Driving behavior based mileage dimension vehicle maintenance dynamic recommendation method and system

Abstract

The present application relates to the technical field of vehicle maintenance, and discloses a mileage dimension vehicle maintenance dynamic recommendation method and system based on driving behavior, which comprises the following steps: acquiring the vehicle brand model, initial mileage and preset maintenance mileage input by a user; querying a vehicle model database based on the vehicle brand model to obtain a standard maintenance project table and a brand load characteristic matrix; calculating a driving wear index according to the brand load characteristic matrix; calculating a dynamic compensation mileage threshold of each maintenance project according to the driving wear index; comparing the difference between the preset maintenance mileage and the initial mileage with the dynamic compensation mileage threshold to generate a recommended maintenance project list; and calculating the cost of each maintenance project and the total cost according to the recommended maintenance project list. The present application can realize the precision and individualization of vehicle maintenance.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of vehicle maintenance, and particularly relates to a mileage dimension vehicle maintenance dynamic recommendation method and system based on driving behavior. BACKGROUND

[0002] Vehicle maintenance technology is a core field of the automobile aftermarket, and its development directly affects road traffic safety and resource utilization efficiency. With the improvement of the intelligent level of automobiles and the upgrading of user service requirements, the traditional maintenance mode relying on manual experience and fixed cycles cannot meet the requirements of precision and transparency.

[0003] Current vehicle maintenance technology mainly relies on fixed cycle recommendations from manufacturers and experience judgments of maintenance personnel, lacking quantitative evaluation of actual driving conditions and vehicle load states. Traditional solutions usually use static threshold to trigger maintenance reminders, which cannot identify the accelerated wear of mechanical parts caused by abnormal working conditions such as high-speed driving and frequent heavy loading, easily leading to failure of key components before reaching theoretical service life or resource waste caused by premature replacement. Existing digital systems are mostly limited to basic information recording and simple mileage reminders, without establishing a dynamic correlation model between driving behavior data and maintenance strategies, making it difficult to achieve accurate prediction. At the same time, the service mode is highly monotonous, lacking flexible solutions to adapt to different user scenarios, and the price system has a black box of information, making it difficult for car owners to verify the necessity and reasonableness of maintenance items. These defects result in high vehicle maintenance costs, uneven maintenance effects, and difficulty in improving user trust.

[0004] Therefore, there is an urgent need to develop a mileage dimension vehicle maintenance dynamic recommendation method and system based on driving behavior, which can achieve precision and individualization of vehicle maintenance. SUMMARY

[0005] To solve the above technical problems, the application provides a mileage dimension vehicle maintenance dynamic recommendation method and system based on driving behavior, which can achieve precision and individualization of vehicle maintenance.

[0006] The application provides a mileage dimension vehicle maintenance dynamic recommendation method based on driving behavior, which comprises the following steps:

[0007] S1, obtaining the vehicle brand and model, initial mileage, and preset maintenance mileage input by the user;

[0008] S2, querying a vehicle model database based on the vehicle brand and model to obtain a standard maintenance item table and a brand load characteristic matrix;

[0009] S3, calculating a driving wear index according to the brand load characteristic matrix;

[0010] S4, calculating a dynamic compensation mileage threshold for each maintenance item according to the driving wear index;

[0011] S5, comparing the difference between the preset maintenance mileage and the initial mileage with the dynamic compensation mileage threshold to generate a recommended maintenance item list;

[0012] S6, calculating the cost of each maintenance item and the total cost according to the recommended maintenance item list.

[0013] Further, in S2, the standard maintenance item table is obtained by querying the vehicle model database based on the vehicle brand and model, including:

[0014] All maintenance items of the target vehicle model are obtained by querying the vehicle model database based on the vehicle brand and model.

[0015] The reference mileage corresponding to the maintenance item, the life coefficient of each accessory, and the cost price of each accessory are obtained to construct the maintenance item table.

[0016] Further, the calculation formula of the life coefficient of each accessory is as follows:

[0017]

[0018] wherein q k represents the life coefficient of the accessory corresponding to the kth maintenance item, represents the reference mileage corresponding to the kth maintenance item, represents the average maintenance mileage of the accessory corresponding to the kth maintenance item.

[0019] Further, in S2, the brand load feature matrix is obtained by querying the vehicle model database based on the vehicle brand and model, including:

[0020] The speed ratio coefficient and the load coefficient of the target vehicle model are obtained by querying the vehicle model database based on the vehicle brand and model.

