of the driving load level corresponding to each driving road is improved, and intelligent traffic regulation and control of each driving road are triggered.

This invention discloses a global optimization method for differentiated traffic control measures that does not violate travel demand, relating to the field of intelligent traffic management technology, and includes the following steps: Step 1: Construct a global optimization framework for differentiated traffic control measures; Step 2: Define the urban traffic network and demand flow parameters; Step 3: Establish a mixed-integer linear programming model for optimizing road network flow distribution; Step 4: Consider factors such as flow demand, flow balance, and travel impedance, approximate the impedance function and objective function with secant lines, and construct constraints for the mixed-integer linear programming model; Step 5: Solve the model to obtain the optimal flow distribution result; Step 6: Obtain differentiated traffic control measures from the model based on the flow distribution result. This invention's method can construct a mixed-integer linear programming model that balances individual interests and overall efficiency based on the complex and diverse travel demands in large-scale urban road networks, design hierarchical control measures, and achieve synergistic optimization of control and path guidance, thereby improving the overall traffic efficiency of the road network.

The invention relates to the technical field of air traffic control, in particular to a multi-mode fusion real-time intelligent monitoring system for air traffic control command. According to the method, a three-dimensional intelligent analysis system of interface situation analysis-voice instruction recognition and understanding-air traffic control information fusion is constructed, and a safety monitoring closed loop of an air traffic control command whole process is realized on the premise that an existing system architecture is not changed; and real-time deduction and risk early warning of the airspace security situation are realized. The generated early warning information is synchronously pushed to a radar controller, an assistant controller and a main controller in a standardized visual and audio coding form through a newly-added safety information prompting module, a three-in-one cooperative cognitive space is constructed, the problem of information asymmetry among multiple seats and multiple on-duty roles in a traditional mode is effectively solved, and the safety of the system is improved. And important technical guarantee is provided for safe operation of civil and aviation pipes.

The invention relates to the technical field of data processing and logistics management, and discloses a cross-border e-commerce logistics order analysis and management system based on big data, and the system comprises a data obtaining module which is used for calculating and forming real-time state data; the potential energy calculation module is used for quantifying the state data into a regional logistics potential energy value; the risk identification module is used for monitoring the potential energy value change to identify a potential risk area; the risk matching module is used for comparing the current risk feature with a preset historical interrupt event feature fingerprint; and the strategy generation module is used for generating a self-adaptive intervention strategy accordingly. According to the method, the potential energy field model and the fingerprint database are constructed, so that the logistics risk is pre-judged in advance and accurately determined, invalid distribution caused by traffic control can be effectively avoided, the decision-making efficiency is improved, the customer recognition degree is guaranteed by generating an intervention strategy, and the analysis and management level of cross-border e-commerce logistics is systematically improved.