Product information analysis method, device and electronic equipment

Abstract

The embodiment of the application provides a product information analysis method and device, electronic equipment and computer readable storage medium, and relates to the technical field of computers. The embodiment of the application obtains product parameter information of a product to be tested; determines a target product with a first product defect according to the product parameter information, and determines a target process in which the target product has the first product defect; determines a target production equipment corresponding to the first product defect according to production equipment information corresponding to the target product, and improves the accuracy of the production equipment that causes the product defect. In addition, the product parameter information and the production equipment information are stored in the same distributed database, so that the information acquisition efficiency can be improved when the product parameter information and the production equipment information are acquired, and the processing efficiency of the overall product information analysis process is also improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a product information analysis method, apparatus, electronic device, and computer-readable storage medium. Background Technology

[0002] With the development of intelligent manufacturing technology, product production efficiency and capacity are gradually improving. However, product defects are inevitable in large-scale production processes. Therefore, in the production field, quickly locating the causes of defects and solving corresponding problems helps improve product yield.

[0003] Currently, in the process of investigating the causes of defects, intelligent product information analysis methods are gradually replacing manual analysis. Among related technologies, how to conduct efficient product information analysis has become a pressing issue that needs to be addressed. Summary of the Invention

[0004] The purpose of this application is to address at least one of the aforementioned technical deficiencies, particularly the technical deficiency of low efficiency in product information analysis.

[0005] According to one aspect of this application, a product information analysis method is provided, the method comprising: acquiring product parameter information of a product to be tested;

[0006] Based on the product parameter information, a target product with a first product defect is identified, and a target process in which the first product defect occurs is identified.

[0007] Based on the production equipment information corresponding to the target product, determine the target production equipment corresponding to the defect of the first product;

[0008] The target production equipment includes the production equipment in the target process.

[0009] The product parameter information and the production equipment information are stored in the same distributed database.

[0010] Optionally, determining the target product with the first product defect based on the product parameter information includes:

[0011] The product parameter information is parsed to determine the first defect information, which includes the defect type.

[0012] A target product is identified as having the first product defect, wherein the defect type includes the first product defect.

[0013] Optionally, parsing the product parameter information to determine the first defect information includes:

[0014] The product parameter information is compared with a preset product parameter threshold to determine defective product parameters that do not meet the preset product parameter threshold.

[0015] The first defect information is determined based on the parameters of the defective product.

[0016] Optionally, the product parameter information includes product image information.

[0017] The step of parsing the product parameter information to determine the first defect information includes:

[0018] The product image information is identified using an image processing algorithm to determine the first defect information.

[0019] Optionally, the product parameter information includes second defect information.

[0020] The step of determining the target product with a first product defect based on the product parameter information includes:

[0021] Based on the second defect information, a target product with the first product defect is identified;

[0022] The second defect information includes product defect information identified based on a neural network model.

[0023] Optionally, the method further includes:

[0024] The first defect information is compared with the second defect information to obtain the comparison result;

[0025] The comparison results are fed back to the neural network model so that the model parameters of the neural network model can be adjusted according to the comparison results.

[0026] Optionally, the second defect information includes product defect information of the target product in the first process.

[0027] The first defect information includes product defect information of the target product in the second process.

[0028] The second process is a production process that follows the first process in the product manufacturing process.

[0029] Optionally, determining the target production equipment corresponding to the first product defect based on the production equipment information corresponding to the target product includes:

[0030] Decision tree algorithm based on the production equipment information corresponding to the target product;

[0031] Determine the target production equipment corresponding to the root node in the decision tree.

[0032] Optionally, the target process for determining that the target product has the first product defect includes:

[0033] Obtain the first position parameter of the defect location of the first product on the target product;

[0034] Determine the first quantity of the target products whose first position parameters satisfy the preset position requirements;

[0035] If the first quantity accounts for a first proportion of the number of products to be tested, which is greater than a first preset threshold, a third process is determined to be the first occurrence of the first product defect in the target product, and the target process includes the third process.

