CN116957402A - Target defect-based processing method and device and computer readable storage medium - Google Patents
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Abstract
The embodiment of the application relates to the field of data processing, and discloses a target defect-based processing method, a target defect-based processing device and a computer-readable storage medium, wherein the method comprises the following steps: acquiring the total number of products in the current batch and the number of products with target defects in the current batch; if the total number of products is detected to be larger than or equal to the preset first number and the number of products with target defects is detected to be smaller than or equal to the preset second number, calculating to obtain defect probability corresponding to the target defects according to the total number of products and the number of products with the target defects; determining whether to perform accumulation operation on the continuous accumulation times according to a matching result of the defect probability and a preset probability interval; if the continuous accumulated times are detected to be larger than or equal to the preset times, degrading the target defect. According to the application, by relaxing the detection conditions of the target defects, the related defects are gradually reduced, so that the analysis processing time in the product detection process is shortened.
Description
Technical Field
The embodiment of the application relates to the field of data processing, in particular to a target defect-based processing method and device and a computer-readable storage medium.
Background
The types and the quantity of the defects of the products generated in the detection process are gradually reduced along with the improvement of the research and development technology from the initial research and development to the mass delivery production of the products, and the defect information presented in the detection report is also reduced.
Product defects are classified as ABCD, corresponding to fatal, severe, general, and minor defects. The CD defects are mostly slight defects, the overall performance is not greatly influenced, the product functions are slightly influenced, and the CD defects can be ignored in the actual detection process. When more slight defects exist in the detection process, the more serious defects are easy to cover, and the analysis processing time of the whole product detection process is longer.
Disclosure of Invention
In view of the foregoing, embodiments of the present application provide a method, an apparatus, and a computer readable storage medium for processing a target defect, so as to shorten an analysis processing time in a product detection process.
According to an aspect of the embodiment of the present application, there is provided a target defect-based processing method, including: acquiring the total number of products in the current batch and the number of products with target defects in the current batch; if the total number of the products is detected to be larger than or equal to the preset first number and the number of the products with the target defects is detected to be smaller than or equal to the preset second number, calculating to obtain defect probability corresponding to the target defects according to the total number of the products and the number of the products with the target defects; determining whether to accumulate the continuous accumulated times according to the matching result of the defect probability and a preset probability interval; and if the continuous accumulated times are detected to be greater than or equal to the preset times, carrying out degradation treatment on the target defect.
In an alternative manner, the processing method further includes: detecting whether the total number of products is greater than or equal to the preset first number, and detecting whether the number of products with target defects is less than or equal to the preset second number; and if the total number of products is detected to be smaller than the preset first number or the number of products with the target defects is detected to be larger than the preset second number, resetting the continuous accumulation times to reset the continuous accumulation times to initial continuous accumulation times.
In an optional manner, the calculating, according to the total number of products and the number of products with the target defect, a defect probability corresponding to the target defect further includes: calculating quotient values of the product quantity with the target defects and the total product quantity, and calculating to obtain quotient values; and taking the quotient as the defect probability corresponding to the target defect.
In an optional manner, the determining whether to perform the accumulating operation on the continuous accumulated times according to the matching result between the defect probability and the preset probability interval further includes: if the matching result represents that the defect probability is successfully matched with a preset probability interval, determining to accumulate the continuous accumulated times; and if the matching result indicates that the defect probability fails to match with the preset probability interval, resetting the continuous accumulation times to reset the continuous accumulation times to initial continuous accumulation times.
In an alternative manner, the processing method further includes: detecting whether the continuous accumulated times are larger than or equal to the preset times; and if the continuous accumulated times are detected to be smaller than the preset times, executing the step of acquiring the total number of products in the current batch and the number of products with target defects in the current batch.
In an alternative manner, the processing method further includes: transmitting the target defect after degradation treatment to a database so that the database updates the stored original defect strategy to obtain an updated defect strategy transmitted by the database; and diagnosing the vehicle defects according to the updated defect strategy.
In an optional manner, the diagnosing the vehicle defect according to the updated defect policy further includes: acquiring message information of a vehicle; wherein, the message information comprises a message code; and matching the message code with the fault code in the updated defect strategy, and determining whether the vehicle has faults according to a matching result.
