A big data processing method, system, storage medium, and device for intelligent transportation.

Abstract

A big data processing method, system, storage medium, and device for intelligent transportation are disclosed, relating to the field of data processing technology. The method includes: acquiring activity reservation information for a target area within a preset time period, the activity reservation information including the activity location, estimated number of participants, and activity time range; determining a scheduling strategy for a target vehicle set based on the estimated number of participants and the activity time range; predicting a traffic information map of the target area within the activity time range, the traffic information map including traffic information for all roads centered on the activity location with a preset radius; determining the driving routes and backup routes corresponding to each target vehicle in the target vehicle set based on the traffic information map and the scheduling strategy; and generating a comprehensive scheduling map of the target vehicle set within the target time range based on the driving routes and backup routes corresponding to each target vehicle. Implementing the technical solution provided in this application improves the effectiveness of vehicle scheduling during large-scale events.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a big data processing method, system, storage medium, and device for intelligent transportation. Background Technology

[0002] With the acceleration of urbanization and the frequent hosting of large-scale events, urban transportation has been placed under tremendous pressure. Especially during large-scale events such as sporting events and concerts, a large influx of people into specific areas within a short period can easily cause traffic congestion, affecting citizens' travel and the efficiency of urban operations. To cope with this situation, it is usually necessary to dispatch a large number of vehicles for crowd dispersal.

[0003] Currently, traffic management plans for large-scale events mainly rely on human experience to formulate. Traffic management departments usually pre-arrange a certain number of vehicles to run on fixed routes at fixed times based on the scale and location of the event. However, due to the uncertainty of the event and the fluctuation of the number of participants, as well as the lack of accurate prediction of traffic conditions during the event, there is a tendency for vehicle scheduling to be mismatched with actual needs, resulting in low effectiveness of vehicle scheduling during large-scale events. Summary of the Invention

[0004] This application provides a big data processing method, system, storage medium, and device for intelligent transportation, which improves the effectiveness of vehicle dispatching during large-scale events.

[0005] Firstly, this application provides a big data processing method for intelligent transportation, the method comprising:

[0006] Obtain activity reservation information for a target area within a preset time period. The activity reservation information includes the activity location, the estimated number of participants, and the activity time range.

[0007] Based on the estimated number of participants and the time range of the activity, a scheduling strategy for the target vehicle set is determined.

[0008] Predict a traffic information map of the target area within the activity time range, the traffic information map including traffic information of all roads centered on the activity location with a preset length as the radius;

[0009] Based on the traffic information map and the scheduling strategy, determine the driving routes and backup routes corresponding to each target vehicle in the target vehicle set;

[0010] Based on the driving routes and backup routes corresponding to each target vehicle, a comprehensive scheduling map of the target vehicle set within the target time range is generated.

[0011] By adopting the above technical solution and acquiring event reservation information for the target area within a preset time period, key information such as event location, estimated number of participants, and event time range can be obtained in advance. Based on the estimated number of participants and the event time range, a dispatching strategy for the target vehicle set can be formulated, achieving precise matching between vehicle dispatching and actual demand. Simultaneously, by predicting the traffic information map of the target area centered on the event location within the event time range, and combining this with the dispatching strategy to plan driving routes and backup routes for each target vehicle, a comprehensive dispatching map is ultimately generated. This makes the vehicle dispatching solution forward-looking and flexible, effectively avoiding the inefficiency of vehicle dispatching caused by event uncertainty and fluctuations in the number of participants. This solution, through advance acquisition of event information and accurate prediction of traffic conditions, achieves scientific and systematic vehicle dispatching, significantly improving the effectiveness of vehicle dispatching during large-scale events.

[0012] A second aspect of this application provides a big data processing system for intelligent transportation, the system comprising:

[0013] The activity information acquisition module is used to acquire activity reservation information of the target area within a preset time period. The activity reservation information includes the activity location, the estimated number of participants, and the activity time range.

[0014] The scheduling strategy determination module is used to determine the scheduling strategy for the target vehicle set based on the estimated number of participants in the activity and the activity time range.

[0015] The traffic information prediction module is used to predict the traffic information map of the target area within the activity time range. The traffic information map includes traffic information of all roads centered on the activity location with a preset length as the radius.

[0016] The vehicle route determination module is used to determine the driving route and backup route corresponding to each target vehicle in the target vehicle set based on the traffic information map and the scheduling strategy.

[0017] The scheduling map generation module is used to generate a comprehensive scheduling map of the target vehicle set within the target time range based on the driving route and backup route corresponding to each target vehicle.

[0018] A third aspect of this application provides a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the method steps described above.

[0019] A fourth aspect of this application provides an electronic device comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to execute the method steps described above.

[0020] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0021] This application, by acquiring event reservation information for a target area within a preset time period, can obtain key information such as event location, estimated number of participants, and event time range in advance. Based on this, a dispatching strategy for the target vehicle set can be formulated, achieving precise matching between vehicle dispatching and actual demand. Simultaneously, by predicting traffic information maps centered on the event location within the target area during the event time range, and combining this with the dispatching strategy, driving routes and backup routes are planned for each target vehicle, ultimately generating a comprehensive dispatch map. This makes the vehicle dispatching solution forward-looking and flexible, effectively avoiding the inefficiency caused by event uncertainty and fluctuations in participant numbers. This solution, through advance acquisition of event information and accurate prediction of traffic conditions, achieves scientific and systematic vehicle dispatching, significantly improving the effectiveness of vehicle dispatching during large-scale events. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a big data processing method for intelligent transportation provided in an embodiment of this application;

[0023] Figure 2 This is a schematic diagram of a big data processing system for intelligent transportation provided in an embodiment of this application;

[0024] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0025] Explanation of reference numerals in the attached drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0027] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0028] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0029] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0030] Please refer to Figure 1 This paper presents a flowchart illustrating a big data processing method for intelligent transportation. This method can be implemented using a computer program, a microcontroller, or run on a big data processing system for intelligent transportation. The computer program can be integrated into a computer device or run as a standalone application. Specifically, the method includes steps 10 to 50, as follows:

[0031] Step 10: Obtain activity reservation information for the target area within a preset time period. The activity reservation information includes the activity location, the estimated number of participants, and the activity time range.

