A method and system for managing online car-hailing operation and maintenance

Abstract

This invention proposes a method and system for ride-hailing operation and maintenance management. The method includes: acquiring the location information of ride-hailing vehicles in real time and synchronizing the location information with trip data; acquiring the first ride-hailing order information based on the trip data, including the real-time location and estimated end time of the first order; acquiring a vehicle order acceptance decision after the first ride-hailing order is completed based on the first order information; and comparing the vehicle order acceptance decision with second ride-hailing order information to match the second ride-hailing order. The system includes a data synchronization module, an order information acquisition module, an order acceptance decision module, and an order matching module. This method and system can accurately match orders that satisfy both customers and drivers, saving vehicle idle time and customer waiting time.

Description

Technical Field

[0001] The present invention proposes an online car-hailing operation and maintenance management method and system, belonging to the technical field of online car-hailing operation and maintenance management. Background Art

[0002] Most online ride-hailing operations and management rely on intelligent network interaction between terminal devices and mobile APPs to provide services to customers. Customers place orders to terminal devices through mobile APPs, and the terminal devices send the mobile APPs assigned to drivers to learn the customer's location, pick up the customer at the location, and send him to the destination. Usually, the system matches orders based on whether the vehicle at the passenger's order location is free. However, in some suburban areas, there are fewer people and a lower density of online ride-hailing vehicles. When a passenger needs a car, he may have to wait for a long time, which makes people's online ride-hailing efficiency low and the experience worse. Summary of the invention

[0003] The present invention provides an online car-hailing operation and maintenance management method and system to solve the above technical problems:

[0004] The present invention provides a method for managing the operation and maintenance of an online car-hailing service, the method comprising:

[0005] Obtain the location information of online ride-hailing vehicles in real time and synchronize the location information with the trip data;

[0006] According to the trip data, obtaining the first order information of the online car-hailing service, the information including the real-time location of the first order and the estimated end time of the first order;

[0007] According to the first order information of the online car-hailing service, obtaining a vehicle order acceptance decision after the online car-hailing service completes the first order;

[0008] The second ride-hailing order is matched based on the comparison between the vehicle order acceptance decision and the second ride-hailing order information.

[0009] Furthermore, the location information of the online car-hailing vehicle is obtained in real time, and the location information is synchronized with the trip data, including:

[0010] Obtain the location information of the online car-hailing service in real time through GPS positioning, where the location information includes at least the order number and longitude and latitude data;

[0011] Using a relational database, a vehicle location information table and a travel data table are created, and the location information table and the travel data table are associated with each other through a vehicle ID so that the location information corresponds to the travel data one by one;

[0012] Monitor changes in the location information and the travel data through a scheduled task or event trigger mechanism, and synchronize the changed data to the relational database to obtain consistency between the location information and the travel data;

[0013] Through the API interface, the vehicle's location information and the corresponding travel data are obtained based on the vehicle ID information.

[0014] Further, according to the travel data, the first order information of the online car-hailing service is obtained, and the information includes the real-time location of the first order and the estimated end time of the first order, including:

[0015] Obtain the real-time location of the first order through the latitude and longitude information in the itinerary data;

[0016] Obtain the remaining mileage of the first order trip by comparing the real-time location of the first order with the destination location of the first order;

[0017] The estimated end time of the first order is obtained according to the total travel time of the trip.

[0018] Further, according to the first order information of the online car-hailing service, obtaining the vehicle order acceptance decision after the online car-hailing service completes the first order includes:

[0019] According to the first order destination location, obtaining the density of online-hailing vehicles at the geographical location of the vehicle through GPS positioning;

[0020] According to the online car-hailing order service system, obtain the number of waiting orders at the geographical location of the vehicle;

[0021] The vehicle order acceptance decision is obtained by analyzing the density of the online car-hailing vehicles and the number of waiting orders.

[0022] Further, matching the second ride-hailing order according to the comparison between the vehicle order acceptance decision and the second ride-hailing order information includes:

[0023] Obtaining the second ride-hailing order information, including the passenger's location, destination, and ride time;

[0024] According to the second car-hailing order information and the vehicle order acceptance decision, an analysis is performed, the number of waiting orders is traversed, and compared with the vehicle order acceptance decision to find the best matching vehicle.

[0025] The present invention proposes an online car-hailing operation and maintenance management system, the system comprising:

[0026] Data synchronization module, which obtains the location information of online ride-hailing vehicles in real time and synchronizes the location information with the trip data;

[0027] An order information acquisition module, which acquires the first order information of the online car-hailing service according to the travel data, wherein the information includes the real-time location of the first order and the estimated end time of the first order;

[0028] An order acceptance decision module, which obtains a vehicle order acceptance decision after the online car-hailing service completes the first order according to the first order information of the online car-hailing service;

[0029] The order matching module compares the vehicle order acceptance decision with the second car-hailing order information and matches the second car-hailing order.

[0030] Furthermore, the data synchronization module includes:

[0031] The GPS positioning module obtains the location information of the online car-hailing vehicle in real time through GPS positioning, and the location information includes at least the order number and longitude and latitude data;

[0032] A data association module, using a relational database, creates a vehicle location information table and a travel data table, and associates the location information table with the travel data table through a vehicle ID, so that the location information corresponds to the travel data one by one;

[0033] A data monitoring module monitors changes in the location information and the travel data through a scheduled task or event triggering mechanism, and synchronizes the changed data to the relational database to obtain consistency between the location information and the travel data;

[0034] The travel data module obtains the vehicle's location information and the corresponding travel data based on the vehicle ID information through the API interface.

[0035] Furthermore, the order information acquisition module includes:

[0036] The real-time location acquisition module acquires the real-time location of the first order through the latitude and longitude information in the itinerary data;

[0037] The remaining mileage acquisition module compares the real-time location of the first order with the destination location of the first order to obtain the remaining mileage of the first order trip;

[0038] The time prediction module obtains the estimated end time of the first order according to the total driving time of the trip.

