Vehicle collaborative dispatching method, device, equipment and storage medium

Abstract

The present application provides a vehicle collaborative dispatching method, device, equipment and storage medium, which relates to the field of vehicle dispatching technology. The method includes: obtaining vehicle usage data of each vehicle deployment area in multiple time periods and vehicle flow data between each vehicle deployment area in each time period based on historical vehicle usage data; determining the number of vehicles to be dispatched in each vehicle deployment area in combination with the remaining vehicles in each of the vehicle deployment areas at the current moment and the vehicle usage data of each vehicle deployment area in the corresponding time period at the current moment; adjusting the number of vehicles to be dispatched in each vehicle deployment area according to the geographical location and user characteristics to obtain the initial number of vehicles to be dispatched; adjusting the initial number of vehicles to be dispatched according to the vehicle flow data between each vehicle deployment area in each time period to obtain the target number of vehicles to be dispatched in each vehicle deployment area. The technical effect of the present application is: refined regional collaborative dispatching of shared cars.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle scheduling, and in particular to a vehicle collaborative scheduling method, device, equipment and storage medium. Background Art

[0002] In recent years, car-sharing has been welcomed by the vast number of users for its convenience and has developed rapidly across the country. However, with the development of car-sharing, the problem of imbalance between supply and demand of car-sharing has also begun to emerge. For example, there is a shortage of cars in some areas during peak hours in the morning and evening, while there is a surplus of cars in other areas, which leads to a decrease in the efficiency of car-sharing and a decline in the return on investment of enterprises.

[0003] In order to solve the above problems, companies usually dispatch vehicles to different areas based on historical data. Although historical data can provide certain predictive guidance for vehicle dispatch, the lack of real-time data integration means that the above methods cannot immediately reflect the current vehicle usage status and user behavior changes, affecting the accuracy of vehicle dispatch decisions. Summary of the invention

[0004] The present application provides a vehicle collaborative scheduling method, device, equipment and storage medium for performing refined regional collaborative scheduling of shared vehicles.

[0005] In a first aspect, the present application provides a vehicle collaborative scheduling method, the method comprising: obtaining historical vehicle usage data of each vehicle deployment area in a target area, and obtaining vehicle usage data of each vehicle deployment area in multiple time periods and vehicle flow data between each vehicle deployment area in each time period based on the historical vehicle usage data; obtaining the remaining vehicles in each vehicle deployment area at a current moment, and determining the number of vehicles to be dispatched for each vehicle deployment area in combination with the remaining vehicles and the vehicle usage data of each vehicle deployment area in the corresponding time period at the current moment; obtaining the geographical location and user characteristics of each vehicle deployment area, and adjusting the number of vehicles to be dispatched for each vehicle deployment area based on the geographical location and the user characteristics to obtain an initial number of vehicles to be dispatched; adjusting the initial number of vehicles to be dispatched based on the vehicle flow data between each vehicle deployment area in each time period to obtain a target number of vehicles to be dispatched for each vehicle deployment area.

[0006] By adopting the above technical solution, dynamic management of regional scheduling of shared cars is achieved by integrating historical data analysis and real-time data monitoring. According to the current real-time data, such as the remaining vehicles in each region, the demand for cars in the current period, etc., timely scheduling optimization is carried out to make the scheduling plan more in line with the current actual situation. At the same time, the method also comprehensively considers multi-dimensional influencing factors such as geographical location and user characteristics, and makes multi-faceted adjustments to the initial scheduling results to make the scheduling more accurate and in line with the actual car needs of different regions. In addition, the method can make dynamic corrections based on vehicle flow data, so that the scheduling not only considers the individual regions, but also the interactive effects between regions, realizes regional collaborative scheduling, and realizes the refined dynamic scheduling of shared cars.

[0007] Optionally, the vehicle usage data includes the number of orders in each of the vehicle deployment areas in different time periods, and the combination of the remaining vehicles and the vehicle usage data of each of the vehicle deployment areas in the corresponding time period at the current moment to determine the number of vehicles to be dispatched in each of the vehicle deployment areas includes: calculating the vehicle usage demand in different time periods in each of the vehicle deployment areas according to the number of orders; comparing the remaining vehicles in each of the vehicle deployment areas at the current moment with the vehicle usage demand in the corresponding time period to determine the number of vehicles transferred out or transferred in of each of the vehicle deployment areas; determining the number of vehicles to be dispatched in each of the vehicle deployment areas according to the number of vehicles transferred out or transferred in of each of the vehicle deployment areas.

[0008] By adopting the above technical solution, by subdividing the number of orders in different time periods, the specific vehicle demand in each time period can be calculated more accurately. On this basis, combined with the current real-time remaining vehicle situation in the area, it can be clearly judged whether there is a surplus or shortage of vehicles in the area in the current time period. That is, the accurate judgment of the supply and demand status of vehicles in different time periods is achieved. Then, the specific outbound or inbound volume of vehicles can be determined as needed, and finally a refined number of vehicle dispatches is formed. The method of considering time period subdivision can be highly targeted, which not only meets the needs of reasonable vehicle flow, but also avoids the problem of imbalance between supply and demand in some time periods. The final dispatch quantity can balance the supply and demand conditions in different time periods to the greatest extent, improve the efficiency of vehicle use, and provide users with a better travel experience.

[0009] Optionally, the user characteristics include the number of users, the number of users of different age groups, and the number of users of different genders, and the adjusting the number of vehicle dispatches in each of the vehicle deployment areas according to the geographical location and the user characteristics to obtain an initial number of vehicle dispatches includes: obtaining vehicle flow data at the geographical location of each of the vehicle deployment areas, and scoring the traffic flow of each of the vehicle deployment areas according to the vehicle flow data to obtain a traffic flow score; obtaining the location of the traffic nodes in each of the vehicle deployment areas and the service frequency of each of the traffic nodes, and scoring the public transportation facilities in each of the vehicle deployment areas according to the location of the traffic nodes and the service frequency of each of the traffic nodes to obtain a public transportation facility score; obtaining the location of each of the vehicle deployment areas and the service frequency of each of the traffic nodes The commercial building distribution, business type and business scale of the vehicle deployment area are obtained, and the commercial activity density of each vehicle deployment area is scored according to the commercial building distribution, business type and business scale to obtain a commercial activity density score; the number of similar vehicles deployed in each vehicle deployment area is obtained, and the degree of competition in each vehicle deployment area is scored according to the number of similar vehicles deployed to obtain a competition degree score; the traffic flow score, the public transportation facility score, the commercial activity density score and the competition degree score are combined to obtain a geographical location score; the initial vehicle dispatch quantity of each vehicle deployment area is obtained by combining the number of users, the number of users of different age groups, the number of users of different genders and the geographical location score.

[0010] By adopting the above technical solution, the solution not only takes into account the number of users, the distribution of users of different age groups and genders, but also evaluates in detail the various characteristics of the geographical location, including traffic flow, public transportation facilities, commercial distribution, competitive situation, etc., and gives special scores to these characteristics. This can fully reflect that the demand for cars in a region is affected by various geographical factors. Then, considering the geographical score together with the user characteristics can make the dispatch quantity more accurately match the actual demand situation in different regions. Compared with dispatching based solely on the number of users, this method is more comprehensive and refined, so that the dispatch plan not only considers the user characteristics of the region itself, but also considers the impact of the surrounding environment on the demand for cars, and realizes accurate dispatch based on multiple factors. This further improves the accuracy of the dispatch quantity, makes the vehicle configuration more reasonable, and provides users with more reliable travel services.