According to another aspect of the embodiments of the present application, there is provided a processing apparatus based on a target defect, the processing apparatus including: the acquisition module is used for acquiring the total quantity of products in the current batch and the quantity of products with target defects in the current batch; the calculating module is used for calculating and obtaining the defect probability corresponding to the target defects according to the total number of the products and the number of the products with the target defects if the total number of the products is detected to be larger than or equal to the preset first number and the number of the products with the target defects is detected to be smaller than or equal to the preset second number; the determining module is used for determining whether to accumulate the continuous accumulated times or not according to the matching result of the defect probability and the preset probability interval; and the processing module is used for carrying out degradation processing on the target defect if the continuous accumulated times are detected to be greater than or equal to preset times.
According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: a controller; and a memory for storing one or more programs which, when executed by the controller, perform the processing method described above.
According to an aspect of the embodiments of the present application, there is also provided a computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor of a computer, cause the computer to perform the above-described processing method.
According to an aspect of embodiments of the present application, there is also provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the computer device performs the above-described processing method.
The embodiment of the application obtains the total quantity of the products in the current batch and the quantity of the products with target defects in the current batch; if the total number of products is detected to be greater than or equal to the preset first number, and the number of products with target defects is detected to be less than or equal to the preset second number, the obtained two samples are characterized to meet the sample requirements, and then the defect probability corresponding to the target defects is calculated according to the total number of products and the number of products with the target defects, namely, only parameters meeting the sample requirements can be used as calculation parameters for calculation, so that the accuracy of the calculation parameters is ensured; determining whether to perform accumulation operation on the continuous accumulation times according to a matching result of the defect probability and a preset probability interval; if the continuous accumulated times are detected to be greater than or equal to the preset times, degrading the target defects, relaxing the detection conditions of the target defects, and gradually reducing the related defects so as to shorten the analysis processing time in the product detection process.
The foregoing description is only an overview of the technical solutions of the embodiments of the present application, and may be implemented according to the content of the specification, so that the technical means of the embodiments of the present application can be more clearly understood, and the following specific embodiments of the present application are given for clarity and understanding.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 is a flow chart illustrating a target defect-based processing method according to an exemplary embodiment of the present application.
Fig. 2 is a flow chart illustrating another target defect-based processing method based on the exemplary embodiment shown in fig. 1.
Fig. 3 is a flow chart illustrating another target defect-based processing method based on the exemplary embodiment shown in fig. 1.
Fig. 4 is a flow chart of another method of target defect based processing shown based on any of the above exemplary embodiments.
FIG. 5 is a schematic diagram illustrating a data interaction process between a processing engine and a database in accordance with an exemplary embodiment of the present application.
Fig. 6 is a flow chart illustrating another target defect-based processing method based on the exemplary embodiment shown in fig. 4.
FIG. 7 is a schematic diagram of a process of data interaction between a processing engine, a database, and a vehicle, as shown in an exemplary embodiment of the application.
Fig. 8 is a schematic diagram of an application scenario of the target defect-based processing method of the present application.
Fig. 9 is a schematic diagram showing a structure of a target defect-based processing apparatus according to an exemplary embodiment of the present application.
Fig. 10 is a schematic diagram of a computer system of an electronic device according to an exemplary embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
In the present application, the term "plurality" means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., a and/or B may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
The existing product detection process has product defects with different degrees, the slight defects can not affect the overall performance of the product, the defects can be ignored in the actual detection process, and the more serious defects are easily covered when more slight defects exist in the detection process, so that the analysis processing time of the whole product detection process is longer.
To this end, an aspect of the present application provides a method for processing a target defect. Referring specifically to fig. 1, fig. 1 is a flow chart illustrating a target defect-based processing method according to an exemplary embodiment of the application. The processing method at least comprises S110 to S140, and is described in detail as follows:
s110: the total number of products in the current batch and the number of products with target defects in the current batch are obtained.
The production time of the product is generally determined in batches on the production line, i.e. the production time of the same batch of products is the same.
The current batch is the batch product corresponding to the current time when the parameter information is acquired. The target defect is a specified defect, such as a slight defect, a defect having no influence on the product performance, a serious defect, a moderate defect, etc., which is a specified defect to be processed, and the present application is not limited in the type of the specific defect.
