Method, System and Storage Medium for Chauffeur Service Management

Abstract

The present invention discloses a method, a system and a storage medium for driver service management. The method includes obtaining an order request and street data, and establishing a mathematical model; calculating an initial driver service path according to the departure place, the destination and the street data; optimizing the initial driver service path using the mathematical model to obtain an optimal driver service path; sending the order request and the optimal driver service path to a driver, and establishing a dispatching plan; and supervising the vehicle position in real time, and when the vehicle arrives at the destination, the dispatching plan ends. The method has the following effects: by obtaining the order request and the street data, establishing a mathematical model, and using the mathematical model to calculate the order request and the street data to obtain an optimal driver service path, and then sending the optimal driver service path to the driver, the management of the driver service process is realized, the operation time is reduced, and the work efficiency is increased. At the same time, the route in the driver service process is also recorded, thereby ensuring the safety of both parties.

Description

Technical Field

[0001] The present invention relates to the technical field of designated driving, and particularly to a method, a system and a storage medium for designated driving management. Background Art

[0002] With the diversified development of China's economy and culture, the material living standards of the people have been significantly improved, and their enthusiasm for entertainment activities has also increased. At this time, alcohol, as a medium for emotional communication, plays an important role in enhancing mutual friendship. Unfortunately, people lack awareness of the importance of avoiding excessive drinking and drunk driving, and are unaware of the harm of drunk driving to personal and others' lives and safety. At the same time, some drivers may not recognize the danger of drunk driving, or are overly confident in their driving ability, thus ignoring the risks of drunk driving. However, alcohol can affect the driver's reaction speed, judgment and attention, greatly increasing the risk of traffic accidents.

[0003] To effectively address this issue, the emergence of designated driving services undoubtedly provides a practical way to solve the problem of drunk driving. Designated driving services can not only help drivers avoid wasting precious time due to being caught for drunk driving, but the time value saved is far more than the serious consequences that may be caused by drunk driving. Designated drivers have received strict professional training, with rich driving experience and good professional qualities, providing a solid and reliable guarantee for the safety of public travel. As a service provider, there is still great room for improvement in improving the service efficiency and reliability of designated driving enterprises, and they have broad development prospects and unlimited potential in the future. In addition, designated driving services have gradually expanded from traditional post-drinking designated driving to various special services such as long-distance designated driving, business designated driving, and tourism designated driving. The innovation of these service models meets the needs of different user groups. This means that the market scale of the designated driving industry continues to expand.

[0004] In an existing technology, after the system obtains the received designated driving order, according to the designated driving order and the currently online designated driving terminals, a backpropagation BP neural network including an input layer, a hidden layer and an output layer is created, and the node value of the hidden node is used as the designated driving order number for dispatching orders to the corresponding designated driving terminals, and the designated driving order number is adjusted based on the gradient descent method of the BP neural network.

[0005] However, in the above-mentioned existing technology, the path allocation of designated driving is not considered, resulting in low efficiency of designated driving order dispatching path planning. Summary of the Invention

[0006] The present invention provides a method and a system for designated driving management to solve the problems of low efficiency of designated driving order dispatching path planning and inability to quickly respond to customers' designated driving needs in the existing technology.

[0007] In a first aspect, to solve the above technical problems, the present invention provides a system method for driving service management, including:

[0008] Obtain a user order request and street data, and establish a mathematical model; wherein, the user order request includes the departure location, destination location, and vehicle location of the user; the mathematical model includes a state machine model and a time function;

[0009] Calculate an initial driving service path based on the departure location, the destination location, and the street data;

[0010] Optimize the initial driving service path using the mathematical model to obtain an optimal driving service path;

[0011] Send the user order request and the optimal driving service path to a driving service driver to establish a dispatching plan;

[0012] Monitor the vehicle location; if the vehicle location reaches the destination, end the current dispatching plan, obtain completed plan data, and end the monitoring; if the vehicle location does not reach the destination, continuously record the current vehicle location in the dispatching plan and maintain the monitoring.

[0013] In an optional implementation manner, the street data includes street paths, street intersections, traffic rules, and real-time traffic conditions.

[0014] In an optional implementation manner, the state machine model includes state descriptions, the total number of states, state transitions, and state change rules.