[0039] Furthermore, the order decision module includes:

[0040] An online car-hailing density acquisition module, which acquires the online car-hailing density at the geographical location of the vehicle through GPS positioning according to the terminal location of the first order;

[0041] The waiting order statistics module obtains the number of waiting orders at the geographical location of the vehicle according to the online car-hailing order service system;

[0042] The order analysis module performs analysis based on the density of online car-hailing vehicles and the number of waiting orders to obtain the vehicle order acceptance decision.

[0043] Furthermore, the order matching module includes:

[0044] A second car booking order information acquisition module is used to acquire the second car booking order information, wherein the information includes the passenger's location, destination and car use time;

[0045] The order comparison module analyzes the second ride-hailing order information and the vehicle order acceptance decision, traverses the number of waiting orders, compares them with the vehicle order acceptance decision, and finds the best matching vehicle.

[0046] Beneficial effects of the present invention: By analyzing the travel data, the platform can predict the vehicle's driving path and expected end time, thereby optimizing the vehicle's scheduling and route planning, reducing idle time, and improving operational efficiency. The provision of real-time location information and expected end time allows passengers to better understand the real-time status of online car-hailing, reducing waiting time and uncertainty, thereby improving passenger satisfaction. The acquisition of real-time location information helps to promptly discover and solve potential safety problems, such as vehicle failures or traffic accidents. In addition, if any unexpected situation occurs, the real-time location information can be quickly used for rescue and accident handling. Combined with the vehicle order decision and the second car-hailing order information, the platform can more intelligently match orders and recommend passengers, thereby improving the success rate and matching degree of orders. The acquisition of real-time location information helps to ensure that the driver's working time and rest time are reasonably arranged, avoid fatigue driving, and thus protect the driver's rights and safety. In summary, the technical solution can bring many beneficial effects by acquiring the location information of online car-hailing and its travel data in real time, including improving service quality, optimizing route planning, improving passenger satisfaction, enhancing safety, realizing intelligent matching and recommendation, promoting sustainable development, enhancing data analysis and decision support, and protecting the rights and interests of drivers. These benefits will not only help improve the operational efficiency and user experience of the online ride-hailing industry, but also promote the healthy and sustainable development of the entire industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The figure is a schematic diagram of an online car-hailing operation and maintenance management method. DETAILED DESCRIPTION

[0048] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0049] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. The embodiments described are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0051] This embodiment provides an online car-hailing operation and maintenance management method, the method comprising:

[0052] Obtain the location information of online ride-hailing vehicles in real time and synchronize the location information with the trip data;

[0053] According to the trip data, obtaining the first order information of the online car-hailing service, the information including the real-time location of the first order and the estimated end time of the first order;

[0054] According to the first order information of the online car-hailing service, obtaining a vehicle order acceptance decision after the online car-hailing service completes the first order;

[0055] The second ride-hailing order is matched based on the comparison between the vehicle order acceptance decision and the second ride-hailing order information.

[0056] The working principle of the above technical solution is: by using technical means such as GPS positioning, the location information of the online car-hailing vehicle is obtained in real time. This location information can be obtained through the API interface of the online car-hailing platform or other data provision channels, and synchronized with the trip data. Ensure the real-time and accuracy of the location information and trip data. According to the trip data, obtain the first order information of the online car-hailing vehicle. This includes the real-time location and expected end time of the first order. Ensure that the information obtained matches the trip data accurately and ensure the real-time nature of the information. According to the first order information, obtain the vehicle's order acceptance decision after the online car-hailing vehicle ends the first order. This may involve the design of algorithms and rules to determine whether the vehicle accepts the order based on factors such as the vehicle's current location, expected end time, and vehicle status (idle, in service, under maintenance, etc.). Ensure the accuracy and efficiency of the order acceptance decision. According to the information of the second car-hailing order, compare it with the vehicle's order acceptance decision. This may involve the design of matching algorithms and rules to determine whether the vehicle can match the order based on factors such as the starting location, destination, and expected departure time of the second order. Ensure the accuracy and efficiency of the matching.

[0057] The effect of the above technical solution is: by obtaining the location information of the online car-hailing in real time, the operating efficiency and user experience of the online car-hailing platform can be improved. The platform can understand the location of the vehicle more accurately, so as to better dispatch and dispatch orders and reduce user waiting time. By synchronizing the location information with the trip data, the real-time and accuracy of the location information and the trip data can be ensured. This helps the platform to accurately monitor the vehicle status and manage orders, improve operational efficiency and service quality. Obtaining the first order information of the online car-hailing, including the real-time location and the expected end time, can provide the platform with more detailed vehicle status information. This helps to make decisions in vehicle scheduling, order management and user notification, and improve operational efficiency and user experience. By making vehicle order decisions based on the first order information, the operating efficiency and order matching accuracy of the platform can be improved. Based on factors such as the current location, expected end time and status of the vehicle, it is possible to better judge whether the vehicle is suitable for receiving orders, avoid invalid dispatching orders and improve the order matching success rate. By comparing the second car-hailing order information with the vehicle's order decision, the accuracy and efficiency of order matching can be improved. Based on factors such as the order’s starting location, destination, and estimated departure time, we can better determine whether the vehicle is suitable for accepting the order, thereby improving the order’s success matching rate and user satisfaction.

[0058] This embodiment provides an online car-hailing operation and maintenance management method, wherein the real-time acquisition of the location information of the online car-hailing vehicle and synchronization of the location information with the travel data include:

[0059] Using a relational database, a vehicle location information table and a travel data table are created, and the location information table and the travel data table are associated with each other through a vehicle ID so that the location information corresponds to the travel data one by one;

[0060] Monitor changes in the location information and the travel data through a scheduled task or event trigger mechanism, and synchronize the changed data to the relational database to obtain consistency between the location information and the travel data;

[0061] Through the API interface, the vehicle's location information and the corresponding travel data are obtained based on the vehicle ID information.

