CN116563052A - Method, apparatus, electronic device and computer readable medium for product detection - Google Patents
Method, apparatus, electronic device and computer readable medium for product detection Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The embodiment of the application provides a method, a device, electronic equipment and a readable storage medium for detecting products. The method comprises the following steps: acquiring first production parameters of a product, wherein the first production parameters are used for representing influences of personnel, machines, materials, methods and environmental factors in each production procedure on the product; determining a second production parameter corresponding to the product according to the first production parameter, wherein the second production parameter is used for indicating the risk degree of the product becoming a defective product; and detecting the product by adopting a corresponding detection mode according to the second production parameter. By the method, the risk degree of the product becoming the defective product can be prejudged, products with different risk degrees are detected by adopting different detection standards, the accuracy of the sampling inspection result is improved, and the waste caused by excessive detection is avoided.
Description
Technical Field
The present disclosure relates to the field of big data analysis technologies, and in particular, to a method, an apparatus, an electronic device, and a computer readable medium for product detection.
Background
After a series of automatic production procedures, in order to ensure the delivery quality of the product, box opening spot inspection is usually carried out before the product is delivered.
Currently, spot inspection is mainly performed by manpower, for example, spot inspection proportion is manually set or selected by a inspector, or products are randomly spot inspected by the inspector. However, because the manual spot check lacks the prejudgment of the risk of the product becoming the defective product, the products with different risk degrees cannot be checked in a targeted manner, and the spot check result has lower accuracy.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, an apparatus, an electronic device, and a computer readable medium for detecting a product, which can predict a risk of a product called a defective product in a production line, and improve accuracy of a sampling inspection result.
In order to solve the technical problems, embodiments of the present application are realized by the following aspects.
In a first aspect, embodiments of the present application provide a method for product detection, including: acquiring first production parameters of a product, wherein the first production parameters are used for representing influences of personnel, machines, materials, methods and environmental factors in each production procedure on the product; determining a second production parameter corresponding to the product according to the first production parameter, wherein the second production parameter is used for indicating the risk degree of the product becoming a defective product; and detecting the product by adopting a corresponding detection mode according to the second production parameter.
In a second aspect, embodiments of the present application provide a device for detecting a product, including: the acquisition module is used for acquiring first production parameters of the product, wherein the first production parameters are used for representing the influence of personnel, machines, materials, methods and environmental factors in each production procedure on the product; the determining module is used for determining a second production parameter corresponding to the product according to the first production parameter, wherein the second production parameter is used for indicating the risk degree of the product becoming a defective product; and the detection module is used for detecting the product by adopting a corresponding detection mode according to the second production parameter.
In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor, and computer-executable instructions stored on the memory and executable on the processor, which when executed by the processor, implement the method of the first aspect described above.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method for product detection according to the first aspect.
In the embodiment of the application, a first production parameter of a product is obtained, wherein the first production parameter is used for representing the influence of personnel, machines, materials, methods and environmental factors on the product in each production procedure; determining a second production parameter corresponding to the product according to the first production parameter, wherein the second production parameter is used for indicating the risk degree of the product becoming a defective product; and detecting the product by adopting a corresponding detection mode according to the second production parameters, so that the risk of the product called defective product of the production line can be prejudged, and the accuracy of the sampling inspection result is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for detecting a product according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for detecting a product according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a method for detecting a product according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a device for detecting a product according to an embodiment of the present application;
fig. 5 is a schematic hardware structure of an electronic device for executing a method for detecting a product according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions in the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
Fig. 1 is a schematic flow chart of a method for detecting a product according to an embodiment of the present application, where the method may be performed by an electronic device, for example, a terminal device or a server device. In other words, the method may be performed by software or hardware installed at a terminal device or a server device. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. As shown, the method may include the following steps.
Step S110: a first production parameter of the product is obtained.
The first production parameter is used for representing the influence of personnel, machines, materials, methods and environmental factors in each production procedure on the product.
