Information management method of automobile quality assurance strategy, electronic equipment and computer medium

Abstract

The invention discloses an information management method for an automobile quality assurance strategy, electronic equipment and a computer medium, and the method comprises the steps: obtaining analysis data of an automobile, and building a first function model between fault data and interval data of the automobile based on the analysis data; based on the distribution relation of the interval data and the first function model, establishing a second function model between the reliability of the automobile and the quality assurance parameters of the automobile; and based on the second function model, establishing a prediction model of profit data of each automobile under different warranty parameters so as to determine warranty information of the automobile based on the prediction model. According to the method, the corresponding warranty information can be subsequently determined through the change trend of the profit data of different warranty parameters output by the prediction model, the management method is simple, the number of analysis parameters is small, the operability is high, the accuracy and theoretical feasibility of the subsequently determined warranty information can be remarkably improved, and the method is suitable for popularization and application. And the practicability of the information management method is improved.

Description

Technical Field

[0001] This application relates to the technical field of quality assurance information management, and particularly to an information management method, an electronic device, and a computer medium for automotive quality assurance strategies. Background Art

[0002] In recent years, with the remarkable improvement of the technical level of the new energy vehicle industry and the enhancement of enterprise competitiveness, existing automotive enterprises, in order to improve their market competitiveness, will choose quality assurance policies with longer cycles or strategies such as lifetime quality assurance. Such practices not only lack a scientific theoretical basis but also bring a heavy economic burden to the enterprises.

[0003] Existing quality assurance strategies are usually made based on market competitiveness or in response to notifications of relevant financial support policies, and do not have theoretical reference value, resulting in enterprises being difficult to select or formulate a quality assurance strategy that meets the needs of their own products, has market competitiveness, and has higher profit value among numerous quality assurance strategies in the market. Therefore, there is an urgent need for an information management method for quality assurance strategies with theoretical feasibility and higher net profit of products to improve the theoretical feasibility of quality assurance information. Summary of the Invention

[0004] To solve the above technical problems, this application provides an information management method, an electronic device, and a computer medium for automotive quality assurance strategies.

[0005] To solve the above problems, this application provides a first technical solution: providing an information management method for automotive quality assurance strategies, the information management method including: obtaining analysis data of the above vehicle, and establishing a first function model between the failure data and the interval data of the above vehicle based on the above analysis data; establishing a second function model between the reliability of the above vehicle and the quality assurance parameters of the above vehicle based on the distribution relationship of the above interval data and the above first function model; establishing a prediction model of the profit data of each above vehicle under different above quality assurance parameters based on the above second function model, so as to determine the quality assurance information of the above vehicle based on the above prediction model.

[0006] Optionally, determining the quality assurance information of the above vehicle based on the above prediction model includes: taking the derivative of the reliability of the above second function model to calculate a third function model between the first failure distribution of the above vehicle and the above quality assurance parameters; obtaining the industry quality assurance parameters of the above vehicle, and performing distribution fitting processing on the above industry quality assurance parameters of the above vehicle to obtain a fourth function model between the second failure distribution of the above vehicle and the above industry quality assurance parameters; calculating the above profit data under the above quality assurance parameters based on the above third function model, the above fourth function model, and the above prediction model to determine the above quality assurance information.

[0007] Optionally, the distribution fitting process for the above-mentioned industry quality assurance parameters of the above-mentioned vehicle includes: performing a distribution fitting process on the above-mentioned industry quality assurance parameters through a normal distribution model to obtain the above-mentioned second failure distribution that conforms to the normal distribution.

[0008] Optionally, based on the above-mentioned third function model, the above-mentioned fourth function model, and the above-mentioned prediction model, calculating the above-mentioned profit data under the above-mentioned quality assurance parameters to determine the above-mentioned quality assurance information, including: calculating, based on the product of the above-mentioned third function model and the above-mentioned fourth function model, a fifth function model between the effective rate of the above-mentioned vehicle and the above-mentioned quality assurance parameters; defining the effective rate of the above-mentioned vehicle to obtain a first quality assurance parameter under the current above-mentioned effective rate based on the above-mentioned fifth function model; and calculating, based on the above-mentioned first quality assurance parameter and the above-mentioned prediction model, a first profit data under the above-mentioned first quality assurance parameter to determine the above-mentioned quality assurance information.