[0011] Optionally, the combining of the traffic flow score, the public transportation facility score, the commercial activity density score and the competition degree score to obtain a geographical location score comprises: substituting the traffic flow score, the public transportation facility score, the commercial activity density score and the competition degree score into a geographical location score formula to obtain a geographical location score; wherein the geographical location score formula is: G = ω T T+ωP P+ω B B-ω C C; where G is the geographical location impact score, ω T ,ω P ,ω B and C All are weight coefficients, T is the traffic flow score, P is the public transportation quantity score, B is the commercial activity density score, and C is the competition level score.

[0012] By adopting the above technical solution, a mathematical formula for geographic location scoring is used to evaluate the impact of various geographical factors, and the importance of each factor can be expressed by different weights. Among them, traffic flow, public transportation and commercial activities have a positive impact on car demand, and the degree of competition has a negative impact on demand. A high overall score indicates that there is a large demand for cars in the area. This quantitative scoring method allows clear numerical comparisons of the geographical characteristics of different regions, and the evaluation results are more intuitive. The scoring formula can reflect the impact of various factors more systematically and comprehensively, and the evaluation results are more accurate and reliable. The final comprehensive geographical location score can well match the actual travel characteristics of different urban areas, providing an accurate basis for subsequent car demand forecasting and scheduling decisions.

[0013] Optionally, combining the number of users, the number of users of different age groups, the number of users of different genders and the geographical location scores to obtain the initial vehicle dispatch quantity for each vehicle deployment area includes: substituting the number of users, the number of users of different age groups, the number of users of different genders and the geographical location scores into a dispatch quantity calculation formula to obtain the initial vehicle dispatch quantity for each vehicle deployment area; wherein the dispatch quantity calculation formula is: Where C is the initial vehicle dispatch quantity of each vehicle deployment area, D is the vehicle dispatch quantity of the i-th vehicle deployment area, U is the number of users in the i-th vehicle deployment area, and U a [i] is the number of users in the i-th age group in the i-th vehicle deployment area, ω a [i] is the weight coefficient corresponding to the number of users in the i-th age group in the i-th vehicle deployment area, U g [j] is the number of users of specific gender j in the i-th vehicle deployment area, ω g [j] is the weight coefficient corresponding to the number of users of specific gender j in the i-th vehicle deployment area, U t [i, j] is the number of users of specific gender j in the i-th age group in the i-th vehicle deployment area, ω t [i, j] is the weight coefficient corresponding to the number of users of specific gender j in the ith age group in the ith vehicle deployment area, α and β are adjustment factors, and G is the geographic location score.

[0014] By adopting the above technical solution, when calculating the initial dispatch quantity, not only the total number of users is taken into account, but also the number of users of different age groups and genders is subdivided, and corresponding weight coefficients are set to reflect the car usage characteristics of different user groups. At the same time, the geographical location score is also added to the calculation as an important influencing factor. This calculation method based on refined mathematical formulas enables the initial dispatch quantity to fully reflect the impact of the user structure within the region and the surrounding geographical environment on car demand. Compared with simply dispatching according to the total number of users, this calculation formula can achieve in-depth consideration of the population structure and geographical distribution, so that the initial dispatch can more accurately match the actual demand situation. In addition, the calculation process is parameterizable, and it can be flexibly adapted to the specific conditions of different cities by adjusting the weight coefficient. The final initial dispatch quantity, as the basis for subsequent optimization, will greatly improve the accuracy of the overall dispatch effect and make the dispatch plan more in line with actual needs.

[0015] Optionally, after obtaining the target vehicle dispatch quantity for each of the vehicle deployment areas, it also includes: calculating the dispatch cost of dispatching the target vehicle dispatch quantity for each of the vehicle deployment areas; predicting the increased revenue value after dispatching the target vehicle dispatch quantity for each of the vehicle deployment areas; if the increased revenue value is less than the dispatch cost, adjusting the target vehicle dispatch quantity until the increased revenue value is greater than the dispatch cost.

[0016] By adopting the above technical solutions, the solution not only considers whether the dispatch quantity meets the actual needs of the region, but also focuses on evaluating the economic rationality of the dispatch. By calculating the dispatch cost and the added value of income, economically unreasonable high-cost dispatch can be avoided. If the initial dispatch quantity causes the increase in income to be lower than the cost, the dispatch quantity should be appropriately reduced and the evaluation should be repeated until the balance of income and expenditure is reached. This ensures that the dispatch plan not only meets the needs but is also economically reasonable. The solution elevates the dispatch effect to the level of economic benefits and achieves the best balance between the dispatch quantity, service quality and enterprise cost-benefit. It avoids enterprises from conducting non-economic dispatch and improves resource utilization efficiency.

[0017] Optionally, after obtaining the target vehicle dispatching number for each vehicle deployment area, the method further includes: obtaining feedback data sent by the user end after each vehicle deployment area dispatches the target vehicle dispatching number; and adjusting the vehicle deployment number for each vehicle deployment area in each time period according to the feedback data.

[0018] By adopting the above technical solution, after the scheduling is executed, the solution can directly obtain the user's evaluation of the scheduling effect as a basis for adjustment. Based on user feedback data, the shortcomings of the scheduling plan in execution can be found. For example, if users in certain time periods or areas report that there are still not enough vehicles, vehicles can be added to these time periods in a targeted manner. In this way, the scheduling plan can be continuously verified and optimized by users, thereby meeting the actual travel needs of users to the greatest extent. The scheduling plan can be refined and improved to make up for the deviation between prediction and reality and improve the user experience.

[0019] In a second aspect, the present application provides a vehicle collaborative scheduling device, the device comprising: the device comprising: an acquisition module, a determination module, a first adjustment module and a second adjustment module; wherein the acquisition module is used to acquire historical vehicle usage data of each vehicle deployment area in a target area, and obtain vehicle usage data of each vehicle deployment area in multiple time periods and vehicle flow data between each vehicle deployment area in each time period according to the historical vehicle usage data; the determination module is used to acquire the remaining vehicles of each vehicle deployment area at the current moment, and determine the number of vehicles to be dispatched for each vehicle deployment area in combination with the remaining vehicles and the vehicle usage data of each vehicle deployment area in the corresponding time period at the current moment; the first adjustment module is used to acquire the geographical location and user characteristics of each vehicle deployment area, and adjust the vehicle scheduling plan of each vehicle deployment area according to the geographical location and the user characteristics to obtain the initial vehicle scheduling number; the second adjustment module is used to adjust the initial vehicle scheduling plan according to the vehicle flow data between each vehicle deployment area in each time period to obtain the target vehicle scheduling number for each vehicle deployment area.

