Vehicle maintenance recommendation method, device, electronic device and storage medium

Abstract

The present application provides a vehicle maintenance recommendation method, device, electronic device and storage medium, which relates to the field of vehicle maintenance technology. The key points of its technical solution are: obtaining the first maintenance order from all maintenance stores within the first preset range of the client's current location; obtaining the first estimated completion time and the first required completion time corresponding to each first maintenance order according to the first maintenance order, and calculating the first difference; calculating the second estimated completion time of the maintenance demand at the corresponding maintenance store according to the first difference corresponding to all the first maintenance orders of each maintenance store; generating recommendation information according to the second estimated completion time corresponding to all maintenance stores, so as to recommend maintenance at the maintenance store with the earliest second estimated completion time. The vehicle maintenance recommendation method, device, electronic device and storage medium provided by the present application have the advantage of improving the accuracy of recommendations.

Description

Technical Field

[0001] The present application relates to the field of vehicle maintenance technology, and in particular to a vehicle maintenance recommendation method, device, electronic device and storage medium. Background Art

[0002] With the rapid growth of the number of cars in use, the demand for vehicle maintenance is also increasing. In order to improve maintenance efficiency and user experience, an intelligent vehicle maintenance recommendation system has emerged. For example, Chinese invention patent application CN112766820A discloses a data processing method, which is applied to a server device, the server device is communicatively connected to a client device, and the server device records the status information of multiple workstations. The method includes obtaining the demand information of the vehicle to be maintained sent by the client device, and then selecting a target workstation that meets the preset matching conditions from the multiple workstations based on the status information and demand information of the multiple workstations, and sending the location of the target workstation to the client device. For workstations in a maintenance state, the method can also perform time prediction processing to obtain the predicted end time of the target maintenance workstation, and send the time to the client device.

[0003] This method improves the utilization efficiency of workstation resources and user experience to a certain extent. However, it still has limitations in estimating the maintenance completion time. Specifically, when calculating the predicted end time of the target maintenance station, this method mainly considers the maintenance work currently in progress, and does not fully consider the situation where new maintenance needs may be inserted into the existing maintenance queue. This simplified time estimation method may lead to inaccurate prediction results, especially when the maintenance workload is large and the order changes are frequent.

[0004] For example, when a new urgent maintenance demand arises, existing methods may not be able to accurately assess the impact of this new order on other pending maintenance tasks. Similarly, for some small maintenance tasks that can be flexibly inserted between other maintenance tasks, existing methods also have difficulty accurately estimating their completion time. This inaccuracy in estimation may cause users to be unable to arrange their time reasonably, increase user waiting time, and reduce user satisfaction.

[0005] Especially for user groups such as online car-hailing and taxis, these users usually hope to complete vehicle maintenance earlier. Although the existing technology provides a method for recommending workstations based on vehicle maintenance needs and workstation status, when predicting the predicted end time of the maintenance workstation, it only calculates based on the number of maintenance items and the time corresponding to the maintenance items. This method usually simply sorts them in order of priority. In some cases, the recommendation is not the optimal result, that is, the maintenance workstation that is estimated to be completed the earliest in order of priority is not necessarily the workstation that actually completes maintenance the earliest.

[0006] Therefore, the existing technology needs to be improved. Summary of the invention

[0007] The purpose of the present application is to provide a vehicle maintenance recommendation method, device, electronic device and storage medium, which have the advantage of improving the accuracy of recommendations.

[0008] In the first aspect, the present application provides a vehicle maintenance recommendation method, the technical solution is as follows:

[0009] include:

[0010] According to the maintenance demand and location information sent by the client, obtain the current first maintenance order of the maintenance station related to the maintenance demand in all maintenance stores within a first preset range of the current location of the client;

[0011] According to the first maintenance order, a first estimated completion time and a first required completion time corresponding to each of the first maintenance orders are obtained, and a first difference is calculated according to the first estimated completion time and the first required completion time;

[0012] Calculating, according to the first difference corresponding to all the first maintenance orders of each maintenance store, a second estimated completion time of the maintenance demand at the corresponding maintenance store;

[0013] Recommendation information is generated based on the second estimated completion times corresponding to all the maintenance stores to recommend maintenance at the maintenance store with the earliest second estimated completion time.

[0014] Further, in the present application, the step of calculating the second estimated completion time of the maintenance demand at the corresponding maintenance store according to the first difference corresponding to all the first maintenance orders of each maintenance store includes:

[0015] Calculate a first estimated maintenance time according to the maintenance demand;

[0016] Comparing the first difference corresponding to all the first maintenance orders of each maintenance store with the first estimated maintenance time;

[0017] When the first difference corresponding to all the first maintenance orders of one maintenance store is less than the first estimated maintenance time, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated according to the first estimated completion time of the last first maintenance order corresponding to the maintenance store and the first estimated maintenance time;

[0018] When a maintenance store has the first difference corresponding to the first maintenance order that is greater than or equal to the first estimated maintenance time, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated according to the arrangement position of the first maintenance order corresponding to the first difference that is greater than or equal to the first estimated maintenance time;

[0019] The step of calculating the second estimated completion time of the maintenance demand at the corresponding maintenance store according to the arrangement position of the first maintenance order corresponding to the first difference value that is greater than or equal to the first estimated maintenance time includes:

[0020] When the first maintenance order corresponding to the first difference that is greater than or equal to the first estimated maintenance time is at the end of the arrangement, the maintenance demand is first inserted before the first maintenance order at the end of the arrangement, and then the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated;

[0021] When the arrangement position of the first maintenance order corresponding to the first difference that is greater than or equal to the first estimated maintenance time is not the last, determine whether the maintenance demand can be inserted before the last first maintenance order in the arrangement position without affecting the subsequent delivery of the first maintenance order;

[0022] If yes, first insert the maintenance demand before the first maintenance order, and then calculate the second estimated completion time of the maintenance demand at the corresponding maintenance store;

[0023] If not, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated based on the first estimated completion time of the last first maintenance order and the first estimated maintenance time.

[0024] Further, in the present application, the step of obtaining, according to the maintenance demand and location information sent by the client, the current first maintenance order of the maintenance station related to the maintenance demand in all maintenance stores within the first preset range of the current location of the client includes:

[0025] Obtaining the maintenance history record of the vehicle corresponding to the client;

[0026] Sending maintenance request information to the client according to the maintenance history record;

[0027] Receiving the maintenance requirement and the location information sent by the client according to the maintenance request information;

[0028] According to the maintenance demand and the location information sent by the client, the first maintenance order currently at the maintenance station related to the maintenance demand in all maintenance stores within a first preset range of the current location of the client is obtained.

[0029] Further, in the present application, the step of sending maintenance request information to the client according to the maintenance history record includes:

[0030] Acquire the unmaintained time of all maintenance items of the vehicle corresponding to the client according to the maintenance history record;

[0031] Obtaining early warning maintenance items based on database information of the vehicle model corresponding to the client and evaluation of the unmaintained time of all maintenance items;

[0032] Obtaining a second estimated maintenance time for the early warning maintenance item;

[0033] Calculating a third estimated completion time based on the second estimated maintenance time;

[0034] Send maintenance request information to the client according to the third estimated completion time.