Alternatively, the product is produced on an automated production line, which is subjected to a plurality of production processes or production stations of the production line during the production process. The production process comprises various links related to product production, detection, packaging and shipment. Personnel, machines, materials, methods, environmental factors are also known as 4M1E (Man, machine, material, method and Environment), which together constitute five major elements of factory production management. The first production parameter is a variation value for representing a product caused by each factor at each production link.
Personnel factors may include skill factors, assessment factors, daily inspection results, experience learning, operational variations, and the like; the equipment factors may include parameter variation, line stoppage, equipment maintenance frequency, etc.; the material factors may include incoming material problems, appearance problems, functional problems, etc.; environmental factors may include plant temperature and humidity, brightness, noise, etc.; method factors may include unsolicited operation, custom operation, change management, etc.
Taking printed circuit board assembly (Printed Circuit Board Assembly, PCBA) production as an example, the production process includes tin printing, solder paste inspection, stamping, reflow soldering, automatic optical inspection (Automatic Optical Inspection, AOI) inspection, plugging, wave crest oven, assembly, testing, automatic appearance inspection, packaging, shipment sampling inspection. Each product can have different degrees of uncontrollable risks through the working procedures, and variable contents, namely variable values, are generated. Alternatively, the variation may be classified into a process variation and a method variation according to the process or factors. The process variation includes variations caused by equipment, fixture depreciation, equipment fixture replacement, process line downtime, equipment maintenance frequency, material changes, process capability parameters (Capability Index of Process, cpk), etc.; the method variation value comprises variation values caused by personnel skill change, product requirement change, management and control method change, program parameter change, operation file change and the like.
Alternatively, each process may automatically capture the corresponding process variation value, i.e. the first production parameter, of each product during reproduction, in the dimensions of the process variation and the method variation.
The variance includes process variations and process variations depending on the factors affected.
Table 1 below shows further examples of the production process and 4M1E factors.
TABLE 1
TABLE 1
Step S120: and determining a second production parameter corresponding to the product according to the first production parameter.
The second production parameter is used to indicate the degree of risk of the product becoming a defective product.
The second production parameters of the products correspond to the first production parameters, the second production parameters of the products can be determined according to the first production parameters of the products, and specifically, the risk level of the products can be judged according to the product variation values. It will be appreciated that the higher the degree of risk expressed by the second production parameter, the greater the likelihood that the product will be detected as a defective product.
Step S130: and detecting the product by adopting a corresponding detection mode according to the second production parameter.
Optionally, the detection mode comprises detecting the product and performing no-inspection on the product, wherein the detection of the product can comprise detecting the product to different degrees, modes and standards. Products with different risk degrees are detected in different modes, so that the products can be detected more specifically, for example, products with high risk levels are detected in high standards and strictly required, missing detection of defective products is avoided, the detection proportion of products with low risk levels can be properly reduced, even non-detection is performed, and labor cost can be saved.
In the embodiment of the application, a first production parameter of a product is obtained, wherein the first production parameter is used for representing the influence of personnel, machines, materials, methods and environmental factors on the product in each production procedure; determining a second production parameter corresponding to the product according to the first production parameter, wherein the second production parameter is used for indicating the risk degree of the product becoming a defective product; and detecting the product by adopting a corresponding detection mode according to the second production parameters, so that the risk of the product called defective product of the production line can be prejudged, and the accuracy of the sampling inspection result is improved.
Fig. 2 is another flow chart of a method for detecting a product according to an embodiment of the present application, including:
step S210: a first production parameter of the product is obtained.
This step may employ the description of the corresponding steps of the previous embodiment, and for the repeatable portion, the description is omitted here.
Step S220: and determining the second production parameter corresponding to the numerical value interval according to the numerical value interval where the product of the first duty ratio, the number of products, the influence degree and the number of working procedures is located.
The first production parameter is determined by a first duty ratio, an affected product quantity, an affected degree, and an affected process quantity, wherein the first duty ratio is used for indicating a duty ratio of a defective product quantity detected by each production process in a defective product total quantity in a history period.
