Designated driving service management method, electronic equipment and storage medium

Abstract

The invention relates to the technical field of transportation, and discloses a designated driving service management method, electronic equipment and a storage medium, and the method comprises the following steps: receiving a designated driving request initiated by a user; generating a designated driving order according to the received designated driving request; the designated driver meets the condition according to the designated driving request; the driving position and the driving speed of the designated driver are tracked in real time through a GPS system, and real-time route adjustment is carried out according to the road condition information; according to the actual driving distance, the service time and the charging standard, calculating service fees and generating a settlement list; historical data and user feedback are collected, and a designated driving service process and a scheduling strategy are optimized through data analysis. According to the invention, by introducing an intelligent scheduling system based on data driving and a real-time road condition analysis and dynamic path adjustment technology, the efficiency and response speed of designated driving service are significantly improved. A market demand fluctuation optimization pricing strategy is combined, an information island is broken through comprehensive data integration, and data sharing and cooperative operation of all departments are ensured.

Description

Technical Field

[0001] The present invention relates to the field of transportation technology, and in particular to a designated driver service management method, electronic equipment, and storage medium. Background Art

[0002] Designated driver services have become an indispensable part of daily life. With changing lifestyles and increasing travel needs, the industry has grown rapidly. However, traditional designated driver service management often relies on manual scheduling and phone calls. This approach is inefficient and prone to errors, leading to poor customer experience and service delays. Especially during peak periods and special circumstances, manual processing lacks flexibility and responsiveness, often failing to respond promptly to market demand fluctuations, seriously impacting overall service quality.

[0003] To address these issues, in recent years, an increasing number of designated driver service platforms have begun using mobile applications (APPs) to manage and dispatch orders. This platform-based management approach can achieve a certain degree of automation and informatization, improving service efficiency. However, while this approach has addressed the efficiency issues of traditional manual dispatching to some extent, it still has many drawbacks. While many existing platforms can accept orders through apps, their relatively limited functionality and highly decentralized systems often fail to meet the diverse needs of users, drivers, the platform, and finance. This leads to the emergence of information silos, increases management complexity, and hinders coordination and collaboration across various links.

[0004] Furthermore, the main shortcoming of existing technologies is that, despite improvements in information processing, comprehensive system integration has yet to be achieved. Many designated driver platforms are relatively independent in their system architecture, and data and service information across multiple departments, such as operations, finance, and customer service, cannot be shared. Matching users with drivers still relies on relatively simple algorithms, lacking real-time traffic analysis and intelligent scheduling. Furthermore, pricing strategies are relatively fixed and cannot be flexibly adjusted based on demand fluctuations, resulting in increased costs for some users. Furthermore, traditional management methods have failed to effectively respond to real-time changes in market demand, leading to low efficiency and unstable service quality. Summary of the Invention

[0005] The purpose of the present invention is to provide a designated driver service management method, electronic equipment and storage medium, which solve the problems of scattered and single functions, information islands and inflexible pricing in existing designated driver service management systems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a designated driver service management method, comprising the following steps: S1. Receive a designated driver request initiated by a user, wherein the designated driver request includes user location information, destination information, and designated driver request type; S2. Generate a designated driver order based on the received designated driver request. The formula for generating the designated driver order is: Order=(L u ,L d ,T,OrderID,TimeStamp); Among them, L u Indicates the user's location information; L d Indicates the user's destination information; OrderID indicates the order number generated by the system; TimeStamp is the timestamp of order creation.

[0007] S3. Select a qualified designated driver based on the type of request, user location information, and driver information in the designated driver request; S4. Track and update the designated driver's driving position and speed in real time through the GPS system, and make real-time route adjustments based on road conditions; S5. Calculate the service fee based on the actual driving distance, service time, and charging standards, and generate a settlement statement including additional fees. The additional fee calculation formula is: F extra =C peak +C special ; Among them, C peak Surcharge for peak hours; C special For special needs expenses; S6. Collect historical data and user feedback, analyze and predict future order demand through data, and optimize the designated driver service process and scheduling strategy. The demand forecast formula is: D t =f(X t ,D t-1 ,D t-2 ,…); Among them, D t is the demand at time t; X t is the external factor at time t; f is the prediction function trained by the ARIMA machine learning method.

[0008] Preferably, receiving a designated driver request includes: The user initiates a designated driver request through the platform, which includes the user's location information, destination information, and the type of designated driver request; The system parses the request, prepares to generate the designated driver order data and pushes it to the scheduling module for subsequent processing and scheduling.

[0009] Preferably, generating a designated driver order includes: The system uses the location information, destination information and demand type in the user's request as the main parameters of the designated driver order and generates order data; The system pushes the generated order data to the dispatch module, which will perform driver matching based on this data.

[0010] Preferably, the matching of designated drivers includes: The system matches drivers based on the user's location information, demand type, and the driver's current online status and current location; the specific matching formula is: Among them, L u is the user's location, is the position of the i-th driver, is the online status of the i-th driver, T (i) is whether the driver meets the demand type, while w1 and w2 are weighted coefficients.

[0011] Preferably, the real-time tracking of the designated driver includes: The system receives and updates the designated driver's driving location and speed data in real time; The system dynamically adjusts routes based on real-time traffic information to ensure efficient designated driver services and avoid congestion and emergencies; The dynamic adjustment is defined as: in, Indicates the new driving route position; Indicates the current driving route position; ΔP s Indicates the offset adjusted according to real-time road conditions and vehicle status; The ΔP s Determined by the following factors: Among them, α1, α2 represent weighting coefficients; R c Indicates the current traffic information; v s Indicates the current speed of the designated driver's vehicle; v max Indicates the maximum permitted speed of the designated driver's vehicle; It represents the ideal driving path by calculating the given starting point and end point; Indicates the current driving route position.