[0036] Optionally, determining the target production equipment corresponding to the first product defect based on the production equipment information corresponding to the target product includes:

[0037] Among the production lines corresponding to the third process and the fourth process, the target production line is determined;

[0038] The target production equipment is determined based on the production equipment information of the candidate production equipment on the target production line;

[0039] The fourth process includes a production process in the product manufacturing process that precedes the third process, and the number of target products corresponding to the target production line is greater than a second preset threshold.

[0040] Optionally, the occurrence time of the first product defect of the target product includes a first time range.

[0041] The step of determining the target production equipment based on the production equipment information of candidate production equipment on the target production line includes:

[0042] Obtain the operating parameters from the production equipment information of the candidate production equipment;

[0043] Identify the target production equipment whose operating parameters are abnormal within the first time range.

[0044] Optionally, the shape information of the first product defect includes first shape information.

[0045] The step of determining the target production equipment based on the production equipment information of candidate production equipment on the target production line includes:

[0046] Obtain the second position parameter of the defect location of the first product on the production table of the candidate production equipment;

[0047] Obtain the component shape information corresponding to the second position parameter from the production equipment information of the candidate production equipment;

[0048] Identify a target production equipment whose component shape information matches the first shape information.

[0049] According to another aspect of this application, a product information analysis apparatus is provided, the apparatus comprising:

[0050] The acquisition module is used to acquire product parameter information of the product under test.

[0051] The first determining module is used to determine, based on the product parameter information, a target product with a first product defect, and to determine the target process in which the first product defect occurs in the target product.

[0052] The second determining module is used to determine the target production equipment corresponding to the defect of the first product based on the production equipment information corresponding to the target product.

[0053] The target production equipment includes the production equipment in the target process.

[0054] The product parameter information and the production equipment information are stored in the same distributed database.

[0055] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0056] One or more processors;

[0057] Memory;

[0058] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to: perform the product information analysis method according to any one of the first aspects of this application.

[0059] For example, in a third aspect of this application, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0060] The memory is used to store at least one executable instruction that causes the processor to perform operations corresponding to the product information analysis method shown in the first aspect of this application.

[0061] According to another aspect of this application, a computer-readable storage medium is provided, wherein when the computer program is executed by a processor, it implements the product information analysis method according to any one of the first aspects of this application.

[0062] For example, in a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the product information analysis method shown in the first aspect of the present application.

[0063] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in various alternative implementations of the first aspect described above.

[0064] The beneficial effects of the technical solution provided in this application are:

[0065] This application embodiment obtains product parameter information of the product under test; determines the target product with a first product defect based on the product parameter information, and determines the target process in which the first product defect occurs in the target product; and determines the target production equipment corresponding to the first product defect based on the production equipment information corresponding to the target product, thereby improving the accuracy of locating the production equipment causing the product defect. Furthermore, this application embodiment stores the product parameter information and the production equipment information in the same distributed database; therefore, when obtaining the product parameter information and the production equipment information, the information acquisition efficiency can be improved, thereby also improving the overall processing efficiency of the product information analysis process. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0067] Figure 1 A flowchart illustrating a product information analysis method provided in an embodiment of this application;

[0068] Figure 2 This is a schematic diagram illustrating an application scenario of a product information analysis method provided in an embodiment of this application.

[0069] Figure 3 This is a schematic diagram illustrating an application scenario of a product information analysis method provided in an embodiment of this application.

[0070] Figure 4 This is a schematic diagram illustrating an application scenario of a product information analysis method provided in an embodiment of this application.

[0071] Figure 5 A flowchart illustrating a product information analysis method provided in an embodiment of this application;

[0072] Figure 6 A schematic diagram of a system architecture for a product information analysis method provided in this application embodiment;

[0073] Figure 7 An interactive schematic diagram of a product information analysis method provided in an embodiment of this application;

[0074] Figure 8 This is a schematic diagram of the structure of a product information analysis device provided in an embodiment of this application;

[0075] Figure 9 This is a schematic diagram of the structure of an electronic device for product information analysis provided in an embodiment of this application. Detailed Implementation

[0076] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0077] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.”

[0078] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0079] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0080] The solutions provided in this application can be executed by any electronic device, such as a terminal device or a server. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein. The product information analysis method, apparatus, electronic device, and storage medium provided in this application aim to solve at least one of the technical problems existing in the prior art.