The execution end determines the product lot i corresponding to the current time according to the time window, and determines the total number X of the products in the lot i i And the number Y of products with target defects i . The present embodiment is not limited to a number unit.
S120: if the total number of products is detected to be larger than or equal to the preset first number and the number of products with target defects is detected to be smaller than or equal to the preset second number, the defect probability corresponding to the target defects is calculated according to the total number of products and the number of products with the target defects.
The preset first number and the preset second number are both preset parameters, and the specific numerical values of the preset first number and the preset second number are not limited in the application, and the size relationship between the preset first number and the preset second number is not limited, i.e. the sizes of the preset first number and the preset second number can be the same or different.
It is noted that, only if the total number of products is detected to be greater than or equal to the preset first number and the number of products with target defects is detected to be less than or equal to the preset second number, the defect probability corresponding to the target defects is calculated according to the total number of products and the number of products with target defects. For example, the total number of products X i And the number Y of products with target defects i Y is taken as i /X i As a defect probability corresponding to the target defect; alternatively, X is i /Y i As a defect probability corresponding to the target defect.
S130: and determining whether to perform accumulation operation on the continuous accumulation times according to the matching result of the defect probability and the preset probability interval.
The continuous accumulated times are used for deciding whether to carry out degradation treatment on the target defect.
For example, if the number of the preset probability intervals is one, the defect probability is only required to be matched with the preset probability intervals once, and whether to perform accumulation operation on the continuous accumulation times is determined according to whether the matching is successful or not.
Another exemplary number of preset probability intervals is a plurality, according to the operation mode of the preset probability intervals shown in table 1 corresponding to the continuous accumulation times, wherein the preset probability intervals include (0.05, 0.1), [0.15,0.2], [ 0.25,0.3], if the defect probability is in [0.15,0.2], the accumulation operation is determined to be performed on the continuous accumulation times, and if the defect probability is not in the three preset probability intervals in table 1, the continuous accumulation times are reset to the initial continuous accumulation times.
TABLE 1
S140: if the continuous accumulated times are detected to be larger than or equal to the preset times, degrading the target defect.
The continuous accumulated times are used for representing whether the target defect is degraded or not, if the continuous accumulated times are detected to be larger than or equal to the preset times, the production conditions such as the production process, the pipeline detection and the like are improved and optimized, the produced product has the target defect which is a small probability event, the type of the target defect is a slight type, and the detection condition of the target defect, namely the degradation treatment of the target defect, can be relaxed, or the detection condition of the target defect is cleared.
The method comprises the steps of obtaining the total quantity of products in a current batch and the quantity of products with target defects in the current batch; if the total number of products is detected to be greater than or equal to the preset first number, and the number of products with target defects is detected to be less than or equal to the preset second number, the obtained two samples are characterized to meet the sample requirements, and then the defect probability corresponding to the target defects is calculated according to the total number of products and the number of products with the target defects, namely, only parameters meeting the sample requirements can be used as calculation parameters for calculation, so that the accuracy of the calculation parameters is ensured; determining whether to perform accumulation operation on the continuous accumulation times according to a matching result of the defect probability and a preset probability interval; if the continuous accumulated times are detected to be greater than or equal to the preset times, degrading the target defects, relaxing the detection conditions of the target defects, and gradually reducing the related defects so as to shorten the analysis processing time in the vehicle detection process.
In another exemplary embodiment of the present application, a precondition for performing a reset operation on a continuous cumulative number is described in detail, referring specifically to fig. 2, fig. 2 is a flow chart illustrating another target defect-based processing method based on the exemplary embodiment shown in fig. 1. The processing method further includes S210 and S220, which are described in detail below:
s210: detecting whether the total number of products is greater than or equal to a preset first number, and detecting whether the number of products with target defects is less than or equal to a preset second number.
The application is not limited to the order of the two detection steps.
S220: and if the total number of the products is detected to be smaller than the preset first number or the number of the products with the target defects is detected to be larger than the preset second number, resetting the continuous accumulation times to reset the continuous accumulation times to the initial continuous accumulation times.