[0035] Further, in the present application, the step of calculating the third estimated completion time according to the second estimated maintenance time includes:

[0036] Acquire, according to the early warning maintenance item, a second maintenance order for the maintenance stations related to the early warning maintenance item in all the maintenance stores within a second preset range;

[0037] The third estimated completion time is calculated according to the second maintenance order and the second estimated maintenance time;

[0038] The step of sending maintenance request information to the client according to the third estimated completion time includes:

[0039] Obtain historical orders corresponding to the client;

[0040] estimating an expected order status within the third estimated completion time based on the historical orders;

[0041] Send maintenance request information to the client according to the expected status of the order.

[0042] Further, in the present application, the step of sending maintenance request information to the client according to the expected status of the order includes:

[0043] Obtain the time ratio of idle time to order processing time in the expected state of the order;

[0044] When the time proportion is greater than a set value, a maintenance request message is sent to the client.

[0045] Furthermore, in the present application, the method further comprises:

[0046] After the client selects a target maintenance store according to the recommendation information, the first maintenance order of the maintenance station related to the maintenance demand of the target maintenance store is obtained in real time;

[0047] After the first maintenance order is updated, the second estimated completion time is recalculated and a corresponding prompt message is sent.

[0048] In a second aspect, the present application also proposes a vehicle maintenance recommendation device, comprising:

[0049] The first module is used to obtain, according to the maintenance demand and location information sent by the client, the current first maintenance order of the maintenance station related to the maintenance demand in all maintenance stores within a first preset range of the current location of the client;

[0050] A second module is used to obtain, according to the first maintenance order, a first estimated completion time and a first required completion time corresponding to each of the first maintenance orders, and calculate a first difference according to the first estimated completion time and the first required completion time;

[0051] A third module is used to calculate a second estimated completion time of the maintenance demand at the corresponding maintenance store according to the first difference corresponding to all the first maintenance orders of each maintenance store;

[0052] The fourth module is used to generate recommendation information based on the second estimated completion time corresponding to all the maintenance stores, so as to recommend maintenance at the maintenance store with the earliest second estimated completion time.

[0053] In a third aspect, the present application further proposes an electronic device, comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the above method are executed.

[0054] In a fourth aspect, the present application further proposes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method are executed.

[0055] From the above, it can be seen that the vehicle maintenance recommendation method, device, electronic device and storage medium provided by the present application calculate the first difference through the first estimated completion time and the first required completion time of the first maintenance order, and calculate the second estimated completion time of the maintenance requirement based on the first difference, and compare the early and late second estimated completion time to generate recommendation information, thereby having the beneficial effect of improving the accuracy of recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flow chart of a vehicle maintenance recommendation method provided in this application.

[0057] Figure 2 This is a structural schematic diagram of a vehicle maintenance recommendation device provided in this application.

[0058] Figure 3 A schematic diagram of the structure of an electronic device provided in this application.

[0059] In the figure: 210, first module; 220, second module; 230, third module; 240, fourth module; 310, processor; 320, memory. DETAILED DESCRIPTION

[0060] The technical solutions in the present application will be clearly and completely described below in conjunction with the drawings in the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0061] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0062] With the rapid growth of the number of cars in the market, the demand for vehicle maintenance is also increasing. However, there are many problems with the traditional vehicle maintenance model, such as it is difficult for users to know the real-time queue situation of the maintenance store and it is impossible to accurately estimate the maintenance completion time. These problems not only affect the user's maintenance experience, but also reduce the operating efficiency of the maintenance store. With the development of technology, some existing technologies have proposed intelligent recommendation methods, such as Chinese patent application CN112766820A. Although this existing technology provides a method for recommending workstations based on vehicle maintenance needs and workstation status, when predicting the predicted end time of the maintenance workstation, it is only calculated based on the number of maintenance items and the corresponding time of the maintenance items. This method usually simply sorts in order of priority. In some cases, the recommendation is not the optimal result.

[0063] In order to solve these problems, the present invention proposes a vehicle maintenance recommendation method, which can intelligently recommend the best maintenance store according to the user's real-time location and maintenance needs, thereby improving user satisfaction and the operating efficiency of the maintenance store.

[0064] First, please refer to Figure 1 , the present application provides a vehicle maintenance recommendation method, the technical solution specifically includes:

[0065] S110, according to the maintenance demand and location information sent by the client, obtaining the current first maintenance order of the maintenance station related to the maintenance demand in all maintenance stores within a first preset range of the current location of the client;

[0066] S120: According to the first maintenance order, obtain a first estimated completion time and a first required completion time corresponding to each first maintenance order, and calculate a first difference according to the first estimated completion time and the first required completion time;

[0067] S130: Calculate a second estimated completion time of the maintenance demand at the corresponding maintenance store according to the first difference corresponding to all the first maintenance orders of each maintenance store;

[0068] S140. Generate recommendation information according to the second estimated completion times corresponding to all maintenance stores, so as to recommend going to the maintenance store with the earliest second estimated completion time for maintenance.

[0069] Among them, the solution of the present application can be implemented by relying on a complete system, which includes a server, a client and a data acquisition terminal. The server communicates with the client and the data acquisition terminal respectively to receive data, process data and send data. The client is mainly used to send maintenance requirements and location information. The data acquisition terminal is set up in the maintenance store to obtain the first maintenance order corresponding to each maintenance station in the corresponding maintenance store.

[0070] In the solution proposed in this application, the system receives maintenance requirements and location information sent by users through mobile devices and other clients. Maintenance requirements may include vehicle model, fault description, expected maintenance items, etc. Location information is usually the user's current geographic coordinates.

[0071] The system determines a first preset range based on the user's location information. This range can be a circular area centered on the user's current location, and the radius can be set according to actual conditions, such as 5 kilometers, 10 kilometers, etc. In some embodiments, this range can also be dynamically adjusted according to the user's preferences or current traffic conditions.

[0072] Next, the system will search for all maintenance stores within this preset range. For each maintenance store, the system will further query the current orders of the maintenance stations related to the user's maintenance needs, that is, the first maintenance order. For example, if the user needs to replace the tire, the system will query the orders of the maintenance station that specializes in tire replacement.

[0073] The first maintenance order here usually includes detailed information on all orders currently being processed and waiting to be processed by each maintenance station, such as order receipt time, estimated completion time, maintenance items, etc. This information provides basic data for subsequent time estimation and recommendations.

[0074] After obtaining the first maintenance order of the maintenance station, the system will analyze each first maintenance order. For each first maintenance order, the system will calculate two key times:

[0075] First estimated completion time: This is the estimated completion time of the first maintenance order based on the maintenance items, vehicle condition, etc. This time can take into account the complexity of the maintenance, the availability of the required parts, and other factors.

[0076] First Required Completion Time: This is the time that the customer requires the maintenance to be completed. Usually, this time is specified by the customer when placing an order. In some cases, the customer will specify a first required completion time. In other cases, the customer is not in a hurry for the vehicle maintenance time. When the customer does not specify a first required completion time, a preset first required completion time can be automatically configured for the first maintenance order. The preset first required completion time can be configured according to the longest time of the maintenance project.