[0015] In an optional implementation manner, the calculating an initial driving service path based on the departure location, the destination location, and the street data includes:

[0016] Perform cleaning and formatting operations on the street data to obtain street information;

[0017] Calculate the initial driving service path based on the departure location, the destination location, and the street information.

[0018] In an optional implementation manner, the optimizing the initial driving service path using the mathematical model to obtain an optimal driving service path includes:

[0019] Divide the initial driving service path into several equally long path segments, and each path segment is regarded as a non-deterministic timed automaton;

[0020] Perform a Cartesian product operation on all the non-deterministic timed automata to obtain a Cartesian product result under a finite state machine; wherein, the Cartesian product result includes all sub-results from one path segment to another path segment;

[0021] Substitute the street data and the Cartesian product result into the mathematical model to calculate the optimal designated driver path.

[0022] In an alternative embodiment, the calculating the initial designated driver path according to the departure location, the destination, and the street information includes:

[0023] Parse the departure location, the destination, and the street information to obtain a simplified path map;

[0024] Establish a path matrix according to the simplified path map;

[0025] Calculate using a path planning algorithm according to the path matrix to obtain the initial designated driver path;

[0026] Among them, the initial designated driver path is calculated according to the following formula,

[0027] dist = min(d[u] + w(u, v), dist)

[0028] where dist represents the initial designated driver path, and the initial value of dist is infinity, d[u] represents the current distance from the departure location to node u, w(u, v) represents the distance value in the u-th row and v-th column of the path matrix, and min() represents selecting the smallest number from several numbers within the parentheses.

[0029] In an alternative embodiment, the substituting the street data and the Cartesian product result into the mathematical model to calculate the optimal designated driver path includes:

[0030] Calculate the travel time using the time function according to the street data;

[0031] Calculate the optimal designated driver path using the state machine model according to the Cartesian product result and the travel time.

[0032] In an alternative embodiment, the calculating the travel time using the time function according to the street data includes:

[0033] According to the street data, obtain the path edges and path points of the street path, and the time required to drive on each path edge;

[0034] Calculate the travel time using the time function according to the path edges, the path points, and the time;

[0035] Among them, the travel time is calculated according to the following formula,

[0036]

[0037] Among them, T represents the travel time, and e j represents the j-th path edge, and p i represents the i-th path point, and t(e j , p i ) represents the time required to pass through the path edge e j to the path point p i , N represents the total number of the path points, and M i represents the total number of the path edges that can reach the path point p i .

[0038] In an alternative embodiment, the calculating the optimal designated-driver path using the state machine model according to the Cartesian product result and the travel time includes:

[0039] Checking the Cartesian product result to find all sub-results that can reach the destination from the departure place;

[0040] Calculating the current time of the path represented by each sub-result and comparing it with the travel time; if the current time is less than the travel time, output the path represented by the current sub-result as the optimal designated-driver path; if the current time is greater than the travel time, continue to judge the next current time and the travel time; if all the current times are greater than the travel time, output the initial designated-driver path as the optimal designated-driver path.

[0041] In a second aspect, the present invention provides a system for designated-driver management, including:

[0042] An information acquisition module, configured to acquire a user order placement request and street data, and establish a mathematical model; wherein, the user order placement request includes the departure place, destination and vehicle position of the user;

[0043] An initial path calculation module, configured to calculate an initial designated-driver path according to the departure place, the destination and the street data;

[0044] A path optimization module, configured to optimize the initial designated-driver path using the mathematical model to obtain an optimal designated-driver path;

[0045] A scheme establishment module, configured to send the user order placement request and the optimal designated-driver path to a designated-driver and establish a dispatching scheme;

[0046] A result feedback module is used to monitor the vehicle position; if the vehicle position reaches the destination, the current dispatching plan is ended, the completed plan data is obtained and the monitoring is ended; if the vehicle position does not reach the destination, the current vehicle position is continuously recorded in the dispatching plan and the monitoring is maintained.

[0047] In a third aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for chauffeur management described in any one of the above is implemented.