Optionally, the first duty cycle is used to represent a historical defective rate. The historical time period may be a period of time of any length of time prior to the current detection time. For example, the historical time period is the last seven days and the first duty cycle is used to represent the ratio of the number of problem products to the total number of defective products that have occurred in the last seven days of the production line. The affected product is used to indicate the affected product when the process and/or factors are varied, and it is understood that when the product is affected, the production parameters are correspondingly changed. The degree of influence may be used to indicate the severity of the product being affected, e.g., the higher the severity of the product being affected, the higher its corresponding degree of influence. The affected process may be a process that is at risk of producing defective products.
Optionally, the first production parameter is a product of the first duty ratio, the number of products, the degree of influence and the number of processes, and has a value ranging from 0 to 100%, including a value ranging from 0 to 30%, 31 to 60%, 61 to 90% and 90 to 100%, for example. It will be appreciated that the number of numerical intervals and the specific numerical values of each interval may be set as desired. Each numerical interval corresponds to a second production parameter. Optionally, the correspondence between the numerical intervals and the second production parameters may be one-to-one correspondence, or may be one-to-many correspondence or many-to-many correspondence. For example, the value interval and the second production parameter are in a many-to-many correspondence, i.e. one value interval may correspond to the values of a plurality of second production parameters, and one second production parameter may also correspond to a plurality of value intervals.
In a possible implementation manner, the determining, according to the first production parameter, a second production parameter corresponding to the product further includes: and determining a second production parameter corresponding to the product when the influence degree is greater than a predetermined first threshold value and/or the number of working procedures is greater than a predetermined second threshold value.
It will be appreciated that the degree of impact to which the product is subjected and the number of affected processes may be different for different 4M1E factors, the degree of impact being positively correlated with the degree of risk, the number of affected processes being positively correlated with the degree of risk. When any one of the processes has a defect, or the product stays in a certain process for too long, or the influence degree is high, or the number of the influenced processes is large, the risk degree of the product called defective product is high even if the determined first production parameter is small. According to the historical experience value or the actual need of production, setting a threshold value corresponding to the degree of influence and/or the number of procedures, and determining a second production parameter indicating a higher risk degree when the degree of influence and/or the number of procedures exceeds the threshold value. For example, if the risk level indicated by the second production parameter determined from the numerical value interval is 1, the determined second production parameter is higher than 1 when the influence level and/or the number of processes exceeds the threshold value.
Alternatively, the degree of influence is determined according to table 2 below.
TABLE 2
For example, it may be determined that the first threshold value of the degree of influence is 6, and when the degree of influence is 6 or more, the corresponding second production parameter is determined.
Similarly, for example, a second threshold value of 2 for the number of processes may be determined, and when the affected process is greater than 2, a corresponding second production parameter is determined.
It is understood that the assignment of the influence level, the value of the first threshold value and the value of the second threshold value may be changed according to actual needs.
Step S231: distributing sampling inspection proportion corresponding to the risk level according to the second production parameter;
step S232: and detecting the product according to the sampling rate.
The second production parameters include a low risk level, a medium risk level, and a high risk level.
In one possible implementation manner, the detecting the product according to the second production parameter by adopting a corresponding detection mode further includes: performing a pass-free on the product if the second production parameter is of a low risk level; in case the second production parameter is a medium risk level or a high risk level, the product is detected using an appearance detection device.
Optionally, assigning a spot check ratio corresponding to the risk level includes determining a standard of acceptance level (Acceptance Quality Level, AQL) corresponding to the risk level and determining a spot check ratio corresponding to the AQL. For example, the proportion of the spot checks of the high risk level is 100% and the proportion of the spot checks of the low risk level is 0%, i.e., the spot checks are performed on the product of the low risk level. When the products with medium risk level and high risk level are inspected, the products can be detected by using appearance detection equipment by adopting an image technology, so that manual intervention is avoided. The appearance detecting device is, for example, a camera, a video camera, a thermal imager, or the like.
For example, PCBA processing for different risk levels may include, low risk level PCBA going to a down-pass process; pushing PCBA of risk grade to a predetermined risk area for rechecking in the current working procedure; for PCBA with high risk level, the image technology is automatically used for rewinding, so that the manual interference is avoided. After the image technology is used for rewinding, the abnormality judgment can be carried out by a variation statistical comparison method, and if a problem exists, the reworking can be automatically triggered.
