CN116596584A - Processing method, device, equipment and medium for automobile quality data - Google Patents

Abstract

The application discloses a method, a device, equipment and a medium for processing automobile quality data. The method comprises the steps of obtaining automobile quality data in an automobile market in a first preset period; the first preset period is a preset time period before the current time, and the automobile quality data comprise vehicle attribute data and vehicle service data; generating a fault data screening table according to the automobile quality data; predicting the cumulative failure frequency of the failure piece in the predicted time period according to the failure piece in the vehicle attribute data, and the sales date, the failure date and the failure frequency value in the vehicle service data, and determining the predicted cumulative failure frequency of the failure piece; and determining a fault analysis result of the failure piece according to the fault data screening table and the predicted accumulated fault frequency. According to the technical scheme, the automobile quality data can be comprehensively and rapidly analyzed, the trend risk of faults can be accurately identified, and the digital management of the automobile quality data is realized.

Description

Processing method, device, equipment and medium for automobile quality data

Technical Field

The present application relates to the field of big data analysis technologies, and in particular, to a method, an apparatus, a device, and a medium for processing automobile quality data.

Background

With the development requirements of environmental protection, energy conservation and emission reduction in the automobile industry, the comprehensive adoption of electric control technology and the large number of assembly of advanced parts, the automobile structure design is increasingly complex; meanwhile, along with the increasing requirements of people on the quality of automobile products, the requirements of users on the quality of the automobile products are continuously improved, so that the quality of the automobile products becomes a focus in competition of the automobile industry. In addition, due to the fact that the market of the automobile industry is large in quantity and the failure claim amount is high, a large quantity of quality failures can cause a large quantity of economic losses to companies, so that resource waste is caused, the environmental influence is aggravated, and the strong complaints of users can be caused, so that the product competitiveness is affected. Therefore, it is important to avoid the risk of automobile faults in time.

At present, manual analysis is usually carried out on automobile product market fault data, but the calculation workload is large, the analysis period is long, the statistical dimension is small, the conclusion accuracy is low, the time rate of risk problem identification is low, so that partial fault products flow into the market in a large quantity when the risk problem is not identified in time, and further, when fault upgrading is treated, great market influence and three-package loss are generated.

Therefore, how to provide a technical scheme capable of comprehensively, rapidly and accurately analyzing and predicting the automobile quality data is a technical problem to be solved by the technicians in the field.

Disclosure of Invention

The application provides a processing method, a device, equipment and a medium for automobile quality data, which are used for realizing

According to an aspect of the present application, there is provided a method of processing vehicle quality data, the method comprising:

acquiring automobile quality data in an automobile market in a first preset period; the first preset period is a preset period before the current time, the automobile quality data comprise vehicle attribute data and vehicle service data, the vehicle attribute data are used for representing equipment inherent attribute data of a vehicle, and the vehicle service data are used for representing equipment maintenance service data of the vehicle;

generating a fault data screening table according to the automobile quality data; wherein the failure data screening table is used for representing a relationship between the vehicle attribute data and the vehicle service data;

predicting the cumulative failure frequency of the failure piece in a prediction time period according to the failure piece in the vehicle attribute data, and the production date, the using time length and the failure frequency value corresponding to the failure piece in the vehicle service data, and determining the predicted cumulative failure frequency of the failure piece;

And determining a fault analysis result of the failure piece according to the fault data screening table and the predicted accumulated fault frequency.

According to another aspect of the present application, there is provided an apparatus for processing vehicle quality data, the apparatus comprising:

the automobile quality data acquisition module is used for acquiring automobile quality data in an automobile market in a first preset period; the first preset period is a preset period before the current time, the automobile quality data comprise vehicle attribute data and vehicle service data, the vehicle attribute data are used for representing equipment inherent attribute data of a vehicle, and the vehicle service data are used for representing equipment maintenance service data of the vehicle;

the fault data screening table generation module is used for generating a fault data screening table according to the automobile quality data; wherein the failure data screening table is used for representing a relationship between the vehicle attribute data and the vehicle service data;

the failure piece failure frequency prediction module is used for predicting the accumulated failure frequency of the failure piece in a prediction time period according to the failure piece in the vehicle attribute data and the production date, the using time length and the failure frequency value corresponding to the failure piece in the vehicle service data, and determining the predicted accumulated failure frequency of the failure piece;

And the failure part fault trend determining module is used for determining a fault analysis result of the failure part according to the fault data screening table and the predicted accumulated fault frequency.

According to another aspect of the present application, there is provided an apparatus for processing vehicle quality data, the apparatus comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of processing vehicle quality data according to any one of the embodiments of the present application.

According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a method for processing vehicle quality data according to any one of the embodiments of the present application.