[0012] Preferably, the calculating of service fees and generating of bills includes: The system calculates the service fee based on the actual distance traveled and service time as well as the charging standards set by the platform. The service fee calculation formula is: F=C base +C rate ×D+C time ×T service ; Among them, C base As the basic cost, C rate is the charging standard per kilometer, C time is the charge per minute; D is the actual driving distance.

[0013] Preferably, the optimized designated driver service process and scheduling strategy include: The system analyzes historical data and user feedback to identify bottlenecks and optimization opportunities in designated driver services; Through data analysis, the system dynamically optimizes scheduling strategies and service quality to improve the overall efficiency of the system and user satisfaction. The optimization scheduling strategy formula is: in, For historical data; is user feedback; w1 and w2 are weighting coefficients.

[0014] Designated driver service management system, including: The order management module is used to receive and analyze user driver requests, generate driver orders, and push them to the scheduling module; The dispatch module selects the appropriate designated driver based on the user's request and driver information, and generates driver matching information; The real-time monitoring module is used to track the driving status of the designated driver in real time, update the vehicle location in real time, and calculate and adjust the driving route; the fee settlement module calculates the service fee based on the actual driving distance and time, generates the bill and pushes it to the user and the driver; The data analysis module collects and analyzes historical data and improves the system service quality through optimization strategies.

[0015] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for managing a designated driver service is implemented.

[0016] A storage medium stores a computer program, which implements a designated driver service management method when executed by a processor.

[0017] In summary, the present invention includes at least one of the following beneficial technical effects: 1. This invention utilizes a data-driven intelligent dispatching system that analyzes user needs and driver locations in real time to optimize driver matching. This improves matching efficiency and reduces user wait times. Compared to existing designated driver services that rely on manual dispatch and phone calls, this system addresses the inefficiencies and error-proneness of traditional methods, significantly improving service response speed and accuracy.

[0018] 2. This invention combines real-time traffic analysis with dynamic route adjustment technology, automatically adjusting driving routes based on real-time traffic information. This optimizes designated driver routes and avoids traffic congestion. Compared to existing designated driver management systems that lack real-time traffic feedback and dynamic adjustment capabilities, this solves the problem of service delays caused by changing traffic conditions in existing technologies, making designated driver services more efficient and intelligent.

[0019] 3. Through comprehensive data collection and analysis, this invention optimizes pricing strategies and automatically adjusts prices based on market demand fluctuations. This achieves the technical effect of rationally pricing based on real-time demand and peak periods. Compared to existing service management systems with fixed, inflexible pricing, this solves the problem of unreasonable or opaque pricing and improves user satisfaction and trust in designated driver platforms.

[0020] 4. By integrating data management platforms across marketing, operations, finance, customer service, and other areas, this invention breaks down information silos and achieves information sharing and collaborative operations across departments. Compared to the existing technology, which features multiple fragmented systems with single functions, this system addresses the serious problems of information silos and poor cross-departmental communication, improves overall service coordination and efficiency, and achieves comprehensive integration and optimization of the designated driver service management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the method flow of the present invention; Figure 2 Schematic diagram of the system architecture of the present invention; Figure 3 It is a schematic diagram of the structure of the electronic device of the present invention.

[0022] Among them, 40, electronic device; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

[0023] The following is combined with Figure 1 -Attached Figure 3 , the present invention is described in further detail.

[0024] The present invention provides a driver service management method, such as Figure 1 As shown, the designated driver service management method may include the following steps: S1. Receive a designated driver request initiated by a user, wherein the designated driver request includes user location information, destination information, and designated driver request type; In this embodiment, receiving a designated driver request from a user is the first step in the designated driver service management method. Its primary purpose is to receive and process the designated driver request from the user to ensure the smooth execution of the subsequent designated driver process. This step not only involves acquiring and transmitting the user's input information, but also requires data parsing, verification, and preprocessing to ensure that subsequent steps can be executed accurately and promptly.

[0025] The user submits a designated driver request through the designated driver service platform. The request contains the following core information: User's location information (L u ) refers to the user's current geographic location, including longitude, latitude, and other location-related information (such as city, street, and specific house number). This information is typically obtained through various methods, such as GPS, Wi-Fi, or base station positioning technology. The platform uses the positioning module to obtain the user's current location in real time.

[0026] Destination Information (L d ) refers to the destination you wish your designated driver to take you to, including latitude and longitude coordinates and other necessary geographic information (such as street address). You can enter your destination manually, select it on the map, or select it automatically.

[0027] Designated driver request type (T): The user specifies the type of designated driver service, indicating the specific driver needs. Examples include standard designated driver, luxury designated driver, and private designated driver. Different service types require different driver qualifications, vehicle types, and service standards, making this information crucial for subsequent driver matching and route planning. The request type (T) will influence the system's selection of appropriate drivers in subsequent steps.

[0028] After a user submits a designated driver request, the system transmits the request to the backend server via the communication module. The communication module uses standard network protocols (such as HTTP, HTTPS, WebSocket, etc.) for data transmission, ensuring that information can be transmitted in real time and efficiently.

[0029] After receiving the user's designated driver request, the server will parse and process the request content and extract key information. Specifically, the server will extract the user's location information (L u )、Destination Information(L d ) and the type of designated driver request (T). The analysis in this step includes: Perform coordinate conversion on location information to ensure that the location data format is unified and standardized (for example, converting geographic location into latitude and longitude coordinates); Perform geocoding on the destination information provided by the user (for example, if user A enters a city name and street name, the system will perform geocoding based on the map API to obtain the accurate latitude and longitude coordinates); Process the demand type T to ensure that the system can identify the type of designated driver service selected by the user.

[0030] After the analysis is completed, the system encapsulates this information into a designated driver order and stores it in the database for subsequent processing.

