Driving resource optimization scheduling system combined with cloud support

Abstract

The invention provides a driving resource optimization scheduling system combined with cloud support, and relates to the technical field of computers, and the system comprises a traffic road network matching module which matches a target traffic road network of a target area; the first prediction module analyzes the real-time driving information to obtain a first predicted vehicle; a driving visual graph generation module obtains a target position of the target service area, and generates a driving visual graph in combination with a first mapping relationship among the first intersection, the first position and the first predicted vehicle; a second prediction module activates a vehicle prediction middle station to analyze the driving views to obtain a target prediction vehicle; and the resource scheduling module analyzes the target prediction vehicle through a resource scheduler to obtain a target resource scheduling strategy, and performs resource scheduling on the target service area. The technical problem of low resource scheduling efficiency caused by lack of real-time dynamic feedback of an existing driving scheduling method is solved, and the technical effects of improving the driving scheduling efficiency and accuracy and optimizing the driving resource utilization rate are achieved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a vehicle resource optimization and scheduling system combined with cloud support. Background Art

[0002] In urban traffic management, vehicle resource scheduling is an important means to improve road traffic efficiency and reduce traffic congestion. Existing vehicle scheduling methods mainly rely on historical traffic data and fixed scheduling rules, predicting future traffic conditions through analysis of past traffic flow, and manually adjusting service area resource allocation. These methods usually use fixed scheduling rules and cannot respond to dynamic changes in traffic flow in a timely manner. Therefore, when emergencies or changes in real-time traffic conditions occur, traditional scheduling methods often cannot quickly adjust resource allocation, resulting in extended waiting time for vehicles and traffic bottlenecks. In addition, the prediction accuracy of existing methods is limited by historical data, and it is difficult to accurately capture the instantaneous characteristics of traffic flow. The lack of real-time data support makes the scheduling strategy lag behind actual needs, resulting in inefficient resource scheduling, which affects road traffic. Summary of the invention

[0003] The present application provides a vehicle resource optimization and scheduling system combined with cloud support, which solves the technical problem that the existing vehicle scheduling method relies on static historical data and fixed scheduling rules, lacks real-time dynamic feedback, and leads to low resource scheduling efficiency. It achieves the technical effect of dynamically responding to changes in the driving environment, improving the efficiency and accuracy of vehicle scheduling, and optimizing the utilization of vehicle resources.

[0004] In view of the above problems, the present application provides a driving resource optimization and scheduling system combined with cloud support, the system comprising: a traffic road network matching module, the traffic road network matching module is used to match the target traffic road network of the target area in the cloud database, the target traffic road network includes multiple intersections with position identifiers, wherein the target area has a target service area; a first prediction module, the first prediction module is used to analyze the real-time driving information of the target traffic road network to obtain a first predicted vehicle at a first intersection among multiple intersections, the first intersection having an identifier of a first position; a driving visible graph generation module, the driving visible graph generation module is used to obtain the target position of the target service area, and generate a driving visible graph in combination with a first mapping relationship between the first intersection, the first position and the first predicted vehicle; a second prediction module, the second prediction module is used to activate the vehicle prediction middle station to analyze the driving visible graph to obtain the target predicted vehicle at the target position in a predetermined time interval; a resource scheduling module, the resource scheduling module is used to analyze the target predicted vehicle through a resource scheduler to obtain a target resource scheduling strategy, and perform resource scheduling on the target service area according to the target resource scheduling strategy.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages: The traffic road network matching module matches the target traffic road network of the target area in the cloud database, identifies multiple intersections, clarifies the geographical scope of the target service area, provides basic geographical information basis for subsequent driving data analysis, ensures that the analyzed traffic environment is consistent with the actual situation, and improves the relevance of the data. The first prediction module obtains the first predicted vehicle at the first intersection among multiple intersections by analyzing the real-time driving information of the target traffic road network, introduces real-time traffic dynamic data, and provides instant feedback on the current traffic flow. The driving visual map generation module obtains the target position of the target service area, and generates a driving visual map in combination with the first mapping relationship between the first intersection, the first position and the first predicted vehicle, intuitively showing the traffic conditions and flow trends. The second prediction module activates the vehicle prediction middle station to analyze the driving visual map, obtains the target predicted vehicle at the target position in the predetermined time interval, refines the prediction of the future traffic conditions of the target service area, and provides more targeted data for resource scheduling. The resource scheduling module analyzes the target predicted vehicle through the resource scheduler to obtain the target resource scheduling strategy, and performs resource scheduling on the target service area according to the target resource scheduling strategy to optimize the resource utilization of the service area.

[0006] To summarize, through the collaborative work of various modules, the traffic road network matching module provides accurate basic data, the first prediction module analyzes real-time traffic information, the driving visualization module visualizes traffic conditions, the second prediction module proactively predicts future traffic, and the resource scheduling module optimizes resource allocation based on the prediction results, thereby achieving accurate prediction and optimal scheduling of driving resources, improving the flexibility and accuracy of driving resource scheduling, thereby more effectively planning resource allocation, reducing vehicle waiting time, and ultimately improving the utilization rate of driving resources and overall traffic flow efficiency.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of the structure of a vehicle resource optimization and scheduling system combined with cloud support provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of a process for obtaining a first predicted vehicle in a vehicle resource optimization and scheduling system combined with cloud support provided in an embodiment of the present application.