[0032] In this embodiment of the application, the target area refers to a specific urban area that requires traffic management. This area includes one or more venues that can host large-scale events, such as stadiums, convention centers, concert halls, etc.

[0033] In this application embodiment, the preset time period refers to a fixed time cycle used for statistical and planning vehicle scheduling. It can be a specific time period such as a day, a week, or a month, during which one or more large-scale events may be held.

[0034] In this embodiment of the application, the activity reservation information refers to the activity information pre-registered by various activity venues in the target area within a preset time period. Specifically, it includes information such as the location of the activity, the expected number of participants, and the specific start and end times of the activity, which is used for subsequent vehicle scheduling planning.

[0035] Specifically, since large-scale events often require advance application and approval, event reservation information can be obtained through the event management system of the target area. The event management system receives event applications from various venues (such as stadiums, convention centers, and concert halls), which include basic information such as the event location, expected number of participants, and event time. The system first categorizes these applications into large-scale and small-scale events, focusing on those with estimated attendance exceeding a preset limit (e.g., 1000 people) or located in a preset congestion area. For these targeted events, the system further obtains detailed reservation information, including specific venue information (such as venue capacity and entrance / exit locations), detailed event schedules (such as start and end times, and peak traffic predictions), and more accurate attendance estimates (such as time-segmented attendance predictions). This method of obtaining more comprehensive and accurate reservation information provides a reliable data foundation for developing targeted vehicle dispatch strategies, avoiding information biases that may arise from relying solely on experience-based estimates, and thus better meeting traffic dispersal needs during events.

[0036] Based on the above embodiments, as an optional embodiment, the step of obtaining activity reservation information for the target area within a preset time period may further include the following steps:

[0037] Step 101: Receive activity applications submitted by various activity venues in the target area.

[0038] Specifically, the system receives activity applications from various venues in the target area through the activity management system. Venue managers need to fill in relevant information through the system's pre-set application interface, including basic data such as activity name, activity type, venue information, expected number of participants, and activity time. The system will standardize this application information to ensure that the data format is uniform and complete, facilitating subsequent classification and processing.

[0039] Step 102: Categorize the event applications to obtain large-scale event applications and small-scale event applications, and determine the number of participants and location for each large-scale event.

[0040] Specifically, the collected event applications are intelligently categorized. First, categorization criteria are set; for example, events with an estimated attendance of over 1000 people are initially classified as large-scale events, while those with fewer than 1000 participants are classified as small-scale events. Simultaneously, the system also considers other characteristics of the event for a comprehensive judgment, such as event duration, event type (e.g., sporting events, concerts, exhibitions), and venue capacity, thereby determining the number of participants and specific location information for each large-scale event.

[0041] Step 103: Select activities with more participants than the preset number and / or whose locations are within the preset congestion area as target activities, and obtain the corresponding activity reservation information for the target activities.

[0042] Specifically, after initial classification, the system further filters and identifies target activities requiring priority attention. Activities with a number of participants exceeding a preset limit (e.g., 2000 people) are automatically marked as target activities. Simultaneously, the system maintains a pre-stored list of congested areas; if an activity is located within these areas, it will be marked as a target activity even if the number of participants does not reach the preset limit. For these identified target activities, the system further acquires more detailed activity reservation information, including venue layout, time-segmented pedestrian flow forecasts, and the distribution of surrounding transportation facilities. This multi-layered filtering and information acquisition mechanism ensures that the system can prioritize the allocation of traffic resources to activities most in need of traffic support, improving the accuracy of traffic scheduling and the efficiency of resource utilization.

[0043] Step 20: Determine the scheduling strategy for the target vehicle set based on the estimated number of participants and the time range of the event.

[0044] In this application embodiment, the target vehicle set refers to a collection of vehicles that can be used to ensure transportation capacity for large-scale events, including but not limited to: bus fleets of transportation companies that have signed pre-signed event transportation capacity guarantee agreements, taxi fleets that can be dispatched flexibly, charter service fleets that cooperate with event organizers, etc. These vehicles are dispatchable, that is, they can be flexibly dispatched as needed during the event.

[0045] In this embodiment, the dispatching strategy refers to a detailed operational plan for the target vehicle set, specifically including but not limited to: the number of vehicles to be dispatched (e.g., 20 buses and 50 taxis are needed based on the estimated number of attendees), the timing of various vehicle types (e.g., arriving in batches 2 hours and 1 hour before the event), vehicle assembly points (e.g., parking lots, temporary stations), and task allocation schemes (e.g., some vehicles are responsible for shuttle services from the event location to the subway station, while others are responsible for cross-regional transportation). The dispatching strategy will be differentiated based on the capacity characteristics and dispatching flexibility of different types of vehicles to achieve optimal allocation of transportation resources.