[0081] This application provides a possible implementation method, such as... Figure 1 As shown, a flowchart of a product information analysis method is provided. This method can be executed by any electronic device. Optionally, the method can be applied to a server or a terminal device. For ease of description, the method provided in the embodiments of this application will be described below with the server as the execution subject.

[0082] This application embodiment can be applied to technical fields such as product data analysis and product information analysis; for example, it can analyze product information of products manufactured within a certain time period or a certain batch of products. Specifically, this application embodiment obtains product parameter information of the product to be tested; determines the target product with a first product defect based on the product parameter information, and determines the target process in which the first product defect occurs in the target product; and determines the target production equipment corresponding to the first product defect based on the production equipment information corresponding to the target product, thereby improving the accuracy of locating the production equipment causing the product defect. In addition, this application embodiment stores the product parameter information and the production equipment information in the same distributed database. Therefore, when obtaining the product parameter information and the production equipment information, the information acquisition efficiency can also be improved, thereby improving the overall processing efficiency of the product information analysis process.

[0083] Specifically, such as Figure 1 As shown, the product information analysis method of this application embodiment may include the following steps:

[0084] S101: Obtain product parameter information of the product to be tested.

[0085] Optionally, the embodiments of this application can be applied to scenarios where product information of the product under test is analyzed. The product under test can include products manufactured in various fields and industries. For example, the product under test can include everyday items such as desks and chairs; it can also include electronic products such as mobile phones and tablets; furthermore, it can include component products, such as the desktop panel of a desk, electronic components of a mobile phone, and the display screen of a tablet computer. For ease of explanation, the embodiments of this application will subsequently use a liquid crystal display (LCD) screen as an example for description; however, it should be understood that this does not constitute a limitation on the embodiments of this application.

[0086] The product parameter information of the product to be tested may include production data, measurement data, and testing data.

[0087] Specifically, production data may include the production process identifiers, assembly line identifiers, and production equipment identifiers that the product under test goes through during production, as well as the time taken by the product under test to go through each production process, assembly line, and production equipment, etc.

[0088] Measurement data may include product characteristic parameters obtained by measuring the product under test; for example, measurement data may include data such as the length, width, thickness, voltage value, and current value of the product under test.

[0089] The testing data may include data information from the testing process of the product under test; for example, the testing data may include testing site identification, product images collected by the testing site, etc. Optionally, in some embodiments, the testing data may also include testing results, such as product defect type, product defect size, product defect location parameters, and other data information.

[0090] Optionally, in this embodiment, the product parameter information of the product under test can be the product parameter information of the product under test within a target time period. For example, the product parameter information may include the product parameter information of the product under test produced on a certain day; in this way, the product parameter information can be used to analyze the product under test produced in the target time period.

[0091] S102: Determine the target product with the first product defect based on the product parameter information, and determine the target process in which the first product defect occurs in the target product.

[0092] Specifically, the first product defect includes product defects in the product under test. Taking an LCD display as an example, the first product defect can include defects such as breakage, foreign objects, uneven film thickness, and broken lines in the circuit board.

[0093] When identifying a target product, the product parameter information can be analyzed to determine the product with the first product defect. This analysis can be performed in various ways: for example, the measured data in the product parameter information can be compared with preset product parameter thresholds to select defective product parameters that do not meet the threshold requirements; then, the type of product defect corresponding to the defective product parameter can be determined; and finally, the target product with the first product defect can be identified among the products to be tested. Alternatively, image processing algorithms can be used to perform image recognition on the product image in the product parameter information to determine what type of product defect exists in the product image; and finally, the target product with the first product defect can be identified among the products to be tested.

[0094] Furthermore, after identifying the target product with the first product defect, the target process in which the first product defect occurs can be determined. For example, the product parameter information may include the production process identifiers that the product under test has gone through, the time that the product under test has gone through each production process, the measurement data and test data of the product under test corresponding to each production process, etc. In this way, the target process in which the first product defect occurs can be determined based on the product parameter information.

[0095] It should be noted that the target process may include one or multiple production processes. That is, the target product may have a first product defect in one production process or in multiple production processes. Furthermore, when a first product defect occurs in multiple production processes, the defect severity of the first product defect in each process may differ.