[0010] Figure 3 A schematic diagram of a process for obtaining a target predicted vehicle in a vehicle resource optimization and scheduling system combined with cloud support provided in an embodiment of the present application.

[0011] Description of the accompanying drawings: traffic road network matching module 10, first prediction module 20, driving visual graph generation module 30, second prediction module 40, resource scheduling module 50. DETAILED DESCRIPTION

[0012] The embodiment of the present application solves the technical problem that the existing vehicle scheduling method relies on static historical data and fixed scheduling rules, lacks real-time dynamic feedback, and leads to low resource scheduling efficiency by providing a vehicle resource optimization and scheduling system combined with cloud support. It achieves the technical effect of dynamically responding to changes in the driving environment, improving the efficiency and accuracy of vehicle scheduling, and optimizing the utilization of vehicle resources.

[0013] like Figure 1 As shown, the embodiment of the present application provides a driving resource optimization and scheduling system combined with cloud support, and the system includes: The traffic road network matching module 10 is used to match a target traffic road network of a target area in a cloud database, wherein the target traffic road network includes a plurality of intersections with location identifiers, wherein the target area has a target service area.

[0014] Specifically, the cloud database is used to store and manage large amounts of traffic data, including roads, intersections, and real-time traffic flow information, which can be accessed via the Internet. The target area refers to a city or region where traffic management and dispatching are required. The target traffic road network is a traffic road network within the target area, including main roads and intersections, which are used to support traffic flow. The location identifier is a unique identifier assigned to each intersection or road to facilitate data matching and processing. The target service area is a comprehensive space that provides traffic services to the target area, including supporting facilities such as vehicle depots (parking lots), comprehensive maintenance centers, and material warehouses. It has a variety of traffic operation and maintenance functions such as information management, dispatch plan management, and crew dispatch management.

[0015] The traffic road network matching module 10 first accesses the cloud database through the Internet, and extracts all the main roads and intersection information in the target area from the cloud database, which is usually stored in the form of vector data. Then, the corresponding location identifier is established according to the specific location of each intersection using the geographic information system (GIS) technology. For example, the location identifier can be the latitude and longitude coordinates of each intersection. By obtaining the target traffic road network, the module provides basic geographic framework information for subsequent driving resource scheduling.

[0016] The first prediction module 20 is used to analyze the real-time driving information of the target traffic road network to obtain a first predicted vehicle at a first intersection among multiple intersections, and the first intersection has an identifier of a first position.

[0017] Specifically, real-time traffic information refers to traffic data collected in real time, including the number of vehicles, vehicle displacement, vehicle speed, passenger flow and congestion conditions, etc. These data are usually collected through sensors, cameras and other equipment. The first intersection is a specific intersection in the target traffic road network, corresponding to a first location identifier. The first predicted vehicle is a prediction result on the number of vehicles obtained after analyzing the real-time traffic information for the first intersection, which is used to evaluate the traffic flow of the intersection.

[0018] The first prediction module 20 obtains real-time driving information of the target traffic road network through network information interaction. Such real-time driving information can be monitored and obtained through sensors, cameras and GPS devices deployed on the road, and transmitted to the cloud database for storage and management. For example, traffic monitoring cameras can capture the real-time vehicle flow at a certain intersection, and sensors can measure the speed and number of passing vehicles.

[0019] Randomly select any intersection from the target traffic road network, record it as the first intersection, and take the first intersection as an example to illustrate the calculation process of the corresponding first predicted vehicle information. The module uses a data analysis algorithm to filter out the driving information related to the first intersection. This algorithm is based on traffic flow theory and determines which vehicles will drive to the first intersection based on information such as the vehicle's driving direction and current position. For example, if you know the vehicle's driving direction and the current road it is on, and know the connection relationship between the road and the first intersection, you can predict which vehicles will arrive at the first intersection. By processing and calculating the filtered driving information, the first predicted vehicle information about the first intersection is obtained. For example, it is predicted that in the next 5 minutes, 5 cars and 2 trucks will arrive at the first intersection with the first position mark.

[0020] By analyzing real-time driving information, this module can calculate the predicted vehicle information at any intersection in the target traffic road network according to the current traffic conditions, providing a data basis for subsequent traffic management and resource scheduling.

[0021] The driving visible graph generation module 30 is used to obtain the target position of the target service area, and generate a driving visible graph in combination with a first mapping relationship between the first intersection, the first position and the first predicted vehicle.