[0020] By adopting the above technical solution, the dynamic management of regional scheduling of shared cars is realized by integrating historical data analysis and real-time data monitoring. According to the current real-time data, such as the remaining vehicles in each region, the demand for cars in the current period, etc., timely scheduling optimization is carried out to make the scheduling plan more in line with the current actual situation. At the same time, the method also comprehensively considers multi-dimensional influencing factors such as geographical location and user characteristics, and makes multi-faceted adjustments to the initial scheduling results to make the scheduling more accurate and in line with the actual car needs of different regions. In addition, the method can be dynamically corrected according to vehicle flow data, so that the scheduling not only considers the individual regions, but also considers the interaction between regions, and realizes regional collaborative scheduling. Refined dynamic scheduling of shared cars is realized.

[0021] In the third aspect, the present application provides an electronic device, adopting the following technical solution: including 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 so that the electronic device executes a computer program such as any one of the above-mentioned vehicle collaborative scheduling methods.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned vehicle collaborative scheduling methods.

[0023] In summary, the present application includes at least one of the following beneficial technical effects:

[0024] 1. Carry out refined regional coordinated dispatch of shared cars;

[0025] 2. Taking into account multi-dimensional influencing factors such as geographical location and user characteristics, the initial scheduling results are adjusted in multiple ways to make the scheduling more accurate and meet the actual vehicle demand in different regions. In addition, this method can be dynamically corrected according to vehicle flow data, so that the scheduling not only considers the individual regions, but also the interaction between regions, and realizes regional coordinated scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a vehicle collaborative scheduling method provided in an embodiment of the present application;

[0027] Figure 2 It is a structural schematic diagram of a vehicle cooperative scheduling device provided in an embodiment of the present application;

[0028] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0029] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0030] In order 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0031] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0032] Figure 1 is a flow chart of a vehicle cooperative dispatching method provided by an embodiment of the present application. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but the steps are not necessarily executed in the order indicated by the arrows; unless otherwise specified in this document, there is no strict order restriction for the execution of the steps, and the steps may be executed in other orders; and Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0033] The present application discloses a vehicle coordinated dispatching method, such as Figure 1 As shown, the method includes S101-S104.

[0034] S101, obtaining historical vehicle usage data of each vehicle deployment area in the target area, and obtaining vehicle usage data of each vehicle deployment area in multiple time periods and vehicle flow data between each vehicle deployment area in each time period based on the historical vehicle usage data.

[0035] The geographical location of the target area in the embodiment of the present application can be a school, residential area, industrial park, business district, etc. Specifically, the historical vehicle usage data mainly includes vehicle order data, vehicle positioning data, vehicle status data, user data, and external environment data. The vehicle order data reflects the demand for vehicle use in different time periods, the vehicle positioning data reflects the use and flow of vehicles, the vehicle status data reflects the intensity of vehicle use, the user data helps to analyze the usage habits of different user groups, and the external environment data examines the impact of external factors. Vehicle flow data refers to the flow patterns of vehicles between multiple vehicle deployment areas, which reflects the vehicle scheduling needs between different regions. The purpose of obtaining vehicle flow data is to analyze the complementary relationship between vehicles in different regions in order to carry out cross-regional vehicle collaborative scheduling.

[0036] In one example, in order to achieve effective coordinated dispatch of vehicles, it is necessary to first obtain the historical vehicle usage data of each vehicle deployment site (such as station A, station B, etc.) in the target area, and analyze the data to obtain the vehicle usage and flow between sites. The vehicle order data, vehicle positioning data, vehicle status data, etc. of each site in the past period of time are extracted from the database of the vehicle sharing service platform. These multi-source heterogeneous data can reflect the vehicle usage demand, vehicle usage intensity, flow patterns, etc. of each site in different time periods. For example, from the vehicle order data, it can be seen that the vehicle usage of station A is large during peak hours in the morning and evening; from the positioning data, it can be analyzed that a large number of vehicles flow from station A to station B in the morning. Then, the average vehicle usage data of each site in different time periods (such as morning peak hours, evening peak hours, etc.) and the vehicle flow data between sites are statistically obtained.

[0037] S102, obtaining the remaining vehicles in each vehicle deployment area at the current moment, and determining the number of vehicles to be dispatched in each vehicle deployment area by combining the remaining vehicles and the vehicle usage data of each vehicle deployment area in the corresponding time period at the current moment.

[0038] In one example, after obtaining the historical vehicle usage data of each vehicle deployment site and analyzing the vehicle usage of the site, the number of vehicles to be dispatched is determined in combination with the real-time data of the site. Specifically, the number of remaining vehicles at each site at the current moment is first obtained through the vehicle monitoring system. For example, there are currently 30 vehicles at Station A. Then, based on the historical vehicle usage data of Station A during the current period (such as the evening peak) (predicted to require 100 vehicles), the number of remaining vehicles is deducted, and it can be calculated that the number of vehicles that Station A needs to dispatch is 70. Similarly, the vehicle dispatch volume at each site at the current moment can be obtained. This is because, considering that the vehicle usage at the site will vary according to the time period, relying solely on historical data cannot accurately reflect the current actual demand. Combining real-time data can achieve more dynamic and accurate vehicle scheduling, and dispatch an appropriate amount of vehicles to the site during the peak period in a timely manner, so as to achieve resource supply and demand matching and avoid the situation where there is an excess of vehicles or a shortage of supply at individual sites.

[0039] Based on the above embodiment, as an optional implementation, in S102: the vehicle usage data includes the number of orders in each vehicle deployment area in different time periods, and the vehicle usage data of the remaining vehicles and each vehicle deployment area in the corresponding time period at the current moment are combined to determine the number of vehicles to be dispatched in each vehicle deployment area, which specifically includes the following steps:

[0040] S20, calculating the vehicle usage demand in each vehicle deployment area in different time periods according to the order quantity.

[0041] In one example, the purpose of calculating the demand for vehicle use based on the number of orders is to more accurately evaluate the actual demand for vehicles in each time period. The specific implementation method is to extract the number of vehicle orders for each vehicle deployment area in different time periods (such as morning peak, evening peak, etc.) in the recent period (for example, in the past two weeks) from the database of the vehicle sharing service platform. For example, the average order volume of area A during the morning peak period of 7-9 o'clock every day last week was 300 orders. Then, the average number of vehicles used for each order can be analyzed with reference to historical data, and an average of 2 vehicles are deployed for one order in area A. Then it can be simply estimated that the demand for vehicle use in area A in the current time period is about 300*2=600 vehicles. In this way, by counting the number of orders in different time periods and combining the average number of vehicles in a single order, the demand for vehicles in each time period can be directly estimated. This method fully utilizes the current actual order data, can accurately and dynamically reflect the actual demand for vehicles in each time period, and provide a basis for the subsequent formulation of a reasonable scheduling plan. .

[0042] S21, comparing the remaining vehicles in each vehicle deployment area at the current moment with the vehicle usage demand in the corresponding time period to determine the vehicle transfer-out quantity or vehicle transfer-in quantity of each vehicle deployment area.

[0043] In one example, after predicting the usage demand of the vehicle deployment area in each period, it is necessary to further combine the current real-time remaining number of vehicles in the area to determine the number of vehicles that need to be deployed. This is because when scheduling vehicles, the actual vehicle situation in the current area must be considered to achieve an accurate match between demand and supply.