According to the technical scheme provided by the application, the automobile quality data in the automobile market in the first preset period are obtained; the first preset period is a preset time period before the current time, and the automobile quality data comprise vehicle attribute data and vehicle service data; generating a fault data screening table according to the automobile quality data; predicting the cumulative failure frequency of the failed part in the predicted time period according to the failed part in the vehicle attribute data and the production date, the using time length and the failure frequency value corresponding to the failed part in the vehicle service data, and determining the predicted cumulative failure frequency of the failed part; and determining a fault analysis result of the failure piece according to the fault data screening table and the predicted accumulated fault frequency. According to the technical scheme, the automobile quality data can be comprehensively and rapidly analyzed, the trend risk of faults can be accurately identified, and the digital management of the automobile quality data is realized.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a method for processing vehicle quality data according to an embodiment of the present application;

FIG. 2a is a 3MIS statistical line graph of various product platforms provided in accordance with one embodiment of the present application;

fig. 2b is a 12MIS statistical area diagram of a key component of a DK national six-product platform provided in the first embodiment of the present application;

fig. 3 is a flowchart of a method for processing vehicle quality data according to a second embodiment of the present application;

FIG. 4 is a diagram showing a statistical representation of cumulative failure frequency provided in a second embodiment of the present application;

FIG. 5a is a schematic diagram illustrating calculation of a candidate growth rate according to a second embodiment of the present application;

FIG. 5b is a schematic diagram showing the calculation of an average growth rate according to the second embodiment of the present application;

FIG. 5c is a schematic diagram illustrating a calculation of a cumulative failure frequency prediction according to a second embodiment of the present application;

fig. 6 is a schematic structural diagram of a processing device for vehicle quality data according to a third embodiment of the present application;

fig. 7 is a schematic structural diagram of an apparatus for implementing a method for processing vehicle quality data according to an embodiment of the present application.

Detailed Description

In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," "preset," "target," and the like in the description and claims of the present application and in the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a method for processing vehicle quality data according to an embodiment of the present application, where the method may be performed by a device for processing vehicle quality data, where the device for processing vehicle quality data may be implemented in hardware and/or software, and the device for processing vehicle quality data may be configured in a device with data processing capability. As shown in fig. 1, the method includes:

s110, acquiring automobile quality data in an automobile market in a first preset period. The first preset period is a preset time period before the current time, and the automobile quality data comprise vehicle attribute data and vehicle service data; the vehicle attribute data at least comprises at least one of a failure piece, a vehicle type and a product model; the vehicle service data at least comprises at least one of failure cause, processing structure, service station code, area, failure mileage, production date, sales date, failure date, maintenance type, data validity, use duration and failure frequency value.

The vehicle quality data may be data that is involved in the failure of the vehicle from production to operation. The vehicle attribute data may be data characterizing vehicle hardware device information and the vehicle service data may be data generated during production, sales, and use of the vehicle.

As an alternative embodiment, the vehicle quality data may be derived from a market three package claim system, a production system, a vehicle operation monitoring system. Specifically, the failure piece, the failure reason, the processing structure, the using time length, the failure frequency value, the failure date, the maintenance type and the like can be derived from the three-package claim system in the market; the production date, the vehicle model, the product model, the production quantity and the like can be derived from the production system; service station codes, areas, mileage, vehicle types, product models, etc. may be derived from the vehicle operation monitoring system.

Wherein, the failure part can be a part of the vehicle which breaks down, such as a water pump, a thermostat, a gasoline pump, a cylinder body and the like; the vehicle model can be the model of the vehicle; the product model can be the model of a product platform, such as the liberated highway national six, the green steam DD national six and the like; the failure cause may be a cause of a failure of the vehicle; the service station code may be a service station code for auto repair; the area may be an area where the service station is located, such as province; the types of repairs may include normal repairs, market rectification, and caretaking repairs; the data validity can be judged according to the maintenance type, the settlement amount and the failure date; the failure date may be a date of repair of the vehicle; the usage time length can be the difference between the sales shipment date and the return date of the invalid piece, and can be represented by the usage days or the usage months, for example, 12MIS (MIS, month in Service) is the factory in quality assurance and the usage time is 12 months; the failure frequency value may be the current failure number of the failed piece and may be set to 1.

In the embodiment of the invention, the data support can be provided for the optimization of the automobile production at the current time by acquiring the automobile quality data in the automobile market in the preset time period before the current time, so that the scientific guidance of the automobile production process is facilitated.

As an alternative embodiment, after the vehicle quality data in the vehicle market in the first preset period is acquired, the acquired vehicle quality data may be subjected to standardization processing, so as to perform further screening processing on the vehicle quality data.

Specifically, the vehicle model can be a vehicle model platform abbreviation; the product model may be a product platform abbreviation; the name field of the corrected failure piece with the first loss can be uniformly identified by the failure piece, the failure reason and the processing structure; the service station code may be a service station name abbreviation; the region may be a province or province code; the fault mileage can be converted into a fault mileage interval according to the actual fault mileage, the fault mileage interval can be 0/2000km/10000km/20000km/30000km/40000km/50000km/60000km/70000km/80000km/90000km/100000km/100000km, and if the actual fault mileage is 1800km, the fault mileage can be converted into 2000km; date of manufacture, date of sale, date of failure may be abbreviations for year and month, such as 2022, 9, 15 may be written as 2209; the maintenance type can be 1 for normal maintenance, 2 for market modification, and 3 for careless maintenance; the validity of the data can be represented by 1 for validity, and 0 for invalidity; the use duration can be converted into a use month interval according to the actual use days, and the actual use days are 82 days, so that the use duration can be 4 months.

S120, generating a fault data screening table according to the automobile quality data. Wherein the failure data screening table is used for representing the relationship between the vehicle attribute data and the vehicle service data.

The fault data screening table can be used for carrying out statistical screening on fault data of the vehicle attribute data under different conditions. It should be noted that at least one fault data filtering table may be generated according to the vehicle quality data. For example, the fault data filtering table may be a fault frequency statistics of each failure part on different production dates, a fault frequency statistics of each vehicle model on different fault dates, a fault frequency statistics of each failure part on different areas, a fault frequency statistics of each failure part on different fault mileage, a fault frequency statistics of each vehicle model on different production dates, and so on. The embodiment of the invention is not limited, and can be determined according to actual needs.