[0022] Specifically, the driving visual map generation module 30 obtains the target location information of the target service area from the cloud database, that is, the specific location of the target service area in the geographic space. Then, according to the corresponding relationship between the first intersection, the first location and the first predicted vehicle, the three types of data are integrated to establish a first mapping relationship to ensure that the predicted vehicle data can be correctly mapped to the corresponding intersection location, so as to accurately reflect the traffic flow in the visual map. Then, a graphics drawing tool or a data visualization tool (such as D3.js, Tableau or Plotly) is used to display the relationship between the location of the target service area, the first intersection and the first predicted vehicle in an intuitive graphical manner according to the integrated information to generate a driving visual map. For example, a visualization tool based on GIS (Geographic Information System) is used to mark the location of the target service area on the map, display the first intersection with a specific icon, and use arrows and other methods to indicate the driving direction of the first predicted vehicle and the expected arrival at the first intersection, thereby generating a view that can clearly display driving-related information.

[0023] By generating a driving visualization, the traffic flow, vehicle distribution and possible traffic jams at the intersection can be intuitively displayed, and reference data can be provided for subsequent target vehicle prediction and driving resource scheduling. The driving visualization is dynamically updated according to the real-time traffic flow changes to reflect the latest intersection traffic conditions.

[0024] The second prediction module 40 is used to activate the vehicle prediction center to analyze the driving visual graph to obtain the target predicted vehicle with the target position in a predetermined time interval.

[0025] Specifically, the vehicle prediction platform is an integrated analysis platform that uses machine learning and data analysis algorithms to process traffic data to generate future traffic forecasts. The predetermined time interval refers to a specific time period for which traffic flow forecasting is required. The target predicted vehicle is the result obtained by the second prediction module 40 after analyzing the driving visibility graph through the vehicle prediction platform, that is, the number of vehicles passing the target location within the predetermined time interval.

[0026] The second prediction module 40 activates the vehicle prediction platform by sending a specific instruction or request signal, and sends the relevant data of the driving visibility map to the vehicle prediction platform. The vehicle prediction platform is usually built using programming languages ​​such as Python, R or Java, and relies on open source machine learning libraries (such as TensorFlow or Scikit-learn) to train and predict the prediction model.

[0027] The vehicle prediction center receives the driving visualization graph as input data, identifies and analyzes various data elements in the driving visualization graph, including the target location of the target service area, the first intersection, etc., and uses built-in analysis algorithms and data processing technology to predict the traffic flow situation in the future predetermined time interval. These algorithms can be traffic flow prediction algorithms based on time series analysis, such as ARIMA (autoregressive integrated moving average model) or LSTM (long short-term memory network). The vehicle prediction center feeds back the analysis results to the second prediction module 40, and this result is the target predicted vehicle at the target location in the predetermined time interval. For example, the predetermined time interval is 30 minutes in the future. The vehicle prediction center analyzes the traffic flow data of the past week and combines the real-time information of the day to predict that 20 cars and 5 buses will arrive at the target location within this time interval.

[0028] The second prediction module 40 analyzes the driving visualization graph through the vehicle prediction center, providing forward-looking data support for subsequent driving resource scheduling, so as to facilitate the subsequent generation of appropriate scheduling strategies and optimize resource allocation to cope with the expected traffic flow.

[0029] The resource scheduling module 50 is used to analyze the target predicted vehicle through a resource scheduler to obtain a target resource scheduling strategy, and perform resource scheduling on the target service area according to the target resource scheduling strategy.

[0030] Specifically, the resource scheduler is a key component in the resource scheduling module 50, which is used to manage and allocate traffic resources and formulate reasonable scheduling strategies based on the predicted number and type of vehicles. The target resource scheduling strategy is a specific strategy formulated based on the predicted data and actual needs, which clarifies how to schedule resources in the target service area, including the allocation and arrangement of human and material resources, to meet the needs of the target predicted vehicles. For example, based on the arrival time and number of vehicles, it is decided how many staff members to send for guidance and service, and how to allocate materials (such as parking spaces, refueling facilities, etc.).

[0031] The resource scheduler in the resource scheduling module 50 obtains the target predicted vehicle information, which includes the number, type, arrival time, etc. of the target predicted vehicles at the target location within the predetermined time interval provided by the second prediction module 40. Then, the resource scheduler analyzes the target predicted vehicle information based on the preset algorithms and rules. For example, if there are a large number of trucks, more large parking spaces and loading and unloading equipment resources may be required; if there are a large number of cars and the arrival time is concentrated, more guides may be required to direct traffic. The resource scheduler may also consider the interval between vehicle arrival times to determine the order of resource allocation.

[0032] Based on the analysis results, the resource scheduler formulates and outputs the target resource scheduling strategy. This strategy is a detailed resource allocation plan. For example, for vehicles that are about to arrive, five guides are arranged to guide vehicles into the parking lot at different entrances. For trucks, 10 large parking spaces are reserved and two loading and unloading workers are arranged to be on standby at any time. For cars, ordinary parking spaces are allocated in sequence according to the expected arrival order.

[0033] The resource scheduling module 50 receives and executes the target resource scheduling strategy formulated by the resource scheduler, and performs resource scheduling for the target service area. For example, the parking lot management system arranges parking spaces for vehicles according to the allocated parking space plan, the human resources management department dispatches guides and loading and unloading workers to corresponding positions as needed, and the material management department ensures that the loading and unloading equipment is in an available state, etc., so as to achieve effective scheduling of resources in the target service area to meet the needs of the target predicted vehicles.