[0044] The specific implementation method is to obtain the number of remaining vehicles in each area in the current period from the vehicle monitoring and dispatching system. Taking area A as an example, the predicted vehicle demand is 500 vehicles, and the current real-time remaining vehicles in area A obtained through the system are 300 vehicles. Then 200 vehicles need to be transferred from other areas to area A. On the contrary, if the current remaining vehicles in area A are 600, which exceeds the demand of 500 vehicles, then area A needs to transfer 100 vehicles to other areas. By combining real-time data with predicted demand for comparison, the amount of vehicles transferred in or out of each area can be accurately obtained, avoiding the situation of excess or shortage of vehicles, and greatly improving the efficiency of vehicle use.

[0045] S22, determining the number of vehicles to be dispatched in each vehicle deployment area according to the number of vehicles dispatched out or the number of vehicles dispatched in each vehicle deployment area.

[0046] In one example, after calculating the vehicle dispatch volume and the vehicle dispatch volume of each vehicle deployment area, the final vehicle dispatch quantity of each area can be determined. The vehicle dispatch volume and the vehicle dispatch volume obtained above are converted into specific dispatch plan execution instructions for subsequent vehicle dispatch coordination.

[0047] The specific implementation method is to first sort out the data of the vehicle transfer-out and transfer-in of each deployment area. Take area A as an example, its vehicle transfer-out is 100 vehicles and its vehicle transfer-in is 200 vehicles. Then it can be directly obtained that the vehicle dispatch quantity of area A is 100 vehicles (net transfer-in quantity). That is, the dispatch plan for area A is to net transfer in 100 vehicles. The same calculation is performed for each other area, and finally the vehicle dispatch quantity plan for the entire area can be obtained.

[0048] S103, obtaining the geographical location and user characteristics of each vehicle deployment area, and adjusting the vehicle dispatch quantity of each vehicle deployment area according to the geographical location and user characteristics to obtain an initial vehicle dispatch quantity.

[0049] User characteristics include user population size: reflecting the overall car demand in the region; user age structure: the car demand varies at different age groups; user occupation composition: there is a difference in the car usage time between office workers and non-office workers; user gender structure: the car usage habits of men and women are also slightly different.

[0050] In one example, after determining the initial dispatch quantity for each vehicle deployment site, it is also necessary to consider the impact of geographic location and user characteristics on vehicle usage demand and adjust and optimize the dispatch quantity. Because different locations and user groups have different vehicle usage habits, if the above factors are not considered, the vehicle configuration will be unreasonable and unable to meet actual needs.

[0051] Specifically, it includes: first extracting the location attributes (such as commercial areas or residential areas) and user characteristics (such as age distribution, occupational groups) of each site from the map service and user database. Then, according to the functional attributes of the area where the site is located, set different geographical location weights; at the same time, according to the user characteristics analysis, obtain the vehicle usage tendencies of different user groups and set specific user characteristic weights. For example, the weight of working hours in commercial areas is 0.8, and the weight of weekends in residential areas is 0.9. Then apply the above weights to the initial vehicle dispatch quantity, perform weighted calculations, and obtain the vehicle dispatch quantity after the geographical location and user characteristics are adjusted. In this way, the vehicle configuration can be more in line with the actual usage of different areas and improve resource utilization efficiency.

[0052] Based on the above embodiment, as an optional implementation, in S103: the user characteristics include the number of users, the number of users of different age groups, and the number of users of different genders. According to the geographical location and the user characteristics, the number of vehicles dispatched in each vehicle deployment area is adjusted to obtain the initial number of vehicles dispatched, which specifically includes the following steps:

[0053] S30, obtaining vehicle flow data of the geographical location of each vehicle deployment area, and scoring the traffic flow of each vehicle deployment area according to the vehicle flow data to obtain a traffic flow score.

[0054] In one example, the vehicle flow data of each vehicle deployment area in the past period of time is first obtained from the vehicle monitoring system, such as the traffic flow monitoring report of the roads around area A. Then the traffic congestion in the area is judged based on the traffic flow data. If the traffic flow is small and the congestion is low, it can be determined that the traffic flow in the area is good; if the traffic flow is large and congestion often occurs, it can be determined that the traffic flow in the area is poor. The specific scoring criteria for traffic flow scoring: the traffic flow score is set to 100 points.

[0055] Based on the traffic flow monitoring data and congestion status reports of regional roads, traffic flow is divided into the following categories: (1) Smooth and unobstructed: small traffic flow, no congestion throughout the day, rated 90-100 points; (2) Basically unobstructed: occasional slight congestion, rated 80-90 points; (3) Intermittent congestion: intermittent congestion during peak hours, rated 60-80 points; (4) Frequent congestion: severe congestion during peak hours, rated 40-60 points; (5) Severe congestion: severe congestion throughout the day, rated 20-40 points. Based on the above standards, the traffic flow status of each deployment area is scored based on actual traffic flow monitoring and statistical data. For example, areas with good traffic flow are given 90 points, areas with poor traffic flow are given 60 points, and so on. In the end, each area can get a traffic flow score.

[0056] After obtaining the traffic flow scores of each area, this factor can be taken into account when formulating the vehicle dispatch plan. More vehicles can be arranged in relatively congested areas, while fewer vehicles can be arranged in areas with smooth traffic. This method makes full use of traffic flow data, which can make vehicle dispatch more in line with actual road conditions and improve dispatch efficiency.

[0057] S31, obtaining the location of the traffic nodes in each vehicle deployment area and the service frequency of each traffic node, and scoring the public transportation facilities in each vehicle deployment area according to the location of the traffic nodes and the service frequency of each traffic node to obtain a public transportation facility score.

[0058] Traffic nodes can be bus traffic nodes and subway traffic nodes. Bus traffic nodes mainly refer to bus stops, especially important transfer stops where multiple bus lines meet. Such nodes allow passengers to transfer between different bus lines and are an important part of the bus system. Subway traffic nodes mainly refer to subway stations, especially transfer stations where different subway lines meet. Such nodes enable transfers between subway lines and are key points in the subway system.

[0059] In one example, the purpose of obtaining public transportation facility information and scoring it is to consider the impact of public transportation on vehicle usage demand.

[0060] First, obtain the location data of public transportation stations such as bus stations and subway stations around each vehicle deployment area from the urban public transportation management department, as well as the bus and subway line information of these stations, and obtain the frequency of departure of each bus and subway line. For example, there are 2 bus stations and 10 bus lines around area A, and the line with the highest departure frequency is one every 10 minutes.

[0061] Then, the public transportation services in each deployment area are scored based on the convenience of the location of public transportation stations and the service frequency of bus and subway lines. The scoring criteria can be: 90 points for areas with multiple transportation hubs and multiple main lines with a departure frequency within 10 minutes; 60 points for areas with fewer stations and fewer lines. In the end, each area can get a public transportation facility score.

[0062] After obtaining the public transportation facility scores, when formulating vehicle dispatch plans, the number of vehicles dispatched in areas with better public transportation service levels can be reduced accordingly, and more vehicles can be dispatched to areas with poorer service levels. This can improve dispatch efficiency, make resource allocation more reasonable, and meet passenger needs.

[0063] S32, obtaining the commercial building distribution, business type and business scale of each vehicle deployment area, and scoring the commercial activity density of each vehicle deployment area according to the commercial building distribution, business type and business scale to obtain a commercial activity density score.