As an alternative but non-limiting implementation, generating a fault data screening table according to the vehicle quality data includes, but is not limited to, the following steps A1-A2:

and A1, selecting target row data, target column data, target values and target screening options from the automobile quality data.

The target line data can be various failure pieces, vehicle models or product models, so that trend statistics can be conveniently carried out on different failure pieces, vehicle models or product models; the target column data can be the dividing condition of the failure frequency of each failure piece, vehicle type or product model; the target value can be an accumulated value of failure frequency of the target line data under a specific screening condition; the target screening item can be a screening condition of failure frequency of each failed part, vehicle model or product model. Specifically, the target row data may be selected from the vehicle attribute data, and the target column data, the target value, and the target screening item may be selected from the vehicle quality data.

In the embodiment of the invention, the selection principle of the target row data, the target column data, the target value and the target screening item can be determined according to the type of the fault data screening table. For example, if the failure data filtering table is the failure frequency statistics of each failure piece on different production dates, the target row data is each failure piece, the target column data is each production date, the target value is the failure frequency value, and the target filtering item is the product platform and the data validity. Also, for example, if the fault data filtering table is a fault frequency statistic of each vehicle model on different fault dates, the target row data is each vehicle model, the target column data is each fault date, the target value is a fault frequency value, and the target filtering item is a product platform and data validity. Also, for example, if the failure data filtering table is a failure frequency statistics of each failure piece in different areas, the target row data is each failure piece, the target column data is each area, the target value is a failure frequency value, and the target filtering item is a product platform, data validity and production date.

And A2, generating a fault data screening table according to the target row data, the target column data, the target value, the target screening item and a preset calculation type of the target value.

The preset calculation type of the target value is counting, namely, the accumulated value of the failure frequency of the target line data under the specific screening condition can be counted. For example, when the failure data filtering table is a statistics of failure frequency of each failure part in different areas, if the obtained vehicle quality data includes failure part 1 of vehicle a, failure part 1 of vehicle B, failure part 2 of vehicle C in the beijing area, failure part 1 of vehicle D in the seagoing area, failure part 2 of vehicle E, the target values corresponding to failure part 1 and beijing area are 2, the target values corresponding to failure part 1 and the seagoing area are 1, the target values corresponding to failure part 2 and the beijing area are 1, and the target values corresponding to failure part 2 and the seagoing area are 1.

According to the embodiment of the invention, importing and filling can be carried out from the acquired automobile quality data in the automobile market in the first preset period according to the selected target row data, the target column data, the target value and the target screening item; the fault data screening table can be generated by calculating the target value based on the function of the pivot table.

It should be noted that the fault data filtering table may include a main filtering table and a custom filtering table, where the main filtering table is a fixed fault data filtering table, and the custom filtering table is a fault data filtering table that is personalized according to actual needs.

S130, predicting the cumulative failure frequency of the failure piece in a prediction time period according to the failure piece in the vehicle attribute data, and the production date, the using time length and the failure frequency value corresponding to the failure piece in the vehicle service data, and determining the predicted cumulative failure frequency of the failure piece.

Wherein the predicted time period may be the next time period to the current time, e.g., the current time period is 2023, 1 month, then the predicted time period may be 2023, 2 months. The cumulative failure frequency may be the frequency at which the failed piece has accumulated to fail before a certain period of time.

Specifically, according to the accumulated failure frequency values of the failure piece in different using time periods and different production dates, the increasing trend of the failure frequency of the failure piece along with the different using time periods can be obtained; and further predicting the accumulated failure frequency of the failure part in the predicted time period according to the failure frequency increasing trend of the failure part, so as to obtain the accumulated failure frequency of the failure part before the next time period.

And S140, determining a fault analysis result of the failure piece according to the fault data screening table and the predicted accumulated fault frequency.

The failure analysis result may be a failure frequency trend of the failed part before the current time period and a failure frequency trend after the current time period. Specifically, the fault data screening table and the predicted accumulated fault frequency can be subjected to statistical arrangement to obtain a data table of fault trends of related failure parts under different using time periods or different production dates, and the data table of fault trends can be further displayed in a chart mode.

As an alternative but non-limiting implementation, determining the failure analysis result of the failed component according to the failure data filtering table and the predicted cumulative failure frequency includes, but is not limited to, the following steps B1-B3:

and B1, marking the data which are larger than the first preset fault frequency in the fault data screening table to obtain a fault data screening identification table.

The first preset failure frequency can be standard data corresponding to the frequency data counted in the failure data screening table, and can be determined according to historical experience. It should be noted that, the first preset failure frequency and the failure data filtering table are in one-to-one correspondence, and when at least two failure data filtering tables exist, at least two first preset failure frequencies exist.

Specifically, the data with high absolute value, high ratio or large amplification of fault frequency in the fault data screening table can be identified, for example, the area of the data to be identified can be filled with other colors to carry out distinguishing identification.

For example, for data with large absolute value of failure frequency in the failure data screening table, for example, for failure frequency table of failure piece relative production date, repair date, area or failure mileage, and thousands of failure frequency screening tables of each production date, the absolute value of failure frequency of failure piece in each table is counted; if the absolute value of failure piece failure frequency is larger than 10, the data area is filled with yellow, and if the absolute value of failure piece failure frequency is larger than 30, the data area is filled with red. For example, for a table of thousands of claim frequencies in each use period, determining an ideal value of standard reaching corresponding to each use period by a standard reaching result of each platform product or market bid, if the absolute value of thousands of claim frequencies in each use period exceeds 8 or exceeds the ideal value of standard reaching, the area of the data is filled with yellow, if the absolute value of thousands of claim frequencies in each use period exceeds 20 or exceeds the ideal value of standard reaching by 150%, the area of the data is filled with red.