[0034] By formulating and executing target resource scheduling strategies, this module can not only effectively respond to predicted traffic flows, but also optimize resource usage in target service areas, thereby improving overall traffic management efficiency and resource utilization.

[0035] Further, such as Figure 2 As shown, the first prediction module 20 in the embodiment of the present application is also used to perform the following steps: Determine a first connecting road of the first intersection according to the target traffic road network, the first connecting road including a plurality of roads; obtain a first road among the plurality of roads, and obtain a travel direction of the first road having a first travel number in combination with the target traffic road network; obtain first real-time driving information of the first road in combination with the real-time driving information; and calculate the first predicted vehicle according to the first real-time driving information and the first travel number.

[0036] Specifically, the first connecting road is a set of roads connected to the first intersection, usually including multiple roads entering and leaving the intersection. For example, at an intersection, the four roads in the east, west, south and north directions are the first connecting roads of the intersection (the first intersection).

[0037] First, the first connecting road of the first intersection is determined by querying the topological structure data of the target traffic road network. For example, in a database storing urban traffic road network information, the road records directly connected to the first intersection are searched, and these records constitute the first connecting road.

[0038] A specific road is selected from the multiple roads of the first connecting road and recorded as the first road. The selection process can be selected according to the priority of the road (such as the priority of the main road) or randomly selected. Then, the road attributes in the traffic road network data are read to determine the travel direction and the first travel number of the first road. The first travel number represents the number of travel directions of the vehicle on the first road.

[0039] The real-time driving information corresponding to the first road is selected from the real-time driving information and recorded as the first real-time driving information, including information such as vehicle speed, vehicle displacement, number of vehicles, number of passengers, etc. A first predicted vehicle is calculated based on the first real-time driving information and the first travel number.

[0040] Through the above series of steps, the first prediction module 20 can accurately predict the vehicle flow at a specific intersection, provide reliable data support for subsequent driving visualization graph generation and driving resource scheduling, and help optimize traffic flow and resource allocation.

[0041] Furthermore, in the embodiment of the present application, the first real-time driving information includes a plurality of vehicles with unit vehicle displacement identifications, and the first prediction module 20 is further configured to perform the following steps: A cluster analysis is performed on multiple vehicles with the unit vehicle displacement as a clustering constraint to obtain a clustering result, wherein the clustering result includes a first cluster cluster, and the first cluster cluster corresponds to a first unit vehicle displacement; the first cluster cluster is counted to obtain a first cluster vehicle number, and combined with the first travel number, a first predicted number of vehicles with the first unit vehicle displacement is calculated; and the first predicted number is added to obtain the first predicted vehicle.

[0042] Specifically, the unit vehicle displacement is a unit value used to measure the size of the vehicle engine displacement, which can be used to distinguish vehicles of different types or levels. The first cluster is a cluster set in the results obtained by cluster analysis. The vehicles in this set have similar unit vehicle displacement characteristics and correspond to a specific first unit vehicle displacement. The first cluster vehicle number refers to the number of vehicles contained in the first cluster, which is obtained by counting the first cluster. This number reflects the proportion of vehicles with the first unit vehicle displacement in multiple vehicles. The first predicted number is the predicted number of vehicles with the first unit vehicle displacement under specific conditions, which is calculated by combining the first cluster vehicle number and the first travel number. It is the intermediate result of calculating the first predicted vehicle.

[0043] Multiple vehicles passing through the first road and the unit vehicle displacement corresponding to each vehicle are read from the first real-time vehicle information, and the multiple vehicles are classified using the unit vehicle displacement size as a clustering constraint by cluster analysis, and vehicles with similar unit vehicle displacement are classified into one category to form clusters. Commonly used clustering algorithms include K-means clustering, hierarchical clustering or DBSCAN, etc.

[0044] Taking the K-means algorithm as an example, the unit vehicle displacement is regarded as a feature dimension, and first determine how many clusters need to be divided. For example, K=3, that is, all vehicles are divided into three categories: small displacement, medium displacement and large displacement. Then the vehicles are grouped iteratively, and for each vehicle, it is assigned to different clusters according to its unit vehicle displacement value, so that the vehicles in the same cluster have the most similar displacement, and finally the clustering result is obtained. It includes multiple clusters, and each cluster corresponds to a unit vehicle displacement. For example, vehicles with unit vehicle displacements of 1.5L, 1.6L, and 1.8L are relatively similar under a certain distance metric, so they are classified as the first cluster, and the first unit vehicle displacement corresponding to this cluster can be set to 1.5~1.8L (the specific value is determined according to the actual situation and analysis accuracy).

[0045] Select one of the multiple clusters, record it as the first cluster, corresponding to the first unit vehicle displacement. Take the first cluster as an example to explain the subsequent prediction number calculation. Count the total number of vehicles in the first cluster to get the first cluster vehicle number. Combined with the first travel number, calculate the first predicted number of vehicles with the first unit vehicle displacement. The calculation process can be calculated by simple average calculation or by weighted average calculation. Taking the average as an example, the formula for calculating the first predicted number is: first predicted number = N / M, where the first cluster vehicle number is N and the first travel number is M. Exemplarily, the first cluster vehicle number is 50 and the first travel number is 2, then the first predicted number is 50 / 2=25 vehicles.