[0064] In one example, first, the distribution data of commercial facilities in each vehicle deployment area is obtained from the urban planning department, including the number and specific location information of shopping malls and shops, as well as the area, industry type, etc. of each commercial facility. For example, there are 2 large shopping malls and 20 catering shops in area A.

[0065] Then, the commercial activity density in the area is evaluated and scored according to the distribution, type and scale of commercial facilities. The scoring criteria can be set as follows: 90 points for areas with many commercial facilities and mainly large shopping malls; 60 points for areas with few commercial facilities and mainly small shops. In the end, each area can get a commercial activity density score. After obtaining the commercial activity density score, when formulating the vehicle dispatch plan, the vehicle dispatch volume in areas with more intensive commercial activities can be increased accordingly to meet the higher vehicle demand in such areas. Make vehicle dispatch more in line with the commercial attributes and related needs in the area.

[0066] S33, obtaining the number of similar vehicles deployed in each vehicle deployment area, and scoring the degree of competition of each vehicle deployment area according to the number of similar vehicles deployed to obtain a competition degree score.

[0067] In one example, obtaining the number of similar vehicles deployed and evaluating the degree of competition is to consider the impact of regional competition on vehicle usage demand. First, the specific number of similar vehicles deployed in each deployment area is obtained from the vehicle sharing service platform. The "similar vehicles" here refer to vehicles that provide similar services, such as small cars, luxury cars, and other categories. Then, the total number of different vehicle categories deployed in each area is counted.

[0068] Next, the competition level in each region is evaluated based on the number of similar vehicles deployed in each region. The competition level can be simply judged by the number of similar vehicles. The more vehicles there are, the more intense the competition is, and the higher the competition level score in the region. For example, if there are 50 small cars in region A, the competition level in region A can be rated 90 points.

[0069] After obtaining the competition score of each area, when formulating the vehicle dispatch plan, the vehicle dispatch volume in the area with high competition can be reduced accordingly to avoid vehicle surplus. This can optimize the vehicle supply and demand relationship in the region and improve the efficiency of vehicle use.

[0070] S34, combining the traffic flow score, public transportation facility score, commercial activity density score and competition level score to obtain a geographical location score.

[0071] In one example, the comprehensive geographical location score of each vehicle deployment area is obtained in order to comprehensively consider the impact of the geographical location on various aspects of vehicle usage demand.

[0072] In the previous steps, we obtained the traffic flow score, public transportation facility score, commercial activity density score and competition degree score of each area. Then we need to integrate them and calculate the comprehensive geographical location score of each area.

[0073] The specific method is to first set a weight for each scoring indicator, such as a traffic flow score weight of 0.3, a public transportation score weight of 0.2, a commercial activity score weight of 0.4, and a competition score weight of 0.1. Then multiply the 4 scores of each area by the corresponding weights, and then sum them up to get the comprehensive score of the geographical location of the area. For example, if the 4 scores of area A are 80 points, 60 points, 90 points, and 70 points respectively, then its comprehensive score of geographical location is calculated as 0.3×80+0.2×60+0.4×90+0.1×70=82 points.

[0074] Combining the traffic flow score, public transportation facility score, commercial activity density score and competition level score, the geographical location score includes:

[0075] Substitute the traffic flow score, public transportation facility score, commercial activity density score and competition level score into the geographical location score formula to obtain the geographical location score; the geographical location score formula is: G = ω T T+ω P P+ω B B-ω C C; where G is the geographical location impact score, ω T ,ω P ,ω B and C All are weight coefficients, T is the traffic flow score, P is the public transportation quantity score, B is the commercial activity density score, and C is the competition level score.

[0076] In one example, each scoring indicator is weighted, with positive points given to traffic flow T, public transportation P, and commercial activities B, and negative points given to competition level C, to comprehensively calculate the impact of geographical location on vehicle demand.

[0077] The formula can be used to quantify and integrate the influencing factors of different geographical locations, and to comprehensively evaluate the geographical location characteristics of each region, making subsequent vehicle scheduling more accurate and reasonable, taking into account both the incremental demand for vehicles in the region and the reduction effect of competition on vehicle demand.

[0078] The weight distribution is determined according to the importance of each scoring indicator on vehicle demand. Among them, the commercial activity density has the greatest impact on vehicle demand, so the weight of the commercial activity score ω B The setting is higher, which can be set to 0.4. Traffic flow also has a great impact on vehicle travel, so the weight of traffic flow score ω T It can be set to 0.3. The impact of public transportation and competition is relatively small, so ω P and ω C They can be set to 0.2 and 0.1 respectively. Of course, the weight setting can also be adjusted according to the actual situation of different cities. For example, in cities with serious traffic congestion, ω T ; Cities with well-developed public transportation can improve P Generally speaking, the weight setting should quantify the impact of each factor as much as possible to make the scoring results more accurate and objective.

[0079] S35, combining the number of users, the number of users of different age groups, the number of users of different genders and the geographical location scores, to obtain the initial vehicle dispatch quantity for each vehicle deployment area.

[0080] In one example, the number of users, the number of users of different age groups, the number of users of different genders, and the geographical location scores are substituted into the dispatch quantity calculation formula to obtain the initial vehicle dispatch quantity for each vehicle deployment area; wherein, the dispatch quantity calculation formula is:

[0081]

[0082] Where C is the initial number of vehicles dispatched in each vehicle deployment area, D is the number of vehicles dispatched in the i-th vehicle deployment area, U is the number of users in the i-th vehicle deployment area, and U a [i] is the number of users in the i-th age group in the i-th vehicle deployment area, ω a [i] is the weight coefficient corresponding to the number of users in the i-th age group in the i-th vehicle deployment area, U g [j] is the number of users of specific gender j in the i-th vehicle deployment area, ω g [j] is the weight coefficient corresponding to the number of users of specific gender j in the i-th vehicle deployment area, U t [i, j] is the number of users of specific gender j in the i-th age group in the i-th vehicle deployment area, ω t [i, j] is the weight coefficient corresponding to the number of users of specific gender j in the ith age group in the ith vehicle deployment area, α and β are adjustment factors, and G is the geographic location score.

[0083] The scheduling calculation formula mainly consists of three parts. The first part is: This part calculates the ratio of the weighted sum of different user groups to the total number of users.

[0084] Among them, U a [i] represents the number of users of the i-th category in the area, such as the number of elderly users, the number of middle-aged users, the number of young users, etc. a [i] is the pre-set weight coefficient of the i-th user. U is the total number of users in the area. This design is because different user groups have different vehicle usage requirements. For example, the usage requirements of the elderly may be higher than those of young people. Weight coefficient ω a [i] is used to represent the differences in usage needs of different user groups. The weight can be set based on the analysis. After summing up and dividing by the total number U, we can get the comprehensive consideration result of user characteristics. This result reflects the impact of different user group compositions on vehicle demand. Adding it to the calculation can improve the dispatch accuracy.

[0085] The second part is: This part calculates the ratio of the weighted sum of users of different genders to the total number of users. g [j] represents the number of users with gender j, such as the number of male users and the number of female users.g [j] is the preset weight coefficient of the user with gender j. U is also the total number of users.

