101 results about "Traffic dynamics" patented technology

The invention relates to the technical field of smart city traffic, and discloses a smart city traffic dynamic optimization system and method based on digital twinning. The system obtains urban traffic network multi-dimensional data from a plurality of heterogeneous data sources through a traffic multi-dimensional data acquisition module and integrates the urban traffic network multi-dimensional data into a traffic related data warehouse; a traffic digital twinning model construction module extracts features from the data warehouse to generate a traffic related feature matrix, and a digital twinning traffic dynamic model is constructed according to the traffic related feature matrix to output a theoretical traffic state value; the traffic flow map construction module determines a dimension link map of each dimension and constructs a traffic flow link map; the traffic core feature screening module screens a traffic core feature sequence based on the map; the traffic multi-dimensional optimization analysis module performs multi-dimensional difference analysis on the theoretical traffic state value and real-time actually measured traffic data, and generates a region-level difference coefficient matrix in combination with the core feature sequence; and the traffic event association positioning module can realize accurate management and dynamic optimization of urban traffic.

The invention provides a construction site monitoring method and system based on Beidou positioning and digital twinning, and relates to the technical field of highway construction supervision. According to the invention, real-time position information of equipment, personnel and vehicles on a construction site is accurately obtained through a Beidou positioning technology, and an equipment and personnel dynamic model and a traffic dynamic model are constructed in combination with vehicle passing data collected by road test equipment. And fusing the dynamic model with a pre-constructed construction site environment model by using a digital twinning technology to generate a high-precision construction site digital twinning model. According to the model, real-time three-dimensional visual monitoring of a construction site is realized, external intrusion and internal collision risks are effectively prevented, illegal behaviors of not wearing a safety helmet and the like are identified in combination with AI, and accident tracing is realized through the Internet of Things. The construction site can be comprehensively monitored without manual inspection, and the safety and inspection efficiency of highway construction are remarkably improved.

The invention discloses an interface request flow dynamic control method and device, a terminal and a medium, and relates to the technical field of the Internet, and the scheme provided by the invention enables a flow control strategy to reflect the actual processing capability of a server in real time by establishing the dynamic association between a flow limiting threshold and a resource state. By analyzing the relationship between the resource utilization rate and the request processing capacity, it is found that higher request processing capacity is allowed in the idle state of the resources, and the processing threshold should be reduced when the resources are nearly fully loaded. A linkage mechanism of a flow pressure index and resource capacity scheduling is further considered, when the flow pressure exceeds a preset interval, an elastic capacity expansion and contraction operation is triggered, cooperative closed-loop control of flow limiting and capacity expansion is achieved, and a flow limiting threshold value and the resource capacity form dynamic matching.

The invention provides an urban traffic dynamic deduction and real-time planning method and system based on multi-source position data fusion. Time-space references of signaling, MDT and MR data are unified through a dynamic rasterization mechanism, and the problem of data isomerism is solved by adopting anchor point selection of signal weighting and a triple criterion time expansion algorithm; generating a minute-level updated dynamic OD matrix based on the hierarchical road network constraint model; a METANET macroscopic traffic flow model and EKF extended Kalman filtering are fused to realize traffic state deduction, and parameter online calibration is supported; signal timing optimization, path induction and emergency control schemes are generated in combination with real-time deduction results, and a sensing-deduction-decision-evaluation closed loop is formed. The method can improve road network traffic efficiency, and is especially suitable for response of sudden traffic events.

The invention relates to the technical field of safety testing, and particularly provides a method, device and equipment for generating an expected function safety testing scene library, and the method comprises the steps: obtaining a parameter combination of key parameters in a pre-constructed basic scene; wherein the key parameters comprise environment parameters, traffic dynamic parameters and vehicle system parameters; generating a plurality of test scenes based on the parameter combination; after screening the plurality of test scenes, constructing a test scene library; wherein the test scene library is used for an expected function safety test of the intelligent driving vehicle. According to the embodiment of the invention, a rich expected function security test scene library can be constructed, and support is provided for expected function security test.