[0021] The brand load feature matrix is constructed according to the speed ratio coefficient and the load coefficient.

[0022] Further, the calculation method of the speed ratio coefficient and the load coefficient is as follows:

[0023]

[0024] wherein α represents the speed ratio coefficient, R avg represents the historical average speed of the target vehicle model, R econ represents the middle value of the economic speed interval of the target vehicle model.

[0025]

[0026] wherein β represents the load coefficient, W median represents the median of the load distribution of the target vehicle model, W maxIndicates the maximum design load of the target vehicle model.

[0027] Further, in S3, the calculation formula of the driving wear index according to the brand load feature matrix is as follows:

[0028] Δm = m p -m0;

[0029]

[0030] Wherein, W represents the driving wear index, Δm represents the difference between the preset maintenance mileage and the initial mileage, m p represents the preset maintenance mileage, m0 represents the initial mileage, a represents the speed ratio coefficient, β represents the load coefficient, D1 represents the speed term wear increment coefficient, and D2 represents the load term wear increment coefficient.

[0031] Further, in S4, the calculation formula of the dynamic compensation mileage threshold of each maintenance item according to the driving wear index is as follows:

[0032]

[0033] Wherein, represents the mileage threshold of the kth maintenance item after dynamic compensation, represents the reference mileage of the kth maintenance item.

[0034] Further, in S5, the difference between the preset maintenance mileage and the initial mileage and the dynamic compensation mileage threshold are compared to generate a recommended maintenance item list, including:

[0035]

[0036] Wherein, N k represents the maintenance coefficient of the kth maintenance item, q k represents the life coefficient of the corresponding spare part of the kth maintenance item.

[0037] The maintenance item with a maintenance coefficient of 1 is exported to generate the recommended maintenance item list.

[0038] Further, in S6, the cost of each maintenance item and the total cost according to the recommended maintenance item list are calculated, including:

[0039] The cost of each maintenance item is determined according to the recommended maintenance item list and the cost price of each spare part corresponding to each maintenance item.

[0040] The total cost is obtained by summing the cost of each maintenance item in the recommended maintenance item list.

[0041] The application also provides a driving behavior-based mileage dimension vehicle maintenance dynamic recommendation system for executing the driving behavior-based mileage dimension vehicle maintenance dynamic recommendation method described above, characterized in that the system comprises the following modules:

[0042] A data acquisition module is configured to acquire a vehicle brand and model, an initial mileage, and a preset maintenance mileage input by a user.

[0043] A matching module is configured to query a vehicle model database based on the vehicle brand and model to acquire a standard maintenance item table and a brand load characteristic matrix.

[0044] A calculation module is configured to calculate a driving wear index according to the brand load characteristic matrix, and to calculate a dynamic compensation mileage threshold of each maintenance item according to the driving wear index.

[0045] A maintenance item recommendation module is configured to compare the difference between the preset maintenance mileage and the initial mileage with the dynamic compensation mileage threshold to generate a recommended maintenance item list.

[0046] A maintenance cost trial module is configured to calculate the cost of each maintenance item and the total cost according to the recommended maintenance item list.

[0047] The embodiments of the application have the following technical effects:

[0048] The application automatically adapts to the corresponding maintenance service based on a vehicle model robustness matching mechanism, saves the analysis time cost of employees, calculates the driving wear index through a nonlinear mileage compensation algorithm and brand characteristic driving, analyzes the coupling effect of the speed ratio coefficient and the load coefficient, dynamically adjusts the recommended period of each maintenance item, avoids excessive maintenance or insufficient maintenance caused by traditional fixed period maintenance, combines the actual working condition of the vehicle with the manufacturer's recommended standard through a standardized service system, realizes the precision and individualization of vehicle maintenance, integrates accessories and maintenance service quotations through a vehicle model database, visualizes the maintenance item decision-making process, reduces the service cost while improving the flexibility of the vehicle owner's decision-making, and finally forms an intelligent maintenance decision-making support capability in the whole life cycle dimension. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the specific embodiments of the application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0050] Figure 1 is a flowchart of the driving behavior-based mileage dimension vehicle maintenance dynamic recommendation method provided by the embodiments of the application.