[0048] In a fourth aspect, the present invention further provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. Wherein, when the computer program runs, the device where the computer-readable storage medium is located is controlled to execute the method for chauffeur management described in any one of the above.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention provides a method for chauffeur management, including obtaining a user order request and street data, and establishing a mathematical model; wherein, the user order request includes the departure place, destination, and vehicle position of the user; the mathematical model includes a state machine model and a time function; according to the departure place, the destination, and the street data, an initial chauffeur path is calculated; the initial chauffeur path is optimized using the mathematical model to obtain an optimal chauffeur path; the user order request and the optimal chauffeur path are sent to a chauffeur, and a dispatching plan is established; the vehicle position is monitored; if the vehicle position reaches the destination, the current dispatching plan is ended, the completed plan data is obtained and the monitoring is ended; if the vehicle position does not reach the destination, the current vehicle position is continuously recorded in the dispatching plan and the monitoring is maintained.

[0051] The method obtains a user order request and street data, establishes a mathematical model including a state machine model and a time function, calculates the user order request and the street data using the mathematical model to obtain an optimal chauffeur path, and then sends the optimal chauffeur path to a chauffeur, thereby realizing the management of the chauffeur process. The method improves the path planning efficiency in the chauffeur dispatching process, thereby effectively realizing a rapid response to the customer's chauffeur needs. In addition, the method also has the function of recording the specific route during the chauffeur process, ensuring the traceability of the chauffeur process, and providing a strong guarantee for the safety of both users and chauffeurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1It is a schematic flowchart of the method for driver service management provided by the first embodiment of the present invention;

[0053] Figure 2 It is a schematic structural diagram of the system for driver service management provided by the second embodiment of the present invention. Detailed implementation manners

[0054] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0055] Refer to Figure 1 , the first embodiment of the present invention provides a method for driver service management, including the following steps:

[0056] S1. Obtain a user order request and street data, and establish a mathematical model; wherein, the user order request includes the departure place, destination, and vehicle position of the user; the mathematical model includes a state machine model and a time function.

[0057] S2. Calculate an initial driver service path according to the departure place, the destination, and the street data.

[0058] S3. Optimize the initial driver service path using the mathematical model to obtain an optimal driver service path.

[0059] S4. Send the user order request and the optimal driver service path to a driver, and establish a dispatching plan.

[0060] S5. Monitor the vehicle position; if the vehicle position reaches the destination, end the current dispatching plan, obtain completed plan data, and end the monitoring; if the vehicle position does not reach the destination, continuously record the current vehicle position in the dispatching plan and maintain the monitoring.

[0061] It should be noted that in some existing methods, after the system obtains a user order request, it can send a dispatching request to a driver according to conditions such as the distance, order time, and distance of the driver using a pre-trained neural network model to achieve a balanced supply and demand dispatching of the driver service between upstream and downstream. However, the path allocation problem during the driver service process is not considered in these technologies, resulting in problems such as low planning efficiency of the driver service dispatching path and inability to quickly respond to the driver service requirements of customers.

[0062] In this method, by accurately obtaining the user's order placement request and detailed street data, a mathematical model including a state machine model and a time function is established. This mathematical model can efficiently perform precise calculations on the user's order placement request and street data, thereby obtaining the optimal driving path for the driver. Then, the optimal driving path is sent to the driver, thus realizing the comprehensive management and optimization of the driving process. The method not only significantly shortens the operation time but also improves work efficiency through the real-time generated optimal path. In addition, this method also has the function of recording the specific route during the driving process, ensuring the traceability of the driving process and providing strong guarantees for the safety of both users and drivers.

[0063] In step S1, first, the user's order placement request and the street data are obtained, and then a suitable mathematical model is established according to specific circumstances. Among them, the user's order placement request needs to include the user's departure location, destination, and vehicle location; the mathematical model includes a state machine model and a time function.

[0064] In one implementation, the street data includes street paths, street intersections, traffic rules, and real-time traffic conditions; the state machine model includes state descriptions, the total number of states, state transitions, and state change rules.

[0065] Specifically, first, detailed street data is obtained from a cooperative map service provider, and the user's order placement request is obtained from the internal database of the system. Then, according to the specific characteristics and requirements of the actual application scenario, a suitable state machine model and a matching time function are selected, and the two together constitute the mathematical model for subsequent use. In the street data, there should be sufficiently detailed street paths, street intersections, traffic rules, and real-time traffic conditions to obtain more accurate results when planning the path.