[0046] For multiple different clusters, after calculating the first predicted number corresponding to each cluster, add these first predicted numbers to get the total first predicted number of vehicles. For example, the first predicted number corresponding to the first unit vehicle displacement is 25 vehicles, the first predicted number corresponding to the second unit vehicle displacement is 15 vehicles, and the first predicted number corresponding to the third unit vehicle displacement is 20 vehicles, then the first predicted number of vehicles is 25+15+20=60 vehicles.

[0047] By classifying and counting vehicles by unit vehicle displacement, the number of different vehicle types can be predicted more accurately, thereby accurately obtaining the first predicted vehicle at the first intersection, providing reliable data support for subsequent modules.

[0048] Further, such as Figure 3As shown, the second prediction module 40 in the embodiment of the present application is also used to perform the following steps: The vehicle prediction center determines the first-level neighborhood of the target position according to the driving visual graph; extracts any first-level intersection in the first-level neighborhood, and determines the second-level neighborhood of the any first-level intersection; samples the second-level intersections in the second-level neighborhood to obtain a target second-level intersection set; fuses the predicted vehicles at each intersection in the target second-level intersection set to obtain any predicted vehicle at the any first-level intersection; and records the fusion result of the any predicted vehicle as the target predicted vehicle.

[0049] Specifically, the first-level neighborhood refers to a specific area around the target location, which is directly related to the target location and is usually close. The second-level neighborhood refers to the area around the first-level intersection as the center. The target second-level intersection set is a set of intersections obtained by sampling the second-level intersections in the second-level neighborhood. The intersections in this set are specific intersections that have been screened for subsequent analysis. Any predicted vehicle refers to the number of vehicles expected to pass through the corresponding first-level intersection within a certain period of time, which is a comprehensive prediction result for the vehicle situation at any first-level intersection.

[0050] First, the vehicle prediction center determines the first-level neighborhood of the target location by analyzing the geographic location information, road connection relationship and other elements in the driving visibility diagram. For example, in the driving visibility diagram, with the target location as the center, an area is circled as the first-level neighborhood according to a certain distance radius or road connection rules. This area may contain information such as roads directly connected to the target location and surrounding buildings.

[0051] Next, after determining the first-level neighborhood, find the intersections in the first-level neighborhood area, that is, the first-level intersections, and randomly select an intersection from it, that is, any first-level intersection. With this intersection as the center, determine the second-level neighborhood of the arbitrary first-level intersection according to the method similar to determining the first-level neighborhood (such as considering factors such as distance and road connection). For example, if the first-level neighborhood is a block range, then the intersection within the block is any first-level intersection, and then with this intersection as the center, a slightly smaller surrounding area is again circled as the second-level neighborhood.

[0052] Then, the intersections in the secondary neighborhood (i.e., secondary intersections) are sampled to obtain the target secondary intersection set. There may be multiple secondary intersections in the secondary neighborhood, and a part of them is selected to form the target secondary intersection set by a certain sampling method (such as random sampling, sampling according to a certain weight ratio, sampling according to a predetermined rule, etc.). For example, in a secondary neighborhood containing 10 secondary intersections, 5 of them are selected to form the target secondary intersection set by the method of equal probability random sampling.

[0053] After that, the predicted vehicles at each intersection in the target secondary intersection set are fused to obtain any predicted vehicle at any first-level intersection. The fusion process can use weighted average method, summation method, etc. For example, there are 3 intersections in the target secondary intersection set, and 5, 3, and 4 vehicles are predicted respectively. The simple summation method is used for fusion, so the number of any predicted vehicles is 5+3+4=12. The results of any predicted vehicles obtained for each arbitrary first-level intersection are sorted and fused to obtain the target predicted vehicle.

[0054] Furthermore, the second prediction module 40 in the embodiment of the present application is also used to perform the following steps: The multiple secondary intersections in the secondary neighborhood are sorted in descending order based on the predicted number of vehicles traveling, to obtain a descending list of intersections; 50% of the intersections in the descending list of intersections are selected to form the target secondary intersection set.

[0055] Specifically, in order to improve the accuracy of target prediction vehicles and the precision of vehicle resource scheduling, the predicted number of vehicles is used as the sampling standard to sample the secondary intersections in the secondary neighborhood. The specific process includes: obtaining the predicted number of vehicles at each secondary intersection through the vehicle visibility graph, and according to these predicted numbers, using sorting algorithms (such as bubble sort, quick sort, etc.) to arrange these secondary intersections to obtain a descending list of intersections.

[0056] Next, 50% of the intersections in the descending list of intersections are selected to form the target secondary intersection set. For example, if there are 10 intersections in the list, the first 5 intersections are selected. For those that are not integers, the method of rounding up is used for sampling. This sampling method can select secondary intersections with relatively more predicted vehicle numbers, so as to more accurately analyze and fuse the predicted vehicles at any primary intersection, thereby obtaining the target predicted vehicle.