[0009] Optionally, based on the above-mentioned second function model, establishing a prediction model for the profit data of each of the above-mentioned vehicles under different above-mentioned quality assurance parameters to determine the above-mentioned quality assurance information of the above-mentioned vehicle based on the above-mentioned prediction model, including: obtaining, based on the above-mentioned second function model, the maintenance costs of the above-mentioned vehicle under different above-mentioned quality assurance parameters; and establishing the above-mentioned prediction model for the above-mentioned profit data of the above-mentioned vehicle under different above-mentioned quality assurance parameters based on the above-mentioned maintenance costs and the sales data of the above-mentioned vehicle.

[0010] Optionally, the above-mentioned analysis data includes the after-sales data and driving data of the above-mentioned vehicle, the above-mentioned failure data is the failure data of a first preset number of the above-mentioned vehicles within a preset time period, and the above-mentioned interval data is the data of the time intervals between two adjacent failures of the above-mentioned vehicle; obtaining the above-mentioned analysis data of the above-mentioned vehicle and establishing a first function model between the above-mentioned failure data and the interval data of the above-mentioned vehicle based on the above-mentioned analysis data, including: obtaining, based on the above-mentioned analysis data, the failure data when a second preset number of failures occur to the above-mentioned first preset number of the above-mentioned vehicles; calculating, based on the above-mentioned analysis data, the ratio of the total operating parameters of the above-mentioned first preset number of the above-mentioned vehicles to the above-mentioned second preset number to obtain the above-mentioned interval data; and establishing the above-mentioned first function model based on the combination of the above-mentioned failure data and the above-mentioned interval data.

[0011] Optionally, the reliability of the above-mentioned vehicle is used to represent the ability of the electric drive system of the above-mentioned vehicle to resume normal operation in case of failure or malfunction, and the above-mentioned reliability conforms to the exponential distribution; establishing a second function model between the above-mentioned reliability of the above-mentioned vehicle and the above-mentioned quality assurance parameters of the above-mentioned vehicle based on the distribution relationship of the above-mentioned interval data and the above-mentioned first function model, including: describing, based on the above-mentioned interval data, the time intervals between events in the Poisson process of the above-mentioned reliability to obtain the operation data of the above-mentioned reliability; and establishing the above-mentioned second function model based on the combination of the above-mentioned operation data and the above-mentioned first function model.

[0012] Optionally, the above warranty parameters include at least one of the warranty mileage and warranty time of the electric drive system of the above vehicle.

[0013] To solve the above problems, the present application provides a second technical solution: providing an electronic device, including a processor and a memory, the above processor is connected to the above memory, wherein the above memory stores program instructions; the above processor is used to execute the program instructions stored in the above memory to implement the above method.

[0014] To solve the above problems, the present application provides a third technical solution: providing a computer-readable storage medium, the above computer-readable storage medium stores program instructions, and the above program instructions can be executed by a processor to implement the above method.

[0015] The present application provides an information management method, an electronic device, and a computer medium for vehicle warranty strategies. The method obtains the analysis data of the vehicle, and based on the analysis data, establishes a first function model between the fault data and the interval data of the vehicle; based on the distribution relationship of the interval data and the first function model, establishes a second function model between the reliability of the vehicle and the warranty parameters of the vehicle; based on the second function model, establishes a prediction model for the profit data of each vehicle under different warranty parameters, so as to determine the warranty information of the vehicle based on the prediction model. The method of the present application can establish the first function model and the second function model through the analysis data of the vehicle, and based on the second function model, establish a prediction model for the profit data of each vehicle under different warranty parameters, so that the subsequent warranty information can be determined by the change trend of the profit data of different warranty parameters output by the prediction model. The management method is simple, the number of analysis parameters is small, and the operability is high, which can significantly improve the theoretical feasibility of the subsequent determined warranty information and improve the practicability of the information management method. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0017] Figure 1 is a schematic flowchart of the first embodiment of the information management method provided by the present application;

[0018] Figure 2 is a schematic flowchart of the second embodiment of the information management method provided by the present application;

[0019] Figure 3It is a schematic flowchart of the third embodiment of the information management method provided by this application;

[0020] Figure 4 It is a schematic flowchart of the fourth embodiment of the information management method provided by this application;

[0021] Figure 5 It is a schematic structural diagram of an embodiment of the electronic device provided by this application;

[0022] Figure 6 It is a schematic structural diagram of an embodiment of the computer-readable storage medium provided by this application. Detailed implementation manners

[0023] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0024] It should be noted that if there are directional indications (such as up, down, left, right, front, back,...) involved in the embodiments of this application, then the directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0025] In addition, if there are descriptions such as "first" and "second" involved in the embodiments of this application, then the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by this application.