[0066] It should be noted that in addition to the above necessary information, the user's order placement request can also record other relevant information, such as the type of vehicle, the user's route requirements, etc., so that a better personalized service relationship can be established between the user and the driver. In addition, the street data and the user's order placement request involve data security and privacy protection issues. Therefore, the system needs to take strict data encryption and access control measures to ensure the security of this information during transmission and storage and prevent data leakage or illegal use.

[0067] In step S2, first, according to the locations of the departure location and the destination, their specific locations are placed into the street data, and then calculations are performed based on the street data to obtain the initial driving path.

[0068] In one embodiment, calculating the initial driving path according to the departure place, the destination, and the street data includes the following steps:

[0069] S21. Clean and format the street data to obtain street information.

[0070] S22. Calculate the initial driving path according to the departure place, the destination, and the street information.

[0071] In step S21, first clean and format the obtained street data to obtain the street information. Among them, the cleaning operation includes removing duplicate data, supplementing data with missing attributes, and deleting data with redundant attributes or values significantly exceeding the normal range; the formatting operation includes converting the data into a unified format and standard, and performing operations such as standardization, normalization, and unit conversion.

[0072] It should be noted that since the street data may contain various errors, incompleteness, repetitions, or information irrelevant to the actual needs during the collection and collation process, the existence of these bad data will greatly reduce the accuracy and reliability of subsequent data analysis. Through data cleaning, these bad data can be removed, improving the accuracy and credibility of the data. At the same time, inaccurate street data may bring security risks, such as leaking privacy information or causing navigation errors. The data cleaning process can accurately identify and correct these problems, thus significantly reducing potential security risks and ensuring the smooth progress of the driving service. Therefore, it is extremely necessary and crucial to clean and format the street data.

[0073] In step S22, calculating the initial driving path according to the departure place, the destination, and the street information includes: parsing the departure place, the destination, and the street information to obtain a simplified path map; establishing a path matrix according to the simplified path map; using a path planning algorithm to calculate according to the path matrix to obtain the initial driving path; among them, the initial driving path is calculated according to the following formula,

[0074] dist = min(d[u] + w(u, v), dist)

[0075] Among them, dist represents the initial designated-driver path, and the initial value of dist is infinity. d[u] represents the current distance from the starting point to node u, w(u, v) represents the distance value in the u-th row and v-th column of the path matrix, and min() represents selecting the smallest number from several numbers within the parentheses. The result calculated in the formula represents the distance from the starting point to the destination under this path. The specific route needs to be output in each iteration of the formula.

[0076] It should be noted that the path matrix details the shortest distances from each path point to all other path points in the simplified path map after the simplification and abstraction process. Using this path matrix enables the program to quickly and accurately find the path with the shortest distance between the starting point and the ending point. However, since the formula only considers the distance factor and does not consider other factors in reality, such as road congestion conditions, traffic light timing, road construction, traffic restriction policies, and weather conditions, etc. This means that the path with the shortest current distance found according to the path matrix may not be the path with the least travel time. Therefore, the initial designated-driver path is only a preliminary result based on an ideal state and does not necessarily reflect the optimal choice in the real traffic environment, and further judgment and optimization are required.

[0077] In step S3, first obtain the previously obtained initial designated-driver path, then substitute the initial designated-driver path into the mathematical model for optimization, and finally calculate the optimal designated-driver path.

[0078] In one implementation manner, using the mathematical model to optimize the initial designated-driver path to obtain the optimal designated-driver path includes the following steps:

[0079] S31, divide the initial designated-driver path into several path segments of equal length, and each path segment is regarded as a non-deterministic timed automaton.

[0080] S32, perform the Cartesian product operation on all the non-deterministic timed automata to obtain the Cartesian product result under the finite state machine; among them, the Cartesian product result includes all sub-results from one path segment to another path segment.

[0081] S33, substitute the street data and the Cartesian product result into the mathematical model to calculate the optimal designated-driver path.

[0082] In step S31, first obtain the calculated initial designated-driver path, then divide the initial designated-driver path into several path segments of equal length, and regard each path segment as a non-deterministic timed automaton for subsequent substitution into the mathematical model.