For example, for the data with high failure frequency ratio in the failure data screening table, for example, for failure frequency tables of failure parts relative to production date, repair date, area or failure mileage, thousands of failure frequency screening tables of each production date and thousands of claim frequency tables in each use duration, the number of failure part failure frequency ratios in each table are counted; if the number of failure part failure frequencies is more than 2% and less than 4%, the area of the data is filled with yellow, and if the number of failure part failure frequencies is more than 4%, the area of the data is filled with red.

Also exemplary, for the data with high failure frequency amplification in the failure data screening table, for example, for the failure frequency table of the failure piece relative production date, repair date, area or failure mileage, and thousands of failure frequency screening tables of each production date and thousands of claim frequency tables in each use duration, the difference value between the failure frequency of the previous date and the current date and the ratio between the difference value and the failure frequency of the current date are counted; if the ratio exceeds 50% and the difference exceeds 5, the area of the data is filled with yellow, and if the ratio exceeds 100% and the difference exceeds 15, the area of the data is filled with red.

And B2, determining the fault trend of the failed part according to the predicted accumulated fault frequency of the failed part in the fault data screening table, the claim frequency of each preset using time period and the fault frequency of each unit production time, each unit repair time, each area and each mileage.

Specifically, the failure frequency of the failure part in the failure data screening table, the claim frequency of each preset using time period, and the failure frequency of each unit production time, each unit repair time, each area and each mileage can be displayed in the form of a line graph, an area graph, a histogram or a comprehensive graph.

For example, table 1 is a 3MIS statistics table for each product platform, and as shown in table 1, is a 3MIS index for six product platforms, such as the free highway state six, the DK state six, the DH state six, the green vapor DD state six, the 4DD1, and the 4DD2, respectively. Wherein, 3MIS index is the failure frequency of the product platform in 6 months of use, 2110 represents 10 months of 2021, 2111 represents 11 months of 2021, and so on.

TABLE 1

Further, a line graph is generated according to the 3MIS statistics table of each product platform shown in table 1, as shown in fig. 2a, and fig. 2a is a 3MIS statistics line graph of each product platform provided in the first embodiment of the present application. The difference of 3MIS indexes among different product platforms and the trend of the 3MIS indexes of the same product platform can be clearly observed according to the line diagram of each product platform shown in fig. 2 a.

By way of further example, table 2 is a table of 12MIS statistics for key components of the six product platform of DK country, as shown in table 2, which are 12MIS indicators for air mass flow meters, EGR valves, throttle valves, rolling bearings, fuel injection pump assemblies, fuel delivery pump assemblies, engine oil pressure sensors, gasket-exhaust pipes, fuel injector assemblies, power steering oil pump assemblies, and other components, respectively. Wherein, 12MIS index is the failure frequency of the product platform within 15 months of use.

TABLE 2

Further, an area diagram is generated according to the 12MIS statistics table of the DK national six-product platform critical component shown in table 2, as shown in fig. 2b, and fig. 2b is a 12MIS statistics area diagram of the DK national six-product platform critical component provided in the first embodiment of the present application. The risk of failure of the various components is clearly observed from the size of the area ratios of the critical components illustrated in fig. 2 b.

And B3, screening an identification table and the failure trend of the failure piece according to the failure data, and determining a failure analysis result of the failure piece.

The fault analysis result can be the influence condition of fault conditions, trends or related market quality information factors. Specifically, risk data identified in the identification table and fault trend representation diagrams of failure parts can be screened through fault data, the risk trend degree of each part is obtained rapidly and intuitively, and management and control measures of each part are further determined according to the risk trend degree, so that the identification of fault risks of the parts is accelerated, and the loss caused by the later faults of the parts is reduced.

The technical scheme has the beneficial effects that the fault risk trend change of each part of the automobile can be intuitively observed through chart display of the fault data screening table and the predicted accumulated fault frequency, and further the part with the fault risk can be rapidly managed and controlled in time.

The embodiment of the invention provides a processing method of automobile quality data, which comprises the steps of obtaining the automobile quality data in an automobile market in a first preset period; the first preset period is a preset time period before the current time, and the automobile quality data comprise vehicle attribute data and vehicle service data; generating a fault data screening table according to the automobile quality data; predicting the cumulative failure frequency of the failed part in the predicted time period according to the failed part in the vehicle attribute data and the production date, the using time length and the failure frequency value corresponding to the failed part in the vehicle service data, and determining the predicted cumulative failure frequency of the failed part; and determining a fault analysis result of the failure piece according to the fault data screening table and the predicted accumulated fault frequency. According to the technical scheme, the automobile quality data can be comprehensively and rapidly analyzed, the trend risk of faults can be accurately identified, and the digital management of the automobile quality data is realized.

Example two

Fig. 3 is a flowchart of a processing method of automobile quality data according to a second embodiment of the present application, where the present embodiment is optimized based on the foregoing embodiment. As shown in fig. 3, the method of this embodiment specifically includes the following steps:

s210, acquiring automobile quality data in an automobile market in a first preset period. Wherein the first preset period is a preset period of time before the current time, the vehicle quality data comprises vehicle attribute data and vehicle service data, the vehicle attribute data is used for representing equipment inherent attribute data of a vehicle, and the vehicle service data is used for representing equipment maintenance service data of the vehicle

S220, generating a fault data screening table according to the automobile quality data; wherein the failure data screening table is used for representing the relationship between the vehicle attribute data and the vehicle service data.