[0026] Specifically, when an enterprise determines the quality assurance strategy of an automobile, the existing determination method can only be based on the requirements of the relevant notice on financial support during the current promotion and application of new energy vehicles as a theoretical support for reference. The notice states that new energy vehicle manufacturers should provide quality assurance for drive motors and motor controllers to consumers. Among them, the quality assurance period for passenger vehicles is not less than 8 years or 120,000 kilometers (whichever comes first), and the quality assurance period for commercial vehicles is not less than 5 years or 200,000 kilometers. However, the above notice is a recommendatory document. More determination methods are to choose a longer quality assurance period in order to maintain an advantage in the fierce market competition. The determined quality assurance strategy not only lacks a scientific theoretical basis but also brings a heavy economic burden to the enterprise, resulting in low theoretical feasibility of the quality assurance strategy.

[0027] In view of this, the embodiment of the present application first provides an information management method for an automobile quality assurance strategy. This information management method is used to assist in determining quality assurance information by establishing a prediction model of profit data of an automobile under different quality assurance parameters, so that subsequent quality assurance information can be managed according to the change trend of profit data under different quality assurance parameters, improving the theoretical feasibility of quality assurance information and the practicality of the information management method. In particular, the information management method of the embodiment of the present application can be applied to the information management of the quality assurance strategy of an automobile, or can also be applied to the information management of the quality assurance strategy of the electric drive system of an automobile. The electric drive system can include at least one of the drive motor, motor controller, and reducer of the automobile.

[0028] Please refer to Figure 1 , Figure 1 is a schematic flowchart of the first embodiment of the information management method provided by the present application. As Figure 1 shown, in this embodiment, the information management method includes the following steps:

[0029] Step S10: Obtain the analysis data of the automobile, and establish a first function model between the failure data and the interval data of the automobile based on the analysis data.

[0030] Specifically, the analysis data of the automobile is the data obtained when analyzing the automobile in terms of sales, quality, maintenance, user driving data, profit, etc. For example, the analysis data at least includes driving data and after-sales data. The driving data is the data obtained by analyzing the user's driving time, driving mileage, etc. of the automobile product to be managed, and the after-sales data is the data obtained by analyzing the sales data, failure conditions, maintenance conditions, etc. of the automobile product to be managed within a certain period of time.

[0031] Therefore, after obtaining the analysis data, a first function model can be established between the failure data and the interval data of the vehicle based on the analysis data. Among them, the failure data can be reflected by the number of failures of a preset number of vehicles within a certain service period. Exemplarily, the failure data can be, but is not limited to, the number of incidents per thousand vehicles (IPTV), such as including IPTV 12mis, IPTV 6mis, or IPTV 2mis, that is, corresponding to the number of failures per thousand vehicles within a 12-month service period, within a 6-month service period, and within a 2-month service period respectively. The interval data can be reflected by the time interval between two failures of the vehicle. For example, the interval data can be, but is not limited to, the mean time between failures (MTBF).

[0032] Step S20: Based on the distribution relationship of the interval data and the first function model, establish a second function model between the reliability of the vehicle and the warranty parameters of the vehicle.

[0033] Specifically, in reliability theory, in electronic components, complex electromechanical systems, or whole machine systems, the failure probability function and reliability usually follow an exponential distribution. Therefore, in this embodiment, by setting the reliability of the vehicle to follow an exponential distribution and determining the operation data of the reliability of the vehicle based on the distribution relationship of the interval data, a second function model between the reliability of the vehicle and the warranty parameters of the vehicle is established based on the operation data and the first function model.

[0034] Step S30: Based on the second function model, establish a prediction model for the profit data of each vehicle under different warranty parameters, so as to determine the warranty information of the vehicle based on the prediction model.

[0035] Specifically, after obtaining the second function model, based on the functional relationship between the reliability and the warranty parameters in the second function model, the profit data corresponding to the warranty parameters can be calculated based on the reliability, so as to establish a prediction model for the profit data of each vehicle under different warranty parameters, so that the change trend of the profit data of different warranty parameters can be predicted based on the prediction model in the future, thereby determining the warranty information that comprehensively considers the analysis data and the profit situation, so as to realize the management of the warranty information.