[0031] To ensure the accuracy of location information, the system of the present invention utilizes a combination of multiple positioning technologies to acquire location data. In practice, the system can obtain user location information through methods such as GPS, Wi-Fi, and base station positioning, selecting the most appropriate positioning method based on the user's environment. For example, when the user is outdoors, GPS positioning systems typically provide relatively accurate location information; however, indoors or in environments with poor signal quality, Wi-Fi or base station positioning may be more reliable options.

[0032] For destination information, the system supports users to enter the address or select the address through the map interface. In the case of entering the address, the system will use geocoding technology to convert the user-entered address into standard latitude and longitude coordinates for subsequent service processes.

[0033] To ensure the timeliness of designated driver requests, the system utilizes optimized communication protocols for data transmission. A highly efficient API interface is used between the platform and backend servers to ensure the speed and accuracy of information transmission. Furthermore, data transmission is encrypted within the system design to ensure the security of user information.

[0034] Step S1 in this embodiment covers the entire process, from the user inputting a designated driver request to the system receiving, parsing, verifying, and storing the order data. This step ensures the system accurately and promptly receives and processes user requests, providing accurate input data for subsequent steps such as driver matching and route planning. By leveraging multiple positioning technologies, data verification, and encrypted transmission, the system efficiently ensures the smooth implementation of designated driver services.

[0035] Through these technologies, users can easily initiate designated driver requests, and the system can accurately understand and process these requests, providing sufficient technical support for subsequent service links.

[0036] S2. Generate a designated driver order based on the received designated driver request, where the designated driver order includes the user's location information, destination information, and demand type; In this embodiment, generating a designated driver order and sending it to the dispatch module is a key step in the designated driver service management method. Its primary function is to generate a complete designated driver order based on the user's designated driver request and send it to the dispatch module for subsequent processing. The core of this step is to convert user requests into specific order data and initiate driver resource allocation through the dispatch system.

[0037] In this step, the system receives the key information (including location information (L u )、Destination Information(L d ) and designated driver demand type (T)) to generate a designated driver order. When generating a designated driver order, the system constructs the order in detail based on the following contents: Location information (L u ): This information represents the user's current location, which is usually obtained through the platform's positioning module (such as GPS module). The location information includes the user's longitude and latitude coordinates (L u =(longitude, latitude)). With this information, the system can accurately know the user's geographical location and provide accurate starting point data for the subsequent scheduling module.

[0038] Destination Information (L d ): Destination information represents the place the user wants to go to, which is usually entered by the user or obtained through map APIs (such as Amap, Baidu Map API, etc.). The system will geocode the destination entered by the user and convert the address information into standard latitude and longitude coordinates (L d =(longitude,latitude))). If the user does not enter the detailed address, the system can also provide address recommendations or auto-fill functions.

[0039] Request Type (T): The request type reflects the user's specific needs for designated driver services, including standard, luxury, and private vehicles. Different service types influence subsequent driver selection. The system determines appropriate driver qualifications and vehicle type requirements based on the request type. The request type (T) helps determine service standards and influences service charges.

[0040] Once a designated driver order is generated, it will be encapsulated into a format that the system can process according to a predetermined data structure. The system will create an order record based on the location information, destination information, and the type of request. The specific format is as follows: Order=(L u ,L d ,T,OrderID,TimeStamp); Among them, L u is the user's current location (latitude and longitude coordinates); L dis the user's destination information (latitude and longitude coordinates); T is the type of designated driver service demand selected by the user (such as ordinary designated driver, luxury designated driver, etc.); OrderID is the generated unique order number used to identify each designated driver request; TimeStamp is the timestamp of order creation, used to record the exact time when the order was initiated.

[0041] The generated designated driver orders will be stored in the system's database, which will store all key information of each order for subsequent query and processing.

[0042] During the process of generating a designated driver order, the system will verify the integrity of the order data to ensure that all necessary fields are filled and the data is valid. The verification includes: Location information verification: Check the location information provided by the user (L u ) is valid, for example, whether the longitude and latitude coordinates are within a reasonable range (longitude between -180 and 180, latitude between -90 and 90). If the location information is invalid, the system will prompt the user with an error and require re-providing valid location information; Destination information verification: The system will check the destination information (L d ) is empty or in an incorrect format. If the user provides address information, the system will call the map API for geocoding to verify whether the address is valid and ensure that the corresponding latitude and longitude coordinates can be successfully obtained. If the input is invalid, the system will prompt the user to re-enter; Request Type Verification: The request type (T) must be one of the service types supported by the system, such as Standard Driver, Luxury Driver, or Private Driver. The system will select different driver qualifications and service standards based on the request type. If the input is not within the supported range, the system will prompt the user to reselect the request type.

[0043] After completing the generation and verification of the order data, the system will push the designated driver order information to the dispatch module through the communication module. The role of the dispatch module is to u )、Destination Information(L d ) and demand type (T), combined with the information of current online drivers, to select the appropriate designated driver.

[0044] During the order push process, the system uses standard data exchange formats (such as JSON, XML, etc.) for data transmission to ensure compatibility with the scheduling module.

[0045] After being pushed to the dispatch module, the dispatch module will process the information in the order and match it with the current driver information. Through this step, the system can pass the user's designated driver request to the dispatch system and start the next step of the driver matching process.

[0046] S3. Select a qualified designated driver based on the request type, user location, and driver information in the designated driver request. In this embodiment, matching designated drivers is a key step in the designated driver service management method, primarily used to optimally select a driver based on the location information, request type, and driver information in the user request. The system uses an intelligent scheduling algorithm to comprehensively evaluate and select the most suitable designated driver based on multiple factors (such as location, request type, and driver status), thereby ensuring efficient and accurate service.

[0047] In this step, the system matches drivers based on the key information in the designated driver request. The core information in the designated driver request includes: User's current location (L u ); user's demand type (T); user's destination (L d ).