[0096] like Figures 2 to 4 As shown, taking an LCD display screen with foreign object defects as an example, the foreign object defects appeared in multiple production processes of this LCD display screen. Figure 2 This is a schematic diagram of product defects in the previous process. The defect level in this process is relatively high (the foreign object area at location A is relatively large), but after repair, the defect level is reduced in the subsequent process (e.g., Figure 3 , Figure 4 As shown, the foreign object at location A has a smaller area.

[0097] S103: Determine the target production equipment corresponding to the defect of the first product based on the production equipment information corresponding to the target product;

[0098] The target production equipment includes the production equipment in the target process.

[0099] The product parameter information and the production equipment information are stored in the same distributed database.

[0100] Specifically, the target production equipment includes the production equipment in the target process. It should be noted that in some target processes, although the target product has a first product defect, due to reasons such as a low defect level or equipment malfunction, it is possible that the defect is missed during detection; that is, the first product defect is not detected in that target process. However, in this embodiment, the target production equipment may include the production equipment in the target process where the first product defect is detected, or it may include the production equipment in the target process where the first product defect is not detected.

[0101] Production equipment information can include details about the production equipment itself, such as its structure, components, operating parameters, and maintenance status.

[0102] Based on the production equipment information corresponding to the target product, the target production equipment corresponding to the defect of the first product can be determined.

[0103] For example, the production equipment can be identified as the target equipment causing the first product defect by checking whether its operating parameters are abnormal. Alternatively, the shape of the components of the production equipment can be compared with the shape of the damaged area of ​​the target product to determine if the production equipment is the target equipment causing the first product defect. Furthermore, in some embodiments, the target production equipment causing the first product defect can be determined based on a decision tree algorithm using the production equipment information corresponding to the target product.

[0104] It should be noted that, in this embodiment, the product parameter information and the production equipment information are stored in the same distributed database. This avoids the situation where storing product parameter information and production equipment information in different databases would lead to low data retrieval efficiency and affect the efficiency of product information analysis.

[0105] This application embodiment obtains product parameter information of the product under test; determines the target product with a first product defect based on the product parameter information, and determines the target process in which the first product defect occurs in the target product; and determines the target production equipment corresponding to the first product defect based on the production equipment information corresponding to the target product, thereby improving the accuracy of locating the production equipment causing the product defect. Furthermore, this application embodiment stores the product parameter information and the production equipment information in the same distributed database; therefore, when obtaining the product parameter information and the production equipment information, the information acquisition efficiency can be improved, thereby also improving the overall processing efficiency of the product information analysis process.

[0106] In another embodiment of this application, determining the target product with a first product defect based on the product parameter information includes:

[0107] The product parameter information is parsed to determine the first defect information, which includes the defect type.

[0108] A target product is identified as having the first product defect, wherein the defect type includes the first product defect.

[0109] Specifically, the first defect information can be used to characterize the defect information obtained by parsing the product parameter information. Optionally, the first defect information may include defect type, defect size, defect shape, defect level, and other information.

[0110] After determining the first defect information of the product under test, the target products containing the first defect can be identified based on the defect type. For example, target products containing a broken wire defect, target products containing a foreign object defect, etc., can be identified separately.

[0111] When parsing the product parameter information, the parsing method can be divided into the following cases depending on the different product parameter information:

[0112] In another embodiment of this application, when the product parameter information includes product characteristic parameters such as measurement data, the product parameter information can be compared with a preset product parameter threshold to determine defective product parameters that do not meet the preset product parameter threshold; then, the first defect information can be determined based on the defective product parameters.

[0113] For example, taking an LCD display as the product under test, the cell thickness is the thickness of the gap between the two substrates of the liquid crystal cell. If the cell thickness of the LCD display is too small, it may cause a black gap defect. Therefore, in this embodiment, when the cell thickness is less than a preset cell thickness threshold, it can be determined that the product under test has a black gap defect.

[0114] In another embodiment of this application, when the product parameter information includes product image information, the product image information can be identified by an image processing algorithm to determine the first defect information.