The invention discloses a real-time road condition-oriented AGV collaborative congestion avoidance path planning method, and relates to the technical field of AGV path planning, and the method comprises the steps: building a global road condition map based on AGV state data, and carrying out the dynamic updating of the global road condition map, and the dynamic updating comprises the weight updating based on an AGV density value and an average speed; based on the updated global road condition map, adopting a path planning algorithm to generate an AGV global optimization cooperative path set; the cooperative path set is converted into a control instruction, dynamic compensation is carried out on the control instruction through load sensing, and a corrected control instruction is obtained and issued to the AGV; according to the method, global road condition dynamic updating is combined with dynamic control of collaborative path optimization and load perception, multi-AGV global flow balance and real-time congestion prevention are achieved, and the problems that under the high load, path conflicts are frequent, local congestion cannot be actively adjusted, and the overall efficiency is reduced due to single AGV optimization are solved.

The invention discloses an electric vehicle charging pile planning method, system and device and a medium, and the method comprises the steps: obtaining urban road data, and building a dynamic traffic network matrix; the influence of different building types on traffic is considered, the dynamic traffic network matrix is corrected, and driving parameters are calculated; through a path search and optimization algorithm and a multi-dimensional traffic demand analysis technology, the travel path of the electric vehicle is simulated, and the charging demand of the electric vehicle is predicted; and establishing an optimization model by taking charging economic benefit maximization as a target, and generating a charging station capacity configuration scheme. The method breaks through the limitation of traditional experience planning, and achieves the efficient configuration of charging resources through the coupling analysis of building features and traffic dynamics.

The invention discloses a multi-lane heterogeneous traffic joint modeling method and system based on a GNN-PINN coupling architecture. The method comprises the following steps: constructing a space-time dynamic graph coding lane changing rule through a GNN, embedding a traffic flow physical constraint through a PINN, and solving a source item; according to the method, CAV behaviors are described by using improved IDM in the mid-microscopic view, an LWR equation subjected to GNN lane change flow correction is solved by using PINN in the macro view, GNN and PINN closed-loop optimization is realized by combining a double-path back propagation mechanism, and a model is optimized based on a residual loss function fusing physical loss, data driving items and boundary constraints. The system comprises a dynamic graph topology module, a GNN coding module, a PINN solving module and the like, the multi-lane traffic dynamic description precision can be improved, the multi-scale characteristic is considered, real-time simulation is supported, and the system is suitable for heterogeneous traffic modeling.

The invention discloses a regional traffic dynamic optimization control method based on vehicle-road cloud coordination, relates to the technical field of traffic control, and designs a self-adaptive space-time dynamic compensation algorithm and a three-level communication fault-tolerant architecture aiming at space-time asynchronization and communication interruption risks existing in multi-source data of a vehicle end, a road side and a cloud end. According to the method, accurate alignment and stable transmission of multi-dimensional heterogeneous data are realized, an attention mechanism enhanced multi-agent deep reinforcement learning architecture is adopted, online element reinforcement learning is combined, global and local optimization control of cloud-side double-loop cooperation is realized, communication is performed by adopting a block chain-based security consensus protocol, the system security and stability are effectively guaranteed, and the system reliability is improved. According to the method, the operation efficiency, the emergency response capability and the environmental friendliness of urban smart traffic are remarkably improved, and the method is suitable for a smart city global traffic optimization scene.

The invention discloses a traffic signal scheduling system and method based on road section congestion information, and relates to the technical field of traffic management and control. Comprising the following steps: S1, data acquisition and preprocessing: acquiring original traffic data of a road section in real time through a multi-source data acquisition unit, the original traffic data comprising traffic flow, vehicle speed, queuing length, occupancy and travel time; meanwhile, external environment data are obtained, wherein the external environment data comprise weather information, holiday and festival information and traffic event information; traffic data and external environment data are acquired in real time through the multi-source data acquisition unit, data quality is guaranteed through cleaning, denoising and standardized preprocessing, and the problem that data fusion of an existing system is insufficient is solved; by means of an adaptive weight learning model, historical traffic data, real-time data and environmental data are combined to dynamically adjust a congestion evaluation index weight, and the defect that a fixed or empirical weight is difficult to adapt to traffic dynamic changes is overcome.