[0057] Furthermore, the resource scheduling module 50 in the embodiment of the present application is also used to perform the following steps: A first service resource prediction evaluation function is introduced to perform resource demand prediction evaluation on the target predicted vehicle to obtain a first target predicted resource demand; the resource scheduler performs resource scheduling on the target service area based on the target predicted energy demand and the target predicted parking demand in the first target predicted resource demand.

[0058] Specifically, the first service resource prediction evaluation function is a mathematical or algorithmic model used to evaluate and predict vehicle service resource requirements. The first target predicted resource demand is the result calculated by the first service resource prediction evaluation function, which includes the resource demand of the target predicted vehicle in the target service area, including the first target predicted energy demand and the first target predicted parking demand, wherein the first target predicted energy demand is the predicted value of the energy required by the target predicted vehicle in the target service area (such as the amount of refueling, the amount of charging, etc.). The first target predicted parking demand is the demand forecast of the target predicted vehicle in the target service area (such as the number of parking spaces required, the distribution of parking time, etc.).

[0059] First, the first service resource prediction and evaluation function is introduced to predict and evaluate the resource demand of the target prediction vehicle to obtain the first target prediction resource demand. In this process, the first service resource prediction and evaluation function will process the input information related to the target prediction vehicle. This information may include the type of vehicle (sedan, truck, etc.), quantity, expected arrival time, expected stay time, etc. For example, if there are a large number of electric vehicles in the target prediction vehicle and they are expected to stay for a long time, the function will calculate the total charging demand based on factors such as the battery capacity and charging efficiency of the vehicle, and calculate the number of parking spaces required based on the number of vehicles and parking habits (such as whether a larger parking space is needed, etc.), thereby obtaining the first target prediction resource demand.

[0060] Then, the resource scheduler schedules resources for the target service area based on the target predicted energy demand and the target predicted parking demand in the first target predicted resource demand. For the target predicted energy demand, if the target service area is a comprehensive service area, the resource scheduler may check whether the refueling facilities (such as the number of oil guns, refueling speed, etc.) and charging facilities (such as the number of charging piles, charging power, etc.) can meet the demand. If the charging demand is large and the number of charging piles is insufficient, measures such as adding temporary charging piles or adjusting the charging power may be arranged. For the target predicted parking demand, the resource scheduler will check the number and layout of existing parking spaces in the parking lot. If the number of parking spaces is insufficient, it may consider opening up temporary parking areas, or guiding vehicles to park reasonably to improve the utilization rate of parking space, etc., so as to achieve effective scheduling of resources in the target service area.

[0061] Furthermore, in the embodiment of the present application, the first real-time driving information includes multiple vehicles with identifications of the number of passengers, and the resource scheduling module 50 is further configured to perform the following steps: Analyze the multiple vehicles with passenger number identifications to obtain a second predicted number of passengers for the first unit vehicle capacity; obtain a first total number of passengers for the first predicted vehicle based on the second predicted number; add the first total number of passengers to obtain a target predicted number of passengers for the target predicted vehicle; introduce a second service resource prediction evaluation function to perform resource demand prediction and evaluation on the target predicted number of passengers to obtain a second target predicted resource demand; and the resource scheduler performs resource scheduling for the target service area based on the target predicted energy demand and the target predicted rest demand in the second target predicted resource demand.

[0062] Specifically, the second service resource prediction evaluation function is a function used to predict and evaluate resource demand for the target predicted number of passengers. The second target predicted resource demand is the result obtained by the second service resource prediction evaluation function, which includes the resource demand based on the target predicted number of passengers, including the target predicted energy demand and the target predicted rest demand, wherein the target predicted energy demand is the energy used by passengers, such as the catering energy supply in the service area, etc., and the target predicted rest demand is the demand for lounges, seats, etc.

[0063] First, read the number of passengers corresponding to each vehicle from the first real-time driving information, that is, the total number of people that each vehicle can carry. According to the aforementioned cluster analysis results, count the number of people carried by vehicles of unit displacement corresponding to each cluster cluster to obtain the corresponding second predicted number. Taking the first unit vehicle displacement as an example, read the number of passengers of all vehicles in the first cluster cluster, calculate their sum, and obtain the second predicted number of the first unit vehicle displacement. According to the cluster analysis results, add up the second predicted numbers corresponding to all vehicles of unit vehicle displacement to obtain the first total number of passengers, which is the sum of the number of people that the first predicted vehicle can carry. Then, add up the first total number of passengers to obtain the target predicted number of passengers for the target predicted vehicle.

[0064] Afterwards, the second service resource prediction evaluation function is introduced to perform resource demand prediction evaluation on the target predicted number of passengers, and the second target predicted resource demand is obtained. For example, the energy demand for catering supply may be proportional to the number of people, and more passengers require more lounges and seats. According to the number of passengers and the corresponding demand ratio, the corresponding energy demand and rest demand can be calculated. For example, every 10 people need 1000 kcal of catering energy supply and 5 rest seats, then the target predicted energy demand and target predicted rest demand can be calculated according to the target predicted number of passengers, thereby obtaining the second target predicted resource demand.