[0083] Specifically, since the state machine model in the mathematical model requires multiple short-term states, the initial designated driving path needs to be divided into several equally long path segments. Then, each of the path segments is regarded as a timed automaton to accurately substitute the dynamic behavior of the vehicle on the path (such as driving time, possible waiting or parking, etc.) into the previously set state machine model.

[0084] It should be noted that when dividing into several equally long path segments, it is necessary to determine the length of each path segment according to the actual situation. Generally, if it is expected that the vehicle travels slower on a certain path, then more path segments need to be divided, that is, the path segments are divided into a larger number to simulate the interference of more traffic lights, pedestrians, and non-motor vehicles.

[0085] In step S32, first, all timed automata are obtained, and then a Cartesian product operation is performed on the timed automata to obtain the Cartesian product result under the finite state machine; where the Cartesian product result includes all sub-results, and each sub-result represents the process from one path segment to another path segment.

[0086] Specifically, calculating the Cartesian product of all timed automata is to regard all timed automata as a set, and then combine each element in the set with another element to form an element pair, and then put all the element pairs into a new set, and this new set is the Cartesian product result. Among them, each sub-result can represent the process from one path segment to another path segment.

[0087] It should be noted that the Cartesian product result obtained after performing the Cartesian product operation includes all path results, that is, all paths from one path point to another path point. However, in actual situations, not all path points can reach all other path points. Moreover, after performing the Cartesian product operation, the number of results will increase to the square of the original set size, which may cause all possible results to not be storable in complex paths. Therefore, after performing the Cartesian product operation, it is necessary to screen the Cartesian product result to delete those paths that cannot be reached or those with extremely high time consumption to reduce the storage burden and improve the calculation efficiency.

[0088] In step S33, first, the determined street data, the Cartesian product result, and the mathematical model are obtained, and then the street data and the Cartesian product result are substituted into the mathematical model for optimization operations to calculate the optimal designated driving path.

[0089] In one implementation, substituting the street data and the Cartesian product result into the mathematical model to calculate the optimal chauffeur path includes the following steps:

[0090] S331. Calculate the travel time according to the street data using the time function.

[0091] S332. Calculate the optimal chauffeur path according to the Cartesian product result and the travel time using the state machine model.

[0092] In step S331, calculating the travel time according to the street data using the time function includes: obtaining the path edges and path points of the street path according to the street data, and the time required to drive on each path edge; calculating the travel time using the time function according to the path edges, the path points, and the time; wherein, the travel time is calculated according to the following formula

[0093]

[0094] wherein, T represents the travel time, e j represents the j-th path edge, p i represents the i-th path point, t(e j , p i ) represents the time required to pass through the path edge e j to the path point p i , N represents the total number of path points, M i represents the total number of path edges that can reach the path point p i . By calculating the time required to reach the path point through different path edges, an optimal chauffeur path with the shortest overall time can be selected.

[0095] It should be noted that when calculating the time required to reach the path point through different path edges, not only the vehicle speed and path length need to be combined, but also the actual situation needs to be considered for calculation. For example, during the morning and evening rush hours, the time required to pass through different roads may change, and some shorter paths may take more time; on roads with temporary traffic control, the time required to pass through will also be more than normal. Therefore, when calculating the optimal chauffeur path, additional information collection devices are also needed to timely obtain the actual road conditions, so as to more accurately find the optimal chauffeur path.

[0096] In step S332, calculating the optimal designated-driver path using the state machine model based on the Cartesian product result and the travel time includes: checking the Cartesian product result to find all sub-results that can reach the destination from the departure location; calculating the current time of the path represented by each sub-result and comparing it with the travel time; if the current time is less than the travel time, outputting the path represented by the current sub-result as the optimal designated-driver path; if the current time is greater than the travel time, continuing to judge the next current time and the travel time; if all current times are greater than the travel time, outputting the initial designated-driver path as the optimal designated-driver path.

[0097] Specifically, after obtaining the Cartesian product result and the travel time, comparison and judgment are required to find the optimal designated-driver path. If the time taken by the path represented by a certain sub-result is less than the travel time of the initial designated-driver path, then the path represented by this sub-result can be output as the optimal designated-driver path; if the time taken by the paths represented by all sub-results is more than the travel time of the initial designated-driver path, then it means that the initial designated-driver path is already optimal, and it is output as the optimal designated-driver path.