[0036] In the embodiment of the present application, the information management method obtains the analysis data of the vehicle, and establishes a first function model between the failure data and the interval data of the vehicle based on the analysis data; based on the distribution relationship of the interval data and the first function model, a second function model between the reliability of the vehicle and the quality assurance parameters of the vehicle is established; based on the second function model, a prediction model of the profit data of each vehicle under different quality assurance parameters is established, so as to determine the quality assurance information of the vehicle based on the prediction model. Therefore, the method of this embodiment can establish the first function model and the second function model through the analysis data of the vehicle, and establish a prediction model of the profit data of each vehicle under different quality assurance parameters based on the second function model, so that the subsequent quality assurance information can be determined by the change trend of the profit data of different quality assurance parameters output by the prediction model. The management method is simple, the number of analysis parameters is small, the operability is high, and the theoretical feasibility of the subsequent determined quality assurance information can be significantly improved, and the practicability of the information management method can be improved.

[0037] In one embodiment, please refer to Figure 2 , Figure 2 which is a schematic flowchart of the second embodiment of the information management method provided by the present application. As Figure 2 shown, in step S30 of the information management method of this embodiment, determining the quality assurance information of the vehicle based on the prediction model further includes:

[0038] Step S310: Take the derivative of the reliability of the second function model to calculate a third function model between the first failure distribution of the vehicle and the quality assurance parameters.

[0039] Specifically, the first failure distribution in this embodiment is specifically the failure probability density, and the failure probability density can be used to reflect the probability density that the vehicle cannot work properly due to natural or external factors within a certain time or number of times. Since in reliability theory, the corresponding failure probability density can usually be obtained based on the description parameters of the distribution characteristics of the reliability, in this embodiment, the derivative of the reliability of the second function model is obtained to calculate a third function model between the first failure distribution of the vehicle and the quality assurance parameters.

[0040] Exemplarily, after taking the derivative of the reliability of the second function model, the following formula can be obtained:

[0041]

[0042] where t is the quality assurance parameter of the vehicle product to be managed, f1(t) is the first failure distribution under the quality assurance parameter; a is the number of vehicle failures per thousand vehicles within a 12-month service period, that is, the failure data IPTV 12mis; b is the average annual mileage of all vehicles corresponding to the failure data, that is, b = average annual mileage * 1000.

[0043] Step S320: Obtain the industry warranty parameters of the vehicle, and perform distribution fitting processing on the industry warranty parameters of the vehicle to obtain a fourth function model between the second failure distribution of the vehicle and the industry warranty parameters.

[0044] Specifically, the information management method of this embodiment can also analyze other vehicle products in the same industry to obtain industry warranty parameters. The industry warranty parameters are used to represent the warranty parameters of the relevant warranty policies of other vehicle products similar to the vehicle products to be managed in this application embodiment. By analyzing the competitiveness of the warranty parameters of the vehicle products to be managed obtained in this embodiment in the market through the industry warranty parameters, the warranty information obtained by the information management method of this embodiment can increase profit data at the level of market competitiveness that meets the requirements, and further improve the practicality and accuracy of the warranty information.

[0045] Therefore, after obtaining the industry warranty parameters of the vehicle, this embodiment can perform distribution fitting processing on the industry warranty parameters to obtain a fourth function model between the second failure distribution of the vehicle and the industry warranty parameters. Among them, the second failure distribution is also the failure probability density, the first failure distribution is the failure probability density under the warranty parameters of the vehicle products to be managed in this embodiment, and the second failure distribution is the failure probability density under the warranty parameters of other vehicle products in the market.

[0046] Step S330: Based on the third function model, the fourth function model, and the prediction model, calculate the profit data under the warranty parameters to determine the warranty information.

[0047] After obtaining the third function model between the first failure distribution and the warranty parameters of the current vehicle products to be managed, and obtaining the fourth function model between the second failure distribution of other vehicle products in the market and the industry warranty parameters, the warranty parameters under a certain appropriate failure probability density can be determined based on the third function model and the fourth function model, so that the corresponding profit data can be calculated based on the distribution of the warranty parameters in the prediction model and the warranty information can be determined.

[0048] In an embodiment of the present application, the information management method calculates a third function model between the first failure distribution of an automobile and the warranty parameters by taking the derivative of the reliability of the second function model, obtains the industry warranty parameters of the automobile, performs distribution fitting processing on the industry warranty parameters of the automobile to obtain a fourth function model between the second failure distribution of the automobile and the industry warranty parameters, and calculates profit data under the warranty parameters based on the third function model, the fourth function model, and the prediction model to determine the warranty information, so that the prediction model of this embodiment can take into account the influence of the analysis data of the automobile and the industry warranty parameters of the market, improve the market competitiveness of the determined warranty information, and further improve the practicability and accuracy of the warranty information.

[0049] Optionally, in step S320 of the information management method of this embodiment, the distribution fitting processing of the industry warranty parameters of the automobile further includes: performing distribution fitting processing on the industry warranty parameters through a normal distribution model to obtain a second failure distribution that conforms to the normal distribution.