[0086] This design is because users of different genders may have different vehicle usage requirements. Weight coefficient ω g [j] is used to indicate the degree of influence of different genders on usage demand. The sum is divided by the total number U to evaluate the comprehensive consideration of user gender characteristics. This result can reflect the impact of gender composition on vehicle demand, and adding it to the calculation can improve scheduling accuracy.

[0087] The third part is: This part calculates the ratio of the weighted sum of users of different age groups and gender combinations to the total number.

[0088] Among them U t [i, j] represents the number of users with age i and gender j. For example, the number of elderly female users, the number of middle-aged male users, etc. t [i, j] is the weight coefficient of the combined user. The sum is divided by the total number U to evaluate the impact of the two characteristics. Weight coefficient ω t [i, j] is used to represent the size of the user demand for this combination. This design can evaluate the differentiated demand for different age groups and gender characteristics. After adding the calculation, the vehicle dispatch can be more in line with the segmented needs of the actual combination of users, and the accuracy can be improved. Overall, it realizes the comprehensive consideration of the multi-dimensional characteristics of users, making the dispatch more in line with the actual situation.

[0089] α is the weight coefficient used to comprehensively consider various user characteristics. β is the weight coefficient of the geographical location score. The allocation of these two coefficients should be based on the impact of the corresponding factors on the demand for cars. Generally speaking, the impact of user characteristics on demand may be greater than that of geographical location, so α can be set slightly larger, such as 0.7; β can be set smaller, such as 0.3. The specific examples can be given according to the actual situation, and no further elaboration will be given here.

[0090] S104, adjusting the initial vehicle dispatch quantity according to the vehicle flow data between each vehicle deployment area within the time period to obtain the target vehicle dispatch quantity of each vehicle deployment area.

[0091] In one example, the initial scheduling plan is adjusted to take into account the flow of vehicles between different areas, so that the scheduling is more in line with actual demand changes. First, the initial scheduling quantity of each vehicle deployment area has been obtained in the previous steps. Then, after the scheduling is executed, the vehicle flow data between different areas within a time period (for example, one day) is continuously collected and counted, that is, the number of vehicles flowing from one area to another. For example, if there are 50 vehicles flowing from area A to area B, it is considered that the demand in area A decreases by 50 vehicles, and the demand in area B increases by 50 vehicles. Next, the initial scheduling quantity of each area is adjusted according to the flow data to form an adjusted target scheduling quantity. The adjustment principle is to reduce the corresponding number of outflow areas and increase the corresponding number of inflow areas. This can make the scheduling more in line with the actual flow of vehicles between regions, meet the needs of dynamic changes, and improve the accuracy of scheduling.

[0092] Assume that there are regions A, B, and C. Based on their respective users and geographical characteristics, their initial dispatch quantities are calculated as follows: Region A: 50 vehicles, Region B: 60 vehicles, and Region C: 80 vehicles. This is the static dispatch quantity based on the conditions of each region. However, through statistics, it is found that within a certain day, the flow of vehicles between regions is: A to B: 20 ​​vehicles, B to C: 15 vehicles, and C to A: 10 vehicles. Then adjust the initial dispatch quantity: Region A adjusted target dispatch quantity = 50-20 = 30 vehicles, Region B adjusted target dispatch quantity = 60+20-15 = 65 vehicles, and Region C adjusted target dispatch quantity = 80+15-10 = 85 vehicles.

[0093] After obtaining the target vehicle dispatching quantity for each vehicle deployment area, it also includes: calculating the dispatching cost of dispatching the target vehicle dispatching quantity for each vehicle deployment area; predicting the increased income value after dispatching the target vehicle dispatching quantity for each vehicle deployment area; if the increased income value is less than the dispatching cost, adjusting the target vehicle dispatching quantity until the increased income value is greater than the dispatching cost.

[0094] In one example, after obtaining the target dispatch quantity for each vehicle deployment area, the economic benefits of the dispatch need to be further evaluated to ensure that an economically reasonable dispatch plan is finally formed. The specific approach is: first calculate the cost required to execute the dispatch according to the target dispatch quantity, including the cost expenditures of shunting and manpower. Then predict the revenue increment of each area after dispatch based on the increased service volume. Then compare the size of the revenue increment with the dispatch cost. If the revenue increment is less than the cost, it means that the current dispatch plan is not economically reasonable enough, and it is necessary to appropriately adjust the target dispatch quantity, reduce the dispatch quantity, and reduce the cost. By repeatedly comparing and adjusting the target quantity until the revenue increment is not less than the dispatch cost, it means that the dispatch plan is economically reasonable. In this way, an executable dispatch plan that not only meets the actual service needs of each region but also takes into account the economic benefits can be obtained. This process fully balances service demand and economic rationality, avoids blindly increasing the dispatch volume and causing economic losses, ensures the scientific nature and execution of the dispatch plan, improves service quality, saves costs for enterprises, and optimizes the economic performance of dispatch services.

[0095] After obtaining the target vehicle dispatching quantity of each vehicle deployment area, it also includes: obtaining feedback data sent by the user end after the target vehicle dispatching quantity of each vehicle deployment area is dispatched; and adjusting the vehicle deployment quantity of each vehicle deployment area in each time period according to the feedback data.

[0096] In one example, after obtaining the target dispatch quantity for each area and implementing the dispatch, it is also necessary to collect user feedback on the dispatch effect to further optimize the number of vehicles deployed in different time periods. The specific approach is to collect users' evaluation and scoring of the waiting time for calling a car, the supply of vehicles, etc. in different time periods in the area. Then collect and count the feedback and scoring data of all users. If a certain area scores poorly in a certain time period, it means that the supply of vehicles in this period is insufficient. It is necessary to increase the number of vehicles deployed in this time period, and adjust the number of vehicles deployed in each time period in a targeted manner to meet the actual vehicle needs of users at different times to the greatest extent. Compared with simple dispatching based on forecast data, this adjustment based on user feedback can more accurately fine-tune the supply and demand situation in each time period, thereby improving user experience satisfaction and improving the execution effect of the dispatch plan.

[0097] Based on the above method, the present application also discloses a vehicle cooperative dispatching device, such as Figure 2 As shown, Figure 2 It is a structural schematic diagram of a vehicle cooperative scheduling device provided in an embodiment of the present application.

[0098] A vehicle collaborative dispatching device comprises: an acquisition module, a determination module, a first adjustment module and a second adjustment module; wherein the acquisition module is used to acquire historical vehicle usage data of each vehicle deployment area in a target area, and obtain vehicle usage data of each vehicle deployment area in multiple time periods and vehicle flow data between each vehicle deployment area in each time period based on the historical vehicle usage data; the determination module is used to acquire the remaining vehicles in each vehicle deployment area at the current moment, and determine the vehicle dispatch quantity of each vehicle deployment area in combination with the remaining vehicles and the vehicle usage data of each vehicle deployment area in the corresponding time period at the current moment; the first adjustment module is used to acquire the geographical location and user characteristics of each vehicle deployment area, and adjust the vehicle dispatch plan of each vehicle deployment area according to the geographical location and user characteristics to obtain the initial vehicle dispatch quantity; the second adjustment module is used to adjust the initial vehicle dispatch quantity according to the vehicle flow data between each vehicle deployment area in each time period to obtain the target vehicle dispatch quantity of each vehicle deployment area.