[0046] Specifically, based on the estimated number of attendees for the event, the required transportation capacity can be calculated. A pre-defined capacity allocation model can be used to allocate the estimated number of attendees according to the proportion of different modes of transportation. For example, if an event with 1000 attendees is estimated to require 30% of its participants to be transported by dedicated vehicles, then the specific number of people needing transportation is calculated to be 300. Then, based on the passenger capacity of different types of vehicles (e.g., buses 40 people / vehicle, taxis 4 people / vehicle) and scheduling feasibility, the number of vehicles needed for each type can be calculated, and a certain proportion of reserve capacity can be reserved to cope with emergencies. Next, a specific vehicle scheduling schedule can be developed based on the event's time frame. For example, the peak periods of crowd flow before and after the event can be analyzed. For instance, for a concert starting at 19:30, the peak entry period might be between 18:00 and 19:00, and the peak exit period might be between 22:30 and 23:30. Based on these time points, the system will develop a phased scheduling plan for the target vehicle group. For example, 60% of the capacity will arrive at the designated area 30 minutes before the peak arrival time, and the remaining 40% will be supplemented in batches to ensure that the capacity supply matches the flow of people. Simultaneously, the system will also consider the geographical characteristics of the event venue to plan specific assembly points and routes for the target vehicle group. For instance, the system will select parking lots or temporary stations at an appropriate distance from the event venue as vehicle assembly points and plan the optimal route based on road conditions.

[0047] Based on the above embodiments, as another optional embodiment, the step of determining the scheduling strategy for the target vehicle set based on the estimated number of participants and the time range of the activity may further include the following steps:

[0048] Step 201: Obtain historical vehicle dispatch data for similar activities, and determine the peak time period within the activity time range based on the historical vehicle dispatch data.

[0049] Specifically, the system retrieves historical vehicle dispatch data from a database that matches the current event type. This historical data includes information such as event scale, time distribution, and vehicle dispatch records. For example, for a concert, the system extracts dispatch data from concerts held at the same or similar venues within the past six months. Through data analysis algorithms, the system extracts peak vehicle usage times from this historical data, thereby identifying the peak periods during the event when vehicle demand is most likely to surge.

[0050] Step 202: Calculate the number of target vehicles required for each peak period based on the estimated number of participants, and the departure information for each target vehicle.

[0051] Specifically, after determining the peak periods, the system calculates the number of target vehicles needed for each peak period based on the estimated number of attendees. The calculation first allocates the estimated attendees to each peak period according to the distribution ratio of historical data. For example, if historical data shows that 40% of the audience needs to be transported one hour before the concert starts and 60% needs to be transported at the end, the system will calculate the actual vehicle demand for each period based on this ratio. Then, the system calculates the number of each type of vehicle needed to meet the capacity demand based on the passenger capacity of different types of target vehicles. Simultaneously, the system generates specific departure information for each target vehicle, including departure time, estimated arrival time, and route. This departure information generation takes into account practical factors such as vehicle speed and road conditions.

[0052] Step 203: Integrate the number of target vehicles and the departure information of each target vehicle into a scheduling strategy for the target vehicle set.

[0053] Specifically, the calculated target vehicle quantity information and the specific departure information of each vehicle are integrated to form a complete scheduling strategy. This strategy includes both macro-level vehicle quantity allocation and micro-level specific vehicle scheduling arrangements. For example, the strategy might specify that 15 buses will be dispatched to assembly point A 90 minutes before the concert starts, and another 10 buses will be dispatched to assembly point B 60 minutes before the start, with detailed departure times and routes for each bus. Through this precise calculation and overall planning based on historical data, the system can more accurately predict and meet vehicle demand during events, improving the accuracy and efficiency of vehicle scheduling.

[0054] Step 30: Predict the traffic information map of the target area within the activity time range. The traffic information map includes traffic information of all roads centered on the activity location with a preset length as the radius.

[0055] Specifically, the prediction range is defined with the geographical coordinates of the event location as the center and a preset length (e.g., 3 kilometers) as the radius. All traffic and road information within this range is retrieved, including road grade, number of lanes, traffic light distribution, and other infrastructure information. Subsequently, multiple data sources are integrated to predict traffic conditions. These data sources include: historical traffic data for the same period (e.g., regular traffic flow from 18:00 to 20:00 on weekdays), weather forecast data (e.g., the impact of rainfall on traffic speed), real-time traffic data of the surrounding area (collected through electronic police, surveillance cameras, etc.), and historical traffic impact data of similar events. The system uses machine learning algorithms to input this data into a preset prediction model, generating traffic information prediction results for various time points within the event's time frame. The prediction results include key indicators such as expected traffic volume, average driving speed, and congestion level.

[0056] This application employs a deep learning-based spatiotemporal sequence prediction model for traffic condition prediction. This model uses a graph neural network structure to construct a directed graph of the road network in the target area, where road nodes and road segments are the nodes, and the connections between roads are the edges. Each node contains multi-dimensional feature information, such as road grade, number of lanes, and historical traffic flow data; each edge contains attribute information such as turning permission and traffic light duration. The input layer of the prediction model receives the node feature matrix and adjacency matrix, and extracts the spatial correlation features of the road network through multi-layer graph convolution operations. Simultaneously, the model also includes a temporal feature extraction module, using a Long Short-Term Memory (LSTM) network structure to process the historical traffic data sequences of each node, capturing the temporal evolution of traffic flow. The output layer of the model fuses spatial and temporal features through a fully connected layer to generate predicted traffic conditions for each road segment within the target time range. During training, the prediction model uses real traffic data collected during historical events as training samples, and optimizes the model parameters through backpropagation. The model also incorporates an attention mechanism that adaptively adjusts the weights of different input features, highlighting the impact of key factors on the prediction results. This deep learning-based prediction method enables the system to accurately capture the complex spatiotemporal dependencies in road networks, providing more reliable traffic prediction results.