S230, determining the accumulated failure frequency of different production dates corresponding to the failure piece in each use time according to the failure piece, the production date, the use time and the failure frequency value corresponding to the failure piece.

Specifically, firstly, the use time length of the failure piece corresponding to different production dates can be determined according to the failure date and the failure frequency value of the failure piece, secondly, a corresponding table of each production date and each use time length can be established, and finally, the accumulated failure frequency of the failure piece is counted according to each production date and each corresponding use time length.

Fig. 4 is a diagram illustrating a statistical representation of cumulative failure frequency provided in a second embodiment of the present application. As shown in fig. 4, the horizontal axis represents the date of production, the vertical axis represents the duration of use, and the table contents represent cumulative failure frequencies of the failure pieces corresponding to the date of production and the duration of use, respectively. For example, the content in 2003/0MIS indicates that a failure element has a cumulative failure frequency of 3 times during use for 3 months at 3 months in 2020 (i.e., 6 months before 2020); the content in 2003/1MIS indicates that a failure piece has a cumulative failure frequency of 50 times over 4 months of use at 3 months of 2020; the content in 2004/12MIS indicates that a failure element has a cumulative failure frequency of 539 times during 15 months of use at 4 months of 2020.

S240, predicting the cumulative failure frequency of the failure piece in the prediction time period according to the cumulative failure frequency of the failure piece in different production dates corresponding to the use time periods, and determining the predicted cumulative failure frequency of the failure piece.

Specifically, the cumulative failure frequency of the failure piece in the prediction time period can be predicted according to the change rule of the cumulative failure frequency corresponding to different use time periods of the failure piece in the same production date, or according to the change rule of the cumulative failure frequency corresponding to different production dates of the failure piece in the same use time period, so as to determine the predicted cumulative failure frequency of the failure piece.

The change rule of the accumulated failure frequency can be obtained by performing linear fitting on each accumulated failure frequency, so that a change function of the accumulated failure frequency is obtained, and further, the predicted accumulated failure frequency of the failure piece in a predicted time period is determined according to the change function. Of course, the embodiment of the invention does not limit the change rule of the accumulated failure frequency, and can also determine the ratio of the accumulated failure frequency between adjacent production dates or the ratio of the accumulated failure frequency between adjacent use periods, and the like.

As an optional but non-limiting implementation manner, according to the accumulated failure frequency of the failure piece in each use time period corresponding to different production dates, the accumulated failure frequency of the failure piece in a predicted time period is predicted, and the predicted accumulated failure frequency of the failure piece is determined, which includes but is not limited to the following processes of steps C1-C2:

and C1, selecting a target accumulated fault frequency from accumulated fault frequencies of different production dates corresponding to the time length of each use of the invalid piece according to the time length of each use, and determining the average growth rate of the accumulated fault frequency of the invalid piece according to the target accumulated fault frequency. The target accumulated fault frequency is a preset number of accumulated fault frequencies which are closest to the time interval of the predicted time period and are continuous and effective in production date and correspond to each using time period.

The effective means that the accumulated failure frequency is the data which actually occurs and is fixed. For example, as illustrated in fig. 4, the current time period is 2110 (i.e., 2021, 10 months), the predicted time period may be 2021, 11 months, 0MIS refers to the cumulative failure frequency of the failed part during 3 months of use, and the effective cumulative failure frequency in 0MIS is the data before the date of production is 2107 (i.e., 2021, 07 months). Similarly, the effective cumulative failure frequency in 1MIS is data prior to date 2106 (i.e., 2021, month 06); the cumulative failure frequency valid in 2MIS is data before date of production 2105 (i.e., month 05 of 2021) until the cumulative failure frequency valid in 12MIS is data before date of production 2007 (i.e., month 07 of 2020).

Illustratively, and as illustrated by way of example in FIG. 4, the current time period is 2021, 10 months, and the predicted time period is 2021, 11 months, as shown in FIG. 4.

First, for 0MIS to 12MIS, a cumulative failure frequency with consecutive and valid production dates in the last 5 months from the 11 months of 2021, which is the data included in the hatched portion in fig. 4, is selected from cumulative failure frequencies of different production dates corresponding to the time period of each use of the failure piece.

And secondly, calculating the ratio of the accumulated fault frequency of the current use time length to the accumulated fault frequency of the last use time length in the same production date respectively, and taking the ratio as the candidate growth rate of the accumulated fault frequency of the current production date in different use time lengths.

Fig. 5a is a schematic diagram illustrating calculation of a candidate growth rate according to an embodiment of the present application. As shown in fig. 5a, 0MIS/0MIS at the same date of production is taken as the candidate growth rate of 0MIS at the date of production, 1MIS/0MIS is taken as the candidate growth rate of 1MIS at the date of production, 2MIS/1MIS is taken as the candidate growth rate of 2MIS at the date of production, and the calculation result of the candidate growth rate shown in fig. 5a is obtained by the same method.

And thirdly, respectively carrying out summation and average on candidate growth rates of the failure piece accumulated failure frequency in different production dates of the same using time length to obtain the average growth rate of the failure piece accumulated failure frequency.