[0062] The working principle of the above technical solution is as follows: using a relational database, two tables are created, namely, a vehicle location information table and a travel data table. The vehicle location information table is used to store the real-time location information of the vehicle, and the travel data table is used to store the travel information of the vehicle. The two tables are associated through the vehicle ID to ensure the one-to-one correspondence between the location information and the travel data. Through a scheduled task or event trigger mechanism, the changes in the vehicle location information and travel data are monitored. For example, a scheduled task can be set to obtain the latest location information and travel data at regular intervals. Or, through an event trigger mechanism, when the location information or travel data changes, the changed data can be obtained immediately. The changed location information and travel data are synchronized to the relational database. By inserting or updating the latest location information and travel data into the corresponding table, it is ensured that the data in the database is consistent with the actual data. In this way, the consistency of the location information and travel data can be obtained for subsequent query and analysis. Through the API interface, the vehicle location information and corresponding travel data can be obtained according to the vehicle ID information. By calling the corresponding API interface and passing in the vehicle ID parameter, the vehicle location information and travel data can be queried and returned from the database. In this way, the real-time location and travel information of the vehicle can be easily obtained for subsequent processing and display.

[0063] The effect of the above technical solution is: by creating a vehicle location information table and a trip data table and associating them with the vehicle ID, the consistency and accuracy of the location information and trip data can be ensured. The scheduled task or event trigger mechanism can monitor data changes in a timely manner and synchronize the changed data to the relational database, thereby ensuring the real-time and accuracy of the data. Through the API interface, external systems or applications can obtain the vehicle's location information and trip data based on the vehicle ID information. This helps to improve the utilization of data, so that other systems or applications can use this data for further analysis, decision-making or services. The privacy and security of data can be ensured through the design of the relational database and the data synchronization mechanism. Encrypting sensitive data and restricting access rights can prevent data leakage and unauthorized access. At the same time, the authentication and authorization mechanism of the API interface can ensure that only authorized personnel can access and operate the data. Data exchange and interoperation with other systems are carried out through the API interface. This design makes the system have better scalability and integration capabilities, and can adapt to the needs of future business growth and technological changes. Real-time acquisition of the location information and trip data of online car-hailing can improve the quality and response speed of online car-hailing services. By monitoring the location and travel status of vehicles in real time, vehicles can be better dispatched, route planning can be optimized, order matching can be improved, etc., thereby improving the overall service quality. By obtaining location information and travel data in real time, the vehicle's driving trajectory and resource utilization can be better managed, and unnecessary driving time and energy consumption can be reduced. This helps to reduce carbon emissions and energy consumption and promote sustainable development. A large amount of vehicle location information and travel data can be collected and analyzed. Real-time acquisition of vehicle location information and travel data allows passengers to better understand the real-time status and estimated arrival time of online ride-hailing vehicles. This helps to improve passenger satisfaction and trust and enhance user experience. In summary, the technical solution has a variety of beneficial effects, including improving data consistency and accuracy, enhancing data security, improving system scalability and integration capabilities, improving service quality, promoting sustainable development, enhancing data analysis and decision support, and improving user experience. These effects help promote the intelligent upgrading and development of the online ride-hailing industry.

[0064] This embodiment provides an online car-hailing operation and maintenance management method, wherein the first order information of the online car-hailing is obtained according to the trip data, and the information includes the real-time location of the first order and the estimated end time of the first order, including:

[0065] Obtain the real-time location of the first order through the latitude and longitude information in the itinerary data;

[0066] Obtain the remaining mileage of the first order trip by comparing the real-time location of the first order with the destination location of the first order;

[0067] The estimated end time of the first order is obtained according to the total travel time of the trip.

[0068] The working principle of the above technical solution is: obtain the longitude and latitude information of the first order from the itinerary data. These longitude and latitude information can be obtained through GPS positioning devices or other location service providers. By parsing the itinerary data, the real-time location information of the first order can be obtained. Compare the real-time location of the first order with the destination location. The destination location can be a destination specified by the user or a preset target location. By calculating the distance between the real-time location and the destination location, the remaining mileage of the first order can be obtained. According to the total driving time of the trip, the end time of the first order can be estimated. The total driving time can be calculated by the start time and the estimated arrival time in the itinerary data. By calculating the ratio of the remaining mileage of the trip to the driving speed, the time required for the remaining trip can be estimated, and adding the current time, the estimated end time can be obtained.

[0069] The effect of the above technical solution is: through the GPS positioning device or other location service provider, the latitude and longitude information of the first order can be obtained in real time, so as to monitor the location of the vehicle in real time. Combined with the destination location, the distance between the real-time location and the destination location can be calculated, and the end time of the first order can be further estimated. This real-time monitoring and prediction capability helps to better dispatch vehicles, optimize route planning and improve service response speed. By parsing the trip data, the real-time location and remaining mileage of the first order can be obtained. This helps to understand the driving status and trip progress of the vehicle, and provides a basis for trip management and route optimization. According to the remaining mileage and driving speed, the time required for the remaining trip can be estimated, providing data support for advance planning. Through real-time monitoring and prediction, the needs of passengers can be better met and the response speed and accuracy of the service can be improved. The estimated end time can be provided to passengers so that they can better arrange their time. This improvement in service quality helps to enhance the trust and satisfaction of passengers. The data and analysis results provided by the technical solution can provide support for decision-making. For example, according to the remaining mileage and driving speed of the trip, it can be determined whether it is necessary to dispatch other vehicles in advance for relay, or adjust the priority of the order according to the expected end time. These decisions help to improve operational efficiency and resource utilization. Through this technical solution, a large amount of travel data can be collected and analyzed to gain insights into driving trajectory, driving speed, order completion, etc. These data-driven insights can help understand market demand, user behavior and operational bottlenecks, and provide a basis for future strategy formulation and improvement. Real-time monitoring and prediction capabilities help optimize vehicle driving trajectory and resource utilization, reducing unnecessary driving time and energy consumption. This helps reduce carbon emissions and energy consumption, and promotes sustainable development and environmental protection.