[0115] For example, Figure 2 The image shows a product image of a product under test. Through image processing algorithms, a foreign object defect is found at position A in the product image.

[0116] In another embodiment of this application, the product parameter information includes second defect information.

[0117] The step of determining the target product with a first product defect based on the product parameter information includes:

[0118] Based on the second defect information, a target product with the first product defect is identified;

[0119] The second defect information includes product defect information identified based on a neural network model.

[0120] Specifically, the second defect information may include product defect information identified based on a neural network model. For example, the second defect information may be product defect information identified by a neural network model based on measurement data (such as product characteristic parameters) and / or product images of the product under test.

[0121] Optionally, the second defect information may include the defect type, as well as information such as defect size, defect shape, and defect level.

[0122] After determining the second defect information of the product under test, the target products containing the first product defect can be identified based on the defect type of the product under test. For example, target products containing a broken wire defect, target products containing a foreign object defect, etc., can be identified separately.

[0123] In another embodiment of this application, the method further includes:

[0124] The first defect information is compared with the second defect information to obtain the comparison result;

[0125] The comparison results are fed back to the neural network model so that the model parameters of the neural network model can be adjusted according to the comparison results.

[0126] Specifically, the first defect information is obtained by analyzing product characteristic parameters or product images, and the second defect information is obtained by identifying product defects based on a neural network model. In this embodiment, the first defect information can be corrected by comparing it with the second defect information; the comparison result can also be fed back to the neural network model so that the model parameters of the neural network model can be adjusted according to the comparison result, thereby improving the recognition accuracy of the neural network model.

[0127] In addition, combined Figure 5As shown, in another embodiment of this application, the second defect information may include product defect information of the target product in the first process, and the first defect information may include product defect information of the target product in the second process. The second process includes a production process located after the first process in the product manufacturing process. In this way, the second defect information corresponding to the preceding process can be verified and checked using the first defect information corresponding to the subsequent process to verify whether the defect information detected by the preceding process is accurate. For example, it can verify whether the detection results of the preceding process are over-tested or under-tested. Furthermore, it can also analyze the repair status of defects in the subsequent process.

[0128] In another embodiment of this application, determining the target production equipment corresponding to the first product defect based on the production equipment information corresponding to the target product includes:

[0129] Decision tree algorithm based on the production equipment information corresponding to the target product;

[0130] Determine the target production equipment corresponding to the root node in the decision tree.

[0131] A decision tree represents the tree structure of a decision set. The generated decision tree can classify data information to achieve the purpose of prediction.

[0132] Specifically, based on the production equipment information corresponding to the target product, a decision tree algorithm is used to generate a decision tree. In the generated decision tree, for the root node and each branch node, the production equipment corresponding to the root node is the production equipment that has the greatest impact on the defect of the first product. Therefore, through the decision tree, the production equipment corresponding to the root node in the decision tree is determined to be the target production equipment.

[0133] In another embodiment of this application, the target process of determining that the target product has the first product defect includes:

[0134] Obtain the first position parameter of the defect location of the first product on the target product;

[0135] Determine the first quantity of the target products whose first position parameters satisfy the preset position requirements;

[0136] If the first quantity accounts for a first proportion of the number of products to be tested, which is greater than a first preset threshold, a third process is determined to be the first occurrence of the first product defect in the target product, and the target process includes the third process.

[0137] Specifically, the first position parameter may include the coordinates of the location of the first product defect on the target product.

[0138] For products under test, such as products in the same batch, if the coordinate values ​​of product defects of multiple products under test are within the preset coordinate range, or the difference between the coordinate values ​​of product defects of multiple products under test is less than the preset spacing threshold, then it can be considered that multiple products under test have the same product defect.

[0139] For example, the location coordinates of the damage to product A are (10, 20), the location coordinates of the damage to product B are (10.1, 20.2), and the location coordinates of the damage to product C are (9.9, 20.1). Since the difference between the location coordinates of the product defects of products A, B, and C is less than the preset spacing threshold (e.g., the preset spacing threshold is 1), it can be considered that the defects of products A, B, and C are the same defect.

[0140] Furthermore, in a real-world scenario, if the first proportion of the number of target products with the same defect to the number of products under test is greater than a first preset threshold, the defect can be analyzed to determine the cause of the defect.