[0048] The system comprehensively considers this information to select the appropriate driver, further improving the efficiency and accuracy of driver matching.

[0049] The system uses an intelligent matching algorithm to evaluate multiple candidate drivers and select the most suitable one. This algorithm is based on multiple factors, including the user's current location, type of request, and the driver's location and online status. To ensure the rationality of the matching results, the system uses a weighted algorithm that assigns different weights to different factors and comprehensively ranks them.

[0050] Location matching: The system first calculates the distance between each driver and the user's current location and selects the driver closest to the user. To calculate the distance, the system uses the following formula: Among them, L u The user's current location; is the current position of the i-th driver; Calculate the geographic distance between the user and the i-th driver (e.g., Euclidean distance or other distance metric).

[0051] Request Type Matching: The system further filters drivers based on the user's request type (T). Different designated driver request types (such as standard designated driver, luxury designated driver, etc.) require drivers with different qualifications. The system prioritizes matching drivers who meet the user's request type. For example, if the user selects a luxury designated driver (T = luxury), the system will select from drivers who meet that requirement.

[0052] Online status matching: Based on the distance and demand type matching, the system will also match the driver's online status (S status ) for screening.

[0053] The system only considers drivers if their online status is 1, as only online drivers can accept orders. An online status value of 1 indicates the driver is currently online, while 0 indicates offline. Therefore, the system prioritizes drivers with an online status of 1.

[0054] Comprehensive weighted matching: After completing the initial screening, the system will conduct a comprehensive weighted matching of multiple qualified drivers. The weighted matching formula is as follows: in: w1 is the weight of position matching, which is used to control the importance of position information in the overall matching. w2 is the weight of online status, which is used to control the impact of the driver's online status on the matching results. w3 is the weight of demand type matching, which is used to control the impact of user demand type on driver selection. S (i) is the online status of the i-th driver, status T (i) Is whether the i-th driver meets the user demand type (T). When the driver matches the user demand type, T (i) is 1, otherwise it is 0.

[0055] The system calculates the weighted parameters in the above formula to finally arrive at a matching score, and selects the driver with the lowest score as the final matching designated driver.

[0056] During the matching process, the system first selects drivers who meet the basic requirements based on the user's demand type and location. The system then scores these drivers according to the weighted matching formula described above, ultimately selecting the driver with the lowest score for dispatch.

[0057] The system calculates the distance between each available driver and the user, and selects drivers who are closer to the user and meet the required type.

[0058] For each qualified driver, calculate its online status and select the driver with an online status of 1.

[0059] For multiple drivers who meet the requirements, the system uses a weighted formula to perform a comprehensive scoring and ultimately selects the driver with the lowest score for task assignment.

[0060] Once a suitable driver is matched, the system will send a task notification to the driver, instructing them to accept the order and go to the user's location. At the same time, the system will also send a confirmation message to the user, informing them of the designated driver's name, contact information, vehicle model information, and estimated arrival time.

[0061] Once the system has matched a driver, a notification message will be sent to the relevant user and driver through the platform. The user will receive detailed information about the designated driver, including the driver's name, vehicle type, and estimated time of arrival. Simultaneously, the driver will receive a task to travel to the user's location and can view the user's location and destination through the platform.

[0062] To ensure timeliness, the system will promptly inform users and drivers of relevant information through push notifications or SMS notifications, ensuring that both parties can confirm and prepare for the pick-up and drop-off tasks in the shortest possible time.

[0063] During the matching process, the system will update relevant information in real time. For example, the driver's online status, location, task status and other information will be continuously updated through GPS and the platform's communication module to ensure that the system always maintains the latest matching results.

[0064] If a driver's status changes during the matching process (such as the driver coming online or offline), the system will re-evaluate the matching results and re-match the driver if necessary.

[0065] In addition to basic matching strategies, the system also uses optimization strategies to improve matching results. The system evaluates driver performance through historical data analysis, user rating feedback, and other information. Based on these evaluation results, the system adjusts the weighting coefficients in the matching algorithm to improve matching accuracy and efficiency. For example, if a driver has a high historical rating, the system may automatically adjust the driver's weight, giving them higher priority in matching.

[0066] To cope with possible system delays and driver unavailability, the system has set up reasonable response times and fault tolerance mechanisms. If the driver does not respond within the specified time or the driver's status changes, the system will re-run the matching process to ensure that users receive timely designated driver service.

[0067] The system also provides users with alternative options, such as selecting another driver or adjusting the service request (such as changing the type of request, etc.).

[0068] The driver matching step in this embodiment uses a weighted algorithm to accurately match designated drivers by comprehensively considering multiple factors such as location information, demand type, and online status. The system's algorithm not only ensures a match between distance and demand type, but also further optimizes the matching results through weighted coefficients based on the driver's online status. Through this process, the system can efficiently provide users with suitable designated drivers and ensure service quality and user satisfaction. The implementation of this step ensures the smooth operation of the designated driver service and provides stable support for the subsequent designated driver process.

[0069] S4. Track the designated driver's driving position and speed in real time through the GPS system, and make real-time route adjustments based on road conditions; In this embodiment, real-time monitoring of the designated driver process is a key step in the designated driver service management method. The system aims to track the designated driver's real-time status and dynamically adjust the driving route to ensure a smooth and efficient designated driver process. Through this process, the system can monitor the designated driver's location, speed, driving path, and road conditions in real time and make necessary adjustments to avoid delays caused by changing road conditions.

[0070] During the designated driving process, the system obtains the location of the designated driver in real time through the GPS device installed on the vehicle (P s ) and speed (v s The location information of the designated driver is an indispensable core data in the designated driver service. It directly affects the route planning, the calculation of the estimated arrival time, and whether the route needs to be adjusted.