[0051] Figure 2 is a logic diagram of a driving behavior-based mileage-dimension vehicle maintenance dynamic recommendation method provided by an embodiment of the present application;

[0052] Figure 3 is a flowchart of a process in which a daily running system acquires the cost prices of various accessories,

[0053] Figure 4 is a schematic diagram of a business interface of a daily running system provided by an embodiment of the present application,

[0054] Figure 5 is a schematic diagram of a product quotation page interface of a daily running system provided by an embodiment of the present application,

[0055] Figure 6 is a schematic diagram of a purchased service interface of a daily running system provided by an embodiment of the present application,

[0056] Figure 7 is a structural schematic diagram of a driving behavior-based mileage-dimension vehicle maintenance dynamic recommendation system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0057] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be described in detail below. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0058] The embodiments of the present application provide a driving behavior-based mileage-dimension vehicle maintenance dynamic recommendation method, which is realized by a driving behavior-based mileage-dimension vehicle maintenance dynamic recommendation system provided by the present application, i.e., a daily running system. The daily running system is a vehicle maintenance and service system, which integrates algorithm logic and application mode of back-end operation and front-end application. When a vehicle owner inputs the brand and model information and the mileage information of the vehicle, the system will recommend corresponding maintenance items and maintenance plans to the vehicle owner according to preset algorithm logic, in combination with the driving mileage of the vehicle in the future. For vehicle owners who lack experience in vehicle maintenance but frequently use vehicles, the service system can effectively guide the vehicle owners on when to maintain and what items to maintain. Meanwhile, due to the standardization and batch processing of the service packages, the service cost of the service team is reduced, and the vehicle owners can intuitively feel the price advantage and the improvement of service quality.

[0059] Figure 1 is a flowchart of a driving behavior-based mileage-dimension vehicle maintenance dynamic recommendation method provided by an embodiment of the present application, Figure 2is a logic diagram of a driving behavior-based mileage dimension vehicle maintenance dynamic recommendation method provided by an embodiment of the present application, referring to Figure 1 and Figure 2 The method comprises the following steps:

[0060] S1, obtaining the vehicle brand model, initial mileage and preset maintenance mileage input by a user.

[0061] Figure 4 is a schematic diagram of a system service interface of a daily running system provided by an embodiment of the present application, Figure 5 is a schematic diagram of a product quotation page interface of a daily running system provided by an embodiment of the present application, referring to Figure 4 and Figure 5 When a user enters a service interface through a daily running applet, the system requires input of the brand model of the vehicle, initial mileage and target maintenance mileage to be used in the future. The vehicle brand model information can be collected through a drop-down menu or text input form, the initial mileage is manually filled in by the user as the current vehicle mileage, and the preset maintenance mileage can be set by dragging a virtual vehicle icon in the interface or manually inputting to the target mileage segment.

[0062] S2, querying a vehicle model database based on the vehicle brand model to obtain a standard maintenance item table and a brand load characteristic matrix.

[0063] In some embodiments, querying the vehicle model database based on the vehicle brand model to obtain the standard maintenance item table comprises:

[0064] querying the vehicle model database according to the vehicle brand model to obtain all maintenance items of the target vehicle model;

[0065] obtaining the reference mileage corresponding to the maintenance items, the life coefficients of each accessory and the cost prices of each accessory, and constructing a maintenance item table.

[0066] The reference mileage corresponding to the maintenance items can be extracted from the official recommended period of each maintenance item in the Maintenance Manual of the target vehicle model or the database of the original equipment manufacturer, for example, the oil change reference mileage of Toyota Camry = 10,000 km.

[0067] The calculation formula of the life coefficient of each accessory is as follows:

[0068]

[0069] wherein q k represents the life coefficient of the accessory corresponding to the kth maintenance item of the target vehicle model, represents the reference mileage corresponding to the kth maintenance item of the target vehicle model, The average maintenance mileage of the kth maintenance item of the target vehicle model corresponds to the accessory. The coefficient is used as a basic parameter for dynamic threshold adjustment to ensure that the maintenance suggestion meets the manufacturer's standard and adapts to the actual use conditions.