[0065] The resource scheduler schedules resources for the target service area based on the target predicted energy demand and target predicted rest demand in the second target predicted resource demand. For the target predicted energy demand, if it is catering energy supply, the resource scheduler will check the food reserves and cooking equipment supply capacity of the restaurant in the service area. If the food reserves are insufficient, replenishment may be arranged; if the cooking equipment supply capacity is limited, the menu may be adjusted or temporary cooking equipment may be added. For the target predicted rest demand, the resource scheduler will check the number of lounges and seats in the service area. If the number of seats is insufficient, it may consider adding temporary seats or guiding passengers to use rest facilities reasonably to meet the resource scheduling needs of the target service area.

[0066] Furthermore, the resource scheduling module 50 described in the embodiment of the present application performs environmental resource scheduling for the target service area based on the target predicted vehicle and the target predicted number of passengers.

[0067] Specifically, the resource scheduling module 50 will also allocate and arrange the environment-related resources of the target service area according to the target predicted vehicles and the target predicted number of passengers. Environmental resources include but are not limited to the operation of air purification equipment in the service area, temperature adjustment (such as the use of air conditioning), environmental sanitation maintenance (such as the number of trash cans, cleaning staff arrangements, etc.) and greening resources (such as the maintenance and layout adjustment of green plants, etc.).

[0068] First, consider the number of target predicted vehicles. If the number of target predicted vehicles is large, the exhaust gas emitted by the vehicles may have a greater impact on the air quality of the service area. At this time, the resource scheduling module 50 will increase the operating power of the air purification equipment or increase the number of operating air purification equipment. For example, through analysis and prediction, a large number of trucks (part of the target predicted vehicles) will enter the service area. Due to the large amount of exhaust emissions from trucks, the resource scheduling module 50 can arrange in advance to increase the power of the air purification equipment from the normal 50% to 80% to ensure good air quality in the service area.

[0069] Secondly, the target predicted number of passengers is analyzed. If the target predicted number of passengers is large, the demand for temperature control resources will increase. The resource scheduling module 50 needs to reasonably arrange the cooling or heating power of the air conditioner according to the number of passengers. For example, when it is predicted that a large number of passengers will arrive in the service area, if it is in summer, the cooling power of the air conditioner may need to be increased from 30 kilowatts to 50 kilowatts to ensure that the temperature in the service area is suitable.

[0070] At the same time, the target predicted number of passengers will also affect the resource scheduling for environmental sanitation maintenance. More people means more garbage generated, and the resource scheduling module 50 may increase the number of garbage bins and the scheduling of cleaning staff. For example, under normal circumstances, one garbage bin is placed for every 50 square meters. When the predicted number of passengers increases, it may be adjusted to place one garbage bin for every 30 square meters; originally, two cleaning staff are arranged to clean every hour, and this may be increased to three cleaning staff per hour.

[0071] In addition, the green resources in the service area will also be adjusted according to the target predicted vehicles and the target predicted number of passengers. If there are a large number of vehicles and passengers, it is necessary to strengthen the maintenance of green plants to better absorb carbon dioxide, purify the air and beautify the environment. For example, increase the frequency of watering and fertilizing green plants, or adjust the layout of green plants to place more green plants in areas with frequent flow of people and vehicles to improve the overall environmental quality of the service area.

[0072] In summary, the vehicle resource optimization and scheduling system combined with cloud support provided in the embodiment of the present application has the following technical effects: The traffic road network matching module 10 matches the target traffic road network of the target area in the cloud database, identifies multiple intersections, clarifies the geographical scope of the target service area, and provides a basic geographical information basis for subsequent driving data analysis, ensuring that the analyzed traffic environment is consistent with the actual situation and improving the relevance of the data. The first prediction module 20 obtains the first predicted vehicle at the first intersection among multiple intersections by analyzing the real-time driving information of the target traffic road network, introduces real-time traffic dynamic data, and provides instant feedback on the current traffic flow. The driving visual map generation module 30 obtains the target position of the target service area, and generates a driving visual map in combination with the first mapping relationship between the first intersection, the first position and the first predicted vehicle, intuitively displaying the traffic conditions and flow trends. The second prediction module 40 activates the vehicle prediction center to analyze the driving visual map, obtains the target predicted vehicle at the target position in the predetermined time interval, refines the prediction of the future traffic conditions of the target service area, and provides more targeted data for resource scheduling. The resource scheduling module 50 analyzes the target predicted vehicle through the resource scheduler, predicts the target predicted energy demand, target predicted parking demand, target predicted energy demand and target predicted rest demand in combination with the service resource prediction evaluation function, formulates a target resource scheduling strategy, and performs resource scheduling on the target service area according to the target resource scheduling strategy to optimize the service area resource utilization.