[0077] Then, the system will calculate the difference between these two times, which is the first difference. The first difference actually represents the maintenance time margin corresponding to the first maintenance order. If the difference is positive, it means that the maintenance is expected to be completed within the time required by the customer; if it is negative, it means there will be a delay.

[0078] After obtaining the first difference, the system will use the first difference to estimate a completion time for the user's new maintenance needs at each maintenance store. This process can take into account multiple factors:

[0079] Current workload of the maintenance store: When there are many positive values ​​of the first difference, it means that the maintenance store currently has a large amount of work margin and may be able to process new orders faster.

[0080] Complexity of new maintenance requirements: The system will estimate the time required for this maintenance project based on the maintenance requirements provided by the user.

[0081] Possibility of inserting new orders: The system will try to insert new maintenance requirements into the existing order sequence and find the best insertion position.

[0082] By taking all these factors into consideration, the system calculates a second estimated completion time for each maintenance store, which reflects when the maintenance is expected to be completed if the user chooses this store for maintenance.

[0083] After calculating the second estimated completion time of all maintenance stores, the system will sort all the second estimated completion times and find the store with the earliest second estimated completion time. This store will be the first choice for the system to recommend.

[0084] Compared with the prior art, the solution of the present application is not to simply query the first maintenance order status of the maintenance station corresponding to each maintenance store, and then estimate the second estimated completion time of the maintenance demand in chronological order, but to calculate the first difference in the first maintenance order of the maintenance station corresponding to each maintenance store, and calculate the second estimated completion time based on the first difference. This can make the recommendation results more accurate and reasonable, improve user experience, and recommend to users the maintenance store that can complete the maintenance work earliest.

[0085] In some specific implementations, the recommendation information generated by the system may include one or more of the following:

[0086] The name and address of the recommended maintenance store;

[0087] Estimated time to complete maintenance;

[0088] The distance and estimated travel time from the user's current location to the maintenance store;

[0089] Ratings and user reviews of maintenance stores;

[0090] Estimated maintenance costs.

[0091] In addition to the preferred recommendation, the system can also provide several alternatives to give users more choices.

[0092] In some embodiments, the system may consider more factors to optimize recommendations. For example, it may combine information such as the user's historical maintenance records, the professional expertise of the maintenance store, and current traffic conditions to provide more personalized and accurate recommendations.

[0093] Through the above steps, the present invention can effectively solve the problems existing in the traditional maintenance model. It not only provides users with more convenient and efficient maintenance options, but also helps maintenance stores to better manage orders and optimize resource allocation. This recommendation method based on real-time data and intelligent algorithms has greatly improved the operational efficiency of the entire vehicle maintenance industry.

[0094] It should be noted that the above embodiment is only one of the preferred implementations of the present invention, and the present invention may also have other various implementations. For example, when calculating the second estimated completion time, a machine learning algorithm may be introduced to continuously optimize the accuracy of the time estimation by analyzing historical maintenance data. For another example, the system may be connected to the on-board diagnostic system (OBD) to obtain more detailed and accurate vehicle status information, thereby providing more accurate maintenance suggestions and time estimates.

[0095] In general, the vehicle maintenance recommendation method provided in this application achieves efficient matching of maintenance needs and maintenance resources through intelligent and data-based means.

[0096] Further, in some of the embodiments, the step of calculating the second estimated completion time of the maintenance demand at the corresponding maintenance store according to the first difference corresponding to all the first maintenance orders of each maintenance store includes:

[0097] The first estimated maintenance time is calculated based on the maintenance demand;

[0098] Compare the first difference value corresponding to all the first maintenance orders of each maintenance store with the first estimated maintenance time;

[0099] When the first differences corresponding to all first maintenance orders of a maintenance store are less than the first estimated maintenance time, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated according to the first estimated completion time of the last first maintenance order of the corresponding maintenance store and the first estimated maintenance time;

[0100] When a maintenance store has a first maintenance order corresponding to a first difference that is greater than or equal to the first estimated maintenance time, a second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated according to the arrangement position of the first maintenance order corresponding to the first difference that is greater than or equal to the first estimated maintenance time;

[0101] The step of calculating the second estimated completion time of the maintenance demand at the corresponding maintenance store according to the arrangement position of the first maintenance order corresponding to the first difference value that is greater than or equal to the first estimated maintenance time includes:

[0102] When the first maintenance order corresponding to the first difference value that is greater than or equal to the first estimated maintenance time is at the end of the arrangement, the maintenance demand is first inserted before the first maintenance order at the end of the arrangement, and then the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated;

[0103] When the first maintenance order corresponding to the first difference value greater than or equal to the first estimated maintenance time is not at the end of the arrangement, determine whether the maintenance demand can be inserted before the last first maintenance order in the arrangement without affecting the delivery of the subsequent first maintenance order;

[0104] If yes, the maintenance demand is inserted before the corresponding first maintenance order, and then the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated;

[0105] If not, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated based on the first estimated completion time of the last first maintenance order and the first estimated maintenance time.

[0106] In the previous description, the application mentioned that the system will calculate the second estimated completion time of the maintenance demand at the corresponding maintenance store based on the first difference corresponding to all the first maintenance orders of each maintenance store. Now, the details of this calculation process will be discussed in more depth below.

[0107] First, the system will estimate the time required for the maintenance work based on the maintenance needs submitted by the user, which is called the "first estimated maintenance time". This time can be calculated based on multiple factors, such as vehicle model, complexity of maintenance items, historical maintenance data, etc. For example, replacing a tire may take 30 minutes, while replacing an engine may take several hours.

[0108] Next, the system will compare this first estimated maintenance time with the first difference of each first maintenance order. This comparison process is actually looking for the best insertion position for the new order, considering the following situations:

[0109] When the first difference values ​​corresponding to all existing first maintenance orders of a maintenance store are less than the first estimated maintenance time, this means that all first maintenance orders are urgent and new maintenance needs cannot be inserted between them. In this case, the system will schedule the new maintenance need after the last existing order. Specifically, the system will calculate the second estimated completion time of the maintenance need at the maintenance store based on the first estimated completion time of the last first maintenance order and the first estimated maintenance time.

[0110] When there is a first maintenance order with a first difference greater than or equal to the first estimated maintenance time, this means there may be an opportunity to insert a new maintenance demand between the existing first maintenance orders, thereby improving overall efficiency. The system will decide how to insert the new maintenance demand based on the arrangement position of the first maintenance order corresponding to the first difference greater than or equal to the first estimated maintenance time.

[0111] If the first maintenance order is at the end of the list, the system will insert the maintenance demand before the first maintenance order and then calculate the second estimated completion time. This is relatively simple because inserting a new order will not affect the completion time of other orders.

[0112] If the first maintenance order is not at the end of the list, the system needs to make a more complex judgment. Specifically, it needs to evaluate whether the maintenance demand can be inserted before the first maintenance order without affecting the delivery of the subsequent first maintenance orders. "Not affecting" here means that after inserting the new order, the completion time of all subsequent first maintenance orders is still not later than the completion time of their respective first demand.