[0070] wherein, Figure 3 is a flowchart for acquiring the cost price of each accessory by the daily running system provided by the embodiment of the application, see Figure 3 The cost price of each accessory includes:

[0071] According to the target vehicle model, the accessories corresponding to each maintenance item are brand accessories or original accessories. The detailed configuration parameters of the corresponding vehicle model are obtained from the brand accessory vehicle model library or the original accessory vehicle model library based on data localization. Based on the target vehicle model, each maintenance item and the corresponding accessory brand, the brand accessory model is obtained in the brand accessory vehicle model accessory relationship library, and the detailed information of the brand accessory is obtained in the accessory brand accessory library, including: accessory brand, accessory model, accessory specification, brand accessory code, maintenance amount, etc. Based on the target vehicle model and each maintenance item, the detailed information of the original accessory is obtained in the original accessory vehicle model accessory relationship library, including: standard price, labor cost, OE number, etc. The brand accessory maintenance item and / or the original accessory maintenance item are matched through the maintenance item library to determine the accessory products that need to be purchased. According to the accessory products that need to be purchased and the detailed information thereof, a supplier accessory library is formed, and the final cost price of each accessory is obtained.

[0072] In some embodiments, the brand load feature matrix is obtained by querying the vehicle model database based on the vehicle brand and model, including:

[0073] The speed ratio coefficient and the load coefficient of the target vehicle model are obtained by querying the vehicle model database based on the vehicle brand and model;

[0074] The brand load feature matrix is constructed based on the speed ratio coefficient and the load coefficient.

[0075] Wherein, the calculation method of the speed ratio coefficient and the load coefficient is as follows:

[0076]

[0077] Wherein, α represents the speed ratio coefficient, R avg represents the historical average speed of the target vehicle, which can be determined by the historical speed data of the same model vehicle obtained through the Internet of Vehicles platform, R econ represents the middle value of the economic speed interval of the target vehicle, which can be extracted from the economic speed interval of the target vehicle brand and model engine performance curve to determine the middle value of the economic speed interval of the target vehicle.

[0078]

[0079] Wherein, β represents the load coefficient, Wmedian The median of the load distribution of the target vehicle model can be determined by the user's typical load distribution statistics through the on-board mass sensor, W max The maximum design load of the target vehicle model can be obtained from the vehicle technical specifications.

[0080] The speed ratio coefficient is used to reflect the degree of speed deviation from the economic interval during driving, and the load coefficient is used to quantify the engine load intensity.

[0081] S3, calculate the driving wear index according to the brand load characteristic matrix.

[0082] The driving wear index calculation method combines mechanical dynamics principles and data-driven methods, and through the synergistic effect of comprehensive speed and load, it evaluates the mechanical wear risk, and realizes the upgrade of wear evaluation from single mileage dimension to multi-physical field coupling. For example, when the speed ratio coefficient is high, the friction between the piston ring and the cylinder wall is intensified, the oil deterioration is accelerated, and the impact load of the valve train (such as camshaft, rocker arm) is increased; when the load coefficient is high, the combustion chamber temperature rises, leading to carbon deposition and cylinder coating wear, and the crankshaft bearing and connecting rod bear greater shear stress. The driving wear index is used to reflect the maintenance strategy under different driving behaviors, for example, under the driving behavior with high driving wear index, the priority items may include replacing high-performance full synthetic oil, checking the sealing of the turbocharger, and cleaning the intake valve carbon deposition; under the driving behavior with low driving wear index, it may be allowed to extend the oil life, and the air filter replacement cycle, etc.

[0083] In some embodiments, the calculation formula for calculating the driving wear index according to the brand load characteristic matrix is as follows:

[0084] Δm = m p -m0;

[0085]

[0086] Wherein, W represents the driving wear index, Δm represents the difference between the preset maintenance mileage and the initial mileage, m p represents the preset maintenance mileage, m0 represents the initial mileage, α represents the speed ratio coefficient, β represents the load coefficient, D1 represents the speed term wear increment coefficient, and D2 represents the load term wear increment coefficient.

[0087] The rotation speed term wear increment coefficient is used to standardize the influence of engine rotation speed on component life into mileage dimension, and the longer the high rotation speed operation time is, the more serious the friction wear is, and the cumulative effect is quantified by mileage equivalent. According to the actual situation, exemplarily, D1 can be set to 1000, indicating that the rotation speed related wear accumulation is 1000 kilometers. The load term wear increment coefficient is used to reflect the accelerated wear of gearbox gears, suspension systems and the like under heavy load working conditions, and the higher the gear contact stress is, the shorter the fatigue life is nonlinearly shortened. According to the actual situation, exemplarily, D2 can be set to 500, indicating that the load related wear increment is 500 kilometers.

[0088] S4, calculating the dynamic compensation mileage threshold of each maintenance item according to the driving wear index.