[0057] Based on the above embodiments, as another optional embodiment, the step of predicting the traffic information map of the target area within the activity time range may further include the following steps:

[0058] Step 301: Obtain historical traffic information of the target area in each preset historical time period, and determine the target historical time period corresponding to the activity time range.

[0059] Specifically, based on the nature and timing of the event, historical traffic information for the target area during preset time periods is retrieved from a historical database. These preset time periods can include weekday periods (e.g., morning rush hour 7:00-9:00 and evening rush hour 17:00-19:00 Monday to Friday), weekend periods (e.g., Saturday and Sunday 10:00-12:00 and 14:00-16:00), and special holiday periods (e.g., Labor Day and National Day 9:00-21:00). The historical traffic information includes data such as traffic volume, average speed, and congestion index for each preset time period. Based on the specific time of the event to be held, the closest target historical time period is determined. For example, for a concert planned for 19:30 on Friday evening, the system will match it to the historical time periods of "Friday evening rush hour 17:00-19:00" and "Friday night 19:00-21:00," because these time periods are closest to the event time in terms of periodicity and traffic characteristics. Through this time-period matching, the system can use traffic data from the target historical time period as a basis for prediction.

[0060] Step 302: Divide the area centered on the activity location and with a preset length as the radius into target traffic areas.

[0061] Specifically, a circular target traffic area is constructed with the geographical coordinates of the activity location as the center and a preset length (e.g., 3 kilometers) as the radius. The system divides this circular area into multiple hexagonal grid cells, each with a side length of 300 meters. This division ensures uniform coverage density while facilitating subsequent traffic analysis and route planning. Each hexagonal grid cell is assigned a unique area number and records basic data such as road information and intersection information contained within it. For example, grid cell numbered A001 might contain a 500-meter section of main road, a 300-meter section of secondary road, and two traffic light intersections. Through this refined area division, the system can more accurately analyze and predict traffic conditions in local areas, providing more detailed spatial references for subsequent traffic forecasting and vehicle scheduling.

[0062] Step 303: Based on the historical traffic information corresponding to the target historical time period, determine the initial traffic information of all roads in the target traffic area.

[0063] Specifically, historical traffic information for the target historical period is extracted as the basis for determining the initial traffic information for each road within the target traffic area. The system performs statistical analysis on the historical traffic information, calculating key indicators such as average traffic volume, average speed, and congestion index for each road during that period. For example, for main road A, the system extracts its historical data during Friday evening rush hour, yielding initial traffic information of an average traffic volume of 800 vehicles per hour and an average speed of 35 kilometers per hour.

[0064] Step 304: Obtain weather information and road construction information for the target traffic area within the target time range, and correct the initial traffic information of each road based on the weather information and road construction information to generate a traffic information map.

[0065] Specifically, the system acquires weather forecast and road construction information within the target time range. Weather information includes meteorological elements such as precipitation, visibility, and wind speed. The system adjusts the initial traffic information based on preset weather impact factors. For example, if the forecast indicates moderate to heavy rain (15-25 mm per hour) on the day of the event, the system will reduce road capacity by 20% and average driving speed by 30%. Simultaneously, the system acquires road construction information released by traffic management departments, including construction location, scope, and lane occupancy. When road construction is detected within the target traffic area, the system adjusts the capacity of the affected road sections accordingly based on the degree of impact. For instance, construction occupying one lane will reduce the capacity of that road section by 40%. The corrected traffic information is then integrated to generate a traffic information map, using a hierarchical coloring method to visually display the traffic conditions of each road section. For example, green indicates smooth traffic (average speed greater than 40 km / h), yellow indicates light congestion (average speed 25-40 km / h), and red indicates severe congestion (average speed less than 25 km / h). Through this dynamic correction and visualization method, traffic information maps can more accurately reflect the actual road traffic conditions during the event, providing a reliable basis for vehicle dispatching decisions.

[0066] Step 40: Based on the traffic information map and scheduling strategy, determine the driving routes and backup routes corresponding to each target vehicle in the target vehicle group.

[0067] Specifically, an improved Dijkstra algorithm is used for route planning, converting the traffic information map into a weighted directed graph. Factors such as segment length, predicted travel time, road grade, and congestion level are integrated into segment weights. For each target vehicle, the system first calculates the optimal route that meets the time requirements based on its departure time and destination specified in the scheduling strategy. During the route search process, the system considers not only the static attributes of road segments but also the dynamic traffic conditions in the traffic information map for comprehensive evaluation. For example, when the system detects that a segment on the shortest route may experience severe congestion during the predicted time period, it automatically adjusts the route selection, prioritizing alternative routes that are slightly longer but have smoother traffic flow. Simultaneously, the system plans 2-3 backup routes for each optimal route, with the overlap rate between these backup routes and the main route controlled below 30%, ensuring a rapid switch to backup routes in case of emergencies on the main route. To improve the reliability of route planning, a segment scoring mechanism is also established. For each road segment, the system comprehensively considers factors such as its historical reliability, the distribution of rescue resources along the route, and the accessibility of alternative roads to score it. For example, although a certain main road may have the shortest distance, if it lacks available alternative roads along its route and its historical traffic records show frequent congestion, its road segment score will be lowered accordingly, thus reducing the probability of this road segment being selected in route planning.

[0068] Based on the above embodiments, as another optional embodiment, the step of determining the driving routes and alternative routes corresponding to each target vehicle in the target vehicle group based on traffic information maps and scheduling strategies may further include the following steps:

[0069] Step 401: Extract traffic flow, average speed and road grade of each road from the traffic information map.