[0113] If the new order can be inserted without affecting the subsequent first maintenance order, the system does so and then calculates the second estimated completion time.

[0114] If it is not possible to insert a new order without affecting subsequent orders, the system will fall back on scheduling the new order after the last first maintenance order and then calculate the second estimated completion time.

[0115] Specifically, the steps for evaluating whether the maintenance requirement can be inserted before the first maintenance order without affecting the subsequent delivery of the first maintenance order include:

[0116] In the original queue order, obtain the insertion position of the maintenance demand;

[0117] According to the insertion position, it is judged whether the first difference values ​​originally corresponding to all the first maintenance orders arranged after the maintenance requirement are greater than or equal to the first estimated completion time corresponding to the maintenance requirement after the maintenance requirement is inserted. If so, it is judged that the maintenance requirement can be inserted before the first maintenance order without affecting the delivery of subsequent first maintenance orders. If not, it is evaluated whether the first maintenance order corresponding to the first difference value less than the first estimated completion time can be swapped with other first maintenance orders so that the delivery of all first maintenance orders is not affected. If so, it is judged that the maintenance requirement can be inserted before the first maintenance order without affecting the delivery of subsequent first maintenance orders. If not, it is judged that the maintenance requirement cannot be inserted before the first maintenance order without affecting the delivery of subsequent first maintenance orders.

[0118] The cleverness of this approach is that the application not only takes into account the urgency of new orders, but also makes full use of the time gaps between existing orders. In this way, the system can maximize the use of maintenance store resources and improve overall work efficiency.

[0119] For example, suppose a maintenance store currently has three orders, and the first estimated completion time and the first required completion time are as follows:

[0120] Order A: First estimated completion time 10:00, first required completion time 11:00, first difference 60 minutes;

[0121] Order B: First estimated completion time 11:30, first required completion time 12:00, first difference 30 minutes;

[0122] Order C: The first estimated completion time is 13:00, the first required completion time is 14:30, and the first difference is 90 minutes.

[0123] Now, there is a new maintenance request with a first estimated maintenance time of 45 minutes.

[0124] First, the system finds that the first difference (90 minutes) of order C is greater than the first estimated maintenance time (45 minutes) of the maintenance requirement.

[0125] The system then checks whether a new requirement corresponding to the maintenance requirement can be inserted between Order B and Order C.

[0126] After calculation, the system found that if the new demand is processed at 11:30 (the first estimated completion time of order B), it can be completed at 12:15, and then order C is processed at 12:15. Order C needs to be processed for 1.5 hours (the first estimated completion time of order B is 11:30 to the first estimated completion time of order C is 13:00, which is 1.5 hours). That is, order C will be completed at 13:45, which will not cause delays in order C. The first demand completion time of order C is (14:30).

[0127] Therefore, the system decides to insert the new requirement between Order B and Order C, and the second estimated completion time of the maintenance requirement is 12:15.

[0128] If conventional existing technology is used, the second estimated completion time of the maintenance requirement will be estimated at 13:45.

[0129] This example shows how the application can intelligently use the time gaps between existing orders to schedule new maintenance needs as early as possible without affecting other orders. This approach effectively improves the accuracy of recommended information.

[0130] In some preferred embodiments, if there are multiple first difference values ​​greater than the first estimated maintenance time, the system will try to insert them one by one to find the optimal insertion method.

[0131] Taking the above example, the first difference of order A is 60 minutes. The system can also try to insert the maintenance demand before order A. However, through subsequent calculations, it is found that inserting it before order A will cause delays in order B, while inserting it between order B and order C will not cause delays in any first maintenance order. Therefore, the system will choose to insert the maintenance demand between order B and order C.

[0132] In some preferred embodiments, the system first compares the first estimated maintenance time of the maintenance requirement with the first difference of all first maintenance orders, and selects the first maintenance order that is at the front of the arrangement and whose subsequent first difference values ​​are all greater than the first estimated maintenance time as the insertion position.

[0133] It is worth noting that the effectiveness of this method depends largely on the accuracy of the first estimated maintenance time. In order to improve the accuracy of this time estimate, the system can use machine learning technology to continuously optimize the estimation model by analyzing a large amount of historical data. For example, the system may consider the following factors: vehicle model and year, specific maintenance items, maintenance technician experience and expertise, current workload, availability of required parts, seasonal factors, etc.

[0134] By taking these factors into account, the system can generate more precise time estimates, further improving the accuracy and reliability of the entire recommendation system.

[0135] In addition, the application is also highly flexible and scalable. For example, the concept of priority can be introduced to allow certain urgent maintenance tasks to jump the queue. Or, the maintenance store's expertise can be considered and the projects they are good at can be given a higher weight when making time estimates.

[0136] In general, this intelligent order insertion and time estimation algorithm is one of the core innovations of this application. It not only improves the utilization efficiency of maintenance resources, but also provides users with more accurate and personalized services.

[0137] It is worth noting that the second estimated completion time is calculated based on the first difference between the existing first maintenance order and the first estimated maintenance time obtained by the maintenance demand sent by the client, and then the recommendation information is generated. The processing and use of this information is one of the core inventions of this application. In actual scenarios, when users have urgent requirements for maintenance time, they usually ask the maintenance store whether they can jump the queue. At this time, the staff of the maintenance store needs to manually arrange the order query, which is a complicated process. Therefore, for the staff, in order to avoid trouble, it is very likely that they will directly feedback that jumping the queue is not allowed and they can only queue in order. In such a scenario, for the staff of the maintenance store, if they want to find the earliest solution to complete the maintenance for the user, they need to do a lot of complicated work, and this process is prone to errors. For users, they usually need to go to a specific maintenance store to inquire. At this time, the user does not know the situation of other maintenance stores. Therefore, the traditional existing technology has brought many problems to users and maintenance store staff.

[0138] In this actual application scenario, the present application proposes obtaining the maintenance requirements and location information sent by the client, and then finally calculating the second estimated completion time. In this process, the user does not need to go to the maintenance store, but can directly send information through the client, and then go to the maintenance store after obtaining the recommended information, thereby improving the user experience. For the staff of the maintenance store, there is no need to perform complicated manual scheduling, which reduces the burden and reduces the possibility of errors.

[0139] Further, in some of the embodiments, according to the maintenance demand and location information sent by the client, the step of obtaining the current first maintenance order of the maintenance station related to the maintenance demand in all maintenance stores within the first preset range of the current location of the client includes:

[0140] Get the maintenance history record of the vehicle corresponding to the client;

[0141] Send maintenance request information to the client based on maintenance history records;

[0142] Receive maintenance requirements and location information sent by the client based on maintenance request information;

[0143] According to the maintenance demand and location information sent by the client, obtain the current first maintenance order of the maintenance station related to the maintenance demand in all maintenance stores within a first preset range of the client's current location.