[0089] In some embodiments, the calculation formula for calculating the dynamic compensation mileage threshold of each maintenance item according to the driving wear index is as follows:

[0090]

[0091] wherein, represents the mileage threshold of the kth maintenance item after dynamic compensation, represents the reference mileage of the kth maintenance item.

[0092] The calculation of the dynamic compensation mileage threshold adopts a semi-logarithmic function model to ensure that the wear index and the mileage adjustment amount are in a nonlinear relationship. For example, when the wear index W is 0.5, the adjusted threshold of the item with a reference mileage of 10,000 kilometers is 10,000*(1-0.1*ln1.5)≈9430 kilometers. The characteristics of the logarithmic function make the initial wear increase significantly affect the threshold, and the adjustment range tends to be flat under extremely high wear conditions, avoiding excessive maintenance frequency caused by extreme driving behavior.

[0093] S5, comparing the difference between the preset maintenance mileage and the initial mileage with the dynamic compensation mileage threshold to generate a recommended maintenance item list.

[0094] In some embodiments, S5 includes the following sub-steps:

[0095] S51, calculating the maintenance coefficient of each maintenance item according to the difference between the preset maintenance mileage and the initial mileage, the dynamic compensation mileage threshold, and the life coefficient of each accessory;

[0096] The calculation formula is as follows:

[0097]

[0098] wherein, N k represents the maintenance coefficient of the kth maintenance item, q krepresents the life coefficient of the spare part corresponding to the kth maintenance item.

[0099] The maintenance item decision algorithm introduces a double verification mechanism, the life coefficient as the basic threshold of the necessity of the item, and the dynamic compensation threshold as the trigger condition of the actual demand. When the actual mileage of the vehicle reaches a certain proportion of the compensation threshold, the system generates a maintenance suggestion of the warning level. First, compare the ratio of Δm and the dynamic compensation threshold, for example, the adjusted threshold of a certain brake pad item is eight thousand kilometers, and the user's planned mileage difference is seven thousand kilometers, so the ratio is 7000 / 8000=0.875. If the life coefficient q of the spare part of this item is 0.9, then 0.875<0.9, the maintenance coefficient is 0, and the system determines that immediate maintenance is not needed. k

[0100] S52, export the maintenance item with a maintenance coefficient of 1 to generate a recommended maintenance item list.

[0101] S6, calculate the cost of each maintenance item and the total cost according to the recommended maintenance item list.

[0102] Specifically, the cost of each maintenance item is determined according to the recommended maintenance item list and the cost price of each spare part corresponding to each maintenance item;

[0103] The total cost is obtained by summing the cost of each maintenance item in the recommended maintenance item list.

[0104] In some embodiments, the cost calculation engine adopts a multi-dimensional pricing strategy. The cost price is composed of the price of the original spare part and / or the price of the brand spare part and the labor cost. The system can also automatically calculate and apply a discount coefficient to generate an optimal price scheme and display the gross profit margin of each product.

[0105] In some embodiments, after the quotation is generated, the system automatically compares the prices of service providers in the same region to achieve transparent service pricing.

[0106] The present application automatically adapts to the corresponding maintenance service based on the robustness matching mechanism of the vehicle model, saves the analysis time cost of employees, calculates the driving wear index through a nonlinear mileage compensation algorithm and brand feature driving, dynamically adjusts the recommended period of each maintenance item by analyzing the coupling effect of the speed ratio coefficient and the load coefficient, avoids excessive maintenance or insufficient maintenance caused by traditional fixed period maintenance, combines the actual working condition of the vehicle with the manufacturer's recommended standard through a standardized service system, realizes the precision and personalization of vehicle maintenance; through the integration of spare parts and maintenance service prices in the vehicle model database, the maintenance item decision process is visualized, the service cost is reduced while the decision flexibility of the vehicle owner is improved, and finally the intelligent maintenance decision support capability in the whole life cycle dimension is formed.

[0107] Reference Figures 4-6 ​An actual operation page of the "everyday run" product provided by the application is provided. First, enter the "everyday run" business interface from the everyday run applet. After inputting the initial mileage, click to get the quotation to jump to the product quotation page. The car can be dragged to change the preset maintenance mileage. The system automatically calculates the information of the items and accessories that need to be maintained according to the algorithm. Alternatively, the selected maintenance items and accessory information can be manually selected to automatically calculate the price of the maintenance items and display the cost price of the required accessories and product introduction. Check the everyday run agreement, read and agree, then purchase, submit an order, and perform a payment operation. After the purchase is successful, the user can view the annual maintenance card information in the service package. Click any maintenance service to enter the detail page to view the card. Click the card to enter the two-dimensional code page, which is used for offline verification. When using the service, show the two-dimensional code in the store, create a work order in the 4S store, scan the two-dimensional code for verification, and the corresponding maintenance items and maintenance accessories will be automatically displayed in the work order to generate a verification record. By clicking the use record button on the operation interface, the user can view the use of the service (remaining times, use times).