Fig. 5b is a schematic diagram illustrating calculation of an average growth rate according to an embodiment of the present application. As shown in fig. 5b, the candidate growth rates that are continuous and effective in production date in the last 5 months from 11 months of 2021 are summed and averaged, respectively, for example, in 1MIS, the candidate growth rates 1, 2, 2.25, 3 and 0 from 2 months to 6 months of 2021 are summed and averaged to obtain 1.65, and the calculation result of the average growth rate as shown in fig. 5b is obtained by the same.

And C2, predicting the failure frequency of the failure piece in the prediction time period according to the average growth rate and the target accumulated failure frequency, and determining the predicted accumulated failure frequency of each failure piece.

Specifically, the predicted cumulative failure frequency of the failure piece under the use duration in the predicted time period can be determined according to the average growth rate of the same use duration and the cumulative failure frequency of the target cumulative failure frequency, wherein the cumulative failure frequency is closest to the time interval of the predicted time period in the use duration and is continuous and effective in production date.

Fig. 5c is a schematic diagram illustrating calculation of a predicted cumulative failure frequency according to an embodiment of the present application. As shown in fig. 5c, the cumulative failure frequency of the failure part, which is 1 month more recently from 2021, 11, and is effective, and the average growth rate calculated in fig. 5b are multiplied to obtain the predicted cumulative failure frequency of the failure part.

After determining the predicted cumulative failure frequency of each failure piece, the predicted cumulative failure frequency can be compared with the actual cumulative failure frequency fed back, and the maximum assignment is obtained.

The technical scheme has the beneficial effects that the accumulated fault frequency of the prediction time period in different use periods is predicted, and the accuracy of predicting the trend of the long fault index in each use period is improved.

S250, determining a fault analysis result of the failure piece according to the fault data screening table and the predicted accumulated fault frequency.

The embodiment of the invention provides a processing method of automobile quality data, which comprises the steps of obtaining the automobile quality data in an automobile market in a first preset period; the vehicle quality data comprises vehicle attribute data and vehicle service data, wherein the vehicle attribute data is used for representing equipment inherent attribute data of a vehicle, and the vehicle service data is used for representing equipment maintenance service data of the vehicle; generating a fault data screening table according to the automobile quality data; wherein the fault data screening table is used for representing the relation between the vehicle attribute data and the vehicle service data; determining the cumulative failure frequency of the failure piece on different production dates corresponding to each use time according to the failure piece and the production date, the failure date and the failure frequency value corresponding to the failure piece; predicting the cumulative failure frequency of the failure piece in a prediction time period according to the cumulative failure frequency of the failure piece in each use time period, and determining the predicted cumulative failure frequency of the failure piece; and determining a fault analysis result of the failure piece according to the fault data screening table and the predicted accumulated fault frequency. According to the technical scheme, the accumulated fault frequency of the prediction time period in different use periods is predicted, and the accuracy of predicting the trend of the long fault index in each use period is improved.

On the basis of the above embodiments, after determining the failure analysis result of the failed component according to the failure data filtering table and the predicted cumulative failure frequency, the method further includes: counting the failure frequency of parts which do not fail in each service station from the automobile quality data; if the service station with the failure frequency of the non-failure part being greater than the second preset failure frequency exists, determining the service station as a target service station, and performing special training and management control on the target service station.

The non-faulty component may be a component that is repaired and replaced during service, but analyzed by the vehicle manufacturer or component manufacturer to meet the "good" requirements.

Specifically, in the first step, the number of faults of non-faulty (No valid Found) parts in each area can be screened from a three-pack claim system in the market, the sales number of products in each area is generated by using a vehicle information screening table in each area, and then the number of faults of the NTF parts is divided by the sales number of products in each area, so that thousands of frequency of faults of the NTF in each area can be obtained; and secondly, screening the areas with more NTF faults in the vehicle information screening table of each area to serve as key areas, adopting a three-pack claim system in the market to further screen vehicle maintenance service stations, and taking the service stations with failure frequencies of non-failure parts larger than the second preset failure frequencies as target service stations to perform special training and management control on the target service stations.

The technical scheme has the advantages that the quality data of the NTF parts with large market claim ratio can be subjected to special analysis, and the key areas or vehicle maintenance service stations with more NTF faults are identified, so that a service department is guided to conduct special guidance and enhanced management and control on the abnormal claim service stations, containment measures are quickly taken, and market claim and user complaints are reduced.

On the basis of the above embodiments, after obtaining the vehicle quality data in the vehicle market in the first preset period, the method further includes: acquiring target automobile quality data of target invalid pieces in an automobile market in a second preset period; the second preset period is greater than the first preset period, the target failure piece is a failure piece with seasonal faults, and the seasonal faults are faults influenced by environments in different seasons.

Specifically, for example, the parts having seasonal faults such as an air-conditioning compressor are used frequently in summer or winter and used frequently in spring or autumn, so that when the frequency of faults of the parts is counted for one year or less, the change rule of the frequency of faults is not easily obtained for the parts having seasonal faults, and effective production guidance advice cannot be provided for the parts having seasonal faults.

In view of the above, for such a failure part with seasonal faults, the embodiment of the present application counts the target vehicle quality data in a period exceeding a general statistical period, for example, the first preset period (i.e., the general statistical period) is 12 months, and the second preset period may be 18 months; and further, the fault frequency and the variation amplitude are analyzed in a time axis transverse ratio mode, so that more accurate evaluation and suggestion can be obtained.

The technical scheme has the beneficial effects that the special data analysis can be carried out on the parts with seasonal faults so as to output more accurate evaluation and further judge the fault risk more timely and accurately.