[0098] It should be noted that the optimal designated-driver path only refers to the optimal path calculated by the program software. The determination of this path depends on various factors, including but not limited to real-time traffic conditions, road rules, vehicle performance parameters, and specific user requirements (such as the fastest travel time, the shortest travel distance, avoiding certain specific areas, etc.). The program software plans a relatively optimal driving route under the current conditions by collecting and analyzing a large amount of real-time data. However, since the actual situation may change at any time, this does not mean that this path is absolutely optimal in any case. In addition, the optimal designated-driver path may also be affected by the user's personal preferences. For example, some users may be more inclined to choose to drive on scenic roads, even if it means a slightly longer travel time; while some users may pay more attention to efficiency and hope to reach the destination as soon as possible. Therefore, when calculating the optimal path, the program software usually also needs to consider these personalized needs of the users.

[0099] In step S4, first obtain the user's order request and the optimal designated-driver path, find a suitable designated-driver, and then send the user's order request and the optimal designated-driver path to the designated-driver to establish the order assignment plan.

[0100] Specifically, obtain the user's order request and the optimal chauffeur path, and promptly notify a suitable chauffeur nearby. After receiving the notification, the chauffeur can decide whether to accept the current chauffeur task based on personal circumstances, and finally determine a suitable chauffeur to perform this chauffeur service. Then, based on the above information, establish a dispatching plan. The dispatching plan includes the user's order request, the optimal chauffeur path, the determined chauffeur, and a record of the actual itinerary of this dispatching plan.

[0101] It should be noted that the dispatching plan will record the actual itinerary of this dispatching plan, including the specific time spent, the actual distance, and the in-car recording. In addition, after the dispatching plan is completed, it will be saved in the enterprise data for a period of time to ensure that in case of an accident, the responsibility can be divided relying on the dispatching plan, thereby effectively safeguarding the legitimate rights and interests of users, chauffeurs, and enterprises.

[0102] In step S5, continuously monitor the vehicle position. If the vehicle position reaches the destination, end the current dispatching plan, obtain the completed plan data and end the monitoring, and at the same time record the completed plan data; if the vehicle position does not reach the destination, continuously record the current vehicle position in the dispatching plan and maintain the monitoring.

[0103] Specifically, continuously monitor the vehicle position to determine whether the vehicle has reached the destination. If the vehicle position reaches the destination, then the current dispatching plan can be ended, and the completed plan data can be obtained to indicate the end of this trip, and at the same time record the completed plan data; if the vehicle position does not reach the destination, continuously record the current vehicle position in the dispatching plan to form the information of the actual itinerary, and maintain the monitoring.

[0104] It should be noted that during the real-time recording process, if it is detected that the actual itinerary is not on the optimal chauffeur path, or the actual road conditions have changed, then a new optimal chauffeur path needs to be recalculated and the chauffeur needs to be prompted; if the actual itinerary is too far from the optimal chauffeur path, then an alarm needs to be issued to the chauffeur to prevent accidents.

[0105] To facilitate the understanding of the present invention, the working process of the present invention will be described below with a relatively common scenario as an example.

[0106] In this embodiment, an enterprise plans to use a system for chauffeur management. The system aims to be able to collect passenger order information in a timely manner and at the same time help chauffeurs reach the destination safely and quickly.

[0107] Step 1: The system collects detailed street data in advance and establishes a perfect mathematical model.

[0108] Step 2: The system collects user order requests in real time.

[0109] Step 3: Based on the user order request and street data, the system calculates the initial driving path with the shortest route.

[0110] Step 4: The system uses the mathematical model to optimize the initial driving path and calculates the optimal driving path with the shortest time according to the real-time road conditions.

[0111] Step 5: The system sends the order request and the optimal driving path to the driver and establishes a dispatching plan.

[0112] Step 6: After the driver arrives at the vehicle and starts driving, the system continuously monitors the vehicle position and records relevant information.

[0113] Step 7: After the vehicle reaches the destination, the system terminates the current dispatching plan, generates completed dispatching data and stores it.

[0114] Through the above steps, the method for driving management can realize the management of the driving process, reduce the operation time and increase the work efficiency. At the same time, the route in the driving process is recorded, thus ensuring the safety of both parties.