The invention discloses a multi-target flow dynamic allocation method based on deep learning, which comprises the following steps: collecting network topology data, link operation data and service flow request data to form a network state sequence; a multi-target constraint set is obtained and written into a target constraint description table; constructing a state vector, and dividing the state vector into multiple groups of input sub-vectors; carrying out conjoint analysis on the service flow, and constructing a candidate allocation action set; constructing an improved CMAC network model, and outputting an action value vector; constructing an anti-multi-arm bandit decision-making module, and outputting action selection distribution; and executing multi-target feasible region projection processing to generate a feasible traffic distribution decision. According to the method, the improved CMAC network model is introduced, and the anti-multi-arm bandit decision-making method is combined, so that stable and self-adaptive dynamic distribution of the network traffic under the multi-target constraint and complex network state is realized.

The invention is suitable for the technical field of traffic management, and provides an urban traffic dynamic command method and system based on digital twinning. According to the method, the high-quality data set is generated through direct multi-source data acquisition, restoration and integration; hierarchical dynamic storage is carried out, and block chain security management is carried out; carrying out stored multi-source data acquisition and processing, and constructing a real-time dynamic traffic twin model; based on the real-time dynamic traffic twinborn model, decision optimization of traffic control is carried out, evaluation feedback is carried out through edge calculation, and model parameters of the real-time dynamic traffic twinborn model are corrected; a three-level data storage system is adopted, a data management center is constructed, and data life cycle management is carried out. According to the method, the real-time dynamic traffic twin model can be constructed by fusing multi-source data, traffic flow simulation, congestion prediction and dynamic optimization are realized, a scientific basis is provided for smart city traffic management, traffic congestion is effectively relieved, and the urban traffic efficiency is improved.

The invention provides a traffic dynamic cooperative control method and system based on multi-source heterogeneous data fusion, and relates to the technical field of traffic control, and the method comprises the steps: obtaining a multi-source heterogeneous data flow of a management and control region through a ubiquitous perception terminal cluster, carrying out the fine-grained data fusion, and outputting a space-time fusion state matrix; performing blind area state completion to generate a panoramic traffic state map; performing dynamic demand prediction based on the space-time deep learning model, and outputting a pressure distribution matrix; dynamically delimiting a traffic control boundary; performing multi-agent collaborative decision, and outputting a collaborative strategy packet; and according to the real-time dissipation rate deviation, performing closed-loop updating of the traffic coordination strategy. The technical problems that traffic data in the prior art mainly depends on a single data source, data missing or coverage blind areas are prone to occurring, real traffic conditions cannot be comprehensively reflected, traffic flow prediction errors and bottleneck prediction errors are caused, and finally traffic congestion is aggravated are solved.

The invention relates to the technical field of big data analysis, and discloses a multi-source data driven low-altitude operation intelligent scheduling method and system, and the method comprises the steps: firstly obtaining multi-source heterogeneous data through the system, and carrying out the standardization processing, and forming a unified multi-source data set; and on this basis, dynamic identification and density evaluation are executed to generate a traffic density distribution map. And then performing clustering analysis on the distribution map, extracting a boundary range of a key region, updating a fence boundary according to the boundary range, and generating a new fence constraint condition. And the system corrects the flight parameters according to the constraint to obtain an adjusted running state, and carries out data acquisition in combination with traffic judgment to form regional traffic dynamic data with higher precision. And finally, the system executes load balancing scheduling and abnormal dynamic updating based on the data to form a newest low-altitude traffic management state. The method can solve the problem of edge resource scheduling staticization.

The invention provides a vehicle trajectory prediction method and system, and relates to the technical field of intelligent vehicles, and the method comprises the steps: obtaining the current trajectory data of all vehicles in a target road section; performing driving intention classification on the current trajectory data of the target vehicle through an intention prediction model constructed based on LSTM to obtain intention features; according to the current trajectory data of all vehicles, through a feature aggregation mechanism of a graph convolutional neural network, feature information of the target vehicle and neighbor vehicles is aggregated, and spatial interaction features are extracted; and based on the intention features and the spatial interaction features, generating future trajectory coordinates of the target vehicle in the target road section by using Transform. According to the method, the prediction precision and robustness are improved, meanwhile, it is ensured that the generated trajectory conforms to the driving intention and the real traffic dynamics constraint, the high-accuracy and high-rationality vehicle trajectory prediction precision in different scenes is remarkably improved, and more reliable environment perception and decision support is provided for an automatic driving vehicle.