In the preferred embodiment, whether the total number of products is greater than or equal to the preset first number is detected first, then whether the number of products with target defects is less than or equal to the preset second number is detected, that is, whether the total number of products with target defects in the current batch meets the corresponding sample requirements is detected first, if not, the number of products with target defects in the current batch is not met, and if so, the number of products with target defects meets the corresponding sample requirements, but the defect probability obtained by calculating the total number of products with target defects in the current batch is not accurate, so that whether the number of products with target defects is less than or equal to the preset second number is not detected later, the continuous accumulation times are reset to the initial continuous accumulation times directly, and calculation of the defect probability is not needed, so that the time consumption of the whole processing process is reduced.
In the embodiment, whether the total number of products and the number of products with target defects meet the corresponding sample requirements is detected, and if the total number of products is detected to be smaller than the preset first number, the total number of products is characterized as not meeting the corresponding sample requirements; if the number of products with the target defects is detected to be larger than the preset second number, the number of products with the target defects is characterized to be not in accordance with the corresponding sample requirements; if any number of the detected samples does not meet the corresponding sample requirement, resetting the continuous accumulation times to the initial continuous accumulation times without calculating the defect probability, so as to reduce the time consumption of the whole processing process.
In another exemplary embodiment of the present application, a specific manner of calculating the defect probability corresponding to the target defect is described in detail, that is, the quotient is calculated by calculating the quotient of the number of products with the target defect and the total number of products; and taking the quotient as the defect probability corresponding to the target defect.
Illustratively, the defect probability corresponding to the target defect is calculated according to the following formula:
wherein P is Target object Representing the defect probability corresponding to the target defect, Y i X represents the number of products of the current batch with target defects i Indicating the total number of current batches of product.
According to the method and the device, a complex calculation process is not needed, and the defect probability corresponding to the target defect is obtained through rapid calculation according to a simple mathematical formula, so that the calculation time of the defect probability is saved.
In another exemplary embodiment of the present application, how to determine whether to perform the accumulating operation on the continuous accumulation times according to the matching result between the defect probability and the preset probability interval is described in detail, with reference to fig. 3, fig. 3 is a flow chart of another processing method based on the target defect shown in the exemplary embodiment of fig. 1. The processing method further includes S310 and S320 in S130 shown in fig. 1, and is described in detail as follows:
s310: if the matching result indicates that the defect probability is successfully matched with the preset probability interval, the continuous accumulation times are determined to be accumulated.
S320: if the matching result indicates that the defect probability fails to match with the preset probability interval, resetting the continuous accumulation times to reset the continuous accumulation times to the initial continuous accumulation times.
The present embodiment is exemplarily described: the preset probability interval is [0.15,0.3], if the defect probability is 0.2, the defect probability is successfully matched with the preset probability interval, and the continuous accumulation times are determined to be accumulated; if the defect probability is 0.1, the defect probability is represented to be failed to match with a preset probability interval, and the continuous accumulation times are reset to be initial continuous accumulation times.
The embodiment details the precondition of the accumulation operation for the continuous accumulation times, namely, the matching result of the defect probability and the preset probability interval represents successful matching, and the accumulation operation for the continuous accumulation times is performed. The matching process only needs to compare the two probability parameters, so that the whole matching process is visual and clear, and the processing mode aiming at continuous accumulated times can be rapidly determined.
In another exemplary embodiment of the present application, it is further described that the above processing method further includes: detecting whether the continuous accumulated times is larger than or equal to preset times; if the continuous accumulated number of times is detected to be smaller than the preset number of times, a step of obtaining the total number of products in the current batch and the number of products with target defects in the current batch is performed, wherein the step represents that the pre-step of S110 is performed.
Or the step of continuously accumulating the number of times in the present embodiment characterizes the same step as the step of S140, because the current lot may have changed when the step of continuously accumulating the number of times is performed to S140, for example, the current lot when the step of continuously accumulating the number of times is performed to S110 is the first lot, the current lot has changed to the second lot, and the step of obtaining the total number of products in the current lot and the number of products with target defects in the current lot is performed to obtain the total number of products in the second lot and the number of products with target defects in the current lot, that is, the processing method for the next lot of products is performed.