[0115] In summary, the present invention provides a method for driving management, including obtaining user order requests and street data and establishing a mathematical model; wherein, the user order request includes the departure place, destination and vehicle position of the user; the mathematical model includes a state machine model and a time function; calculating an initial driving path according to the departure place, the destination and the street data; optimizing the initial driving path using the mathematical model to obtain an optimal driving path; sending the user order request and the optimal driving path to the driver to establish a dispatching plan; monitoring the vehicle position; if the vehicle position reaches the destination, ending the current dispatching plan to obtain completed plan data and ending the monitoring; if the vehicle position does not reach the destination, continuously recording the current vehicle position in the dispatching plan and maintaining the monitoring.

[0116] The method realizes the management of the driving service process by obtaining the user's order request and street data, establishing a mathematical model including a state machine model and a time function, using the mathematical model to calculate the user's order request and the street data to obtain the optimal driving path, and then sending the optimal driving path to the driver. The method not only significantly shortens the operation time but also improves the work efficiency through the real-time generated optimal path. In addition, the method also has the function of recording the specific route during the driving service process, ensuring the traceability of the driving service process and providing a strong guarantee for the safety of both users and drivers.

[0117] Referring to Figure 2 , the second embodiment of the present invention provides a system for driving service management, including:

[0118] An information acquisition module for obtaining the user's order request and street data and establishing a mathematical model; wherein, the user's order request includes the user's departure location, destination, and vehicle location.

[0119] Among them, the information acquisition module includes a data collection module for obtaining the user's order request and street data; and a model establishment module for establishing a mathematical model.

[0120] An initial path calculation module for calculating an initial driving path according to the departure location, the destination, and the street data.

[0121] Among them, the initial path calculation module includes a data sorting module for cleaning and formatting the street data; and a path calculation module for calculating the initial driving path according to the departure location, the destination, and the street information.

[0122] A path optimization module for optimizing the initial driving path using the mathematical model to obtain the optimal driving path.

[0123] Among them, the path optimization module includes a path division module for dividing the initial driving path into several equal-length path segments and recording them as the non-continuous time automaton; a result calculation module for performing a Cartesian product operation on all the non-continuous time automata to obtain a Cartesian product result under the finite state machine; and a result optimization module for substituting the street data and the Cartesian product result into the mathematical model to calculate the optimal driving path.

[0124] A plan establishment module for sending the user's order request and the optimal driving path to the driver to establish a dispatching plan.

[0125] Among them, the solution establishment module includes a solution delivery module for sending the user's order request and the optimal chauffeur path to the chauffeur; and an order assignment establishment module for recording the user's order request, the optimal chauffeur path, and the information of the chauffeur to establish an order assignment solution.

[0126] The result feedback module is used to monitor the vehicle position; if the vehicle position reaches the destination, the current order assignment solution is ended, the completed solution data is obtained and the monitoring is ended; if the vehicle position does not reach the destination, the current vehicle position is continuously recorded in the order assignment solution and the monitoring is maintained.

[0127] Among them, the result feedback module includes a vehicle monitoring module for monitoring the vehicle position in real time; and an information recording module for recording information such as the actual distance, time spent, and conversations during the vehicle's driving process.

[0128] It should be noted that the system for chauffeur management provided in the embodiments of the present invention is used to execute all the process steps of the method for chauffeur management in the above embodiments. The working principles and beneficial effects of the two correspond one by one, and thus will not be elaborated here.

[0129] The embodiments of the present invention also provide an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for chauffeur management. When the processor executes the computer program, the steps in the above embodiments of the methods for chauffeur management are implemented, such as Figure 1 the step S11 shown. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above system embodiments are implemented, such as the initial path calculation module.

[0130] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the electronic device.

[0131] The electronic device can be a computing device such as a desktop computer, a notebook, a handheld computer, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device, and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, a bus, etc.

[0132] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects all parts of the entire electronic device using various interfaces and circuits.

[0133] The memory can be used to store the computer program and / or module. The processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0134] Among them, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or system capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0135] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the system embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative effort.