[0070] Specifically, basic traffic information is extracted from the collected traffic information maps, such as traffic flow, average speed, and road classification information for each road. Traffic flow represents the number of vehicles passing through the road per unit time, average speed represents the average speed of vehicles on the road, and road classification includes highways, arterial roads, secondary arterial roads, and local roads.

[0071] Step 402: Calculate the congestion coefficient of each traffic road based on traffic volume and average speed, and set the segment weight of each traffic road in combination with the road grade.

[0072] Specifically, based on the extracted basic information, the congestion coefficient of each traffic road is calculated. The congestion coefficient can be calculated as the ratio of traffic flow to average vehicle speed. The specific calculation formula is: Congestion coefficient = Traffic flow / Average vehicle speed. The higher the congestion coefficient, the more congested the road is and the greater the difficulty of passage. Introducing the congestion coefficient can objectively reflect the actual traffic conditions of the road and provide a basis for subsequently establishing a passage cost matrix.

[0073] By combining road classification information, segment weights are assigned to each type of road. Higher-class roads typically have greater traffic capacity and are therefore assigned smaller weights; lower-class roads have relatively smaller traffic capacity and are assigned larger weights. For example, the weights for expressways, arterial roads, secondary arterial roads, and local roads can be set to 0.2, 0.4, 0.6, and 0.8, respectively. The introduction of segment weights reflects the differences in traffic difficulty among roads of different classifications.

[0074] Step 403: Establish a road traffic cost matrix based on the congestion coefficient and road segment weight. The road traffic cost matrix includes the travel time and difficulty of each traffic road.

[0075] Specifically, after obtaining the congestion coefficient and road segment weights, a road traffic cost matrix is ​​established. This matrix contains two dimensions: travel time and traffic difficulty. Travel time can be calculated based on road length and average vehicle speed. Traffic difficulty is determined by both the congestion coefficient and road segment weights, specifically calculated as: Traffic Difficulty = Congestion Coefficient × Road Segment Weight. By establishing the traffic cost matrix, the traffic cost of each road can be comprehensively reflected.

[0076] Step 404: Based on the road traffic cost matrix, plan the driving route and backup route for each target vehicle using the minimum cost principle.

[0077] Specifically, after obtaining the road traffic cost matrix, in order to scientifically and rationally plan the driving routes for target vehicles, it is necessary to establish a road connectivity map that reflects the actual road connectivity. This map not only includes the connection relationships between various traffic roads, but also the turning information at intersections, such as whether left turns and U-turns are permitted. By establishing the road connectivity map, it can be ensured that the planned driving routes meet the actual road traffic requirements.

[0078] Based on the established road connectivity graph, a minimum cost principle is used to plan a travel route for the target vehicle. Specifically, all feasible paths that meet the turning information requirements are searched in the road connectivity graph, and the total cost of each path is calculated. The total cost consists of the travel time and difficulty of each segment on the path. Selecting the path with the minimum total cost as the travel route can maximize traffic efficiency while ensuring safe passage.

[0079] To proactively mitigate the impact of traffic congestion on travel, risk assessments of planned routes are necessary. Specifically, sections of the route with congestion coefficients exceeding a preset threshold are marked as high-risk sections. These high-risk sections may experience severe congestion during actual travel, leading to a significant decrease in traffic efficiency; therefore, alternative routes need to be planned in advance.

[0080] After identifying high-risk road sections, to ensure the feasibility of alternative routes, parallel or adjacent road segments are selected as alternative routes at the starting point of the high-risk road section. When selecting alternative routes, the difference in toll cost between the alternative route and the corresponding high-risk road section must be less than a preset value to ensure that the traffic efficiency of the alternative route is not significantly lower than the original route. By selecting suitable alternative routes, reliable route selection can be provided for the subsequent generation of alternative routes. After obtaining alternative routes, at least two complete alternative routes are generated based on these alternative routes. These alternative routes need to meet two conditions: first, they do not pass through the marked high-risk road sections; second, they constitute a complete path from the starting point to the destination. By generating multiple alternative routes, multiple detour options can be provided to the target vehicles, allowing them to switch routes in a timely manner when traffic congestion occurs, avoiding getting stuck in congested sections.

[0081] For example, suppose that within a target area, a route needs to be planned for a vehicle traveling from point A to point B. This area contains multiple roads of different levels, specifically including one east-west main road M1, two east-west secondary roads S1 and S2, and several connecting roads C1, C2, and C3. Basic information for each road is extracted from a traffic information map. For example, the main road M1 has a traffic flow of 1200 vehicles / hour and an average speed of 40 km / h; secondary road S1 has a traffic flow of 800 vehicles / hour and an average speed of 35 km / h; and secondary road S2 has a traffic flow of 600 vehicles / hour and an average speed of 30 km / h. The calculated congestion coefficients for these roads are: M1 = 30 (1200 / 40), S1 approximately 22.9 (800 / 35), and S2 = 20 (600 / 30). Road segment weights are assigned based on road classification: the weight of arterial road M1 is set to 0.4, and the weights of secondary arterial roads S1 and S2 are set to 0.6. The traffic difficulty of each road is calculated as follows: M1 is 12 (30 × 0.4), S1 is approximately 13.7 (22.9 × 0.6), and S2 is 12 (20 × 0.6). Simultaneously, travel time is calculated based on road length and average vehicle speed, thus establishing a complete road traffic cost matrix.

[0082] A road connectivity graph is established based on the traffic cost matrix. For example, at the intersection of M1 and C1, both left and right turns are allowed, while at the intersection of S1 and C2, only right turns are allowed. Assuming the congestion coefficient threshold is set to 25, the road segment with a congestion coefficient of 30 on the main road M1 is marked as a high-risk road segment.