[0144] In some of the above embodiments, through the solution of this application, recommendation information can be generated to recommend users to go to the recommended maintenance stores, without the need for users to go to the maintenance stores first and then make inquiries. In this process, how to provide users with maintenance services more actively and accurately, this problem guides this application to make further improvements, so that it can not only passively respond to users' maintenance needs, but also actively predict and recommend maintenance services. This initiative not only improves the user experience, but also helps prevent potential vehicle problems, thereby improving road safety and vehicle service life.

[0145] To achieve this goal, this application introduces intelligent maintenance history analysis and prediction. The core idea is: by analyzing the user's vehicle maintenance history, combined with the characteristics and usage of the vehicle model, actively predict possible maintenance needs, and send maintenance suggestions to the user at the appropriate time.

[0146] Specifically, the workflow is as follows:

[0147] Obtain vehicle maintenance history;

[0148] The system first obtains the maintenance history of the vehicle corresponding to the client (usually the user's smartphone or in-vehicle system). These records may include: past maintenance items and dates, the time and content of regular maintenance, the record of replaced parts, the vehicle mileage, any of the problems or abnormal conditions reported by the user.

[0149] This historical data provides the basis for the system to perform predictive analysis. By analyzing this data, the system can understand the vehicle's usage, maintenance frequency, and potential problems that may exist.

[0150] Send maintenance request information;

[0151] Based on the analysis of maintenance history, the system will generate and send maintenance request information to the client. This process can take into account multiple factors: the recommended maintenance cycle of each maintenance item, the user's actual usage (such as mileage, usage environment, etc.), the characteristics and common problems of the vehicle model, seasonal factors (some maintenance items may be more important in certain seasons), and the user's personal preferences (such as whether they prefer more frequent maintenance).

[0152] For example: For a high-mileage commercial vehicle, the system may recommend tire inspection and replacement more frequently; while for a family car mainly used for short-distance commuting, the system may pay more attention to the maintenance of the battery and brake system.

[0153] Receive maintenance requirements and location information;

[0154] When a user receives a maintenance request and decides to perform maintenance, he or she can reply with specific maintenance requirements and current location information through the client. This information may include: the maintenance items confirmed by the user, other maintenance requirements supplemented by the user, the user's current location, and any one of the maintenance time ranges expected by the user.

[0155] Obtain maintenance store information;

[0156] With the user's maintenance needs and location information, the system can start to query qualified maintenance stores. In this process, the system will search for all relevant maintenance stores within the first preset range of the user's current location, and obtain the current orders of maintenance stations in these stores that are related to the user's maintenance needs.

[0157] This proactive approach to prediction and recommendations offers several benefits:

[0158] Through timely maintenance, many potential problems can be prevented and the overall reliability and performance of the vehicle can be improved. Users do not need to remember various maintenance cycles themselves. The system will remind them at the appropriate time, greatly reducing the risk of missing important maintenance. The system will provide customized suggestions based on the actual use of each vehicle, rather than simply following the standard maintenance cycle. By understanding the user's maintenance needs in advance, maintenance shops can better arrange work and prepare the required parts, thereby improving overall efficiency. As time goes by, the system accumulates more and more data, and the accuracy of its predictions and recommendations will continue to improve.

[0159] Specifically, suppose a user owns a mid-sized SUV that has been used for many years. By analyzing the user's maintenance history, the system finds that the last oil change was 6 months ago, the tires have been used for about 40,000 kilometers, the air conditioning system had a minor problem last summer, and the user's average annual mileage is about 20,000 kilometers.

[0160] Based on this information, the system may send the following maintenance request information to the user before summer arrives:

[0161] "It is recommended to consider the following maintenance items: 1. Replace the engine oil and oil filter; 2. Tire inspection and tire balancing; 3. Air conditioning system inspection and cleaning; 4. Regular battery and brake system inspection."

[0162] If the user confirms the maintenance request, the system will start searching for nearby maintenance stores based on the location information and generate recommendation information.

[0163] Further, in some of the embodiments, the step of sending maintenance request information to the client according to the maintenance history record includes:

[0164] Obtain the unmaintained time of all maintenance items of the client's corresponding vehicle based on the maintenance history records;

[0165] According to the database information of the vehicle model corresponding to the client and the unmaintained time of all maintenance items, the early warning maintenance items are obtained;

[0166] Obtain the second estimated maintenance time for the early warning maintenance project;

[0167] Calculate a third estimated completion time based on the second estimated maintenance time;

[0168] A maintenance request message is sent to the client according to the third estimated completion time.

[0169] Specifically, the step of calculating the third estimated completion time according to the second estimated maintenance time includes:

[0170] According to the early warning maintenance item, the second maintenance order of the maintenance station related to the early warning maintenance item in all maintenance stores within the second preset range is obtained;

[0171] A third estimated completion time is calculated based on the second maintenance order and the second estimated maintenance time;

[0172] The step of sending maintenance request information to the client according to the third estimated completion time includes:

[0173] Get the historical orders corresponding to the client;

[0174] The expected status of the order within the third estimated completion time based on historical order estimates;

[0175] Send maintenance request information to the client based on the expected status of the order.

[0176] In some of the above-mentioned embodiments, the present application proposes sending maintenance request information to the client based on the maintenance history records. On this basis, a key issue emerges: how to ensure that the maintenance request information sent by the system is not only accurate, but also has practical significance and urgency. This issue guides the present application to further optimize the generation process of the maintenance request information to make it more intelligent and accurate.

[0177] To achieve this goal, a multi-level evaluation mechanism is introduced into the system, which not only takes into account the vehicle's maintenance history, but also combines the characteristic data of the vehicle model and real-time usage to generate more accurate and personalized maintenance recommendations. The core of this evaluation mechanism includes the following steps:

[0178] Get the unmaintained time;

[0179] First, the system will obtain the unmaintained time of all maintenance items of the client's corresponding vehicle based on the maintenance history records. This time can take into account multiple factors: time span: the actual time from the last maintenance to now; mileage: the distance driven since the last maintenance; frequency of use: the frequency and intensity of daily use of the vehicle; environmental factors: such as the climate conditions and road conditions of the permanent location, etc.

[0180] For example, for the maintenance item of oil change, the system will not only calculate the time since the last change, but also take into account the actual mileage during this period. If a car has only traveled 1,000 kilometers in 6 months, then the system may not list it as an urgent maintenance item even if the regular change cycle has been reached.

[0181] Evaluation of early warning maintenance projects;

[0182] Next, the system will evaluate the early warning maintenance items based on the database information of the client's corresponding vehicle model and the unmaintained time of all maintenance items. This process involves the following key points: Vehicle model database: Each vehicle model has its own specific maintenance requirements and common problems. The system will set different weights and early warning thresholds for different maintenance items according to the characteristics of the vehicle model; Dynamic threshold: The early warning threshold can be dynamic, that is, dynamically adjusted according to the use of the vehicle. For example, for vehicles that often drive in harsh environments, the early warning threshold of some maintenance items may be lowered; Comprehensive score: The system will calculate a comprehensive score for each maintenance item. This score takes into account multiple factors such as the unmaintained time, vehicle model characteristics, and usage. Only when the score exceeds a certain threshold will the item be listed as an early warning maintenance item.