[0136] The specific embodiments described above have further elaborated on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for designated driver management, characterized in that: include: Obtaining a user order request and street data, and establishing a mathematical model; wherein the user order request includes the user's departure place, destination, and vehicle location; the mathematical model includes a state machine model and a time function; Calculate an initial designated driver route according to the departure place, the destination and the street data; Using the mathematical model to optimize the initial designated driver path to obtain the optimal designated driver path; Sending the user's order request and the optimal designated driving route to the designated driving driver to establish an order dispatching plan; Monitor the vehicle position; if the vehicle position reaches the destination, end the current dispatch plan, obtain the completion plan data and end the supervision; if the vehicle position does not reach the destination, continue to record the current vehicle position in the dispatch plan and keep monitoring; The step of optimizing the initial designated driver path using the mathematical model to obtain the optimal designated driver path includes: Divide the initial designated driver path into a number of path segments of equal length, each of the path segments being regarded as a non-continuous time automaton; Performing a Cartesian product operation on all the non-continuous time automata to obtain a Cartesian product result under a finite state machine; wherein the Cartesian product result includes all sub-results from one path segment to another path segment; Substituting the street data and the Cartesian product result into the mathematical model to calculate the optimal designated driver route; The step of calculating the initial designated driver route according to the departure place, the destination and the street data includes: Parsing the departure place, the destination and the street data to obtain a simplified route map; According to the simplified path map, a path matrix is ​​established; the path matrix records the shortest distance from each path point to all other path points in the simplified path map after simplification and abstraction; According to the path matrix, a path planning algorithm is used to calculate and obtain the initial designated driver path; The initial designated driver path is calculated according to the following formula: ； in, represents the initial designated driver path, and The initial value of is infinite. Indicates the distance from the departure point to the node The current distance, Indicates the path matrix Row, No. The distance value of the column, It means to select the smallest number from the numbers in the brackets; The step of substituting the street data and the Cartesian product result into the mathematical model to calculate the optimal designated driver route includes: According to the street data, the travel time is calculated using the time function; The optimal designated driver route is calculated using the state machine model according to the Cartesian product result and the travel time.

2. The method for designated driver management according to claim 1, characterized in that: The street data includes street routes, street intersections, traffic regulations, and real-time traffic conditions.

3. The method for designated driver management according to claim 1, characterized in that: The calculating an initial designated driver route according to the departure place, the destination and the street data includes: Cleaning and formatting the street data to obtain street information; The initial designated driver route is calculated based on the departure place, the destination and the street information.

4. The method for designated driver management according to claim 1, characterized in that , the travel time is calculated using the time function according to the street data, including: According to the street data, the path edges and path points of the street path are obtained, as well as the time required to travel on each path edge; The travel time is calculated using the time function according to the path edge, the path point, and the time; The travel time is calculated according to the following formula: ； in, represents the travel time, Indicates Path edges, Indicates Waypoints, Indicates the path edge To waypoint The time required, represents the total number of path points, Indicates that the path point can be reached The total number of edges in the path.

5. The method for designated driver management according to claim 1, characterized in that , the optimal designated driver route is calculated using the state machine model according to the Cartesian product result and the travel time, including: Check the Cartesian product result to find all sub-results that can reach the destination from the departure point; The current time of the path represented by each sub-result is calculated and compared with the travel time; if the current time is less than the travel time, the path represented by the current sub-result is output as the optimal designated driver path; if the current time is greater than the travel time, the next current time and the travel time are continuously determined; if all the current times are greater than the travel time, the initial designated driver path is output as the optimal designated driver path.

6. A system for designated driver management, characterized in that: A method for managing designated drivers as claimed in any one of claims 1 to 5, comprising: An information acquisition module, used to acquire user order requests and street data, and establish a mathematical model; wherein the user order request includes the user's departure place, destination and vehicle location; An initial route calculation module, used to calculate an initial designated driver route according to the departure place, the destination and the street data; A path optimization module, used to optimize the initial designated driver path using the mathematical model to obtain an optimal designated driver path; A plan establishment module, used to send the user's order request and the optimal designated driving route to the designated driving driver to establish an order dispatch plan; The result feedback module is used to monitor the vehicle position; if the vehicle position reaches the destination, the current dispatch plan is terminated, the completion plan data is obtained and the supervision is terminated; if the vehicle position does not reach the destination, the current vehicle position is continuously recorded in the dispatch plan and supervision is maintained.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for designated driver management as described in any one of claims 1 to 5.

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