[0070] This embodiment provides an online car-hailing operation and maintenance management method, wherein obtaining a vehicle order acceptance decision after the online car-hailing ends the first order according to the first order information of the online car-hailing comprises:

[0071] According to the first order destination location, obtaining the density of online-hailing vehicles at the geographical location of the vehicle through GPS positioning;

[0072] According to the online car-hailing order service system, obtain the number of waiting orders at the geographical location of the vehicle;

[0073] The vehicle order acceptance decision is obtained by analyzing the density of the online car-hailing vehicle and the number of waiting orders, wherein

[0074]

[0075] Among them, C represents the order decision, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, M S represents the real-time density of online ride-hailing vehicles, M 平 represents the average density of online ride-hailing vehicles every five minutes, D s Indicates the real-time quantity of waiting orders, D 平 Indicates the average number of waiting orders every five minutes.

[0076] The working principle of the above technical solution is: according to the first order terminal location, the current geographic location of the vehicle is obtained by GPS positioning. Then, the density of online car-hailing vehicles near the geographic location is calculated using the corresponding algorithms and data. This can be achieved by counting the number of online car-hailing vehicles within a certain range nearby, such as calculating the number of online car-hailing vehicles within a radius of 1 km nearby, and obtaining the number of waiting orders at the geographic location of the vehicle through the online car-hailing order service system. This can be achieved through real-time communication and data interaction with the order service system. The system can query the waiting orders nearby and count the number. According to the density of online car-hailing vehicles and the number of waiting orders, an analysis is performed to determine the order acceptance decision. The specific analysis method can be defined and adjusted according to actual conditions and needs. One possible analysis method is to compare the relationship between the density of online car-hailing vehicles and the number of waiting orders, and formulate an order acceptance decision strategy according to different situations. For example, if the density of online car-hailing vehicles is low and the number of waiting orders is high, the vehicle can be encouraged to accept orders; if the density of online car-hailing vehicles is high and the number of waiting orders is low, the opportunity to accept orders can be reduced. According to the analysis results, the order acceptance decision is implemented. The vehicle order acceptance decision is obtained by analyzing the density of online car-hailing vehicles and the number of waiting orders, where

[0077]

[0078] Among them, C represents the order decision, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, MS represents the real-time density of online ride-hailing vehicles, M 平 represents the average density of online ride-hailing vehicles every five minutes, D s Indicates the real-time quantity of waiting orders, D 平 It indicates the average number of waiting orders every five minutes. Based on the result of the order acceptance decision, the system can send corresponding instructions or prompts to the vehicle to inform the vehicle whether to accept the order.

[0079] The effect of the above technical solution is: first, it uses GPS positioning technology to obtain the real-time location of the vehicle. Then, based on this location, the system can calculate the density M of online car-hailing vehicles in the area. S In addition, taking into account the time factor, the system also calculates the average online car-hailing density M every five minutes. 平 , providing more comprehensive information for decision making. The system obtains the number of waiting orders D at the geographical location of the vehicle from the online car-hailing order service system s Similarly, in order to better reflect the situation, the system also calculates the average number of waiting orders D every five minutes. 平 The above-mentioned online car-hailing density and the number of waiting orders are calculated by a specific formula

[0080]

[0081] The weights w1 and w2 can be adjusted according to actual conditions. This formula aims to comprehensively consider the two factors of online car-hailing density and the number of waiting orders, so as to make smarter order-taking decisions. Through the above method, the system can make order-taking decisions in real time and dynamically based on the density of online car-hailing and the number of waiting orders. This method helps to improve the vehicle's order-taking efficiency and overall service quality. By making order-taking decisions based on the real-time density of online car-hailing and the number of waiting orders, the system can more efficiently assign orders to vehicles and avoid excessive congestion or long waiting times. At the same time, it can also improve the user experience, reduce user waiting time and increase the order satisfaction rate.

[0082] This embodiment provides an online car-hailing operation and maintenance management method, wherein the comparison between the vehicle order acceptance decision and the second car-hailing order information is performed to match the second car-hailing order, including:

[0083] Obtaining the second ride-hailing order information, including the passenger's location, destination, and ride time;

[0084] According to the second car-hailing order information and the vehicle order decision, the number of waiting orders is traversed and compared with the vehicle order decision to find the best matching vehicle, where

[0085]

[0086] Among them, P represents the vehicle matching value, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, M S represents the real-time density of online ride-hailing vehicles, M 平 represents the average density of online ride-hailing vehicles every five minutes, D s Indicates the real-time quantity of waiting orders, D 平 represents the average number of waiting orders every five minutes, Z c represents the initial position of the second passenger, Z d Indicates the second passenger's arrival location, Z 车 Indicates the location of the online car-hailing vehicle, T k represents the time the second passenger uses the car, T C It represents the time when the online-hailing car arrives at the passenger’s initial location, a and b represent weights, a=0.7, b=0.3, and β represents the adjustment coefficient, which is affected by the density of online-hailing cars.

[0087] The working principle of the above technical solution is: first obtain the second car-hailing order information, which includes the passenger's location, destination and car-use time; analyze the second car-hailing order information and the vehicle order decision, traverse the number of waiting orders, compare with the vehicle order decision, and find the best matching vehicle, where

[0088]

[0089] Among them, P represents the vehicle matching value, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, M S represents the real-time density of online ride-hailing vehicles, M 平 represents the average density of online ride-hailing vehicles every five minutes, D s Indicates the real-time quantity of waiting orders, D 平 represents the average number of waiting orders every five minutes, Z c represents the initial position of the second passenger, Z d Indicates the second passenger's arrival location, Z 车 Indicates the location of the online car-hailing vehicle, T k represents the time the second passenger uses the car, T C It represents the time when the online-hailing car arrives at the passenger’s initial location, a and b represent weights, a=0.7, b=0.3, and β represents the adjustment coefficient, which is affected by the density of online-hailing cars.