[0183] Estimated maintenance time;

[0184] For maintenance items that are listed as early warning, the system will estimate a "second estimated maintenance time". This time can be calculated based on multiple factors: the complexity of the maintenance project, the specific model and year of the vehicle, the average efficiency of the maintenance store, the supply of parts that may be needed, seasonal factors (some maintenance items may take longer in certain seasons), etc.

[0185] Calculate the third estimated completion time;

[0186] After obtaining the second estimated maintenance time, the system will not directly send it to the user as a recommended time. Instead, it will conduct a more in-depth analysis and calculate a "third estimated completion time". This process includes:

[0187] Obtain information of all maintenance stores within the second preset range;

[0188] The second preset range may be the same as or different from the first preset range.

[0189] Analyze the workload of each store;

[0190] The system will query the current orders of the maintenance stations related to the early warning maintenance items.

[0191] Simulate order insertion;

[0192] The system will attempt to insert the early warning maintenance items into the existing order sequence of each store.

[0193] Calculate the optimal completion time;

[0194] Through this simulation, the system can calculate a theoretical earliest completion time for each store. The earliest of these times is the third estimated completion time.

[0195] Generate maintenance request information;

[0196] Finally, the system will generate a maintenance request message based on the calculated third estimated completion time. But before sending this message, the system will perform an additional check:

[0197] Get the user's historical orders;

[0198] Historical orders refer to the user’s past vehicle usage status. For online ride-hailing and taxis, it refers to their past order status.

[0199] Estimate the expected status of the order;

[0200] The system will analyze the user's possible itinerary and arrangements at the third estimated completion time.

[0201] Assess timing;

[0202] If the system predicts that the user may be busy or have other important arrangements during the third estimated completion time, the recommended time will be adjusted or the maintenance request information will not be sent temporarily.

[0203] Through this multi-level evaluation mechanism, the maintenance request information generated by the system not only accurately reflects the actual maintenance needs of the vehicle, but also fully considers the user's personal situation and convenience. This approach brings several significant advantages: each maintenance suggestion is generated based on the actual situation of a specific vehicle and the user's personal usage pattern, rather than simply following a general maintenance cycle. By identifying potential problems in a timely manner, the system can help users avoid more serious failures, thereby reducing overall maintenance costs and vehicle downtime. The system will choose the most appropriate time to send maintenance suggestions to avoid disturbing users when they are busy or inconvenient. By pre-evaluating the workload of maintenance stores, the system can help balance the workload of each store and improve overall service efficiency. Over time, the system can continuously optimize its prediction model and recommendation strategy by analyzing maintenance results and user feedback.

[0204] Specifically, suppose a user has a compact electric car that has been used for many years. When analyzing the user's vehicle data, the system finds that: the battery has been used for about 40,000 kilometers, which is close to the recommended mileage for the first in-depth inspection; the tires have been used for 18 months, but the actual mileage is relatively low because the user mainly commutes short distances in the city; the air conditioning system is about to enter its third summer, but has not been fully inspected; the brake system was last inspected 10 months ago.

[0205] Based on this information, the system made the following assessment:

[0206] Evaluation of unmaintained time:

[0207] Battery: The score is high and close to the warning threshold;

[0208] Tires: The score is medium and has not yet reached the warning threshold;

[0209] Air conditioning system: The score is high, and considering the upcoming summer, it has reached the warning threshold;

[0210] Braking system: Average score, close to but not reaching the warning threshold;

[0211] Early warning maintenance project determination:

[0212] The system identifies battery inspection and air conditioning system maintenance as early warning maintenance items;

[0213] Maintenance time estimate:

[0214] Battery in-depth inspection: estimated to take 2 hours;

[0215] Air conditioning system inspection and cleaning: estimated to take 1.5 hours;

[0216] Third estimated completion time calculation:

[0217] The system queries all maintenance stores with electric vehicle maintenance capabilities within 20 kilometers of the user's permanent residence. Through simulated order insertion, it finds one that can start maintenance at 14:00, and the third estimated completion time is 17:30;

[0218] Maintenance request information generation:

[0219] After calculating the third estimated completion time, the system will obtain the corresponding historical orders of the client. Assuming that the user is an online car-hailing or taxi driver, if it is found according to the client's historical orders that the vehicle is idle from 14:00 to 17:30, that is, there is no order execution, then the expected state of the order will be idle. In this case, a maintenance request information will be sent to the client, so that the user can know that the vehicle can be maintained from 14:00 to 17:30.

[0220] However, if the client's historical orders show that the order is busy from 14:00 to 17:30, the expected order status will be busy. In this case, no maintenance request information will be sent to the client. On the one hand, this can avoid disturbing the user, and on the other hand, it can prevent the user from performing maintenance during busy hours.

[0221] Among them, when the expected status of the order is busy and the maintenance request information is not sent to the client, it can be decided whether to execute the above method based on the urgency of the warning maintenance item. That is, when the urgency of the warning maintenance item is very high, that is, the vehicle at this time is likely to break down. In this case, even if the expected status of the order is busy, the maintenance request information is still sent to the client. If the urgency of the warning maintenance item is low or medium, the expected status of the order is busy and the maintenance request information is not sent to the client.

[0222] It is worth noting that the reason why the present application controls the sending timing of the maintenance request information, in addition to avoiding interference to users and preventing users from performing maintenance directly during busy hours after receiving the maintenance request information, is that the solution of the present application does not adopt an appointment solution, that is, when the maintenance request information is sent to the client according to the third estimated completion time, the third estimated completion time obtained is real-time. At this time, the user cannot make an appointment for maintenance in the time period corresponding to the third estimated completion time by means of an appointment, but can only go to the corresponding maintenance store for maintenance at the current time, so that the maintenance can be completed at the third estimated completion time. For this reason, it is necessary to control the sending timing of the maintenance request information. The reason why the present application does not adopt an appointment solution is that the appointment solution will bring many uncertain factors to the estimated calculation of time, because the scheduled orders may be canceled or changed, which will cause a series of impacts on the estimated calculation of subsequent orders, and may cause a large number of orders to be affected by chain reactions. Therefore, in some preferred embodiments of the present application, the appointment solution is not adopted.

[0223] In general, this intelligent maintenance management system represents the future development direction of automotive aftermarket services. It not only improves the quality and efficiency of services, but also lays the foundation for the realization of true "smart cars" and "smart transportation".

[0224] Further, in some of the embodiments, the step of sending maintenance request information to the client according to the expected status of the order includes:

[0225] Get the ratio of idle time to order progress time in the expected order status;

[0226] When the time ratio is greater than the set value, a maintenance request message is sent to the client.

[0227] If the time proportion is greater than the set value, it means that the expected status of the order is more likely to be in idle time. In this case, a maintenance request message is sent to the client. After receiving the maintenance request message, the client can directly send the maintenance requirements and location information, so that the maintenance can be completed in the time period corresponding to the third estimated completion time which is more likely to be in spatial time.

[0228] Furthermore, in some embodiments, the method further comprises:

[0229] After the client selects the target maintenance store based on the recommended information, the first maintenance order of the maintenance station related to the maintenance demand of the target maintenance store is obtained in real time;

[0230] After the first maintenance order is updated, the second estimated completion time is recalculated and a corresponding prompt message is sent.