[0071] Whenever the driver's location changes, the system will update the location data in real time. The location of the designated driver usually includes its latitude and longitude coordinates (L s ), and the speed (v s ) represents the vehicle's real-time speed. By tracking the driver's location and speed in real time, the system can understand the designated driver's driving status at any time.

[0072] The location information of the designated driver is updated continuously, so its new location needs to be calculated continuously. In the system, the current location of the designated driver is calculated by the following update formula: in: For the updated designated driver location, The last location of the designated driver. ΔP s is the current position offset, indicating the driver's offset from the previous position to the current position. The calculation formula is: ΔP s =v s Δt; in: v s is the current speed of the designated driver’s vehicle, and Δt is the time interval (i.e., the time between the last update and this update).

[0073] In this way, the system can accurately calculate the current location of the designated driver and update it in real time.

[0074] During the designated driver service, real-time traffic information (R c) is crucial for route adjustments. The system accesses traffic monitoring systems, map APIs, or third-party services (such as AutoNavi Maps and Baidu Maps) to obtain real-time information on traffic conditions, congestion, and accidents. By analyzing this information, the system can determine whether routes need to be adjusted to avoid congestion or accident areas.

[0075] Traffic information (R c ) typically include: The current traffic volume of the road (e.g., traffic flow index), Is there an accident or road closure? Special traffic events (such as construction, weather impact, etc.).

[0076] The system processes this information in real time and dynamically adjusts the driving route based on the current driver's location and destination to ensure the efficiency of the designated driver service.

[0077] When the system receives new traffic information (R c ) will calculate a new optimal driving route. The goal of route optimization is to select the optimal route based on the driver's current speed, location, destination, and real-time traffic information. To avoid traffic congestion or accidents, the system continuously updates the route and provides real-time feedback to users and drivers.

[0078] The path optimization process depends on many factors. The system adjusts the path using the following formula: in: is the adjusted driving path, is the current driving path, α1, α2 represent weighting coefficients; R c Indicates current traffic information; v s Indicates the current speed of the designated driver's vehicle; v max Indicates the maximum permitted speed of the designated driver's vehicle; Represents the ideal driving path calculated for a given start and end point.

[0079] The route adjustment is dynamic, and the system will adjust the route based on the current location of the designated driver. and real-time traffic information (R c ) to make adjustments. If the driver's location changes or traffic information is updated, the system will recalculate the route and adjust the path by updating the formula. Each route adjustment is based on the following two factors: Current road conditions: factors that affect route selection, such as traffic flow, accidents, and road closures.

[0080] Vehicle speed: Based on the vehicle's speed, the system dynamically calculates a new path.

[0081] The adjusted route will be promptly fed back to the designated driver, and the updated estimated arrival time and route information will be provided to the user through the platform to ensure that both parties have a clear understanding of the designated driver process.

[0082] Once the route adjustment is complete, the system will notify the user and driver of the updated route and estimated time of arrival. This information is communicated in real time via push notifications, text messages, or app alerts. The driver will receive the new navigation information and continue driving according to the updated route. The user will also be kept up to date on the designated driver's status, ensuring transparency and control over the designated driver process.

[0083] During the designated driving process, the system may encounter various abnormal situations, such as driver equipment failure, communication interruption, driver location failure to update, etc. In order to ensure the stability of the designated driving service, the system has set up a fault-tolerant mechanism.

[0084] Device failure or data loss: When the system fails to receive location updates from the driver, it automatically triggers backup devices or data sources to supplement and ensure the continuity of location tracking.

[0085] Communication interruption: If communication between the system and the driver is interrupted, the system will try to reconnect after a short period of time and continue calculating the route from the last valid location.

[0086] Traffic emergencies: If a major traffic accident or other force majeure event occurs during the designated driving process, the system will automatically detect and adjust the route, and send emergency handling plans to the user and the driver, such as replacing the driver or adjusting the service time.

[0087] To improve the efficiency and accuracy of route adjustments, the system uses machine learning algorithms during route optimization, analyzing historical traffic data, user feedback, and driver performance to further refine route adjustment strategies. The system learns from historical data and adjusts route optimization algorithms based on different scenarios (such as peak periods and holidays), ensuring optimal service in specific situations.

[0088] Step S4 in this embodiment ensures the smooth operation of the designated driver service by obtaining the designated driver's location information, speed, path, and real-time traffic information in real time. The system continuously updates and adjusts the driver's driving path to avoid traffic jams and emergencies, thereby improving the efficiency of the designated driver service. Through real-time feedback and notification mechanisms, the system ensures that users and drivers can understand the service status in a timely manner. Under the protection of exception handling and fault tolerance mechanisms, the system can operate stably and provide users with continuous and reliable designated driver services. This process ensures the efficiency and reliability of the designated driver service, greatly improving the user experience and service quality.

[0089] S5. Calculate the service fee and generate a settlement invoice based on the actual driving distance, service time and charging standards; In this embodiment, calculating the service fee and generating the bill is one of the key steps in the designated driver service management method. The goal of this step is to calculate the service fee based on the actual driving time (T service ), driving distance (D), and other factors, combined with the platform's fee schedule, automatically calculates the designated driver fee and generates the final bill. This step allows users to clearly understand the cost structure and detailed fee breakdown of the designated driver service.

[0090] The cost calculation process of the designated driver service is based on the core data of the designated driver process: the user's actual driving time (T service ), driving distance (D) and the charging standards set by the platform (such as charging standards per kilometer and charging standards per minute). The platform calculates the total cost of the designated driver service based on these data and other additional fees (such as peak period surcharges). The specific fee calculation formula is: F=C base +C rate ×D+C time ×T service +C peak +C additional ; in: F is the final fee for the designated driver service, C base The basic fee (usually the starting price for a designated driver) does not change with time and distance. C rate The charging standard per kilometer is in Yuan / km. D is the actual driving distance during the designated driving process, in kilometers (km), C time The charge per minute is in yuan / minute. T service The actual duration of the designated driver service, in minutes (mì), C peakThe price increase during peak period is in RMB and is calculated based on the peak period rules set by the system. C additional Other additional fees may include additional charges for specific service requirements, such as luxury car requirements, special service requirements, etc.