[0099] Optionally, the determination module is also used to calculate the vehicle usage demand of each vehicle deployment area in different time periods based on the order quantity; compare the remaining vehicles in each vehicle deployment area at the current moment with the vehicle usage demand in the corresponding time period to determine the vehicle transfer-out or vehicle transfer-in of each vehicle deployment area; determine the vehicle dispatch quantity of each vehicle deployment area based on the vehicle transfer-out or vehicle transfer-in of each vehicle deployment area.

[0100] Optionally, the first adjustment module is also used to obtain vehicle flow data of the geographical location of each vehicle deployment area, and score the traffic flow of each vehicle deployment area based on the vehicle flow data to obtain a traffic flow score; obtain the location of the traffic nodes of each vehicle deployment area and the service frequency of each traffic node, and score the public transportation facilities of each vehicle deployment area based on the location of the traffic nodes and the service frequency of each traffic node to obtain a public transportation facility score; obtain the distribution of commercial buildings, business types and business scales of each vehicle deployment area, and score the commercial activity density of each vehicle deployment area based on the distribution of commercial buildings, business types and business scales to obtain a commercial activity density score; obtain the number of similar vehicles deployed in each vehicle deployment area, and score the degree of competition of each vehicle deployment area based on the number of similar vehicles deployed to obtain a competition degree score; combine the traffic flow score, public transportation facility score, commercial activity density score and competition degree score to obtain a geographical location score; combine the number of users, the number of users of different age groups, the number of users of different genders and the geographical location score to obtain the initial vehicle dispatch number of each vehicle deployment area.

[0101] Optionally, the first adjustment module is further used to substitute the traffic flow score, public transportation facility score, commercial activity density score and competition degree score into the geographical location score formula to obtain the geographical location score; wherein the geographical location score formula is: G = ω T T+ω P P+ω B B-ω C C; where G is the geographical location impact score, ω T ,ω P ,ω B and C All are weight coefficients, T is the traffic flow score, P is the public transportation quantity score, B is the commercial activity density score, and C is the competition level score.

[0102] Optionally, the first adjustment module is further used to substitute the number of users, the number of users of different age groups, the number of users of different genders and the geographical location score into the dispatch quantity calculation formula to obtain the initial vehicle dispatch quantity of each vehicle deployment area; wherein the dispatch quantity calculation formula is:

[0103] Where C is the initial number of vehicles dispatched in each vehicle deployment area, D is the number of vehicles dispatched in the i-th vehicle deployment area, U is the number of users in the i-th vehicle deployment area, and U a [i] is the number of users in the i-th age group in the i-th vehicle deployment area, ω a [i] is the weight coefficient corresponding to the number of users in the i-th age group in the i-th vehicle deployment area, U g [j] is the number of users of specific gender j in the i-th vehicle deployment area, ω g [j] is the weight coefficient corresponding to the number of users of specific gender j in the i-th vehicle deployment area, U t [i, j] is the number of users of specific gender j in the i-th age group in the i-th vehicle deployment area, ω t [i, j] is the weight coefficient corresponding to the number of users of specific gender j in the ith age group in the ith vehicle deployment area, α and β are adjustment factors, and G is the geographic location score.

[0104] Optionally, the above-mentioned device is also used to calculate the dispatching cost of dispatching the target number of vehicles in each vehicle deployment area; predict the increased revenue after dispatching the target number of vehicles in each vehicle deployment area; if the increased revenue is less than the dispatching cost, adjust the target number of vehicles until the increased revenue is greater than the dispatching cost.

[0105] Optionally, the above-mentioned device is also used to obtain feedback data sent by the user end after each vehicle deployment area dispatches the target vehicle dispatch quantity; according to the feedback data, adjust the number of vehicles deployed in each vehicle deployment area in each time period.

[0106] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0107] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0108] The communication bus 1002 is used to realize the connection and communication between these components.

[0109] The user interface 1003 may include a display screen (Display) and a camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.

[0110] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0111] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0112] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 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 a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a vehicle cooperative scheduling method.

[0113] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program for a vehicle collaborative scheduling method stored in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0114] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0115] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0116] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0117] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0120] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0121] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A vehicle collaborative scheduling method, characterized in that: The method comprises: Acquire historical vehicle usage data of each vehicle deployment area in the target area, and obtain vehicle usage data of each vehicle deployment area in multiple time periods and vehicle flow data between each vehicle deployment area in each time period based on the historical vehicle usage data; Obtain the remaining vehicles in each of the vehicle deployment areas at the current moment, and determine the number of vehicles to be dispatched in each of the vehicle deployment areas by combining the remaining vehicles with the vehicle usage data of each of the vehicle deployment areas in the corresponding time period at the current moment; Acquiring the geographical location and user characteristics of each of the vehicle deployment areas, and adjusting the number of vehicle dispatches in each of the vehicle deployment areas according to the geographical location and the user characteristics to obtain an initial number of vehicle dispatches; the user characteristics include the number of users, the number of users of different age groups, and the number of users of different genders, and adjusting the number of vehicle dispatches in each of the vehicle deployment areas according to the geographical location and the user characteristics to obtain an initial number of vehicle dispatches, including: Acquire vehicle flow data of the geographical location of each vehicle deployment area, and score the traffic flow of each vehicle deployment area according to the vehicle flow data to obtain a traffic flow score; Obtain the location of each of the traffic nodes in the vehicle deployment area and the service frequency of each of the traffic nodes. Scoring the public transportation facilities in each vehicle deployment area according to the location of the transportation nodes and the service frequency of each transportation node to obtain a public transportation facility score; Obtaining the commercial building distribution, business type and business scale of each of the vehicle deployment areas, and scoring the commercial activity density of each of the vehicle deployment areas according to the commercial building distribution, business type and business scale to obtain a commercial activity density score; Obtaining the number of similar vehicles deployed in each of the vehicle deployment areas, and scoring the degree of competition in each of the vehicle deployment areas according to the number of similar vehicles deployed to obtain a degree of competition score; Combining the traffic flow score, the public transportation facility score, the commercial activity density score and the competition level score to obtain a geographical location score; The geographic location score is obtained by combining the traffic flow score, the public transportation facility score, the commercial activity density score and the competition degree score, including: Substituting the traffic flow score, the public transportation facility score, the commercial activity density score and the competition degree score into the geographical location score formula to obtain a geographical location score; The geographic location scoring formula is: G=ω T T+ω P P+ω B B-oh C C; Where G is the geographical location impact score, ω T ,ω P ,ω B and C All are weight coefficients, T is the traffic flow score, P is the public transportation quantity score, B is the commercial activity density score, and C is the competition degree score; Combining the number of users, the number of users of different age groups, the number of users of different genders and the geographical location scores, obtaining the initial vehicle dispatch number for each vehicle deployment area; The initial vehicle dispatch quantity for each vehicle deployment area is obtained by combining the number of users, the number of users of different age groups, the number of users of different genders and the geographical location scores, including: Substituting the number of users, the number of users of different age groups, the number of users of different genders and the geographical location score into the dispatch quantity calculation formula to obtain the initial vehicle dispatch quantity for each vehicle deployment area; The scheduling amount calculation formula is: Where C is the initial vehicle dispatch quantity of each vehicle deployment area, D is the vehicle dispatch quantity of the i-th vehicle deployment area, U is the number of users in the i-th vehicle deployment area, and U a [i] is the number of users in the i-th age group in the i-th vehicle deployment area, ω a [i] is the weight coefficient corresponding to the number of users in the i-th age group in the i-th vehicle deployment area, U g [j] is the number of users of specific gender j in the i-th vehicle deployment area, ω g [j] is the weight coefficient corresponding to the number of users of specific gender j in the i-th vehicle deployment area, U t [i, j] is the number of users of specific gender j in the i-th age group in the i-th vehicle deployment area, ω t [i, j] is the weight coefficient corresponding to the number of users of specific gender j in the i-th age group in the i-th vehicle deployment area, α and β are adjustment factors, and G is the geographic location score; The initial vehicle dispatch quantity is adjusted according to the vehicle flow data between the vehicle deployment areas in each of the time periods to obtain a target vehicle dispatch quantity for each of the vehicle deployment areas.