[0231] After the user selects the target maintenance store, the system continues to monitor the entire maintenance process in real time and make intelligent adjustments to ensure that the user gets the best service experience. This process mainly includes the following key steps:

[0232] Get maintenance store information in real time;

[0233] When the user selects the target maintenance store based on the system's recommended information, the system does not stop working. Instead, it continues to obtain the first maintenance order of the maintenance station related to the user's maintenance needs in real time. This real-time monitoring process takes into account multiple factors:

[0234] The current working status of the maintenance station;

[0235] The progress of ongoing maintenance projects;

[0236] The status of orders in the waiting queue;

[0237] Unexpected events that may affect the progress of maintenance (such as equipment failure, emergency order insertion, etc.);

[0238] Through this continuous real-time monitoring, the system can keep abreast of the latest situation of the maintenance store and provide a basis for subsequent dynamic adjustments. Since in some preferred implementations of the present application, an appointment plan is not adopted, when the user goes to the recommended maintenance store, the maintenance store may have a new first maintenance order.

[0239] Dynamically update the estimated completion time;

[0240] Based on real-time monitoring, the system will immediately trigger a recalculation mechanism when it detects an update to the first maintenance order. This mechanism includes:

[0241] Re-evaluate the progress of each first maintenance order;

[0242] Update the estimated start time of orders in the waiting queue;

[0243] Consider the impact of newly inserted urgent orders on the overall schedule;

[0244] Recalculate the second estimated completion time of the user's maintenance needs based on the latest situation;

[0245] This dynamic update process ensures that the system always has the most accurate time estimate and can respond to various changes and emergencies in a timely manner.

[0246] Intelligent prompt information generation

[0247] Based on the recalculated second estimated completion time, the system will generate corresponding prompt information. This process is not a simple information transmission, but an intelligent decision-making process:

[0248] Change assessment: The system will assess the degree of deviation between the new estimated completion time and the original plan. Only when the deviation exceeds a certain threshold (for example, 15 minutes) will it consider sending a reminder message.

[0249] Reminder timing selection: The system will consider the user's current state (for example, whether they are driving or in an important meeting) to select the most appropriate reminder timing.

[0250] Customized information content: Depending on the nature (advance or delay) and degree of time change, the system will customize different information content. For example, if the time is significantly advanced, the system may suggest that the user leave earlier; if it is seriously delayed, the system may provide alternative options.

[0251] Interactive options: The prompt may include multiple options, allowing users to make real-time adjustments, such as confirming a new time, selecting an alternative maintenance store, or rescheduling the maintenance date.

[0252] Through real-time monitoring and dynamic adjustments, timely and personalized solutions are provided to users in the face of emergencies. This approach brings several significant advantages: through timely information updates and flexible options, user uncertainty and potential disappointment are greatly reduced. Through dynamic adjustments, the system can better balance the workload of the maintenance center and improve overall service efficiency. Providing users with multiple options makes them feel that they are always in control of their maintenance plans. Real-time updates and detailed explanations increase the transparency of the entire maintenance process and build user trust in the system and services. By identifying and handling potential delays in advance, the system effectively prevents possible user complaints and negative experiences. The data collected in this process can be used to further optimize the system's predictive algorithms and service processes.

[0253] In summary, the present application calculates the first difference through the first estimated completion time and the first required completion time of the first maintenance order, calculates the second estimated completion time of the maintenance requirement based on the first difference, and compares the early and late second estimated completion time to generate recommendation information, thereby having the beneficial effect of improving the accuracy of recommendations.

[0254] Second, refer to Figure 2 , the present application also proposes a vehicle maintenance recommendation device, comprising:

[0255] The first module 210 is used to obtain the current first maintenance order of the maintenance station related to the maintenance demand in all maintenance stores within a first preset range of the current location of the client according to the maintenance demand and location information sent by the client;

[0256] The second module 220 is used to obtain a first estimated completion time and a first required completion time corresponding to each first maintenance order according to the first maintenance order, and calculate a first difference according to the first estimated completion time and the first required completion time;

[0257] The third module 230 is used to calculate the second estimated completion time of the maintenance demand at the corresponding maintenance store according to the first difference corresponding to all the first maintenance orders of each maintenance store;

[0258] The fourth module 240 is used to generate recommendation information according to the second estimated completion times corresponding to all maintenance stores, so as to recommend maintenance at the maintenance store with the earliest second estimated completion time.

[0259] Through the above technical solution, the first difference is calculated by the first estimated completion time of the first maintenance order and the first required completion time, and the second estimated completion time of the maintenance requirement is calculated based on the first difference. The early and late second estimated completion time are compared to generate recommendation information, thereby having the beneficial effect of improving the accuracy of recommendations.

[0260] In addition, in some preferred embodiments, a vehicle maintenance recommendation device proposed in the present application can perform any one of the steps in the above method.

[0261] Thirdly, refer to Figure 3 The present application also proposes an electronic device, including a processor 310 and a memory 320, wherein the memory 320 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 310, the steps in the above method are executed.

[0262] Through the above technical solution, the processor 310 and the memory 320 are interconnected and communicate with each other through a communication bus and / or other forms of connection mechanisms (not marked), and the memory 320 stores computer-readable instructions executable by the processor 310. When the electronic device is running, the processor 310 executes the computer-readable instructions to execute the method in any optional implementation of the above embodiment to achieve the following functions: according to the maintenance demand and location information sent by the client, the current first maintenance order of the maintenance station related to the maintenance demand in all maintenance stores within the first preset range of the current location of the client is obtained; according to the first maintenance order, the first estimated completion time and the first required completion time corresponding to each first maintenance order are obtained, and the first difference is calculated according to the first estimated completion time and the first required completion time; according to the first difference corresponding to all the first maintenance orders of each maintenance store, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated; according to the second estimated completion time corresponding to all maintenance stores, recommendation information is generated to recommend going to the maintenance store with the earliest second estimated completion time for maintenance.

[0263] In a fourth aspect, the present application further proposes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method are executed.

[0264] Through the above technical solution, when the computer program is executed by the processor, the method in any optional implementation method of the above embodiments is executed to achieve the following functions: according to the maintenance needs and location information sent by the client, the current first maintenance order for the maintenance station related to the maintenance needs in all maintenance stores within the first preset range of the client's current location is obtained; according to the first maintenance order, the first estimated completion time corresponding to each first maintenance order is obtained, and the first difference is calculated based on the first estimated completion time and the first required completion time; according to the first difference corresponding to all the first maintenance orders of each maintenance store, the second estimated completion time of the maintenance need at the corresponding maintenance store is calculated; according to the second estimated completion time corresponding to all maintenance stores, recommendation information is generated to recommend going to the maintenance store with the earliest second estimated completion time for maintenance.