[0091] The base fee is the starting price for designated driver services, typically set by the platform upon user request. This fee is fixed at the start of each designated driver service and does not change over time or over distance. It covers the platform's basic operating costs, such as the driver's fixed salary and vehicle preparation fees.

[0092] The driving distance fee is calculated based on the actual distance (D) traveled by the driver during the driving process. The fee is calculated based on the charging standard per kilometer (C rate ) is calculated based on the distance traveled, usually in kilometers. For example, if the charge is 5 yuan per kilometer and the designated driver travels 10 kilometers, the fee for this portion of the trip is 10 × 5 = 50 yuan.

[0093] In addition to the driving distance, the time of the designated driver service will also affect the fee, especially in the event of traffic jams, waiting, etc. The system will monitor the length of the designated driver service in real time and charge a fee based on the per minute standard (C time ) to calculate this portion of the fee. For example, if the designated driver service lasts 30 minutes and the charge is 2 yuan per minute, the fee for this portion of the service is 30 × 2 = 60 yuan.

[0094] During certain time periods, such as peak season, holidays or bad weather, the designated driver fee may be increased. This part of the fee is based on the peak season price increase rules set by the platform (C peak For example, during peak hours, the platform may add a certain percentage (such as 20%) to the service fee, which will be calculated based on the actual service time and travel distance.

[0095] In some special cases, the designated driver service may incur additional fees (C special These additional fees include, but are not limited to, luxury vehicle requirements, additional insurance costs, specific vehicle model requirements, etc. The platform will automatically calculate these additional fees based on the user's specific needs and include them in the final fee list.

[0096] During the designated driving process, the system automatically identifies peak hours and other special circumstances that may impact fees. Based on platform-defined rules, the system dynamically adjusts fees. For example, if the designated driving occurs during holidays, inclement weather, or during special traffic events, the system will automatically charge additional fees based on pre-set rules.

[0097] For example, if the peak markup is 20%, and the original fee (excluding the markup) is $100, the final fee is calculated as: F final =(100+20)=120 yuan Once the cost calculation is completed, the system will generate a detailed bill based on the calculated cost. The bill will include: Basic Fee (C base ), cost calculated by distance (C rate ×D), cost calculated by time (C time ×T service ), Peak fare increase (C peak ), other additional costs (C special ), total cost (F).

[0098] The bill will be presented in a format that is easy for users to understand, and users can view the detailed components of all expenses, ensuring transparency and fairness in fee settlement.

[0099] The generated bills will be pushed to users and drivers through the platform's communication module, usually in the form of electronic bills sent through the App or SMS, ensuring that users and drivers receive relevant information on fee settlement in a timely manner.

[0100] After the bill is generated, users can settle the payment through the platform's payment methods (such as credit card payment, e-wallet payment, etc.). After confirming the bill information is correct, users select a payment method and proceed with payment. Once payment is complete, the system generates a payment confirmation and sends a notification to the user and driver confirming the successful payment. The system updates the designated driver service status to "settled" and stores the payment record in the database for subsequent query.

[0101] In actual operation, the system adopts the following technical means to ensure the efficiency, accuracy and security of fee calculation and settlement: Real-time data collection: The system collects the driving time and distance in real time during the designated driving process through GPS and timestamp data to ensure that the fee calculation is based on real-time data.

[0102] Dynamic surcharge calculation: The system dynamically adjusts the surcharge ratio based on real-time time information (such as peak periods, holidays, etc.) to ensure that the fees are consistent with the actual situation.

[0103] Support for multiple payment methods: The platform supports multiple payment methods. Users can choose the appropriate payment method according to their needs. The system processes transactions through a secure payment interface to ensure the security of the payment process.

[0104] Data encryption and security: All fare calculations and payment information are encrypted to ensure the privacy and transaction information security of users and drivers.

[0105] In step S5 of this embodiment, an automated fee calculation model is used to accurately calculate the service fee for designated drivers based on factors such as driving time, driving distance, and additional fees during the designated driver process. The fee calculation follows the principles of transparency and fairness and can be dynamically adjusted according to real-time conditions (such as peak periods, holidays, etc.). At the same time, the system provides detailed and transparent billing information and supports multiple payment methods to ensure that users and drivers can complete payments conveniently. The system's automated processing not only improves efficiency, but also ensures the accuracy and transparency of fee settlement.

[0106] S6. Collect historical data and user feedback, and optimize the designated driver service process through data analysis and scheduling strategies.

[0107] In this embodiment, optimizing the service process is a key step in the designated driver service management method. This method primarily involves collecting and analyzing historical data to optimize various aspects of the designated driver service, including scheduling, pricing, and service quality. Using data analysis and machine learning technologies, the system continuously identifies and resolves bottlenecks in the service process, automatically adjusting and optimizing various service strategies, and thereby improving overall service efficiency, user experience, and system operational efficiency.

[0108] The core goal of optimizing service processes is to improve the efficiency of designated driver services, reduce user wait times, and optimize scheduling strategies, pricing strategies, and service quality through data-driven decision-making. Through data analysis, the system can identify potential bottlenecks and resource waste in the service process and propose improvement measures. For example, the system might optimize driver allocation strategies, predict demand fluctuations based on historical data, or adjust pricing structures based on user and driver feedback.

[0109] To support service process optimization, the system first needs to collect and store historical data. This data contains various information about the service process, including but not limited to: User data: including user's demand type (T), starting location (L u ), destination (L d ), service time (T service ), driving distance (D) and other information.

[0110] Driver data: including the driver's real-time online status (S status ), service ratings, historical performance (such as completion rate, task timeliness, etc.).