[0108] The "everyday run" product provided by the application is based on vehicle mileage, combined with brand manufacturer maintenance manuals and vehicle actual use environment, driver habits and other multi-dimensional factors, to provide a full range of customized basic maintenance service discount plans. This feature can better meet the personalized needs of car owners and improve the maintenance effect compared to the one-size-fits-all maintenance service in the prior art.

[0109] The "everyday run" product can also provide a variety of scene selection such as commercial vehicle maintenance, community vehicle maintenance, and 4S store vehicle maintenance, and is equipped with door-to-door pickup and delivery services, so that car owners can choose the appropriate maintenance method according to their needs and time arrangement, greatly improving the convenience and flexibility of maintenance.

[0110] With more than 20 years of industry experience and more than one million vehicle data accumulation, the product has established a standardized service system from vehicle information collection and registration to product after-sales service, ensuring the quality and efficiency of maintenance services. The product realizes traceability, full-process visualization, and transparent management of team inspection, so that car owners can clearly understand the whole process of maintenance services, and the service credibility and satisfaction are enhanced. Covering all brand fuel, hybrid, and pure electric vehicle types, car owners only need to perform simple operations, and the system can quickly identify vehicle information and generate a customized maintenance discount plan, avoiding tricks and hidden consumption, so that car owners can clearly understand the maintenance cost.

[0111] The product makes vehicle maintenance more accurate and effective through customized services, prolongs the service life of the vehicle, shortens the service time and improves the service efficiency through a standardized service system and a convenient pricing system, enhances the trust and satisfaction of car owners through transparent management and diversified scene solutions, and enables car owners to enjoy high-quality maintenance services at a lower cost through customized maintenance discount plans.

[0112] The embodiment of the present application also provides a driving behavior based mileage dimension vehicle maintenance dynamic recommendation system for executing the driving behavior based mileage dimension vehicle maintenance dynamic recommendation method, Figure 7 is a structural schematic diagram of the driving behavior based mileage dimension vehicle maintenance dynamic recommendation system provided by the embodiment of the present application, referring to Figure 7 The system comprises the following modules:

[0113] A data acquisition module is configured to acquire a vehicle brand and model, an initial mileage and a preset maintenance mileage input by a user; wherein the vehicle brand and model are not necessarily selected and can be matched at a minimum granularity;

[0114] A matching module is configured to query a vehicle model database based on the vehicle brand and model, and acquire a standard maintenance item table and a brand load characteristic matrix;

[0115] A calculation module is configured to calculate a driving wear index according to the brand load characteristic matrix, and calculate a dynamic compensation mileage threshold of each maintenance item according to the driving wear index;

[0116] A maintenance item recommendation module is configured to compare a difference between the preset maintenance mileage and the initial mileage with the dynamic compensation mileage threshold, and generate a recommended maintenance item list;

[0117] A maintenance cost trial module is configured to calculate a cost of each maintenance item and a total cost according to the recommended maintenance item list.

[0118] Further, the driving behavior based mileage dimension vehicle maintenance dynamic recommendation system further comprises:

[0119] A worry-free item module comprises basic service construction such as maintenance service, travel service, insurance service, other service, comprehensive service and item formula;

[0120] A discount coefficient module is configured to maintain a customized discount coefficient according to a vehicle model matching result and a business version;

[0121] A product gross profit rate module is configured to maintain a customized gross profit rate according to the vehicle model matching result and the business version.

[0122] In some embodiments, the maintenance cost trial module calculates the cost of each maintenance item and the total cost according to the recommended maintenance item list, the discount coefficient and the gross profit rate.

[0123] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application.