[0050] Classification Guarantee mileage / 10,000 km Competitor 1 10 Competitor 2 12 Competitor 3 15 Competitor 4 15 Competitor 5 16 Competitor 6 16

[0051] Table 1

[0052] Specifically, in this embodiment, the industry warranty parameters to be observed are compared with a preset normal distribution model to fit the distribution of the industry warranty parameters, and the fitting degree of the industry warranty parameters is evaluated through a significance level p representative function. The closer the p value is to 1, the closer the distribution of the industry warranty parameters fitted in this embodiment is to the actual data, that is, the more accurate the distribution of the second failure distribution is.

[0053] Among them, in this embodiment, the industry warranty parameters of several other automotive products can be obtained to calculate the mean and standard deviation of the industry warranty parameters of several products. Exemplarily, the industry warranty parameters may include the warranty mileage of the competitors of the automotive product to be managed in the industry, and the obtained industry warranty parameters can be as shown in Table 1 above.

[0054] Since the second failure distribution is the failure probability density, the expression of the second failure distribution under the normal distribution can be:

[0055]

[0056] Among them, f2(m) is used to represent the failure probability density of the industry warranty parameter m, u is the mean of the industry warranty parameter, and σ is the standard deviation of the industry warranty parameter. Through the industry warranty parameters in the above table, u = 14 and σ = 0.91 can be calculated.

[0057] Optionally, please refer to Figure 3 , Figure 3It is a schematic flowchart of the third embodiment of the information management method provided by this application. As Figure 3 shown, step S330 of the information management method in this embodiment further includes:

[0058] Step S331: Based on the product of the third function model and the fourth function model, calculate the fifth function model between the efficiency of the vehicle and the quality assurance parameter.

[0059] Specifically, after obtaining the third function model and the fourth function model, in this embodiment, by obtaining the product of the third function model and the fourth function model, the product of the third function model and the fourth function model is used as the joint distribution function to represent the fifth function model between the efficiency of the vehicle and the quality assurance parameter. Among them, since the third function model is used to represent the functional relationship of the quality assurance parameters of the vehicle products to be managed, and the third function model is used to represent the functional relationship of the industry quality assurance parameters of competing products, that is, in the fifth function model obtained by the product of the third function model and the fourth function model, the efficiency can be used to represent the competitiveness level of the vehicle products to be managed in this embodiment in the industry.

[0060] Among them, based on the above formulas (1) and (2), the formula of the fifth function model can be calculated as follows:

[0061] f(t) = f1(t) * f2(m); (3)

[0062] Among them, f(t) is used to represent the efficiency of the vehicle products to be managed under the quality assurance parameters, f1(t) is the first failure distribution, and f2(m) is the second failure distribution.

[0063] Step S332: Define the efficiency of the vehicle to obtain the first quality assurance parameter at the current efficiency based on the fifth function model.

[0064] After obtaining the fifth function model, in this embodiment, by defining the competitiveness level that the vehicle products to be managed need to maintain in the industry, the efficiency of the vehicle can be obtained, so that the efficiency can be substituted into the fifth function model to obtain the first quality assurance parameter at this efficiency.

[0065] Step S333: Based on the first quality assurance parameter and the prediction model, calculate the first profit data at the first quality assurance parameter to determine the quality assurance information.

[0066] After obtaining the first quality assurance parameter and the prediction model, the first quality assurance parameter can be substituted into the prediction model to calculate the first profit data at the first quality assurance parameter, and then based on the first profit data, determine whether to use this quality assurance parameter as the quality assurance strategy for the vehicle products to be managed, that is, determine the quality assurance information.

[0067] Exemplarily, it can be assumed that the quality guarantee parameter of the automotive product to be managed needs to have a market competitiveness of approximately 80%, that is, f(t) = 0.8. Combining with the above formula (3), the corresponding first quality guarantee parameter t can be calculated. Substituting the first quality guarantee parameter t into the prediction model, the profit data under the first quality guarantee parameter t can be calculated.

[0068] In the embodiment of the present application, this information management method calculates the fifth function model between the efficiency of the vehicle and the quality guarantee parameter based on the product of the third function model and the fourth function model, defines the efficiency of the vehicle, and based on the fifth function model, obtains the first quality guarantee parameter under the current efficiency. Based on the first quality guarantee parameter and the prediction model, the first profit data under the first quality guarantee parameter is calculated to determine the quality guarantee information, so that the profit data determined by the prediction model can take into account the influence of the analysis data of the vehicle and the industry quality guarantee parameters of the market, thereby improving the market competitiveness of the quality guarantee information and further improving the practicability and accuracy of the quality guarantee information.