[0090] The effect of the above technical solution is: by connecting with the online car-hailing order service system, relevant information of the second car-hailing order can be obtained, including the passenger's location, destination and car-use time. This information can help the system understand the passenger's needs and itinerary requirements. Compare and analyze the second car-hailing order information with the vehicle's order-taking decision. According to the passenger's location, destination and car-use time, the system can evaluate whether the vehicle is suitable for accepting orders and make corresponding matches based on the vehicle's order-taking decision. The system can traverse the number of waiting orders based on the information of the second car-hailing order and find the vehicle that best matches the order. This can be achieved by comparing the passenger's location, destination and car-use time with the relevant information of the waiting order. By traversing the number of waiting orders and comparing it with the vehicle's order-taking decision, the system can find the best matching vehicle. This vehicle can meet the passenger's needs and meet the vehicle's order-taking decision. Through the above technology, analysis and matching can be performed based on the information of the second car-hailing order and the vehicle's order-taking decision to find the best matching vehicle. This can improve the efficiency of the online car-hailing system, ensure that passengers can find suitable vehicles as soon as possible, and enhance the user experience. At the same time, it can also help vehicles better accept orders and improve the order satisfaction rate.

[0091] This embodiment provides an online car-hailing operation and maintenance management system, the system comprising:

[0092] Data synchronization module, which obtains the location information of online ride-hailing vehicles in real time and synchronizes the location information with the trip data;

[0093] An order information acquisition module, which acquires the first order information of the online car-hailing service according to the travel data, wherein the information includes the real-time location of the first order and the estimated end time of the first order;

[0094] An order acceptance decision module, which obtains a vehicle order acceptance decision after the online car-hailing service completes the first order according to the first order information of the online car-hailing service;

[0095] The order matching module compares the vehicle order acceptance decision with the second car-hailing order information and matches the second car-hailing order.

[0096] The working principle of the above technical solution is: by using technical means such as GPS positioning, the location information of the online car-hailing vehicle is obtained in real time. This location information can be obtained through the API interface of the online car-hailing platform or other data provision channels, and synchronized with the trip data. Ensure the real-time and accuracy of the location information and trip data. According to the trip data, obtain the first order information of the online car-hailing vehicle. This includes the real-time location and expected end time of the first order. Ensure that the information obtained matches the trip data accurately and ensure the real-time nature of the information. According to the first order information, obtain the vehicle's order acceptance decision after the online car-hailing vehicle ends the first order. This may involve the design of algorithms and rules to determine whether the vehicle accepts the order based on factors such as the vehicle's current location, expected end time, and vehicle status (idle, in service, under maintenance, etc.). Ensure the accuracy and efficiency of the order acceptance decision. According to the information of the second car-hailing order, compare it with the vehicle's order acceptance decision. This may involve the design of matching algorithms and rules to determine whether the vehicle can match the order based on factors such as the starting location, destination, and expected departure time of the second order. Ensure the accuracy and efficiency of the matching.

[0097] The effect of the above technical solution is: by obtaining the location information of the online car-hailing in real time, the operating efficiency and user experience of the online car-hailing platform can be improved. The platform can understand the location of the vehicle more accurately, so as to better dispatch and dispatch orders and reduce user waiting time. By synchronizing the location information with the trip data, the real-time and accuracy of the location information and the trip data can be ensured. This helps the platform to accurately monitor the vehicle status and manage orders, improve operational efficiency and service quality. Obtaining the first order information of the online car-hailing, including the real-time location and the expected end time, can provide the platform with more detailed vehicle status information. This helps to make decisions in vehicle scheduling, order management and user notification, and improve operational efficiency and user experience. By making vehicle order decisions based on the first order information, the operating efficiency and order matching accuracy of the platform can be improved. Based on factors such as the current location, expected end time and status of the vehicle, it is possible to better judge whether the vehicle is suitable for receiving orders, avoid invalid dispatching orders and improve the order matching success rate. By comparing the second car-hailing order information with the vehicle's order decision, the accuracy and efficiency of order matching can be improved. Based on factors such as the order’s starting location, destination, and estimated departure time, we can better determine whether the vehicle is suitable for accepting the order, thereby improving the order’s success matching rate and user satisfaction.

[0098] In this embodiment, an online car-hailing operation and maintenance management system is provided, wherein the data synchronization module includes:

[0099] The GPS positioning module obtains the location information of the online car-hailing vehicle in real time through GPS positioning, and the location information includes at least the order number and longitude and latitude data;

[0100] A data association module, using a relational database, creates a vehicle location information table and a travel data table, and associates the location information table with the travel data table through a vehicle ID, so that the location information corresponds to the travel data one by one;

[0101] The data monitoring module monitors the changes of the location information and the travel data through a scheduled task or event triggering mechanism, and synchronizes the changed data to the relational database to obtain the consistency of the location information and the travel data.

[0102] The travel data module obtains the vehicle's location information and the corresponding travel data based on the vehicle ID information through the API interface.

[0103] The working principle of the above technical solution is as follows: using a relational database, two tables are created, namely, a vehicle location information table and a travel data table. The vehicle location information table is used to store the real-time location information of the vehicle, and the travel data table is used to store the travel information of the vehicle. The two tables are associated through the vehicle ID to ensure the one-to-one correspondence between the location information and the travel data. Through a scheduled task or event trigger mechanism, the changes in the vehicle location information and travel data are monitored. For example, a scheduled task can be set to obtain the latest location information and travel data at regular intervals. Or, through an event trigger mechanism, when the location information or travel data changes, the changed data can be obtained immediately. The changed location information and travel data are synchronized to the relational database. By inserting or updating the latest location information and travel data into the corresponding table, it is ensured that the data in the database is consistent with the actual data. In this way, the consistency of the location information and travel data can be obtained for subsequent query and analysis. Through the API interface, the vehicle location information and corresponding travel data can be obtained according to the vehicle ID information. By calling the corresponding API interface and passing in the vehicle ID parameter, the vehicle location information and travel data can be queried and returned from the database. In this way, the real-time location and travel information of the vehicle can be easily obtained for subsequent processing and display.