[0083] When planning the route, the system identifies multiple feasible paths from point A to point B: Path 1 is "A-M1-C2-B", Path 2 is "A-S1-C2-B", and Path 3 is "A-S2-C3-B". By calculating the total cost of each path, Path 1 is chosen as the preferred route, assuming it has the lowest total cost. Since Path 1 contains a high-risk section (a segment of M1), alternative routes need to be selected. The system finds that secondary road S1 is parallel to M1, and its cost difference from M1 is within a preset range; therefore, S1 is selected as an alternative route. Two alternative routes are generated based on S1: Alternative Route 1 is "A-S1-C2-B", and Alternative Route 2 is "A-S1-C3-B". During actual driving, if severe congestion is detected on M1, the vehicle can switch to Alternative Route 1 or Alternative Route 2 before reaching the high-risk section to avoid the congestion. For example, when a vehicle is traveling on section C1 and finds that section M1 is congested, it can turn onto section S1 via C1 and then continue along alternative route 1 to its destination point B.

[0084] Step 50: Based on the driving routes and backup routes corresponding to each target vehicle, generate a comprehensive scheduling map of the target vehicle set within the target time range.

[0085] Specifically, after planning the driving routes and backup routes for each target vehicle, a comprehensive scheduling map for the target time range needs to be generated to achieve coordinated scheduling of multiple vehicles. This comprehensive scheduling map not only includes the route information of each target vehicle, but also needs to consider the spatiotemporal relationships between vehicles to avoid path conflicts or exacerbate road congestion.

[0086] First, the system determines the vehicle set within the target time range. For example, during the morning rush hour from 7:00 to 9:00, the system acquires the travel demands of 100 target vehicles. Each vehicle has its planned departure time, estimated arrival time, planned route, and alternative route. This information forms the foundational data for the integrated dispatch map. Next, the target time range is divided into several time segments, such as every 5 minutes. Within each time segment, the estimated number of vehicles on each road is counted. Specifically, based on the travel routes and schedules of each target vehicle, its road position within each time segment is calculated. For example, during the 7:30-7:35 time segment, there might be 15 target vehicles traveling on main road M1, with 10 traveling along their original routes and 5 choosing alternative routes due to congestion. To prevent certain roads from becoming overly congested during specific time periods, the system sets road capacity thresholds. When the estimated number of vehicles on a road exceeds the threshold within a certain time segment, the system automatically adjusts the travel times of some vehicles or suggests they choose alternative routes. For example, when the system predicts that the traffic flow on the main road M1 will exceed the road capacity threshold between 8:00 and 8:05, it will suggest that some vehicles planning to pass through M1 during that time depart earlier or later, or choose an alternative route.

[0087] In generating the integrated dispatch map, the system also needs to consider the actual traffic capacity and historical congestion patterns of each road segment. For example, if an intersection frequently experiences congestion during the morning rush hour, the system will allocate a certain amount of time during dispatching to avoid a chain reaction of delays for subsequent vehicles due to minor delays. Simultaneously, the system will monitor the load status of backup routes to ensure that they still have sufficient capacity to accommodate diverted traffic when the main route becomes congested. The final integrated dispatch map is a dynamic, multi-dimensional information carrier. It not only displays the distribution of target vehicles at different time segments but also includes the real-time load status of the road network and potential congestion risks. Dispatchers can make traffic control decisions based on this map, such as notifying relevant vehicles to activate backup routes in advance when congestion is predicted to occur on a certain road segment.

[0088] Based on the above embodiments, as another optional embodiment, the big data processing method for intelligent transportation may further include the following processes:

[0089] Specifically, to ensure the effectiveness of the planned route and respond promptly to changes in road conditions, the system needs to monitor and dynamically adjust the actual driving status of the target vehicles in real time. This can be achieved by acquiring the real-time location information of each target vehicle through onboard equipment or mobile terminals, thereby generating the vehicle's actual driving trajectory. The system then compares and analyzes the actual driving trajectory with the pre-planned route, calculating the trajectory deviation value.

[0090] When the system detects that the actual driving trajectory of a target vehicle deviates from the planned route and the deviation distance exceeds a first preset distance (e.g., 500 meters), it needs to promptly send a route deviation warning to the vehicle. The warning information may include current location information, deviation distance, and suggested return route. For example, if a vehicle originally planned to travel along the main road M1, but its actual driving trajectory shows that it has entered the parallel secondary road S1, and the deviation distance reaches 300 meters, the system will send a warning to the vehicle, indicating that it has deviated from the planned route and providing navigation suggestions to return to M1. Simultaneously, the system will monitor the traffic conditions of the target vehicle's driving segment in real time. When the real-time congestion coefficient of a certain road segment exceeds a first preset threshold (e.g., congestion coefficient greater than 25), the system will proactively push alternative route information to target vehicles on that road segment. For example, when the system detects that the real-time congestion coefficient of a section of the main road M1 rises to 30, it will push pre-planned alternative routes to vehicles on that road segment (such as suggesting turning onto the secondary road S1 to continue driving) to help vehicles avoid congested sections in a timely manner.