[0069] In one embodiment, the step of establishing the prediction model of the profit data of each vehicle under different quality guarantee parameters based on the second function model in step S30 further includes: obtaining the maintenance cost of the vehicle under different quality guarantee parameters based on the second function model; establishing the prediction model of the profit data of the vehicle under different quality guarantee parameters based on the maintenance cost and the sales data of the vehicle.

[0070] Specifically, since the second function model can represent the functional relationship between the reliability of the vehicle and the quality guarantee parameter, based on the reliability parameter of the second function model, the average maintenance cost and the expected sales volume of each vehicle of the automotive product to be managed can be obtained, and thus the total maintenance cost required for the vehicles with the expected sales volume to be repaired within the corresponding quality guarantee parameters can be determined. Further, the expected sales volume of the vehicle within a certain period of time and the single-piece profit that can be obtained from the sale of each vehicle are used to obtain the sales data of the vehicle with the expected sales volume. The sales data indicates the total profit that can be obtained after the vehicle with the expected sales volume is sold. Based on the maintenance cost and the sales data of the vehicle, the profit data of the vehicle under different quality guarantee parameters can be calculated. The profit data can be understood as the net profit that can be obtained after excluding the possible maintenance costs during the quality guarantee period, so that the corresponding quality guarantee information can be determined based on the profit data later. The management method is simple, the number of analysis parameters is small, and the operability is high, which can significantly improve the theoretical feasibility of the subsequent determined quality guarantee information and improve the practicability of the information management method.

[0071] Among them, the prediction model of the profit data under different quality guarantee parameters can be expressed as follows:

[0072] P(t) = x * y - (1 - R(t)) * x * z; (4)

[0073] Among them, P(t) can be expressed as profit data under the quality assurance parameters, R(t) represents the reliability under the quality assurance parameters, x is the estimated sales volume, y is the profit per unit of the sold car, and z is the average maintenance cost per car. In a possible way, the quality assurance parameters of the embodiments of the present application can be the relevant parameters for providing quality assurance for a certain part of the car, that is, the above profit-related data can be understood as the relevant profit parameters corresponding to the quality assurance part. For example, the quality assurance parameters are the quality assurance mileage or quality assurance time for providing quality assurance for the electric drive system of the car, that is, the above profit-related data are the relevant data of the electric drive system, which are not specifically limited here.

[0074] In one embodiment, the analysis data includes the after-sales data and driving data of the car, the failure data is the failure data of the first preset number of cars within a preset time period, and the interval data is the data of the interval time between two adjacent failures of the car. Please refer to Figure 4 , Figure 4 is the flowchart of the fourth embodiment of the information management method provided by the present application. As Figure 4 shown, step S10 includes the following steps:

[0075] Step S110: Based on the analysis data, obtain the failure data when the first preset number of cars have the second preset number of failures.

[0076] Specifically, the after-sales data of the analysis data may include the sales volume data, profit per unit data, average maintenance cost per car, and IPTV 12mis of the car products to be managed, and the driving data may include the average driving time and average driving mileage of the car users. Exemplarily, the demand parameters that need to be input before obtaining the quality assurance information in the information management method of the embodiments of the present application are shown in Table 2 below:

[0077]

[0078] Table 2

[0079] Among them, the above analysis data can be the models of the same positioning that have been mass-produced in the automobile enterprise for reference, or obtained through research on the models of the same positioning in the market, or the relevant data of the models of the same positioning and the data obtained through prediction. The above driving data can be the user driving data obtained through big data platforms, big data statistical analysis, etc. to consider more user behaviors.

[0080] Therefore, after obtaining the analysis data in this embodiment, the fault data when the first preset number of vehicles have the second preset number of faults can be obtained through the analysis data, that is, it can be determined that the fault data can be IPTV. When the fault data is IPTV 12mis, the first preset number can be 1000, and the second number can be the number of faults of a thousand vehicles within a 12-month service period, denoted as a. Specifically, the annual sales volume data can be defined as M. A total of n faults occur for M vehicles within 12 months. Then, the fault data can be determined as described by the following formula:

[0081] IPTV 12mis = n / M * 1000; (5)

[0082] Among them, IPTV 12mis is the fault data within a 12-month service period.

[0083] Step S120: Based on the analysis data, calculate the ratio of the total operating parameters of the first preset number of vehicles to the second preset number to obtain the interval data.