Claims

1. A dynamic vehicle maintenance recommendation method based on driving behavior and mileage dimension, characterized in that, The method includes the following steps: S1. Obtain the vehicle brand and model, initial mileage, and preset maintenance mileage input by the user; S2. Based on the vehicle brand and model, query the vehicle model database to obtain the standard maintenance item table and brand load feature matrix; Specifically, it includes: The vehicle model database is queried based on the vehicle brand and model to obtain the speed ratio coefficient and load coefficient of the target vehicle model; Construct a brand load characteristic matrix based on the speed ratio coefficient and load coefficient; The calculation methods for the speed ratio coefficient and load coefficient are as follows: ； Where α represents the speed ratio coefficient, R avg R represents the historical average RPM of the target vehicle model. econ This represents the midpoint of the economic speed range for the target vehicle model; ； Where β represents the load factor, W median W represents the median of the load distribution of the target vehicle model. max Indicates the maximum design load of the target vehicle model; S3. Calculate the driving wear index based on the brand load characteristic matrix; The calculation formula is as follows: ； ； Where W represents the driving wear index, This represents the difference between the preset maintenance mileage and the initial mileage, m. p The preset maintenance mileage is represented by m0, the initial mileage is represented by α, the speed ratio coefficient is represented by β, the load coefficient is represented by D1, the wear increment coefficient of the speed item is represented by D2, and the wear increment coefficient of the load item is represented by D2. S4. Calculate the dynamic compensation mileage threshold for each maintenance item based on the driving wear index. The calculation formula is as follows: ； in, This represents the mileage threshold after dynamic compensation for the k-th maintenance item. This represents the base mileage for the k-th maintenance item; S5. Compare the difference between the preset maintenance mileage and the initial mileage with the dynamic compensation mileage threshold to generate a recommended maintenance item list; S6. Calculate the cost of each maintenance item and the total cost based on the recommended maintenance item list, and generate a quotation.

2. The vehicle maintenance dynamic recommendation method based on driving behavior and mileage dimension according to claim 1, characterized in that, In step S2, the standard maintenance item table is obtained by querying the vehicle model database based on the vehicle brand and model, including: Based on the vehicle brand and model, query the vehicle model database to obtain all maintenance items for the target vehicle model; Obtain the baseline mileage, life coefficient of each component, and cost price of each component corresponding to the maintenance items, and construct a maintenance item table.

3. The vehicle maintenance dynamic recommendation method based on driving behavior and mileage dimension according to claim 2, characterized in that, The formulas for calculating the life coefficient of each component are as follows: ； Where, q k This represents the lifespan coefficient of the part corresponding to the k-th maintenance item. This represents the base mileage corresponding to the k-th maintenance item. This represents the average maintenance mileage of the parts corresponding to the k-th maintenance item.

4. The vehicle maintenance dynamic recommendation method based on driving behavior and mileage dimension according to claim 1, characterized in that, In step S5, the difference between the preset maintenance mileage and the initial mileage is compared with the dynamic compensation mileage threshold to generate a recommended maintenance item list, including: ； Where, N k q represents the maintenance coefficient for the k-th maintenance item. k This represents the lifespan coefficient of the part corresponding to the k-th maintenance item; Export the maintenance items with a maintenance factor of 1 to generate a recommended maintenance item list.

5. The vehicle maintenance dynamic recommendation method based on driving behavior and mileage dimension according to claim 2, characterized in that, In step S6, the cost of each maintenance item and the total cost are calculated based on the recommended maintenance item list, and a quotation is generated, including: The cost of each maintenance item is determined based on the recommended maintenance item list and the cost price of each part corresponding to each maintenance item. The total cost is calculated by summing the costs of each maintenance item in the recommended maintenance list, and a quote is generated.

6. A mileage-based dynamic vehicle maintenance recommendation system based on driving behavior, used to execute the mileage-based dynamic vehicle maintenance recommendation method based on driving behavior as described in any one of claims 1-5, characterized in that, The system includes the following modules: The data acquisition module is used to acquire the vehicle brand and model, initial mileage, and preset maintenance mileage input by the user; The matching module is used to query the vehicle model database based on the vehicle brand and model to obtain the standard maintenance item table and brand load feature matrix; The calculation module is used to calculate the driving wear index based on the brand load feature matrix; and to calculate the dynamic compensation mileage threshold for each maintenance item based on the driving wear index. The maintenance item recommendation module is used to compare the difference between the preset maintenance mileage and the initial mileage with the dynamic compensation mileage threshold to generate a recommended maintenance item list. The maintenance cost calculation module is used to calculate the cost of each maintenance item and the total cost based on the recommended maintenance item list, and generate a quotation.

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