Table 3 below is one example of determining a second production parameter from a first production parameter.
TABLE 3 Table 3
In one possible implementation, the close-coupled product of the high risk level product may be determined to be a high risk product. For example, products that go through the same process or the same station per unit time as the high risk grade products. The unit time may be a user-defined duration. For example, if 14 points of products subjected to the assembly process are detected as high risk level products and the unit time is defined as 5 minutes, then determination 13:55-14:00 the product after the assembly procedure is a close-fitting product, and the risk level is high risk.
In the embodiment of the application, the second production parameters corresponding to the products can be determined according to the production needs and the user demands, the products can be detected in a targeted manner by adopting a corresponding detection mode according to the second production parameters, the products with different risk degrees can be checked in a targeted manner, and the accuracy of the spot check result is further improved.
Fig. 3 is another flow chart of a method for detecting a product according to an embodiment of the present application.
In one possible implementation manner, before the obtaining the first production parameter of the product, the method further includes: storing personnel, machines, materials, methods, environmental factors and product data of each production procedure into a risk database; and determining a first production parameter of the product according to the corresponding relation between personnel, machines, materials, methods, environmental factors and product data.
Optionally, the risk database automatically collects production data pushed by the production system and data based on the internet of things, and stores the data according to the system requirements. The data of each working procedure of the product in the production process can be mastered through the data of the risk database. Optionally, the data of the risk database further includes a defect part list, and the list information includes a product Model Number (Model), a product Serial Number (SN), collection report data (Collect Report Data, CRD), a product picture, and the like.
After creating the data in S301, a Digital Out-of-Box audio (Digital OBA) system automatically determines the risk level (i.e., the second production parameter) of the received data (i.e., the first production parameter) according to the data in the risk database in S302, where the close-connected products before and after the high risk product are determined to be the high risk level. Optionally, the number of high risk products is equal to 0, determined as a low risk level; the number of high risk products is greater than 0, determined to be a high risk level. S303, triggering a corresponding AQL sampling rate by the Digital OBA system according to the determined risk level. Such as low risk products, automatically performing a leave-on or loose spot check; high risk products are tested according to the ratio of the added spot check or the 100% spot check. The product to be detected needs to carry risk information, and the risk information comprises, but is not limited to, risk grade and sampling rate corresponding to the product; further, the system recommends a detection mode suitable for the product according to the risk information. .
According to step S304, the product is detected by using a corresponding detection method to determine whether the product passes the detection.
In one possible implementation manner, after the product is detected by adopting a corresponding detection mode, the method further includes: and updating the risk database according to the detection result.
The detection result obtained by the appearance detection device may be fed back to the risk database to update the risk database.
Optionally, the product with high risk level can be provided for inspectors to judge, and the judging result is fed back to the risk database for automatic study of the system. This way of combining manual inspection is particularly suitable for phases where the sample data of the risk database is insufficient at the beginning of the use of the system.
In one possible implementation manner, after the product is detected by adopting a corresponding detection mode, the method further includes: and recommending reworking measures for the product when the detection result indicates that the product is a defective product.
Optionally, the Digital OBA system rejects products that do not pass the detection, automatically determines products that need to be reworked, and lists a list of products.
Optionally, for products that are rejected for rework, the Digital OBA system automatically recommends rework improvements. According to the reworking measures provided by the system, reworking products can be improved more pertinently. After the improvement is completed, the process returns to step S301, and the reworked product is inspected again. The Digital OBA system presents a trend chart of high-risk products distributed on each production line in real time according to the high-risk information, and automatically recommends detection personnel arrangement, worker returning arrangement and the like to management personnel according to the trend data. Therefore, personnel arrangement can be timely made according to trend data of the high risk information, and production efficiency and production quality are further improved.
Optionally, according to the first production parameter of each process, the station and the process with more bad items can be pre-warned, so that staff can intervene in advance, and the possibility of producing defective products is reduced.