[0084] By obtaining the after-sales data and driving data in the analysis data, the total operating parameters of the first preset number of vehicles can be calculated according to the annual average mileage of vehicle use and the annual sales volume data in the after-sales data. The total operating parameters can indicate the total mileage traveled by the vehicles with the annual sales volume. Based on the ratio of the total operating parameters to the second preset number, the interval data can be calculated as described by the following formula:

[0085] MTBF = M * c / n; (6)

[0086] Among them, MTBF is the interval data, c is the annual average mileage of vehicle use by users, M is the annual sales volume data, and n is the number of faults that occur for M vehicles within 12 months.

[0087] Step S130: Based on the combination of the fault data and the interval data, establish a first function model.

[0088] After obtaining the fault data and the interval data, the above formulas (5) and (6) can be combined to obtain the functional relationship between the fault data and the interval data of the vehicle, that is, the first function model is shown as the following formula:

[0089] IPTV 12mis = c * 1000 / MTBF; (7)

[0090] Among them, IPTV 12mis is the fault data, MTBF is the interval data, and c is the annual average mileage of vehicle use by users.

[0091] Therefore, the information management method of this embodiment obtains the failure data when the first preset number of vehicles have the second preset number of failures by analyzing data, calculates the ratio of the total operating parameters of the first preset number of vehicles to the second preset number to obtain the interval data, and establishes the first function model based on the combination of the failure data and the interval data, so that the second function model can be calculated based on the first function model subsequently, and the derivative of the first function model is obtained to obtain the third function model.

[0092] In one embodiment, the reliability of a vehicle is used to represent the ability of the vehicle's electric drive system to resume normal operation in case of failure or malfunction, and the reliability conforms to the exponential distribution. Step S20 includes: describing the time interval between events in the Poisson process of reliability based on the interval data to obtain the operation data of reliability; establishing the second function model based on the combination of the operation data and the first function model.

[0093] Specifically, after obtaining the first function model, since the failure probability function and reliability of the vehicle's electric drive system usually follow the exponential distribution, in this embodiment, the reciprocal of the interval data can be used as the parameter for describing the distribution in the exponential distribution of reliability to describe the time interval between events in the Poisson process of reliability, and the operation data of reliability can be obtained. The operation data of reliability can be represented by the following formula:

[0094] R(t) = e -λt ;(8)

[0095] where R(t) is the reliability, λ is the parameter of the exponential distribution, and λ ≈ 1 / MTBF, and t is the quality assurance parameter.

[0096] After obtaining the operation data, the combination of the operation data and the first function model is used to obtain the functional relationship between the reliability of the vehicle and the quality assurance parameter of the vehicle, that is, the second function model is shown as the following formula:

[0097]

[0098] where R(t) is the reliability, IPTV 12mis is the failure data, c is the average annual mileage of the user, and t is the quality assurance parameter.

[0099] In one embodiment, the quality assurance parameter includes at least one of the quality assurance mileage and the quality assurance time of the vehicle's electric drive system.

[0100] Specifically, the warranty parameters obtained in the information management method of this embodiment can be understood as the warranty mileage and / or warranty time for providing warranty services for the electric drive system of the vehicle. That is, the above prediction model can include a relationship model of profit data at different warranty mileages, and / or the above prediction model can include a relationship model of profit data at different warranty times. Therefore, the user can select the corresponding prediction model based on the needs, further improving the practicality of the information management method.

[0101] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of an embodiment of the electronic device provided by this application. As Figure 5 shown, the electronic device 50 of this embodiment includes a memory 52 and a processor 51, and the processor 51 is connected to the memory 52. The memory 52 is used to store program instructions. The processor 51 is used to execute the program instructions stored in the memory 52 to implement the method described in any of the above embodiments.

[0102] Among them, the processor 51 can also be referred to as a CPU (Central Processing Unit, central processing unit). The processor 51 may be an integrated circuit chip with signaling processing capabilities. The processor 51 can also be a general-purpose processor, a digital signaling processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0103] The memory 52 can be a memory module, a TF card, etc., and can store all the information in the electronic device 50. The input original data, computer programs, intermediate operation results, and final operation results are all stored in the memory. It stores and retrieves information according to the positions specified by the controller. With the memory, the string matching prediction device has a memory function and can ensure normal operation. The memory of the string matching prediction device can be classified into a main memory (internal memory) and an auxiliary memory (external memory) according to its use, and there is also a classification method of dividing it into an external memory and an internal memory. The external memory is usually a magnetic medium or an optical disc, etc., which can store information for a long time. The internal memory refers to the storage component on the motherboard, which is used to store the data and programs being currently executed, but only temporarily stores the programs and data. When the power is turned off or interrupted, the data will be lost.