The invention provides an intelligent driving assisting method based on cloud collaboration and a traffic dynamic real-time processing system. The method comprises the steps that road environment data around a vehicle are collected in real time through a vehicle end sensing device; performing real-time processing on the road environment data by using a vehicle-mounted edge computing device, and extracting dynamic obstacle feature information; uploading the dynamic obstacle feature information after multi-vehicle processing to a cloud coordination center, and integrating and verifying multi-vehicle data through the cloud coordination center to generate real-time traffic obstacle distribution information; dynamically adjusting a navigation path planning algorithm according to the real-time traffic obstacle distribution information, and pushing an updated navigation instruction to the vehicle end; and dynamically adjusting a navigation path planning algorithm according to the real-time traffic obstacle distribution information, and pushing an updated navigation instruction to the vehicle end. According to the invention, the problem of intelligent driving vehicle path planning failure caused by single data analyzed by navigation software due to lack of multi-dimensional information acquired by the vehicle in real time is solved.

The invention discloses a short video traffic dynamic transaction and multi-dimensional value evaluation intelligent system, which relates to the field of electronic commerce, and is characterized in that in a delivery window optimization module, a fixed delivery window length twinwindow is determined; according to the method, the delivery period can be adaptively adjusted according to the fractal robustness of the short video under multiple time scales such as minute scale, hour scale and day scale, and when the multi-time-dimension fractal continuous robust index FCSms is relatively high, the fixed delivery window length twinwindow is prolonged to utilize the long tail effect of the short video; and when the multi-time-dimension fractal continuous robust index FCSms is relatively low, the fixed delivery window length twinwindow is shortened to avoid excessive delivery. Compared with an existing fixed-time-length delivery mode, the system enables a related short-time delivery window of a short video platform to be closer to the life cycle of video traffic, and improves the accuracy and timeliness of advertisement delivery.

The invention provides an embankment traffic load simulation multi-degree-of-freedom device and test system, and the device comprises an X-direction linear motion unit which employs a conveyor belt mechanical drive structure and is used for driving a Z-direction linear motion unit to move back and forth; the Y-direction linear motion unit adopts a linear motor electromagnetic driving structure and is used for driving the loading device to move left and right; the Z-direction linear motion unit adopts a linear motor electromagnetic driving structure and is used for driving the Y-direction linear motion unit to move up and down; the loading device is mounted at the top of the Y-direction linear motion unit and is used for outputting a dynamic load to simulate a stress test of the embankment roadbed under the action of a traffic dynamic load; and the motor control mainboard is in control connection with the X-direction linear motion unit, the Y-direction linear motion unit, the Z-direction linear motion unit and the loading device and is used for coordinating three-dimensional motion and dynamic loading control. Through cooperative control of spatial multi-degree-of-freedom motion and dynamic load, the technical bottleneck of dike traffic load simulation in a complex environment is solved.

The application discloses a multi-service traffic grouping scheduling method for a P4 switch processor core, mainly comprising a feature extraction method based on a statistical interval and a Sketch, comprehensive data packet time correlation and traffic dynamic change, an optimized Bloom filter conflict detection mechanism, improved adaptability and accuracy of the algorithm in a limited resource environment; a decision tree model is combined with scheduling, a decision tree is reconstructed to adapt to a P4 switch operation mode and data processing demand, high-precision service flow classification under line-speed operation is ensured; a grouping scheduling method AD-PIFO is designed based on variable priority, data packet forwarding priority is dynamically adjusted, different service QoS demand is met, and the overall performance and efficiency of the switch are improved. The method is outstanding in high-priority traffic throughput and low-latency performance, and the medium-low-priority traffic throughput is better than that of similar algorithms, is stable and effective under different load conditions, has reasonable additional time overhead of data packet transmission, and is widely used in SDN network optimization data packet scheduling scenes.

The invention relates to the technical field of data processing, and particularly provides a traffic dynamic cooperative control method, system and device and a storage medium, and the method comprises the steps: obtaining the monitoring data of each road node, and determining the traffic state parameters of the road nodes based on the monitoring data; determining a control instruction range of a signal lamp of the corresponding road node based on the traffic state parameters; and optimizing the control instruction range of the signal lamps of the plurality of road nodes by using a multi-objective function, and solving a global optimal control instruction set. According to the invention, the bottleneck of isolated optimization is broken through, and a quantifiable technical path is provided for urban-level intelligent traffic management and control.