In another exemplary embodiment of the present application, a method for storing and updating a defect policy is described in detail, and referring specifically to fig. 4, fig. 4 is a schematic flow chart of another method for processing a target defect based on any one of the foregoing exemplary embodiments. Any of the above processing methods is applied to the processing engine, and further the processing method further includes S410 to S420, which are described in detail as follows:
s410: and sending the target defect subjected to degradation treatment to a database so as to enable the database to update the stored original defect strategy to obtain the updated defect strategy sent by the database.
S420: and diagnosing the vehicle defects according to the updated defect strategy.
Illustratively, as shown in fig. 5, fig. 5 is a schematic diagram illustrating a data interaction process between a processing engine and a database according to an exemplary embodiment of the present application. The database is MySQL, and the processing engine is big data flink. The method comprises the steps that target defects are degraded according to any processing method, the degraded target defects are sent to a database, the database updates the target defects in the original defect strategy according to the received degraded target defects, namely the original defect strategy is updated, so that updated defect strategies are obtained, the updated defect strategies are returned to the link by the database, and vehicle defect diagnosis is carried out by the link according to the updated defect strategies.
In another exemplary embodiment of the present application, how to diagnose a vehicle defect according to an updated defect policy is described in detail, and referring specifically to fig. 6, fig. 6 is a flow chart illustrating another target defect-based processing method based on the exemplary embodiment shown in fig. 4. S610 to S620 are further included in S420 shown in fig. 4, and are described in detail as follows:
s610: acquiring message information of a vehicle; wherein the message information includes a message code.
S620: and carrying out matching operation on the message code and the fault code in the updated defect strategy, and determining whether the vehicle has faults according to a matching result.
The processing engine can dynamically adjust the relevant defect grades according to the acquired message information, namely carrying out degradation processing or upgrading processing on the grade corresponding to the relevant defect. In addition, if a new defect is detected, the new defect is fed back to the database so that the database updates the current defect strategy.
Illustratively, as shown in FIG. 7, FIG. 7 is a schematic diagram of a process of data interaction between a processing engine, a database, and a vehicle, as shown in an exemplary embodiment of the present application. The database is MySQL, and the processing engine is big data flink. The flink receives updated defect strategies returned by the database to diagnose the defects of the vehicle in real time: the vehicle uploads message information comprising message codes to the link, wherein the message information comprises DTC information, the updated defect strategy corresponds to preset defect DTC information, and the preset defect DTC information is shown in table 2:
TABLE 2
And analyzing the DTC information in the message information and the defect DTC information preset in the updated defect strategy, and if the message code in the received DTC information is 0x7F9, determining that the fault that the front safety airbag of the driver is not connected occurs to the vehicle.
In some embodiments, if it is detected that the message code matches the fault code in the updated defect policy, then other reference values in the message information need to be further detected to determine whether the vehicle is faulty. For example, the message information includes CAN information, and the updated defect policy corresponds to preset defect CAN information, where the preset defect CAN information is shown in table 3:
TABLE 3 Table 3
The link analyzes CAN information in the received message information and defect CAN information preset in the updated defect strategy, if the message code in the received CAN information is 0x110, other reference values in the heat preservation information need to be further detected, and if the other reference values are 0x0 or 0x1, the vehicle is characterized that no dipped headlight fault occurs; if the other reference value is 0x3, the vehicle is characterized by a dipped headlight failure.
The processing engine of the embodiment can rapidly diagnose the faults of the vehicle based on the updated defect strategy and the message information uploaded by the vehicle, and can dynamically carry out degradation or upgrading processing on the grades corresponding to the related defects according to the real-time fault diagnosis result, so that the database can update the defect strategy in real time, and the defects of the vehicle can be diagnosed efficiently, flexibly and accurately.
In another exemplary embodiment of the present application, an application scenario of the above-mentioned multiple target defect-based processing methods is illustrated, and referring specifically to fig. 8, fig. 8 is a schematic diagram of an application scenario of the target defect-based processing method of the present application. The system comprises a vehicle 100, a management person 200, a platform 300, a MySQL 400 and a flink 500, wherein data transmission can be performed between the ends in a wireless communication mode, and the application is not limited by the connection mode between the ends.