[0104] In several embodiments provided in this application, it should be understood that the disclosed methods and apparatuses can be implemented in other ways. For example, the inverter control method described above is merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0105] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, in each embodiment of this application, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a system server, or a network device, etc.) or a processor to execute all or part of the steps of the methods in each embodiment of this application.

[0108] Please refer to Figure 6 , Figure 6 is a schematic structural diagram of an embodiment of the computer-readable storage medium provided in this application. As Figure 6As shown, the computer-readable storage medium of the present application stores program instructions 61 that can implement all of the above methods. Among them, the program instructions 61 can be stored in the above storage medium in the form of a software product, including several instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods in various embodiments of the present application. The aforementioned storage device includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, or electronic devices such as computers, servers, mobile phones, and tablets. The above are only the embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A method for managing automobile warranty information, characterized in that: The information management method comprises: Acquire analysis data of the vehicle, and establish a first function model between fault data and interval data of the vehicle based on the analysis data; Based on the distribution relationship of the interval data and the first function model, establishing a second function model between the reliability of the automobile and the warranty parameter of the automobile; Based on the second function model, a prediction model of the profit data of each of the automobiles under different warranty parameters is established, so as to determine the warranty information of the automobile based on the prediction model.

2. The information management method according to claim 1, characterized in that: Determining the warranty information of the automobile based on the prediction model includes: Deriving the reliability of the second function model to calculate a third function model between the first failure distribution of the automobile and the warranty parameter; Acquiring industry warranty parameters of the automobile, and performing distribution fitting processing on the industry warranty parameters of the automobile to obtain a fourth function model between the second failure distribution of the automobile and the industry warranty parameters; Based on the third function model, the fourth function model and the prediction model, the profit data under the warranty parameters is calculated to determine the warranty information.

3. The information management method according to claim 2, characterized in that: The performing distribution fitting processing on the industry warranty parameter of the automobile includes: The industry warranty parameter is subjected to distribution fitting processing through a normal distribution model to obtain the second failure distribution that conforms to the normal distribution.

4. The information management method according to claim 2, characterized in that: The calculating the profit data under the warranty parameters based on the third function model, the fourth function model and the prediction model to determine the warranty information includes: Based on the product of the third function model and the fourth function model, a fifth function model between the efficiency of the automobile and the warranty parameter is calculated; defining the efficiency of the automobile to obtain a first warranty parameter at the current efficiency based on the fifth function model; Based on the first quality assurance parameter and the prediction model, first profit data under the first quality assurance parameter is calculated to determine the quality assurance information.

5. The information management method according to claim 1, characterized in that: The step of establishing a prediction model of profit data of each of the automobiles under different warranty parameters based on the second function model, so as to determine warranty information of the automobile based on the prediction model, includes: Based on the second function model, obtaining the maintenance cost of the automobile under different warranty parameters; Based on the maintenance cost and the sales data of the automobile, the prediction model of the profit data of the automobile under different warranty parameters is established.

6. The information management method according to claim 1, characterized in that: The analysis data includes after-sales data and driving data of the automobile, the fault data is the fault data of a first preset number of the automobiles within a preset time period, and the interval data is the interval time data between two adjacent faults of the automobile; The obtaining of the analysis data of the vehicle and establishing a first function model between the fault data and the interval data of the vehicle based on the analysis data comprises: Based on the analysis data, obtaining fault data when a second preset number of faults occur to the first preset number of vehicles; Based on the analysis data, calculating a ratio of the total operating parameters of the first preset number of the vehicles to the second preset number to obtain the interval data; The first function model is established based on the combination of the fault data and the interval data.

7. The information management method according to claim 1, characterized in that: The reliability of the automobile is used to indicate the ability of the electric drive system of the automobile to resume normal operation in the event of a fault or failure, and the reliability conforms to an exponential distribution; The step of establishing a second function model between the reliability of the automobile and the warranty parameter of the automobile based on the distribution relationship of the interval data and the first function model comprises: Based on the interval data, describing the time intervals between events in the Poisson process of the reliability to obtain the operation data of the reliability; Based on the combination of the operation data and the first function model, the second function model is established.

8. The information management method according to claim 1, characterized in that: The warranty parameter includes at least one of a warranty mileage and a warranty time of the electric drive system of the vehicle.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the processor is connected to the memory, wherein: The memory stores program instructions; The processor is configured to execute program instructions stored in the memory to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and the program instructions can be executed by a processor to implement the method according to any one of claims 1 to 8.

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