The invention relates to the technical field of data processing, and discloses a data stream processing method and device based on dynamic bandwidth allocation, equipment and a medium, when a data request of a client is received, a new redirection request is generated by judging a data stream type and applying redirection logic when the data stream type is confirmed to be a target data type, and the data stream is redirected. Therefore, the data streams can be quickly and accurately classified and guided, congestion of the data streams on unnecessary paths is avoided, and the condition of resource competition is relieved. According to the method, the channel specially used for transmitting the target data stream is determined, and independent transmission paths are provided for different types of data streams, so that mutual interference among data is reduced, and the occurrence probability of system jamming is remarkably reduced. According to the method, the traffic resource of each channel is calculated and allocated by applying a traffic dynamic allocation algorithm, so that the processing capability of high-concurrency scenes of financial and medical data processing is greatly improved, and the processing efficiency and the response speed of data streams are remarkably improved.

The invention discloses a traffic dynamic shortest path distribution method and system based on a triangular fuzzy number weight, and relates to the technical field of intelligent traffic systems.Fuzzy information in a path evaluation attribute is represented through a triangular fuzzy number, a comprehensive weight is analyzed and calculated in combination with deviation maximization and a moisture value, and the decision reliability is effectively improved; a K-Means algorithm is adopted to perform dynamic path search, and through a representative vertex subset pre-calculation and increment updating strategy, the path retrieval efficiency is remarkably improved; a travel time prediction model related to departure time and real-time flow is established, and accurate response to accidental congestion is realized; and finally, outputting an optimal path scheme through a multi-objective decision-making method. Compared with a traditional algorithm, the speed is improved on a real road network data set, the path distribution accuracy is improved, meanwhile, the path safety, comfort and efficiency are effectively balanced, real-time path planning under a large-scale road network is supported, and an efficient solution is provided for intelligent traffic management.

The invention belongs to the technical field of unmanned vehicles, and discloses an unmanned vehicle path planning method and system based on big data, and the method comprises the steps: obtaining traffic static data, traffic dynamic data and vehicle state data, and sequentially carrying out the data preprocessing and data fusion operation, and obtaining a vehicle traffic information data set; obtaining a vehicle path target and user preference, and performing path decision by using a pre-configured path decision model in combination with the vehicle traffic information data set to obtain a global initial path; and continuously acquiring real-time data of the vehicle in the process of executing the global initial path, and performing dynamic path adjustment by using a dynamic obstacle avoidance algorithm and a comprehensive cost function until the vehicle arrives at a destination. According to the method, the safety and efficiency of the unmanned vehicle are remarkably improved, the method is closer to the cognition and decision logic of real human driving, and the method has high engineering application value and popularization prospects.

The invention discloses a traffic dynamic evaluation method based on multi-source data fusion, and the method comprises the steps: obtaining multi-source data, obtaining a mapping feature value and a weight coefficient which reflect a prediction condition from the multi-source data based on the prediction condition of a region to be predicted, and enabling the feature input data to be determined through the mapping feature and the weight coefficient, and inputting the feature input data into a pre-trained analysis neural network to obtain analysis data, and inputting the analysis data into a pre-trained trend neural network to obtain trend data so as to perform traffic prediction according to the trend data. The method comprises the following steps: firstly, extracting value data through independent quantization, association mapping and weight weighting of multi-source data; analyzing a neural network to obtain analysis data; obtaining trend data through a trend neural network; and finally, obtaining a congestion prediction conclusion according to the analysis data and the trend data, thereby remarkably improving the stability and accuracy of congestion prediction.

The invention discloses an optical network routing and spectrum allocation algorithm based on a GCN-LSTM, and the algorithm comprises the steps: S1, defining an optical network environment, initializing the algorithm, and constructing a basic framework of a traffic prediction GCN-LSTM model; s2, according to historical traffic data in the network, network traffic data prediction is carried out in combination with a machine learning method; s3, predetermining an optimal modulation format based on the predicted flow result; and S4, performing efficient RSA decision according to the determined modulation format. According to the optical network routing and spectrum allocation algorithm based on the GCN-LSTM, optimization and improvement are carried out in the aspects of flow data prediction, flow dynamic threshold setting, modulation format determination and the like, and the problems that in a traditional heuristic algorithm, the spectrum resource utilization rate is low and the like can be solved.