Example III

Fig. 6 is a schematic structural diagram of a processing device for vehicle quality data according to a third embodiment of the present application. As shown in fig. 6, the apparatus includes:

the automobile quality data acquisition module 310 is configured to acquire automobile quality data in an automobile market in a first preset period; the first preset period is a preset time period before the current time, and the automobile quality data comprise vehicle attribute data and vehicle service data;

a fault data screening table generating module 320, configured to generate a fault data screening table according to the vehicle quality data; wherein the failure data screening table is used for representing a relationship between the vehicle attribute data and the vehicle service data; wherein the vehicle attribute data at least comprises at least one of a failure piece, a vehicle model and a product model; the vehicle service data at least comprises at least one of a fault reason, a processing structure, a service station code, an area, a fault mileage, a production date, a sales date, a fault date, a maintenance type, data validity, a use duration and a fault frequency value;

A failure piece failure frequency predicting module 330, configured to predict an accumulated failure frequency of the failure piece in a predicted time period according to the failure piece in the vehicle attribute data, and a production date, a use duration and a failure frequency value corresponding to the failure piece in the vehicle service data, and determine a predicted accumulated failure frequency of the failure piece;

and the fault analysis result determining module 340 is configured to determine a fault analysis result of the failed component according to the fault data filtering table and the predicted cumulative fault frequency.

The embodiment of the invention provides a processing device of automobile quality data, which is used for acquiring the automobile quality data in an automobile market in a first preset period; the first preset period is a preset time period before the current time, and the automobile quality data comprise vehicle attribute data and vehicle service data; generating a fault data screening table according to the automobile quality data; predicting the cumulative failure frequency of the failure piece in the predicted time period according to the failure piece in the vehicle attribute data, and the sales date, the failure date and the failure frequency value in the vehicle service data, and determining the predicted cumulative failure frequency of the failure piece; and determining a fault analysis result of the failure piece according to the fault data screening table and the predicted accumulated fault frequency. According to the technical scheme, the automobile quality data can be comprehensively and rapidly analyzed, the trend risk of faults can be accurately identified, and the digital management of the automobile quality data is realized.

Further, the fault data filtering table generating module 320 includes:

the fault data screening table determining unit is used for selecting target row data, target column data, target values and target screening options from the automobile quality data;

and the fault data screening table generating unit is used for generating a fault data screening table according to the target row data, the target column data, the target value, the target screening item and the preset calculation type of the target value.

Further, the failure part failure frequency prediction module 330 includes:

the cumulative failure frequency determining unit is used for determining different production dates corresponding to the failure piece in each use time according to the failure piece, the production date, the use time length and the failure frequency value corresponding to the failure piece;

the cumulative failure frequency prediction unit is used for predicting the cumulative failure frequency of the failure piece in the prediction time period according to the cumulative failure frequency of different production dates corresponding to the use time length of the failure piece, and determining the predicted cumulative failure frequency of the failure piece.

Further, the cumulative failure frequency prediction unit includes:

a target cumulative failure frequency determining subunit, configured to select, for each usage duration, a target cumulative failure frequency from cumulative failure frequencies of different production dates corresponding to each usage duration of a failure part, and determine an average growth rate of the cumulative failure frequencies of the failure part according to the target cumulative failure frequency; the target accumulated fault frequency is a preset number of accumulated fault frequencies which are closest to the time interval of the predicted time period and are continuous and effective in production date and correspond to each using time period;

And the accumulated failure frequency prediction subunit is used for predicting the failure frequency of the failure piece in the prediction time period according to the average growth rate and the target accumulated failure frequency, and determining the predicted accumulated failure frequency of each failure piece.

Further, the fault analysis result determining module 340 includes:

the fault data screening table identification unit is used for identifying the data which are larger than the first preset fault frequency in the fault data screening table to obtain a fault data screening identification table;

the failure part fault trend determining unit is used for determining the fault trend of the failure part according to the predicted accumulated fault frequency of the failure part, the claim frequency of each preset using time period and the fault frequency of each unit production time, each unit repair time, each area and each mileage section in the fault data screening table;

and the fault analysis result determining unit is used for screening the identification table and the fault trend of the failed part according to the fault data and determining the fault analysis result of the failed part.

Further, the device further comprises:

the non-failure part statistics module is used for counting the failure frequency of the non-failure part of each service station from the automobile quality data after determining the failure analysis result of the failure part according to the failure data screening table and the predicted accumulated failure frequency;

And the target service station determining module is used for determining the service station as a target service station if the service station with the failure frequency of the non-failure part being greater than the second preset failure frequency exists, so as to carry out special training and management control on the target service station.

Further, the device further comprises:

the target automobile quality data acquisition module is used for acquiring the target automobile quality data of the target invalid piece in the automobile market in the second preset period after acquiring the automobile quality data in the automobile market in the first preset period; the second preset period is greater than the first preset period, the target failure piece is a failure piece with seasonal faults, and the seasonal faults are faults influenced by environments in different seasons.

The processing device for the automobile quality data provided by the embodiment of the application can execute the processing method for the automobile quality data provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.

Example IV

Fig. 7 shows a schematic diagram of the structure of a device 10 that may be used to implement an embodiment of the application. Devices are intended to represent various forms of digital computers, such as laptops, desktops, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The device may also represent various forms of mobile apparatuses such as personal digital processing, cellular telephones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing apparatuses. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.

As shown in fig. 7, the apparatus 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the device 10 can also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

The various components in the device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, for example, the processing method of the vehicle quality data.

In some embodiments, the method of processing vehicle quality data may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the above-described processing method of the vehicle quality data may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the processing method of the vehicle quality data in any other suitable way (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present application, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present application are achieved, and the present application is not limited herein.