The following illustrates a data interaction procedure in an application scenario:
first, the operator 200 imports the original defect policy into the platform 300 and stores it into MySQL 400. The flink 500 monitors whether the defect policy in the MySQL 400 is changed by adopting the flink-cdc, and if so, the MySQL 400 sends the changed current defect policy to the flink 500 so that the flink 500 obtains the current defect policy.
Then, the flink 500 obtains the total number of products in the current batch and the number of products with target defects in the current batch from the current batch of vehicle information uploaded by the vehicle 100; if the total number of products is detected to be larger than or equal to the preset first number and the number of products with target defects is detected to be smaller than or equal to the preset second number, calculating to obtain defect probability corresponding to the target defects according to the total number of products and the number of products with the target defects; determining whether to perform accumulation operation on the continuous accumulation times according to a matching result of the defect probability and a preset probability interval; if the continuous accumulated times are detected to be larger than or equal to the preset times, degrading the target defect. Secondly, the link 500 sends the target defect after degradation to the MySQL 400, so that the MySQL 400 updates the stored original defect policy, the MySQL 400 returns the updated defect policy to the link 500, and the subsequent link 500 performs defect diagnosis on the related vehicle according to the updated defect policy.
Specifically, the flink500 performs defect diagnosis on the vehicle 100 according to the message information uploaded by the vehicle 100 and the updated defect policy, and if the flink500 detects a new defect, the new defect is sent to the MySQL 400, so that the MySQL 400 returns the latest defect policy to the flink500 after updating the defect policy.
The execution end of the processing method of the present application may be the server 501 disposed in the link, which may execute the processing method in each of the foregoing embodiments, which is not described herein. The server 501 may be an independent physical server, or may be a server cluster or a distributed system formed by a plurality of physical servers, where a plurality of servers may form a blockchain, and the servers are nodes on the blockchain, and the server 501 may also be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network ), and basic cloud computing services such as big data and artificial intelligence platforms, which are not limited in this respect.
In another aspect of the present application, a target defect-based processing apparatus is provided, as shown in fig. 9, and fig. 9 is a schematic structural diagram of a target defect-based processing apparatus according to an exemplary embodiment of the present application. The processing apparatus 900 includes:
An obtaining module 910, configured to obtain a total number of products in the current batch and a number of products with target defects in the current batch.
The calculating module 930 is configured to calculate, if the total number of products is detected to be greater than or equal to the preset first number and the number of products with the target defects is detected to be less than or equal to the preset second number, a defect probability corresponding to the target defects according to the total number of products and the number of products with the target defects.
The determining module 950 is configured to determine whether to perform the accumulating operation on the continuous accumulation times according to a matching result between the defect probability and the preset probability interval.
And the processing module 970 is configured to perform degradation processing on the target defect if the continuous accumulated number of times is detected to be greater than or equal to the preset number of times.
In an alternative manner, the processing apparatus 900 further includes:
the detection module is used for detecting whether the total quantity of products is larger than or equal to a preset first quantity and detecting whether the quantity of products with target defects is smaller than or equal to a preset second quantity.
And the resetting module is used for resetting the continuous accumulation times to reset the continuous accumulation times to the initial continuous accumulation times if the total number of the products is detected to be smaller than the preset first number or the number of the products with the target defects is detected to be larger than the preset second number.
In an alternative manner, the computing module 930 further includes:
and the quotient calculating unit is used for calculating the quotient of the number of the products with the target defects and the total number of the products, and calculating the quotient.
And the defect probability unit is used for taking the quotient value as the defect probability corresponding to the target defect.
In an alternative manner, the determining module 950 further includes:
and the accumulation unit is used for determining to accumulate the continuous accumulated times if the matching result represents that the defect probability is successfully matched with the preset probability interval.
And the resetting unit is used for resetting the continuous accumulation times to reset the continuous accumulation times to the initial continuous accumulation times if the matching result indicates that the defect probability fails to match with the preset probability interval.
In an alternative manner, the processing apparatus 900 further includes:
the continuous accumulated time detection module is used for detecting whether the continuous accumulated time is greater than or equal to the preset time.
And the execution module is used for executing the step of acquiring the total number of the products in the current batch and the number of the products with target defects in the current batch if the continuous accumulated times are detected to be smaller than the preset times.