[0111] Order data: including user demand categories (such as ordinary chauffeur services, luxury chauffeur services, etc.), as well as order execution status (such as execution time, service quality rating, etc.).

[0112] User feedback data: including user service evaluations, complaint information, suggestion feedback, etc.

[0113] All of this historical data will be stored in the system's database and updated regularly. Through in-depth analysis of historical data, the data system identifies potential bottlenecks in the current designated driver service process and formulates optimization strategies based on the analysis results. The data analysis process uses machine learning and statistical methods to generate more accurate optimization suggestions and adjustment strategies. The main optimization strategies include but are not limited to the following aspects: Scheduling optimization: The system optimizes the scheduling strategy for designated driver services by analyzing historical order data, driver performance, and time period demand.

[0114] Predicting demand fluctuations: Using historical order data, the system can predict demand fluctuations during certain periods or in certain areas. For example, during certain periods (such as rush hour), the system can dispatch more drivers in advance to meet demand.

[0115] Driver matching: The system prioritizes matching drivers with better performance based on their historical performance (such as order acceptance rate, customer ratings, etc.) to ensure service quality and efficiency.

[0116] The specific formula for scheduling optimization is as follows: Among them, L u The user's location; is the position of the i-th driver; T (i) Whether the driver meets the user's needs (such as ordinary driver, luxury driver, etc.); is the driver's online status, w1, w2, and w3 are weighted coefficients used to adjust the relative importance of factors such as location matching, demand type matching, and driver status.

[0117] The formula calculates the optimal driver match by comprehensively considering factors such as user location, driver demand matching degree, and driver online status.

[0118] Pricing strategy optimization is based on analysis of historical orders and user feedback to ensure that the charging structure is fair, transparent, and able to adapt to demand fluctuations. For example: Dynamic pricing: The system automatically adjusts prices based on forecasted demand fluctuations. For example, it adjusts prices during peak hours or periods of surging demand, balancing supply and demand through dynamic pricing.

[0119] Personalized pricing: The system provides personalized offers or discounts based on the user's historical needs, frequently selected service types, and other data. The pricing formula is: F=(C base +C rate ×D+C time ×T service )×(1+α); in: F is the final cost, C base As basic fee, C rate ×D is the distance-based fee. C time ×T service For time-based charges, α is a dynamic price increase factor, which usually takes a larger value during high-demand periods (such as peak periods, holidays, etc.) and a smaller value otherwise.

[0120] By adjusting the markup factor (α) in real time, the system can automatically optimize the pricing strategy based on real-time demand, traffic conditions and other factors.

[0121] Optimizing service quality relies on in-depth analysis of user feedback. The system uses machine learning algorithms to analyze user ratings and complaints, identify key factors affecting service quality, and take corresponding improvement measures. Service quality optimization mainly includes: Driver training and feedback: The system provides targeted training to drivers with low scores based on historical feedback data to help them improve their service quality.

[0122] Real-time feedback mechanism: The system can obtain real-time user feedback on the designated driver service and dynamically adjust the service based on this feedback. For example, if a user gives a low rating, the system will automatically mark the driver's priority and follow up in the system.

[0123] The core of optimizing service quality is to continuously analyze data, identify problems and respond quickly based on actual conditions to ensure continuous improvement of the designated driver service.

[0124] The optimization process in this embodiment makes full use of machine learning and data analysis technology. The system gradually forms a more accurate scheduling, pricing and service quality optimization model by training a large amount of historical data. For example, the following machine learning algorithm is used: Cluster analysis: Through clustering algorithms, the system can identify different demand patterns and efficient driver patterns, thus providing a basis for optimizing scheduling strategies.

[0125] Regression analysis: Through regression analysis, the system can predict the impact of different factors on the efficiency and quality of designated driver services, and then adjust scheduling and pricing strategies.

[0126] Reinforcement learning: Through reinforcement learning, the system can automatically adjust scheduling decisions according to the environment (such as traffic flow, weather, etc.) to achieve optimal service efficiency.

[0127] The continuous training and optimization of machine learning algorithms help the system better adapt to different service scenarios and changes in demand, ensuring that the designated driver service is always in an efficient operating state.

[0128] Based on historical data, the platform can use time series analysis or regression models to predict future demand. Assume the following variables: D t is the demand at time t (such as the number of orders).

[0129] X t is the external factors at time t, such as weather, holidays, etc.

[0130] Demand forecast can be expressed by the following formula: D t =f(X t ,D t-1 ,D t-2 ,…); Where f is a prediction function, which can usually be trained by machine learning methods (such as ARIMA, LSTM and other time series models). With this model, the platform can predict in advance the demand for designated drivers in certain time periods or under certain conditions, and then reasonably allocate driver resources.

[0131] Once the optimization strategies are generated, the system will apply them to the actual designated driver service in real time. For example: Optimized scheduling: The system will immediately adjust the driver matching strategy and assign qualified drivers to users to ensure a quick response.

[0132] The optimization scheduling strategy formula is: in, For historical data; is user feedback; w1 and w2 are weighting coefficients.

[0133] Optimized pricing: The system automatically adjusts service fees based on the optimized pricing model to ensure fairness and transparency of service fees.

[0134] These optimization results are fed back to users and drivers in real time. For example, the system will push optimized orders to drivers and display the optimized fee structure to users.

[0135] To continuously improve optimization results, the system continuously trains and optimizes its machine learning models based on new historical data. After each designated driver service is completed, the system automatically analyzes the newly generated data (such as order duration, user feedback, driver performance, etc.) and feeds it back into the model for adjustment. This continuous learning mechanism enables the system to continuously improve the optimization accuracy of scheduling, pricing, and service quality over time.