The invention discloses a dynamic regulation and control method and system for highway reconstruction and extension traffic, and relates to the technical field of traffic engineering and intelligent traffic systems. The method comprises the following steps: acquiring vehicle information, generating charge labels and grouping the charge labels; calculating retention intensity to identify priority vehicles; extracting boundary parameters to judge traffic conditions; calculating a path index marking state; according to the invention, through collecting the axle number, tonnage and vehicle type codes of the vehicles and generating charge labels, fine classification of passing vehicles is realized, queuing time and charge data are combined to calculate retention intensity, priority passing vehicles and lanes are identified, decision-making accuracy is improved, lane boundaries and capacity parameters are extracted, and the accuracy of the lane boundaries is improved. The event conflict path is identified in combination with the path structure and the spatial position, a path segment linkage relationship is established, the spatial coupling and path identification capability of regulation is enhanced, fine regulation is realized, and the traffic efficiency and the event handling level are improved.

The invention discloses a vehicle and road cloud intelligent scheduling method and system based on dynamic signal timing, and relates to the related technical field of intelligent traffic, and the method comprises the steps: collecting multi-source traffic data of a target traffic region, including vehicle real-time position data, traffic signal state data, road topology data and environment perception data; a regional traffic dynamic model is adopted, and traffic flow space-time distribution is predicted according to the multi-source traffic data; according to the traffic flow space-time distribution, planning a dynamic signal timing strategy; and issuing the dynamic signal timing strategy to a signal controller of each intersection of the target traffic area, and synchronously pushing the dynamic signal timing strategy to a vehicle terminal. The technical problems that in the prior art, traffic signal control is rigid, dynamic traffic flow changes are difficult to respond in real time, and the overall traffic efficiency of a road network is poor due to insufficient vehicle-road cloud collaboration are solved, and intelligent collaboration of a signal strategy and vehicle driving is achieved. The overall traffic efficiency of the road network is improved, and vehicle delay and queuing are reduced.

The application relates to the technical field of big data analysis, and discloses a multi-source data driven low-altitude operation intelligent scheduling method and system, the method comprising the following steps: firstly, a system acquires multi-source heterogeneous data, and carries out standardization processing to form a unified multi-source data set. On this basis, dynamic identification and density evaluation are carried out to generate a traffic density distribution map. Then, clustering analysis is carried out on the distribution map, the boundary range of a key area is extracted, and the fence boundary is updated according to the boundary range to generate new fence constraint conditions. The system corrects flight parameters according to the constraint conditions, obtains an adjusted operation state, carries out data collection in combination with traffic judgment, and forms higher-precision regional traffic dynamic data. Finally, the system executes load balancing scheduling and abnormal dynamic updating based on the data to form the latest low-altitude traffic management state. The method can solve the problem of static scheduling of edge resources.

The application discloses a tunnel flow dynamic scheduling system for small-granularity slice networks and relates to the fields of network slicing and traffic engineering.The application aims to solve the problems of low resource utilization of small-granularity slices and the difficulty of guaranteeing service experience by passive response scheduling in the prior art.The system comprises a small-granularity slice sensing layer, a dynamic strategy engine and a tunnel execution layer.The sensing layer collects queue depth and time delay data of tunnels in real time;the strategy engine predicts congestion based on a prediction model and identifies idle slices to initiate resource lending; and the execution layer dynamically adjusts paths and queue parameters to complete the lending.The application realizes prediction-based active elastic scheduling, significantly improves network resource utilization and guarantees the performance of high-priority services.

The invention belongs to the technical field of intelligent logistics, and discloses an intelligent order distribution method and system considering rider load balancing, and the method comprises the steps: obtaining the real-time position data, historical order data and dynamic traffic data of a rider in a distribution region, and constructing a load balancing distribution scheme according to the data; performing rider cluster division on the distribution area, generating a load distribution thermodynamic diagram, and calculating a load balancing index based on the load distribution thermodynamic diagram; based on the load balancing index and the load balancing distribution scheme, distribution instruction generation is carried out on to-be-distributed orders, a hierarchical distribution sequence is formed through priority ranking, and an optimal order distribution strategy is obtained after matching optimization; according to the configuration instruction, order pushing is adjusted, the execution state is monitored, the load efficiency is evaluated, an adjustment and optimization parameter is obtained, and the adjustment and optimization parameter is fed back to the distribution scheme; according to the method, the dynamic optimal configuration of the distribution network is realized, the load of a rider is effectively balanced, and the distribution efficiency and the service quality are improved.