In an alternative manner, the processing device 900 is applied to a processing engine, and the processing device 900 further includes:
And the sending module is used for sending the target defect subjected to degradation processing to the database so as to enable the database to update the stored original defect strategy to obtain the updated defect strategy sent by the database.
And the diagnosis module is used for diagnosing the vehicle defects according to the updated defect strategy.
In an alternative, the diagnostic module further comprises:
the message information acquisition unit is used for acquiring the message information of the vehicle; wherein the message information includes a message code.
And the fault diagnosis unit is used for carrying out matching operation on the message code and the fault code in the updated defect strategy, and determining whether the vehicle has faults or not according to the matching result.
The processing device acquires the total quantity of products in the current batch and the quantity of products with target defects in the current batch; if the total number of products is detected to be greater than or equal to the preset first number, and the number of products with target defects is detected to be less than or equal to the preset second number, the obtained two samples are characterized to meet the sample requirements, and then the defect probability corresponding to the target defects is calculated according to the total number of products and the number of products with the target defects, namely, only parameters meeting the sample requirements can be used as calculation parameters for calculation, so that the accuracy of the calculation parameters is ensured; determining whether to perform accumulation operation on the continuous accumulation times according to a matching result of the defect probability and a preset probability interval; if the continuous accumulated times are detected to be greater than or equal to the preset times, degrading the target defects, relaxing the detection conditions of the target defects, and gradually reducing the related defects so as to shorten the analysis processing time in the product detection process.
It should be noted that, the processing apparatus provided in the foregoing embodiment and the processing method provided in the foregoing embodiment belong to the same concept, and a specific manner in which each module and unit perform an operation has been described in detail in the method embodiment, which is not described herein again.
Another aspect of the present application also provides an electronic device, including: a controller; and a memory for storing one or more programs which, when executed by the controller, perform the processing method described above.
Referring to fig. 10, fig. 10 is a schematic diagram of a computer system of an electronic device according to an exemplary embodiment of the present application, which illustrates a schematic diagram of a computer system of an electronic device suitable for implementing an embodiment of the present application.
It should be noted that, the computer system 1000 of the electronic device shown in fig. 10 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 10, the computer system 1000 includes a central processing unit (Central Processing Unit, CPU) 1001 which can perform various appropriate actions and processes, such as performing the method in the above-described embodiment, according to a program stored in a Read-Only Memory (ROM) 1002 or a program loaded from a storage section 1008 into a random access Memory (Random Access Memory, RAM) 1003. In the RAM 1003, various programs and data required for system operation are also stored. The CPU 1001, ROM 1002, and RAM 1003 are connected to each other by a bus 1004. An Input/Output (I/O) interface 1005 is also connected to bus 1004.
The following components are connected to the I/O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output portion 1007 including a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and a speaker; a storage portion 1008 including a hard disk or the like; and a communication section 1009 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 1009 performs communication processing via a network such as the internet. The drive 1010 is also connected to the I/O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed as needed in the drive 1010, so that a computer program read out therefrom is installed as needed in the storage section 1008.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 1009, and/or installed from the removable medium 1011. When executed by a Central Processing Unit (CPU) 1001, the computer program performs various functions defined in the system of the present application.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having 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 (Erasable Programmable Read Only Memory, EPROM), 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. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
Another aspect of the application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of processing as before. The computer-readable storage medium may be included in the electronic device described in the above embodiment or may exist alone without being incorporated in the electronic device.
Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the computer device performs the processing methods provided in the respective embodiments described above.
According to an aspect of the embodiment of the present application, there is also provided a computer system including a central processing unit (Central Processing Unit, CPU) which can perform various appropriate actions and processes, such as performing the method in the above-described embodiment, according to a program stored in a Read-Only Memory (ROM) or a program loaded from a storage section into a random access Memory (Random Access Memory, RAM). In the RAM, various programs and data required for the system operation are also stored. The CPU, ROM and RAM are connected to each other by a bus. An Input/Output (I/O) interface is also connected to the bus.