[0136] The optimized service process steps in this embodiment automatically adjust the dispatching strategy, pricing strategy, and service quality of the designated driver service through the collection and in-depth analysis of large amounts of data and the application of machine learning technology. By continuously optimizing these processes, the system can improve service efficiency, reduce user wait times, and dynamically adjust service strategies based on real-time demand and traffic conditions. This process not only ensures the efficiency and accuracy of the designated driver service, but also enables the system to continuously learn and optimize, thereby achieving long-term service improvements and increased user satisfaction.

[0137] The designated driver service management system described below and the designated driver service management method described above can be referenced to each other.

[0138] Please see the attached Figure 2 The present invention also provides a designated driver service management system, including: The order management module is used to receive and analyze user driver requests, generate driver orders, and push them to the scheduling module; The dispatch module selects the appropriate designated driver based on the user's request and driver information, and generates driver matching information; The real-time monitoring module is used to track the driving status of the designated driver in real time, update the vehicle location in real time, and calculate and adjust the driving route; the fee settlement module calculates the service fee based on the actual driving distance and time, generates the bill and pushes it to the user and the driver; The data analysis module collects and analyzes historical data and improves the system service quality through optimization strategies.

[0139] The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, so they will not be repeated here.

[0140] Please see the attached Figure 3 The present invention further provides an electronic device 40, comprising: a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor, and when the computer program is executed by the processor, the above method is performed.

[0141] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41 , the above method is executed.

[0142] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0143] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A designated driver service management method, characterized in that: The following steps are involved: S1. Receive a designated driver request initiated by a user, wherein the designated driver request includes user location information, destination information, and designated driver request type; S2. Generate a designated driver order based on the received designated driver request. The formula for generating the designated driver order is: Order=(L u ,L d ,T,OrderID,TimeStamp); Among them, L u Indicates the user's location information; L d Indicates the user's destination information; OrderID indicates the order number generated by the system; TimeStamp is the timestamp of order creation; S3. Select a qualified designated driver based on the type of demand, user location information, and driver information in the designated driver request; S4. Track and update the designated driver's driving position and speed in real time through the GPS system, and make real-time route adjustments based on road conditions; S5. Calculate the service fee based on the actual driving distance, service time, and charging standards, and generate a settlement statement including additional fees. The additional fee calculation formula is: F extra =C peak +C special ; Among them, C peak Surcharge for peak hours; C special For special needs expenses; S6. Collect historical data and user feedback, analyze and predict future order demand through data, and optimize the designated driver service process and scheduling strategy. The demand forecast formula is: D t =f(X t ,D t-1 ,D t-2 ,…); Among them, D t is the demand at time t; X t is the external factor at time t; f is the prediction function trained by the ARIMA machine learning method.

2. The method for managing designated driver services according to claim 1, wherein: Receiving a designated driver request includes: The user initiates a designated driver request through the platform, which includes the user's location information, destination information, and the type of designated driver request; The system parses the request, prepares to generate the designated driver order data and pushes it to the scheduling module for subsequent processing and scheduling.

3. The method for managing designated driver services according to claim 1, wherein: Generating a designated driver order includes: The system uses the location information, destination information and demand type in the user's request as the main parameters of the designated driver order and generates order data; The system pushes the generated order data to the dispatch module, which will perform driver matching based on this data.

4. The method for managing designated driver services according to claim 1, wherein: The matching designated drivers include: The system matches drivers based on the user's location information, demand type, and the driver's current online status and current location; the specific matching formula is: Among them, L u is the user's location, is the position of the i-th driver, is the online status of the i-th driver, T (i) is whether the driver meets the demand type, while w1 and w2 are weighted coefficients.

5. The method for managing designated driver services according to claim 1, wherein: The real-time tracking of the designated driver includes: the system receiving and updating the driving position and driving speed data of the designated driver in real time; The system dynamically adjusts routes based on real-time traffic information to ensure efficient designated driver services and avoid congestion and emergencies; The dynamic adjustment is defined as: P snew =P so ld +ΔP s ; in, Indicates the new driving route position; Indicates the current driving route position; ΔP s Indicates the offset adjusted according to real-time road conditions and vehicle status; The ΔP s Determined by the following factors: Among them, α1, α2 represent weighting coefficients; R c Indicates the current traffic information; v s Indicates the current speed of the designated driver's vehicle; v max Indicates the maximum permitted speed of the designated driver's vehicle; It represents the ideal driving path by calculating the given starting point and end point; Indicates the current driving route position.

6. The method for managing designated driver services according to claim 1, wherein: Calculating service fees and generating bills includes: The system calculates the service fee based on the actual distance traveled and service time as well as the charging standards set by the platform. The service fee calculation formula is: F=C base +C rate ×D+C time ×T service ; Among them, C base As the basic cost, C rate is the charging standard per kilometer, C time is the charge per minute; D is the actual driving distance.

7. The method for managing designated driver services according to claim 1, wherein: The optimized designated driver service process and scheduling strategy include: The system analyzes historical data and user feedback to identify bottlenecks and optimization opportunities in designated driver services; Through data analysis, the system dynamically optimizes scheduling strategies and service quality to improve the overall efficiency of the system and user satisfaction; The optimization scheduling strategy formula is: in, For historical data; is user feedback; w1 and w2 are weighting coefficients.

8. A designated driver service management system, applied to the designated driver service management method according to any one of claims 1 to 7, characterized in that: include: The order management module is used to receive and analyze user driver requests, generate driver orders, and push them to the scheduling module; The dispatch module selects the appropriate designated driver based on the user's request and driver information, and generates driver matching information; Real-time monitoring module, used to track the driving status of the designated driver in real time, update the vehicle location in real time, calculate and adjust the driving route; The fee settlement module calculates the service fee based on the actual driving distance and time, generates the bill and pushes it to the user and driver; The data analysis module collects and analyzes historical data and improves the system service quality through optimization strategies.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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