The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.

Claims (10)

1. A method for processing vehicle quality data, the method comprising:

acquiring automobile quality data in an automobile market in a first preset period; the first preset period is a preset time period before the current time, and the automobile quality data comprise vehicle attribute data and vehicle service data;

generating a fault data screening table according to the automobile quality data; wherein the failure data screening table is used for representing a relationship between the vehicle attribute data and the vehicle service data; wherein the vehicle attribute data at least comprises at least one of a failure piece, a vehicle model and a product model; the vehicle service data at least comprises at least one of a fault reason, a processing structure, a service station code, an area, a fault mileage, a production date, a sales date, a fault date, a maintenance type, data validity, a use duration and a fault frequency value;

Predicting the cumulative failure frequency of the failure piece in a prediction time period according to the failure piece in the vehicle attribute data, and the production date, the using time length and the failure frequency value corresponding to the failure piece in the vehicle service data, and determining the predicted cumulative failure frequency of the failure piece;

and determining a fault analysis result of the failure piece according to the fault data screening table and the predicted accumulated fault frequency.

2. The method of claim 1, wherein generating a fault data screening table from the vehicle quality data comprises:

selecting target row data, target column data, target values and target screening options from the automobile quality data;

and generating a fault data screening table according to the target row data, the target column data, the target value, the target screening item and the preset calculation type of the target value.

3. The method of claim 1, wherein predicting the cumulative failure frequency of the failed part over a predicted period of time based on the failed part in the vehicle attribute data and the production date, the usage time, and the failure frequency value corresponding to the failed part in the vehicle service data, determining the predicted cumulative failure frequency of the failed part comprises:

Determining the accumulated failure frequency of different production dates corresponding to each use time length of the failure piece according to the failure piece and the production date, the use time length and the failure frequency value corresponding to the failure piece;

and predicting the cumulative failure frequency of the failure piece in the predicted time period according to the cumulative failure frequency of the failure piece in different production dates corresponding to the use time periods, and determining the predicted cumulative failure frequency of the failure piece.

4. A method according to claim 3, wherein predicting the cumulative failure frequency of the failed part in the predicted time period according to the cumulative failure frequency of the failed part in each use time period corresponding to different production dates, and determining the predicted cumulative failure frequency of the failed part comprises:

for each use duration, selecting a target accumulated failure frequency from accumulated failure frequencies of different production dates corresponding to each use duration of the failure piece, and determining an average increase rate of the accumulated failure frequency of the failure piece according to the target accumulated failure frequency; the target accumulated fault frequency is a preset number of accumulated fault frequencies which are closest to the time interval of the predicted time period and are continuous and effective in production date and correspond to each using time period;

And predicting the failure frequency of the failure piece in the prediction time period according to the average growth rate and the target accumulated failure frequency, and determining the predicted accumulated failure frequency of each failure piece.

5. The method of claim 1, wherein determining a failure analysis result for the failed component based on the failure data screening table and the predicted cumulative failure frequency comprises:

marking the data which are larger than the first preset failure frequency in the failure data screening table to obtain a failure data screening identification table;

determining a fault trend of the failed part according to the predicted accumulated fault frequency of the failed part in the fault data screening table, the claim frequency of each preset using time period, and the fault frequency of each unit production time, each unit repair time, each area and each mileage;

and screening an identification table and the fault trend of the failed part according to the fault data, and determining a fault analysis result of the failed part.

6. The method of claim 1, wherein after determining a failure analysis result for the failed component based on the failure data screening table and the predicted cumulative failure frequency, the method further comprises:

Counting the failure frequency of parts which do not fail in each service station from the automobile quality data;

if the service station with the failure frequency of the non-failure part being greater than the second preset failure frequency exists, determining the service station as a target service station, and performing special training and management control on the target service station.

7. The method of claim 1, wherein after obtaining the vehicle quality data in the vehicle market for the first predetermined period, the method further comprises:

acquiring target automobile quality data of target invalid pieces in an automobile market in a second preset period; the second preset period is greater than the first preset period, the target failure piece is a failure piece with seasonal faults, and the seasonal faults are faults influenced by environments in different seasons.

8. A processing device for vehicle quality data, the device comprising:

the automobile quality data acquisition module is used for acquiring automobile quality data in an automobile market in a first preset period; the first preset period is a preset time period before the current time, and the automobile quality data comprise vehicle attribute data and vehicle service data;

The fault data screening table generation module is used for generating a fault data screening table according to the automobile quality data; wherein the failure data screening table is used for representing a relationship between the vehicle attribute data and the vehicle service data; wherein the vehicle attribute data at least comprises at least one of a failure piece, a vehicle model and a product model; the vehicle service data at least comprises at least one of a fault reason, a processing structure, a service station code, an area, a fault mileage, a production date, a sales date, a fault date, a maintenance type, data validity, a use duration and a fault frequency value;

the failure piece failure frequency prediction module is used for predicting the accumulated failure frequency of the failure piece in a prediction time period according to the failure piece in the vehicle attribute data and the production date, the using time length and the failure frequency value corresponding to the failure piece in the vehicle service data, and determining the predicted accumulated failure frequency of the failure piece;

and the fault analysis result determining module is used for determining the fault analysis result of the failure piece according to the fault data screening table and the predicted accumulated fault frequency.

9. An electronic device, the device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of processing vehicle quality data according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions for causing a processor to execute the method of processing vehicle quality data according to any one of claims 1-7.