2. The vehicle coordinated dispatching method according to claim 1, characterized in that: The vehicle usage data includes the number of orders in each of the vehicle deployment areas in different time periods, and the determination of the number of vehicles to be dispatched in each of the vehicle deployment areas by combining the remaining vehicles with the vehicle usage data of each of the vehicle deployment areas in the corresponding time period at the current moment includes: Calculate the demand for vehicles in each of the vehicle deployment areas in different time periods according to the order quantity; Compare the remaining vehicles in each of the vehicle deployment areas at the current moment with the vehicle usage demand in the corresponding time period to determine the vehicle transfer-out quantity or vehicle transfer-in quantity of each of the vehicle deployment areas; The number of vehicles to be dispatched in each of the vehicle deployment areas is determined according to the number of vehicles dispatched out or the number of vehicles dispatched in each of the vehicle deployment areas.

3. The vehicle coordinated dispatching method according to claim 1, characterized in that: After obtaining the target vehicle dispatch quantity of each vehicle deployment area, the method further includes: Calculate the dispatch cost of dispatching the target number of vehicles in each of the vehicle deployment areas; Predicting the incremental revenue after dispatching the target number of vehicles to each of the vehicle deployment areas; If the revenue increase value is less than the dispatch cost, the target vehicle dispatch quantity is adjusted until the revenue increase value is greater than the dispatch cost.

4. The vehicle coordinated dispatching method according to claim 1, characterized in that: After obtaining the target vehicle dispatch quantity of each vehicle deployment area, the method further includes: Acquire feedback data sent by the user end after each of the vehicle deployment areas dispatches the target number of vehicles; According to the feedback data, the number of vehicles deployed in each vehicle deployment area in each time period is adjusted.

5. A vehicle cooperative dispatching device, characterized in that: The device comprises: an acquisition module, a determination module, a first adjustment module and a second adjustment module; wherein, The acquisition module is used to acquire the historical vehicle usage data of each vehicle deployment area in the target area, and obtain the vehicle usage data of each vehicle deployment area in multiple time periods and the vehicle flow data between each vehicle deployment area in each time period according to the historical vehicle usage data; The determination module is used to obtain the remaining vehicles in each of the vehicle deployment areas at the current moment, and determine the number of vehicles to be dispatched in each of the vehicle deployment areas by combining the remaining vehicles and the vehicle usage data of each of the vehicle deployment areas in the corresponding time period at the current moment; The first adjustment module is used to obtain the geographical location and user characteristics of each vehicle deployment area, and adjust the vehicle dispatch plan of each vehicle deployment area according to the geographical location and the user characteristics to obtain the initial vehicle dispatch quantity; the user characteristics include the number of users, the number of users of different age groups, and the number of users of different genders. The vehicle dispatch quantity of each vehicle deployment area is adjusted according to the geographical location and the user characteristics to obtain the initial vehicle dispatch quantity, including: Acquire vehicle flow data of the geographical location of each vehicle deployment area, and score the traffic flow of each vehicle deployment area according to the vehicle flow data to obtain a traffic flow score; Obtain the location of each of the traffic nodes in the vehicle deployment area and the service frequency of each of the traffic nodes. Scoring the public transportation facilities in each vehicle deployment area according to the location of the transportation nodes and the service frequency of each transportation node to obtain a public transportation facility score; Obtaining the commercial building distribution, business type and business scale of each of the vehicle deployment areas, and scoring the commercial activity density of each of the vehicle deployment areas according to the commercial building distribution, business type and business scale to obtain a commercial activity density score; Obtaining the number of similar vehicles deployed in each of the vehicle deployment areas, and scoring the degree of competition in each of the vehicle deployment areas according to the number of similar vehicles deployed to obtain a degree of competition score; Combining the traffic flow score, the public transportation facility score, the commercial activity density score and the competition level score to obtain a geographical location score; The geographic location score is obtained by combining the traffic flow score, the public transportation facility score, the commercial activity density score and the competition degree score, including: Substituting the traffic flow score, the public transportation facility score, the commercial activity density score and the competition degree score into the geographical location score formula to obtain a geographical location score; The geographic location scoring formula is: G=ω T T+ω P P+ω B B-oh c C; Where G is the geographical location impact score, ω T ,ω P ,ω B and C All are weight coefficients, T is the traffic flow score, P is the public transportation quantity score, B is the commercial activity density score, and C is the competition degree score; Combining the number of users, the number of users of different age groups, the number of users of different genders and the geographical location scores, obtaining the initial vehicle dispatch number for each vehicle deployment area; The initial vehicle dispatch quantity for each vehicle deployment area is obtained by combining the number of users, the number of users of different age groups, the number of users of different genders and the geographical location scores, including: Substituting the number of users, the number of users of different age groups, the number of users of different genders and the geographical location score into the dispatch quantity calculation formula to obtain the initial vehicle dispatch quantity for each vehicle deployment area; The scheduling amount calculation formula is: Where C is the initial vehicle dispatch quantity of each vehicle deployment area, D is the vehicle dispatch quantity of the i-th vehicle deployment area, U is the number of users in the i-th vehicle deployment area, and U a [i] is the number of users in the i-th age group in the i-th vehicle deployment area, ω a [i] is the weight coefficient corresponding to the number of users in the i-th age group in the i-th vehicle deployment area, U g [j] is the number of users of specific gender j in the i-th vehicle deployment area, ω g [j] is the weight coefficient corresponding to the number of users of specific gender j in the i-th vehicle deployment area, U t [i, j] is the number of users of specific gender j in the i-th age group in the i-th vehicle deployment area, ω t [i, j] is the weight coefficient corresponding to the number of users of specific gender j in the i-th age group in the i-th vehicle deployment area, α and β are adjustment factors, and G is the geographic location score; The second adjustment module is used to adjust the initial vehicle dispatch quantity according to the vehicle flow data between the vehicle deployment areas in each time period to obtain the target vehicle dispatch quantity of each vehicle deployment area.

6. An electronic device, characterized in that: It 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 so that the electronic device executes the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 4.

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