[0265] Among them, the computer-readable storage medium 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 red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0266] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0267] In addition, 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, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0268] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0269] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A vehicle maintenance recommendation method, characterized in that: include: According to the maintenance demand and location information sent by the client, obtain the current first maintenance order of the maintenance station related to the maintenance demand in all maintenance stores within a first preset range of the current location of the client; According to the first maintenance order, a first estimated completion time and a first required completion time corresponding to each of the first maintenance orders are obtained, and a first difference is calculated according to the first estimated completion time and the first required completion time; Calculating, according to the first difference corresponding to all the first maintenance orders of each maintenance store, a second estimated completion time of the maintenance demand at the corresponding maintenance store; Generate recommendation information according to the second estimated completion time corresponding to all the maintenance stores, so as to recommend the maintenance store with the earliest second estimated completion time for maintenance; The step of calculating the second estimated completion time of the maintenance demand at the corresponding maintenance store according to the first difference corresponding to all the first maintenance orders of each maintenance store comprises: Calculate a first estimated maintenance time according to the maintenance demand; Comparing the first difference corresponding to all the first maintenance orders of each maintenance store with the first estimated maintenance time; When the first difference corresponding to all the first maintenance orders of one maintenance store is less than the first estimated maintenance time, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated according to the first estimated completion time of the last first maintenance order corresponding to the maintenance store and the first estimated maintenance time; When a maintenance store has the first difference corresponding to the first maintenance order that is greater than or equal to the first estimated maintenance time, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated according to the arrangement position of the first maintenance order corresponding to the first difference that is greater than or equal to the first estimated maintenance time; The step of calculating the second estimated completion time of the maintenance demand at the corresponding maintenance store according to the arrangement position of the first maintenance order corresponding to the first difference value that is greater than or equal to the first estimated maintenance time includes: When the first maintenance order corresponding to the first difference that is greater than or equal to the first estimated maintenance time is at the end of the arrangement, the maintenance demand is first inserted before the first maintenance order at the end of the arrangement, and then the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated; When the arrangement position of the first maintenance order corresponding to the first difference that is greater than or equal to the first estimated maintenance time is not the last, determine whether the maintenance demand can be inserted before the last first maintenance order in the arrangement position without affecting the subsequent delivery of the first maintenance order; If yes, first insert the maintenance demand before the first maintenance order, and then calculate the second estimated completion time of the maintenance demand at the corresponding maintenance store; If not, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated based on the first estimated completion time of the last first maintenance order and the first estimated maintenance time.

2. A vehicle maintenance recommendation method according to claim 1, characterized in that: The step of obtaining, according to the maintenance demand and location information sent by the client, a current first maintenance order of a maintenance station related to the maintenance demand in all maintenance stores within a first preset range of the client's current location comprises: Obtaining the maintenance history record of the vehicle corresponding to the client; Sending maintenance request information to the client according to the maintenance history record; Receiving the maintenance requirement and the location information sent by the client according to the maintenance request information; According to the maintenance demand and the location information sent by the client, the first maintenance order currently at the maintenance station related to the maintenance demand in all maintenance stores within a first preset range of the current location of the client is obtained.

3. A vehicle maintenance recommendation method according to claim 2, characterized in that: The step of sending maintenance request information to the client according to the maintenance history record comprises: Acquire the unmaintained time of all maintenance items of the vehicle corresponding to the client according to the maintenance history record; Obtaining early warning maintenance items based on database information of the vehicle model corresponding to the client and evaluation of the unmaintained time of all maintenance items; Obtaining a second estimated maintenance time for the early warning maintenance item; Calculating a third estimated completion time based on the second estimated maintenance time; Send maintenance request information to the client according to the third estimated completion time.

4. A vehicle maintenance recommendation method according to claim 3, characterized in that: The step of calculating the third estimated completion time according to the second estimated maintenance time comprises: Acquire, according to the early warning maintenance item, a second maintenance order for the maintenance stations related to the early warning maintenance item in all the maintenance stores within a second preset range; The third estimated completion time is calculated according to the second maintenance order and the second estimated maintenance time; The step of sending maintenance request information to the client according to the third estimated completion time includes: Obtain historical orders corresponding to the client; estimating an expected order status within the third estimated completion time based on the historical orders; Send maintenance request information to the client according to the expected status of the order.

5. A vehicle maintenance recommendation method according to claim 4, characterized in that: The step of sending maintenance request information to the client according to the expected status of the order includes: Obtain the time ratio of idle time to order processing time in the expected state of the order; When the time proportion is greater than a set value, a maintenance request message is sent to the client.

6. A vehicle maintenance recommendation method according to claim 1, characterized in that: The method further includes: After the client selects a target maintenance store according to the recommendation information, the first maintenance order of the maintenance station related to the maintenance demand of the target maintenance store is obtained in real time; After the first maintenance order is updated, the second estimated completion time is recalculated and a corresponding prompt message is sent.

7. A vehicle maintenance recommendation device, characterized in that: include: The first module is used to obtain, according to the maintenance demand and location information sent by the client, the current first maintenance order of the maintenance station related to the maintenance demand in all maintenance stores within a first preset range of the current location of the client; A second module is used to obtain, according to the first maintenance order, a first estimated completion time and a first required completion time corresponding to each of the first maintenance orders, and calculate a first difference according to the first estimated completion time and the first required completion time; A third module is used to calculate a second estimated completion time of the maintenance demand at the corresponding maintenance store according to the first difference corresponding to all the first maintenance orders of each maintenance store; A fourth module is used to generate recommendation information according to the second estimated completion time corresponding to all the maintenance stores, so as to recommend the maintenance store with the earliest second estimated completion time for maintenance; The step of calculating the second estimated completion time of the maintenance demand at the corresponding maintenance store according to the first difference corresponding to all the first maintenance orders of each maintenance store comprises: Calculate a first estimated maintenance time according to the maintenance demand; Comparing the first difference corresponding to all the first maintenance orders of each maintenance store with the first estimated maintenance time; When the first difference corresponding to all the first maintenance orders of one maintenance store is less than the first estimated maintenance time, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated according to the first estimated completion time of the last first maintenance order corresponding to the maintenance store and the first estimated maintenance time; When a maintenance store has the first difference corresponding to the first maintenance order that is greater than or equal to the first estimated maintenance time, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated according to the arrangement position of the first maintenance order corresponding to the first difference that is greater than or equal to the first estimated maintenance time; The step of calculating the second estimated completion time of the maintenance demand at the corresponding maintenance store according to the arrangement position of the first maintenance order corresponding to the first difference value that is greater than or equal to the first estimated maintenance time includes: When the first maintenance order corresponding to the first difference that is greater than or equal to the first estimated maintenance time is at the end of the arrangement, the maintenance demand is first inserted before the first maintenance order at the end of the arrangement, and then the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated; When the arrangement position of the first maintenance order corresponding to the first difference that is greater than or equal to the first estimated maintenance time is not the last, determine whether the maintenance demand can be inserted before the last first maintenance order in the arrangement position without affecting the subsequent delivery of the first maintenance order; If yes, first insert the maintenance demand before the first maintenance order, and then calculate the second estimated completion time of the maintenance demand at the corresponding maintenance store; If not, the second estimated completion time of the maintenance demand at the corresponding maintenance store is calculated based on the first estimated completion time of the last first maintenance order and the first estimated maintenance time.

8. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 6 are executed.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are executed.

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