The following components are connected to the I/O interface: an input section including a keyboard, a mouse, etc.; an output section including a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and the like, and a speaker, and the like; a storage section including a hard disk or the like; and a communication section including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section performs communication processing via a network such as the internet. The drives are also connected to the I/O interfaces as needed. Removable media such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, and the like are mounted on the drive as needed so that a computer program read therefrom is mounted into the storage section as needed.
The foregoing is merely illustrative of the preferred embodiments of the present application and is not intended to limit the embodiments of the present application, and those skilled in the art can easily make corresponding variations or modifications according to the main concept and spirit of the present application, so that the protection scope of the present application shall be defined by the claims.
Claims (10)
1. A method of processing a target defect, the method comprising:
Acquiring the total number of products in the current batch and the number of products with target defects in the current batch;
if the total number of the products is detected to be larger than or equal to the preset first number and the number of the products with the target defects is detected to be smaller than or equal to the preset second number, calculating to obtain defect probability corresponding to the target defects according to the total number of the products and the number of the products with the target defects;
determining whether to accumulate the continuous accumulated times according to the matching result of the defect probability and a preset probability interval;
and if the continuous accumulated times are detected to be greater than or equal to the preset times, carrying out degradation treatment on the target defect.
2. The processing method according to claim 1, characterized in that the processing method further comprises:
detecting whether the total number of products is greater than or equal to the preset first number, and detecting whether the number of products with target defects is less than or equal to the preset second number;
and if the total number of products is detected to be smaller than the preset first number or the number of products with the target defects is detected to be larger than the preset second number, resetting the continuous accumulation times to reset the continuous accumulation times to initial continuous accumulation times.
3. The method according to claim 1, wherein the calculating the defect probability corresponding to the target defect according to the total number of products and the number of products having the target defect further comprises:
calculating quotient values of the product quantity with the target defects and the total product quantity, and calculating to obtain quotient values;
and taking the quotient as the defect probability corresponding to the target defect.
4. The method according to claim 1, wherein determining whether to accumulate the consecutive accumulated times according to the matching result between the defect probability and the preset probability interval, further comprises:
if the matching result represents that the defect probability is successfully matched with a preset probability interval, determining to accumulate the continuous accumulated times;
and if the matching result indicates that the defect probability fails to match with the preset probability interval, resetting the continuous accumulation times to reset the continuous accumulation times to initial continuous accumulation times.
5. The processing method according to claim 1, characterized in that the processing method further comprises:
Detecting whether the continuous accumulated times are larger than or equal to the preset times;
and if the continuous accumulated times are detected to be smaller than the preset times, executing the step of acquiring the total number of products in the current batch and the number of products with target defects in the current batch.
6. The processing method according to any one of claims 1 to 5, which is applied to a processing engine, characterized in that the processing method further comprises:
transmitting the target defect after degradation treatment to a database so that the database updates the stored original defect strategy to obtain an updated defect strategy transmitted by the database;
and diagnosing the vehicle defects according to the updated defect strategy.
7. The method of processing of claim 6, wherein the performing vehicle defect diagnosis according to the updated defect policy further comprises:
acquiring message information of a vehicle; wherein, the message information comprises a message code;
and matching the message code with the fault code in the updated defect strategy, and determining whether the vehicle has faults according to a matching result.
8. A target defect-based processing apparatus, the processing apparatus comprising:
The acquisition module is used for acquiring the total quantity of products in the current batch and the quantity of products with target defects in the current batch;
the calculating module is used for calculating and obtaining the defect probability corresponding to the target defects according to the total number of the products and the number of the products with the target defects if the total number of the products is detected to be larger than or equal to the preset first number and the number of the products with the target defects is detected to be smaller than or equal to the preset second number;
the determining module is used for determining whether to accumulate the continuous accumulated times or not according to the matching result of the defect probability and the preset probability interval; the continuous accumulated times are used for deciding whether to carry out degradation treatment on the target defect or not;
and the processing module is used for carrying out degradation processing on the target defect if the continuous accumulated times are detected to be greater than or equal to preset times.
9. An electronic device, comprising:
a controller;
a memory for storing one or more programs that, when executed by the controller, cause the controller to implement the processing method of any of claims 1-7.
10. A computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor of a computer, cause the computer to perform the processing method of any of claims 1 to 7.
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