[0091] Furthermore, the system statistically analyzes the actual traffic conditions of target vehicles, specifically recording the actual travel time, route, and difficulty of each vehicle. This actual traffic information is then compared with the predicted traffic information in the integrated dispatch map to generate deviation information. For example, if it is found that the actual travel time for most vehicles on a certain road segment increased by 20% compared to the predicted time, or if a road segment that was originally expected to be smooth is actually frequently congested, this deviation information will be recorded. Based on the collected deviation information, the system corrects and updates the road traffic cost matrix. For example, if statistics show that the actual congestion level of a certain road segment is consistently higher than expected during a specific time period, the system will correspondingly increase the traffic difficulty weight of that road segment during that time period; if the actual traffic performance of an alternative route is better than expected, its priority in route planning may be adjusted. Through this dynamic update mechanism, the road traffic cost matrix can more accurately reflect the actual road conditions, thereby providing more reasonable route planning suggestions.

[0092] Please see Figure 2 This is a schematic diagram of a big data processing system for intelligent transportation provided in an embodiment of this application, wherein the system includes:

[0093] The activity information acquisition module is used to acquire activity reservation information of the target area within a preset time period. The activity reservation information includes the activity location, the estimated number of participants, and the activity time range.

[0094] The scheduling strategy determination module is used to determine the scheduling strategy for the target vehicle set based on the estimated number of participants in the activity and the activity time range.

[0095] The traffic information prediction module is used to predict the traffic information map of the target area within the activity time range. The traffic information map includes traffic information of all roads centered on the activity location with a preset length as the radius.

[0096] The vehicle route determination module is used to determine the driving route and backup route corresponding to each target vehicle in the target vehicle set based on the traffic information map and the scheduling strategy.

[0097] The scheduling map generation module is used to generate a comprehensive scheduling map of the target vehicle set within the target time range based on the driving route and backup route corresponding to each target vehicle.

[0098] Optionally, the activity information acquisition module is also used to receive activity applications reported by various activity venues in the target area;

[0099] The activity applications are categorized into large-scale activity applications and small-scale activity applications, and the number of participants and the location of each large-scale activity are determined.

[0100] The number of participants in the activity exceeds the preset number and / or the activity location is within a preset congestion area.

[0101] The activity is designated as the target activity, and the corresponding activity reservation information is obtained.

[0102] Optionally, the scheduling strategy determination module is further configured to acquire historical vehicle scheduling data for similar activities, and determine the peak period within the time range of the activity based on the historical vehicle scheduling data;

[0103] The number of target vehicles required for each peak period is calculated based on the estimated number of participants in the activity, as well as the departure information for each target vehicle;

[0104] The number of target vehicles and the departure information of each target vehicle are integrated into a scheduling strategy for the target vehicle set.

[0105] Optionally, the traffic information prediction module is further configured to acquire historical traffic information of the target area in preset historical time periods, and determine the target historical time period corresponding to the activity time range;

[0106] The area centered on the activity location and with a preset length as the radius is divided into target traffic areas;

[0107] Based on the historical traffic information corresponding to the target historical time period, the initial traffic information of all roads in the target traffic area is determined;

[0108] The system acquires weather information and road construction information for the target traffic area within the target time range, and corrects the initial traffic information of each traffic road based on the weather information and road construction information to generate the traffic information map.

[0109] Optionally, the vehicle route determination module is further configured to extract the traffic flow, average vehicle speed and road grade of each traffic road from the traffic information map;

[0110] The congestion coefficient of each of the aforementioned traffic roads is calculated based on the traffic volume and average vehicle speed, and the segment weight of each of the aforementioned traffic roads is set in conjunction with the road grade.

[0111] A road traffic cost matrix is ​​established based on the congestion coefficient and the road segment weight. The road traffic cost matrix includes the travel time and traffic difficulty of each of the traffic roads.

[0112] Based on the road traffic cost matrix, the minimum cost principle is used to plan the driving route and backup route for each target vehicle.

[0113] Optionally, the vehicle route determination module is further configured to establish a road connectivity graph based on the road traffic cost matrix, the road connectivity graph including the connection relationships and turning information of each of the traffic roads;

[0114] In the road connectivity graph, the candidate path with the lowest travel cost and that satisfies the turning information is selected as the driving route, and the road segments on the driving route with a congestion coefficient greater than the congestion coefficient threshold are marked as high-risk road segments.

[0115] At the starting point of the high-risk road section, a parallel or adjacent road section is selected as a candidate road section, and the difference in toll cost between the candidate road section and the corresponding high-risk road section is less than a preset value.

[0116] Based on the candidate road segments, at least two complete paths that do not pass through the high-risk road segments are generated as backup routes.

[0117] Optionally, the system also includes a correction and update module, which is further used to obtain the actual driving trajectory of each target vehicle in the target vehicle set and perform deviation analysis between the actual driving trajectory and the corresponding driving route;

[0118] When the target vehicle is detected to have deviated from the driving route and the deviation distance is greater than a first preset distance, a route deviation warning message is sent to the target vehicle;

[0119] When the real-time congestion coefficient of the road segment where the target vehicle is traveling is detected to be greater than the first preset threshold, the corresponding alternative route is pushed to the target vehicle.

[0120] The actual traffic information of each target vehicle is statistically analyzed, and the deviation information between the actual traffic information and the corresponding expected traffic information in the integrated dispatch map is recorded. The road traffic cost matrix is ​​then corrected and updated based on the deviation information.

[0121] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0122] This application also provides a computer storage medium that can store multiple instructions. The instructions are adapted to be loaded by a processor and executed by the processor to provide a big data processing method for intelligent transportation according to the above embodiments. For the specific execution process, please refer to the detailed description of the above embodiments, which will not be repeated here.

[0123] Please refer to Figure 3 This application also discloses an electronic device. Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0124] The communication bus 302 is used to enable communication between these components.

[0125] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0126] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0127] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0128] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a big data processing method for intelligent transportation.