[0104] The effect of the above technical solution is: by creating a vehicle location information table and a trip data table and associating them with the vehicle ID, the consistency and accuracy of the location information and trip data can be ensured. The scheduled task or event trigger mechanism can monitor data changes in a timely manner and synchronize the changed data to the relational database, thereby ensuring the real-time and accuracy of the data. Through the API interface, external systems or applications can obtain the vehicle's location information and trip data based on the vehicle ID information. This helps to improve the utilization of data, so that other systems or applications can use this data for further analysis, decision-making or services. The privacy and security of data can be ensured through the design of the relational database and the data synchronization mechanism. Encrypting sensitive data and restricting access rights can prevent data leakage and unauthorized access. At the same time, the authentication and authorization mechanism of the API interface can ensure that only authorized personnel can access and operate the data. Data exchange and interoperation with other systems are carried out through the API interface. This design makes the system have better scalability and integration capabilities, and can adapt to the needs of future business growth and technological changes. Real-time acquisition of the location information and trip data of online car-hailing can improve the quality and response speed of online car-hailing services. By monitoring the location and travel status of vehicles in real time, vehicles can be better dispatched, route planning can be optimized, order matching can be improved, etc., thereby improving the overall service quality. By obtaining location information and travel data in real time, the vehicle's driving trajectory and resource utilization can be better managed, and unnecessary driving time and energy consumption can be reduced. This helps to reduce carbon emissions and energy consumption and promote sustainable development. A large amount of vehicle location information and travel data can be collected and analyzed. Real-time acquisition of vehicle location information and travel data allows passengers to better understand the real-time status and estimated arrival time of online ride-hailing vehicles. This helps to improve passenger satisfaction and trust and enhance user experience. In summary, the technical solution has a variety of beneficial effects, including improving data consistency and accuracy, enhancing data security, improving system scalability and integration capabilities, improving service quality, promoting sustainable development, enhancing data analysis and decision support, and improving user experience. These effects help promote the intelligent upgrading and development of the online ride-hailing industry.

[0105] In this embodiment, an online car-hailing operation and maintenance management system is provided, wherein the order information acquisition module includes:

[0106] The real-time location acquisition module acquires the real-time location of the first order through the latitude and longitude information in the itinerary data;

[0107] The remaining mileage acquisition module compares the real-time location of the first order with the destination location of the first order to obtain the remaining mileage of the first order trip;

[0108] The time prediction module obtains the estimated end time of the first order according to the total driving time of the trip.

[0109] The working principle of the above technical solution is: obtain the longitude and latitude information of the first order from the itinerary data. These longitude and latitude information can be obtained through GPS positioning devices or other location service providers. By parsing the itinerary data, the real-time location information of the first order can be obtained. Compare the real-time location of the first order with the destination location. The destination location can be a destination specified by the user or a preset target location. By calculating the distance between the real-time location and the destination location, the remaining mileage of the first order can be obtained. According to the total driving time of the trip, the end time of the first order can be estimated. The total driving time can be calculated by the start time and the estimated arrival time in the itinerary data. By calculating the ratio of the remaining mileage of the trip to the driving speed, the time required for the remaining trip can be estimated, and adding the current time, the estimated end time can be obtained.

[0110] The effect of the above technical solution is: through the GPS positioning device or other location service provider, the latitude and longitude information of the first order can be obtained in real time, so as to monitor the location of the vehicle in real time. Combined with the destination location, the distance between the real-time location and the destination location can be calculated, and the end time of the first order can be further estimated. This real-time monitoring and prediction capability helps to better dispatch vehicles, optimize route planning and improve service response speed. By parsing the trip data, the real-time location and remaining mileage of the first order can be obtained. This helps to understand the driving status and trip progress of the vehicle, and provides a basis for trip management and route optimization. According to the remaining mileage and driving speed, the time required for the remaining trip can be estimated, providing data support for advance planning. Through real-time monitoring and prediction, the needs of passengers can be better met and the response speed and accuracy of the service can be improved. The estimated end time can be provided to passengers so that they can better arrange their time. This improvement in service quality helps to enhance the trust and satisfaction of passengers. The data and analysis results provided by the technical solution can provide support for decision-making. For example, according to the remaining mileage and driving speed of the trip, it can be determined whether it is necessary to dispatch other vehicles in advance for relay, or adjust the priority of the order according to the expected end time. These decisions help to improve operational efficiency and resource utilization. Through this technical solution, a large amount of travel data can be collected and analyzed to gain insights into driving trajectory, driving speed, order completion, etc. These data-driven insights can help understand market demand, user behavior and operational bottlenecks, and provide a basis for future strategy formulation and improvement. Real-time monitoring and prediction capabilities help optimize vehicle driving trajectory and resource utilization, reducing unnecessary driving time and energy consumption. This helps reduce carbon emissions and energy consumption, and promotes sustainable development and environmental protection.

[0111] In this embodiment, an online car-hailing operation and maintenance management system is provided, wherein the order decision module includes:

[0112] An online car-hailing density acquisition module, which acquires the online car-hailing density at the geographical location of the vehicle through GPS positioning according to the terminal location of the first order;

[0113] The waiting order statistics module obtains the number of waiting orders at the geographical location of the vehicle according to the online car-hailing order service system;

[0114] The order analysis module analyzes the density of online car-hailing vehicles and the number of waiting orders to obtain the vehicle order acceptance decision, where

[0115]

[0116] Among them, C represents the order decision, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, M S represents the real-time density of online ride-hailing vehicles, M 平 represents the average density of online ride-hailing vehicles every five minutes, D s Indicates the real-time quantity of waiting orders, D 平 Indicates the average number of waiting orders every five minutes.

[0117] The working principle of the above technical solution is: according to the first order terminal location, the current geographic location of the vehicle is obtained by GPS positioning. Then, the density of online car-hailing vehicles near the geographic location is calculated using the corresponding algorithms and data. This can be achieved by counting the number of online car-hailing vehicles within a certain range nearby, such as calculating the number of online car-hailing vehicles within a radius of 1 km nearby, and obtaining the number of waiting orders at the geographic location of the vehicle through the online car-hailing order service system. This can be achieved through real-time communication and data interaction with the order service system. The system can query the waiting orders nearby and count the number. According to the density of online car-hailing vehicles and the number of waiting orders, an analysis is performed to determine the order acceptance decision. The specific analysis method can be defined and adjusted according to actual conditions and needs. One possible analysis method is to compare the relationship between the density of online car-hailing vehicles and the number of waiting orders, and formulate an order acceptance decision strategy according to different situations. For example, if the density of online car-hailing vehicles is low and the number of waiting orders is high, the vehicle can be encouraged to accept orders; if the density of online car-hailing vehicles is high and the number of waiting orders is low, the opportunity to accept orders can be reduced. According to the analysis results, the order acceptance decision is implemented. The vehicle order acceptance decision is obtained by analyzing the density of online car-hailing vehicles and the number of waiting orders, where

[0118]

[0119] Among them, C represents the order decision, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, M S represents the real-time density of online ride-hailing vehicles, M 平 represents the average density of online ride-hailing vehicles every five minutes, D sIndicates the real-time quantity of waiting orders, D 平 It indicates the average number of waiting orders every five minutes. Based on the result of the order acceptance decision, the system can send corresponding instructions or prompts to the vehicle to inform the vehicle whether to accept the order.