[0073] In general, the embodiment of the present application realizes the collaborative work of various modules. The traffic road network matching module 10 provides accurate basic data, the first prediction module 20 analyzes real-time traffic information, the driving visualization graph generation module 30 visualizes traffic conditions, the second prediction module 40 prospectively predicts future traffic, and the resource scheduling module 50 optimizes resource allocation according to the prediction results, thereby achieving accurate prediction and optimized scheduling of driving resources, improving the flexibility and accuracy of driving resource scheduling, thereby more effectively planning resource allocation, reducing vehicle waiting time, and ultimately improving the utilization rate of driving resources and overall traffic flow efficiency.

[0074] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. Combined with the cloud-supported vehicle resource optimization and dispatching system, it is characterized by: include: A traffic road network matching module, the traffic road network matching module is used to match a target traffic road network of a target area in a cloud database, the target traffic road network includes a plurality of intersections with location identifiers, wherein the target area has a target service area; A first prediction module, the first prediction module is used to analyze the real-time driving information of the target traffic road network to obtain a first predicted vehicle at a first intersection among multiple intersections, the first intersection having a first position identifier; A driving visible graph generation module, the driving visible graph generation module is used to obtain the target position of the target service area, and generate a driving visible graph in combination with a first mapping relationship between the first intersection, the first position and the first predicted vehicle; A second prediction module, the second prediction module is used to activate the vehicle prediction center to analyze the driving visual map to obtain the target predicted vehicle at the target position within a predetermined time interval; A resource scheduling module is used to analyze the target predicted vehicle through a resource scheduler to obtain a target resource scheduling strategy, and perform resource scheduling on the target service area according to the target resource scheduling strategy.

2. The vehicle resource optimization and dispatching system combined with cloud support according to claim 1 is characterized in that: The execution step of the first prediction module also includes: Determine a first connecting road of the first intersection according to the target traffic road network, wherein the first connecting road includes a plurality of roads; Acquire a first road from the plurality of roads, and obtain a travel direction of the first road having a first travel number in combination with the target traffic road network; obtaining first real-time driving information of the first road in combination with the real-time driving information; The first predicted vehicle is calculated based on the first real-time driving information and the first travel number.

3. The vehicle resource optimization and dispatching system combined with cloud support according to claim 2 is characterized in that: The first real-time driving information includes a plurality of vehicles with unit vehicle displacement identifications, and the execution steps of the first prediction module further include: Performing cluster analysis on multiple vehicles using the unit vehicle displacement as a clustering constraint to obtain a clustering result, wherein the clustering result includes a first cluster cluster, and the first cluster cluster corresponds to a first unit vehicle displacement; Counting the first clusters to obtain a first cluster driving number, and combining the first driving number to obtain a first predicted number of driving of the first unit vehicle displacement; The first predicted number is added to obtain the first predicted vehicle.

4. The vehicle resource optimization and dispatching system combined with cloud support according to claim 1 is characterized in that: The execution step of the second prediction module also includes: The vehicle prediction center determines the first-level neighborhood of the target location according to the driving visibility graph; Extract any first-level intersection in the first-level neighborhood, and determine a second-level neighborhood of the any first-level intersection; Sampling the secondary intersections in the secondary neighborhood to obtain a target secondary intersection set; The predicted vehicles at each intersection in the target secondary intersection set are integrated to obtain any predicted vehicle at any primary intersection; The fusion result of the arbitrary predicted vehicle is recorded as the target predicted vehicle.

5. The vehicle resource optimization and dispatching system combined with cloud support according to claim 4 is characterized in that: The execution step of the second prediction module also includes: The plurality of secondary intersections in the secondary neighborhood are sorted in descending order based on the predicted number of vehicles traveling, to obtain a descending list of intersections; 50% of the intersections in the descending list of intersections are selected to form the target secondary intersection set.

6. The vehicle resource optimization and dispatching system combined with cloud support according to claim 3 is characterized in that: The execution steps of the resource scheduling module also include: Introducing a first service resource prediction evaluation function to perform resource demand prediction evaluation on the target prediction vehicle to obtain a first target prediction resource demand; The resource scheduler performs resource scheduling for the target service area based on the target predicted energy demand and the target predicted parking demand in the first target predicted resource demand.

7. The vehicle resource optimization and dispatching system combined with cloud support according to claim 6 is characterized in that: The first real-time driving information includes a plurality of vehicles with identifications of the number of passengers, and the execution steps of the resource scheduling module further include: Analyze the plurality of vehicles with identifications of the number of passengers to obtain a second predicted number of passengers in the vehicles with the first unit vehicle displacement; Obtaining a first total number of passengers of the first predicted vehicle according to the second predicted number; Adding the first total number of passengers to obtain the target predicted number of passengers for the target predicted vehicle; Introducing a second service resource prediction evaluation function to perform resource demand prediction evaluation on the target predicted number of passengers to obtain a second target predicted resource demand; The resource scheduler performs resource scheduling for the target service area based on the target predicted energy demand and the target predicted rest demand in the second target predicted resource demand.

8. The vehicle resource optimization and dispatching system combined with cloud support according to claim 7 is characterized in that: The resource scheduling module performs environmental resource scheduling for the target service area based on the target predicted vehicle and the target predicted number of passengers.

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