[0129] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a big data processing method for intelligent transportation. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0130] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0131] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0135] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0136] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A big data processing method for intelligent transportation, characterized in that, The method includes: Obtain activity reservation information for a target area within a preset time period. The activity reservation information includes the activity location, the estimated number of participants, and the activity time range. Based on the estimated number of participants and the time range of the activity, a scheduling strategy for the target vehicle set is determined. Predict a traffic information map of the target area within the activity time range, the traffic information map including traffic information of all roads centered on the activity location with a preset length as the radius; Based on the traffic information map and the scheduling strategy, determine the driving routes and backup routes corresponding to each target vehicle in the target vehicle set; Based on the driving routes and backup routes corresponding to each target vehicle, a comprehensive scheduling map of the target vehicle set is generated within the target time range; The step of determining the driving routes and alternative routes corresponding to each target vehicle in the target vehicle set based on the traffic information map and the scheduling strategy includes: The traffic flow, average speed, and road grade of each road are extracted from the traffic information map. The congestion coefficient of each of the aforementioned traffic roads is calculated based on the traffic volume and average vehicle speed, and the segment weight of each of the aforementioned traffic roads is set in conjunction with the road grade. A road traffic cost matrix is ​​established based on the congestion coefficient and the road segment weight. The road traffic cost matrix includes the travel time and traffic difficulty of each traffic road. The traffic difficulty is jointly determined by the congestion coefficient and the road segment weight. Based on the road traffic cost matrix, the minimum cost principle is used to plan the driving route and backup route for each target vehicle. The step of planning driving routes and alternative routes for each target vehicle based on the road traffic cost matrix and using the minimum cost principle includes: A road connectivity graph is established based on the road traffic cost matrix, and the road connectivity graph includes the connection relationships and turning information of each of the traffic roads; In the road connectivity graph, the candidate path with the lowest travel cost and that satisfies the turning information is selected as the driving route, and the road segments on the driving route with a congestion coefficient greater than the congestion coefficient threshold are marked as high-risk road segments. At the starting point of the high-risk road section, a parallel or adjacent road section is selected as a candidate road section, and the difference in toll cost between the candidate road section and the corresponding high-risk road section is less than a preset value. Based on the candidate road segments, at least two complete paths that do not pass through the high-risk road segments are generated as backup routes.

2. The big data processing method for intelligent transportation according to claim 1, characterized in that, The process of obtaining activity reservation information for the target area within a preset time period includes: Receive activity applications submitted by various activity venues in the target area; The activity applications are categorized into large-scale activity applications and small-scale activity applications, and the number of participants and the location of each large-scale activity are determined. The number of participants in the activity exceeds the preset number and / or the activity location is within a preset congestion area. The activity is designated as the target activity, and the corresponding activity reservation information is obtained.

3. The big data processing method for intelligent transportation according to claim 1, characterized in that, The step of determining the scheduling strategy for the target vehicle set based on the estimated number of participants and the time range of the activity includes: Obtain historical vehicle dispatch data for similar activities, and determine the peak time period within the time range of the activity based on the historical vehicle dispatch data; The number of target vehicles required for each peak period is calculated based on the estimated number of participants in the activity, as well as the departure information for each target vehicle; The number of target vehicles and the departure information of each target vehicle are integrated into a scheduling strategy for the target vehicle set.

4. The big data processing method for intelligent transportation according to claim 1, characterized in that, The predicted traffic information map of the target area within the activity time range includes: Obtain historical traffic information of the target area in preset historical time periods, and determine the target historical time period corresponding to the activity time range; The area centered on the activity location and with a preset length as the radius is divided into target traffic areas; Based on the historical traffic information corresponding to the target historical time period, the initial traffic information of all roads in the target traffic area is determined; The system acquires weather information and road construction information for the target traffic area within the target time range, and corrects the initial traffic information of each traffic road based on the weather information and road construction information to generate the traffic information map.

5. The big data processing method for intelligent transportation according to claim 1, characterized in that, The method further includes: The actual driving trajectory of each target vehicle in the target vehicle set is obtained, and the deviation between the actual driving trajectory and the corresponding driving route is analyzed. When the target vehicle is detected to have deviated from the driving route and the deviation distance is greater than a first preset distance, a route deviation warning message is sent to the target vehicle; When the real-time congestion coefficient of the road segment where the target vehicle is traveling is detected to be greater than the first preset threshold, the corresponding alternative route is pushed to the target vehicle. The actual traffic information of each target vehicle is statistically analyzed, and the deviation information between the actual traffic information and the corresponding expected traffic information in the integrated dispatch map is recorded. The road traffic cost matrix is ​​then corrected and updated based on the deviation information.

6. A big data processing system for intelligent transportation, characterized in that, The system is used to perform the big data processing method for intelligent transportation as described in claim 1, the system comprising: The activity information acquisition module is used to acquire activity reservation information of the target area within a preset time period. The activity reservation information includes the activity location, the estimated number of participants, and the activity time range. The scheduling strategy determination module is used to determine the scheduling strategy for the target vehicle set based on the estimated number of participants in the activity and the activity time range. The traffic information prediction module is used to predict the traffic information map of the target area within the activity time range. The traffic information map includes traffic information of all roads centered on the activity location with a preset length as the radius. The vehicle route determination module is used to determine the driving route and backup route corresponding to each target vehicle in the target vehicle set based on the traffic information map and the scheduling strategy. The scheduling map generation module is used to generate a comprehensive scheduling map of the target vehicle set within the target time range based on the driving route and backup route corresponding to each target vehicle.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted to be loaded by a processor and executed as described in any one of claims 1-5.

8. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-5.

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