[0120] The effect of the above technical solution is: first, it uses GPS positioning technology to obtain the real-time location of the vehicle. Then, based on this location, the system can calculate the density M of online car-hailing vehicles in the area. S In addition, taking into account the time factor, the system also calculates the average online car-hailing density M every five minutes. 平 , providing more comprehensive information for decision making. The system obtains the number of waiting orders D at the geographical location of the vehicle from the online car-hailing order service system s Similarly, in order to better reflect the situation, the system also calculates the average number of waiting orders D every five minutes. 平 The above-mentioned online car-hailing density and the number of waiting orders are calculated by a specific formula

[0121]

[0122] The weights w1 and w2 can be adjusted according to actual conditions. This formula aims to comprehensively consider the two factors of online car-hailing density and the number of waiting orders, so as to make smarter order-taking decisions. Through the above method, the system can make order-taking decisions in real time and dynamically based on the density of online car-hailing and the number of waiting orders. This method helps to improve the vehicle's order-taking efficiency and overall service quality. By making order-taking decisions based on the real-time density of online car-hailing and the number of waiting orders, the system can more efficiently assign orders to vehicles and avoid excessive congestion or long waiting times. At the same time, it can also improve the user experience, reduce user waiting time and increase the order satisfaction rate.

[0123] In this embodiment, an online car-hailing operation and maintenance management system is provided, wherein the order matching module includes:

[0124] A second ride-hailing order information acquisition module, which acquires the second ride-hailing order information, the information including the passenger's location, destination, and passenger demand;

[0125] The order comparison module analyzes the second car-hailing order information and the vehicle order acceptance decision, traverses the number of waiting orders, compares it with the vehicle order acceptance decision, and finds the best matching vehicle, wherein

[0126]

[0127] Among them, P represents the vehicle matching value, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, M S represents the real-time density of online ride-hailing vehicles, M 平represents the average density of online ride-hailing vehicles every five minutes, D s Indicates the real-time quantity of waiting orders, D 平 represents the average number of waiting orders every five minutes, Z c represents the initial position of the second passenger, Z d Indicates the second passenger's arrival location, Z 车 Indicates the location of the online car-hailing vehicle, T k represents the time the second passenger uses the car, T C It represents the time when the online-hailing car arrives at the passenger’s initial location, a and b represent weights, a=0.7, b=0.3, and β represents the adjustment coefficient, which is affected by the density of online-hailing cars.

[0128] The working principle of the above technical solution is: first obtain the second car-hailing order information, which includes the passenger's location, destination and car-use time; analyze the second car-hailing order information and the vehicle order decision, traverse the number of waiting orders, compare with the vehicle order decision, and find the best matching vehicle, where

[0129]

[0130] Among them, P represents the vehicle matching value, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, M S represents the real-time density of online ride-hailing vehicles, M 平 represents the average density of online ride-hailing vehicles every five minutes, D s Indicates the real-time quantity of waiting orders, D 平 represents the average number of waiting orders every five minutes, Z c represents the initial position of the second passenger, Z d Indicates the second passenger's arrival location, Z 车 Indicates the location of the online car-hailing vehicle, T k represents the time the second passenger uses the car, T C It represents the time when the online-hailing car arrives at the passenger’s initial location, a and b represent weights, a=0.7, b=0.3, and β represents the adjustment coefficient, which is affected by the density of online-hailing cars.

[0131] The effect of the above technical solution is: by connecting with the online car-hailing order service system, relevant information of the second car-hailing order can be obtained, including the passenger's location, destination and car-use time. This information can help the system understand the passenger's needs and itinerary requirements. Compare and analyze the second car-hailing order information with the vehicle's order-taking decision. According to the passenger's location, destination and car-use time, the system can evaluate whether the vehicle is suitable for accepting orders and make corresponding matches based on the vehicle's order-taking decision. The system can traverse the number of waiting orders based on the information of the second car-hailing order and find the vehicle that best matches the order. This can be achieved by comparing the passenger's location, destination and car-use time with the relevant information of the waiting order. By traversing the number of waiting orders and comparing it with the vehicle's order-taking decision, the system can find the best matching vehicle. This vehicle can meet the passenger's needs and meet the vehicle's order-taking decision. Through the above technology, analysis and matching can be performed based on the information of the second car-hailing order and the vehicle's order-taking decision to find the best matching vehicle. This can improve the efficiency of the online car-hailing system, ensure that passengers can find suitable vehicles as soon as possible, and enhance the user experience. At the same time, it can also help vehicles better accept orders and improve the order satisfaction rate.