In the embodiment of the application, the data of each factor variable are collected at one time node, a series of different input variables are considered, the data judgment and the risk level association are carried out on the variables according to the target rule set by the Digital OBA system, the system algorithm can be optimized, the labor cost is reduced, the probability of defective goods delivery is reduced, and the quality of the delivered products is ensured.
Fig. 4 is a schematic structural diagram of an apparatus for detecting a product according to an embodiment of the present application, where the apparatus 400 includes: an acquisition module 410, a determination module 420, and a detection module 430.
The obtaining module 410 is configured to obtain a first production parameter of a product, where the first production parameter is used to represent an influence of personnel, machines, materials, methods, and environmental factors in each production process on the product; a determining module 420, configured to determine a second production parameter corresponding to the product according to the first production parameter, where the second production parameter is used to indicate a risk degree of the product becoming a defective product; and the detection module 430 is configured to detect the product according to the second production parameter by adopting a corresponding detection mode.
In one possible implementation, the first production parameter is determined by a first duty ratio, an affected product quantity, an affected degree of influence, and an affected process quantity, wherein the first duty ratio is used for indicating a duty ratio of a defective product quantity detected by each production process in a defective product total quantity in a history period; the determining module 420 is configured to determine the second production parameter corresponding to a numerical interval according to the numerical interval where the product of the first duty ratio, the number of products, the degree of influence, and the number of processes is located.
In a possible implementation, the determining module 420 is further configured to determine a second production parameter corresponding to the product when the degree of influence is greater than a predetermined first threshold and/or the number of procedures is greater than a predetermined second threshold.
In one possible implementation, the second production parameters include a low risk level, a medium risk level, and a high risk level; the detection module 430 is configured to allocate a sampling rate corresponding to the risk level according to the second production parameter; and detecting the product according to the sampling rate.
In a possible implementation, the detection module 430 is further configured to perform a no-check release on the product if the second production parameter is at a low risk level; in case the second production parameter is a medium risk level or a high risk level, the product is detected using an appearance detection device.
In a possible implementation manner, the apparatus 400 is further configured to recommend reworking measures for the product if the detection result indicates that the product is a defective product after the product is detected by the corresponding detection method.
In a possible implementation manner, the apparatus 400 is further configured to store personnel, machines, materials, methods, environmental factors, and product data of each production process into a risk database before the first production parameter of the product is obtained; and determining a first production parameter of the product according to the corresponding relation between personnel, machines, materials, methods, environmental factors and product data.
In a possible implementation manner, the apparatus 400 is further configured to update the risk database according to the detection result after the product is detected by using the corresponding detection method.
The apparatus 400 provided in this embodiment of the present application may perform the methods described in the foregoing method embodiments, and implement the functions and beneficial effects of the methods described in the foregoing method embodiments, which are not described herein again.
Fig. 5 shows a schematic diagram of a hardware structure of an electronic device for performing a method for product detection according to an embodiment of the present application, and referring to the figure, at a hardware level, the electronic device includes a processor 510, optionally including an internal bus 520, a network interface 530, and a memory. The Memory may include a Memory 540, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory) 550, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor 510, network interface 530, and memory may be interconnected by an internal bus 520, which may be an industry standard architecture (Industry Standard Architecture, ISA) bus, a peripheral component interconnect (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in the figure, but not only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The storage may include memory 540 and non-volatile storage 550, and provides instructions and data to the processor 510.
Processor 510 reads the corresponding computer program from non-volatile memory 550 into memory 540 and then runs to form a means of locating the target user at a logical level. The processor 510 executes the program stored in the memory, and is specifically configured to perform the method described in the embodiments of fig. 1 to 3, and achieve the same or corresponding technical effects.
The methods disclosed above in the embodiments of fig. 1-3 of the present application may be applied to a processor or implemented by processor 510. The processor 510 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in processor 510. The processor 510 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor 510 reads information in the memory and performs the steps of the method described above in connection with its hardware.