[0132] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for managing the operation and maintenance of online car-hailing services, characterized in that: The method comprises: Obtain the location information of online ride-hailing vehicles in real time and synchronize the location information with the trip data; According to the trip data, obtaining the first order information of the online car-hailing service, the information including the real-time location of the first order and the estimated end time of the first order; According to the first order information of the online car-hailing service, obtaining a vehicle order acceptance decision after the online car-hailing service completes the first order; Wherein, according to the first order information of the online car-hailing service, obtaining the vehicle order acceptance decision after the online car-hailing service completes the first order includes: According to the first order destination location, obtaining the density of online-hailing vehicles at the geographical location of the vehicle through GPS positioning; According to the online car-hailing order service system, obtain the number of waiting orders at the geographical location of the vehicle; The vehicle order acceptance decision is obtained by analyzing the density of the online car-hailing vehicle and the number of waiting orders, wherein Among them, C represents the order decision, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, M S represents the real-time density of online ride-hailing vehicles, M 平 represents the average density of online ride-hailing vehicles every five minutes, D s Indicates the real-time quantity of waiting orders, D 平 Indicates the average number of waiting orders every five minutes; Matching the second ride-hailing order according to the comparison between the vehicle order acceptance decision and the second ride-hailing order information; Wherein, matching the second ride-hailing order according to the comparison between the vehicle order acceptance decision and the second ride-hailing order information includes: Obtaining the second ride-hailing order information, including the passenger's location, destination, and ride time; According to the second car-hailing order information and the vehicle order decision, the number of waiting orders is traversed and compared with the vehicle order decision to find the best matching vehicle, where Among them, P represents the vehicle matching value, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, M S represents the real-time density of online ride-hailing vehicles, M 平 represents the average density of online ride-hailing vehicles every five minutes, D s Indicates the real-time quantity of waiting orders, D 平 represents the average number of waiting orders every five minutes, Z c represents the initial position of the second passenger, Z d Indicates the second passenger's arrival location, Z 车 Indicates the location of the online car-hailing vehicle, T k represents the time the second passenger uses the car, T C It represents the time when the online-hailing car arrives at the passenger’s initial location, a and b represent weights, a=0.7, b=0.3, and β represents the adjustment coefficient, which is affected by the density of online-hailing cars.

2. According to claim 1, a method for managing the operation and maintenance of online car-hailing service, characterized in that: Obtain the location information of online ride-hailing vehicles in real time and synchronize the location information with the trip data, including: Obtain the location information of the online car-hailing service in real time through GPS positioning, where the location information includes at least the order number and longitude and latitude data; Using a relational database, a vehicle location information table and a travel data table are created, and the location information table and the travel data table are associated with each other through a vehicle ID so that the location information corresponds to the travel data one by one; Monitor changes in the location information and the travel data through a scheduled task or event trigger mechanism, and synchronize the changed data to the relational database to obtain consistency between the location information and the travel data; Through the API interface, the vehicle's location information and the corresponding travel data are obtained based on the vehicle ID information.

3. According to claim 1, a method for managing the operation and maintenance of online car-hailing service, characterized in that: According to the trip data, the first order information of the online car-hailing service is obtained, wherein the information includes the real-time location of the first order and the estimated end time of the first order, including: Obtain the real-time location of the first order through the latitude and longitude information in the itinerary data; Obtain the remaining mileage of the first order trip by comparing the real-time location of the first order with the destination location of the first order; The estimated end time of the first order is obtained according to the total travel time of the trip.

4. An online car-hailing operation and maintenance management system, characterized in that: The system comprises: Data synchronization module, which obtains the location information of online ride-hailing vehicles in real time and synchronizes the location information with the trip data; An order information acquisition module, which acquires the first order information of the online car-hailing service according to the travel data, wherein the information includes the real-time location of the first order and the estimated end time of the first order; An order acceptance decision module, which obtains a vehicle order acceptance decision after the online car-hailing service completes the first order according to the first order information of the online car-hailing service; Wherein, the order decision module includes: An online car-hailing density acquisition module, which acquires the online car-hailing density at the geographical location of the vehicle through GPS positioning according to the terminal location of the first order; The waiting order statistics module obtains the number of waiting orders at the geographical location of the vehicle according to the online car-hailing order service system; The order analysis module analyzes the density of online car-hailing vehicles and the number of waiting orders to obtain the vehicle order acceptance decision, where Among them, C represents the order decision, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, M S represents the real-time density of online ride-hailing vehicles, M 平 represents the average density of online ride-hailing vehicles every five minutes, D s Indicates the real-time quantity of waiting orders, D 平 Indicates the average number of waiting orders every five minutes; An order matching module, which compares the vehicle order acceptance decision with the second car-hailing order information and matches the second car-hailing order; Wherein, matching the second ride-hailing order according to the comparison between the vehicle order acceptance decision and the second ride-hailing order information includes: Obtaining the second ride-hailing order information, including the passenger's location, destination, and ride time; According to the second car-hailing order information and the vehicle order decision, the number of waiting orders is traversed and compared with the vehicle order decision to find the best matching vehicle, where Among them, P represents the vehicle matching value, w1 and w2 represent weights, w1 = 0.6, w2 = 0.4, M S represents the real-time density of online ride-hailing vehicles, M 平 represents the average density of online ride-hailing vehicles every five minutes, D s Indicates the real-time quantity of waiting orders, D 平 represents the average number of waiting orders every five minutes, Z c represents the initial position of the second passenger, Z d Indicates the second passenger's arrival location, Z 车 Indicates the location of the online car-hailing vehicle, T k represents the time the second passenger uses the car, T C It represents the time when the online-hailing car arrives at the passenger’s initial location, a and b represent weights, a=0.7, b=0.3, and β represents the adjustment coefficient, which is affected by the density of online-hailing cars.

5. According to claim 4, the online car-hailing operation and maintenance management system is characterized in that: The data synchronization module comprises: The GPS positioning module obtains the location information of the online car-hailing vehicle in real time through GPS positioning, and the location information includes at least the order number and longitude and latitude data; A data association module, using a relational database, creates a vehicle location information table and a travel data table, and associates the location information table with the travel data table through a vehicle ID, so that the location information corresponds to the travel data one by one; A data monitoring module monitors changes in the location information and the travel data through a scheduled task or event triggering mechanism, and synchronizes the changed data to the relational database to obtain consistency between the location information and the travel data; The travel data module obtains the vehicle's location information and the corresponding travel data based on the vehicle ID information through the API interface.

6. According to claim 4, the online car-hailing operation and maintenance management system is characterized in that: The order information acquisition module includes: The real-time location acquisition module acquires the real-time location of the first order through the latitude and longitude information in the itinerary data; A remaining mileage acquisition module is used to obtain the remaining mileage of the first order trip by comparing the real-time location of the first order with the destination location of the first order; The time prediction module obtains the estimated end time of the first order according to the total driving time of the trip.

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