The electronic device may also execute the methods described in the foregoing method embodiments, and implement the functions and beneficial effects of the methods described in the foregoing method embodiments, which are not described herein.
Of course, other implementations, such as a logic device or a combination of hardware and software, are not excluded from the electronic device of the present application, that is, the execution subject of the following processing flow is not limited to each logic unit, but may be hardware or a logic device.
The embodiments of the present application also provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to perform the methods described in the embodiments of fig. 1 to 3, and achieve the same or corresponding technical effects.
The computer readable storage medium includes Read-Only Memory (ROM), random access Memory (Random Access Memory RAM), magnetic disk or optical disk, etc.
Further, embodiments of the present application also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, implement the methods described in the embodiments of fig. 1 to 3 and achieve the same or corresponding technical effects.
In summary, the foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
Claims (11)
1. A method of product inspection, comprising:
acquiring first production parameters of a product, wherein the first production parameters are used for representing influences of personnel, machines, materials, methods and environmental factors in each production procedure on the product;
determining a second production parameter corresponding to the product according to the first production parameter, wherein the second production parameter is used for indicating the risk degree of the product becoming a defective product;
and detecting the product by adopting a corresponding detection mode according to the second production parameter.
2. The method of claim 1, wherein the first production parameter is determined by a first duty cycle, an affected product quantity, an extent of the effect, and an affected process quantity, wherein the first duty cycle is used to indicate a duty cycle of a quantity of defective products detected by each production process in a total quantity of defective products over a historical period of time;
the determining, according to the first production parameter, a second production parameter corresponding to the product includes:
and determining the second production parameter corresponding to the numerical value interval according to the numerical value interval where the product of the first duty ratio, the number of products, the influence degree and the number of working procedures is located.
3. The method of claim 2, wherein the determining a second production parameter corresponding to the product from the first production parameter further comprises:
and determining a second production parameter corresponding to the product when the influence degree is greater than a predetermined first threshold value and/or the number of working procedures is greater than a predetermined second threshold value.
4. The method of claim 1, wherein the second production parameters include a low risk level, a medium risk level, and a high risk level;
and detecting the product by adopting a corresponding detection mode according to the second production parameter, wherein the method comprises the following steps:
distributing sampling inspection proportion corresponding to the risk level according to the second production parameter;
and detecting the product according to the sampling rate.
5. The method of claim 4, wherein said detecting said product according to said second production parameter using a corresponding detection method, further comprises:
performing a pass-free on the product if the second production parameter is of a low risk level;
in case the second production parameter is a medium risk level or a high risk level, the product is detected using an appearance detection device.
6. The method of claim 1, wherein after the detecting the product by the corresponding detecting method, further comprising:
and recommending reworking measures for the product when the detection result indicates that the product is a defective product.
7. The method of claim 1, wherein prior to the obtaining the first production parameter of the product, further comprising:
storing personnel, machines, materials, methods, environmental factors and product data of each production procedure into a risk database;
and determining a first production parameter of the product according to the corresponding relation between personnel, machines, materials, methods, environmental factors and product data.
8. The method of claim 7, wherein after said detecting said product with a corresponding detection means, further comprising:
and updating the risk database according to the detection result.
9. An apparatus for product testing, comprising:
the acquisition module is used for acquiring first production parameters of the product, wherein the first production parameters are used for representing the influence of personnel, machines, materials, methods and environmental factors in each production procedure on the product;
the determining module is used for determining a second production parameter corresponding to the product according to the first production parameter, wherein the second production parameter is used for indicating the risk degree of the product becoming a defective product;
and the detection module is used for detecting the product by adopting a corresponding detection mode according to the second production parameter.
10. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions which, when executed, perform the method of product detection of any of claims 1-8 using the processor.
11. A computer readable medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of product detection of any of claims 1-8 below.
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| CN202310512513.XA CN116563052A (en) | 2023-05-08 | 2023-05-08 | Method, apparatus, electronic device and computer readable medium for product detection |
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| CN202310512513.XA CN116563052A (en) | 2023-05-08 | 2023-05-08 | Method, apparatus, electronic device and computer readable medium for product detection |
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