[0141] Specifically, the third process in which the first product defect first appears in the target product can be determined. For example, if the first product defect appears in processes b, c, and d of the target product, and process b is located before processes c and d in the product manufacturing process, then process b is the process in which the first product defect first appears in the target product.

[0142] In another embodiment of this application, determining the target production equipment corresponding to the first product defect based on the production equipment information corresponding to the target product includes:

[0143] Among the production lines corresponding to the third process and the fourth process, the target production line is determined;

[0144] The target production equipment is determined based on the production equipment information of the candidate production equipment on the target production line;

[0145] The fourth process includes a production process in the product manufacturing process that precedes the third process, and the number of target products corresponding to the target production line is greater than a second preset threshold.

[0146] Specifically, since the third process is the process in which the first product defect first appears in the target product, it is understandable that the production equipment that caused the first product defect may be located in the third process or in the fourth process before the third process.

[0147] In this embodiment, the third and fourth processes may each include multiple production lines. The target production line is the production line containing the equipment that caused the defect in the first product. Specifically, the production line where the quantity of the target product exceeds a second preset threshold can be defined as the target production line.

[0148] Furthermore, the target production equipment can be determined based on the production equipment information of the candidate production equipment on the target production line.

[0149] Candidate production equipment includes the production equipment on the target production line.

[0150] Production equipment information can include details about the production equipment itself, such as its structure, components, operating parameters, and maintenance status.

[0151] In another embodiment of this application, the occurrence time of the first product defect of the target product includes a first time range.

[0152] The step of determining the target production equipment based on the production equipment information of candidate production equipment on the target production line includes:

[0153] Obtain the operating parameters from the production equipment information of the candidate production equipment;

[0154] Identify the target production equipment whose operating parameters are abnormal within the first time range.

[0155] Specifically, the first timeframe refers to the timeframe within which the target product develops its first product defect. Operating parameters can include characteristic parameters of the production equipment during operation, such as current, voltage, pressure, and liquid flow rate.

[0156] Since abnormal operation of production equipment can cause defects in the products produced, if the operating parameters of a candidate production equipment are abnormal within the first time frame, the candidate production equipment with abnormal operating parameters can be identified as the target production equipment causing the first product defect.

[0157] In another embodiment of this application, the shape information of the first product defect includes first shape information.

[0158] The step of determining the target production equipment based on the production equipment information of candidate production equipment on the target production line includes:

[0159] Obtain the second position parameter of the defect location of the first product on the production table of the candidate production equipment;

[0160] Obtain the component shape information corresponding to the second position parameter from the production equipment information of the candidate production equipment;

[0161] Identify a target production equipment whose component shape information matches the first shape information.

[0162] Specifically, the first shape information is the shape information of the first product defect.

[0163] The second location parameter may include the coordinates of the location of the first product defect on the production table of the candidate production equipment.

[0164] In real-world scenarios, contact between components on production equipment and the manufactured products, or the squeezing of the manufactured products, can cause defects in the products. Therefore, if the shape information of the component corresponding to the second position parameter on the production table of the candidate production equipment is consistent with the first shape information, then the candidate production equipment can be identified as the target production equipment causing the first product defect.

[0165] It should be noted that in some embodiments, the coordinates of the first product defect location on the target product are different from the coordinates of the defect location on the production table of the candidate production equipment. For example, the target product may be a part of the product produced by the candidate production equipment; that is, the candidate production equipment produces a larger screen panel, while the target product is a smaller screen panel cut from the larger screen panel. In this case, when determining the second position parameter, it is necessary to convert the coordinates of the defect location on the target product to the coordinates on the production table of the candidate production equipment based on the position of the target product on the production table of the candidate production equipment.

[0166] This application embodiment obtains product parameter information of the product under test; determines the target product with a first product defect based on the product parameter information, and determines the target process in which the first product defect occurs in the target product; and determines the target production equipment corresponding to the first product defect based on the production equipment information corresponding to the target product, thereby improving the accuracy of locating the production equipment causing the product defect. Furthermore, this application embodiment stores the product parameter information and the production equipment information in the same distributed database; therefore, when obtaining the product parameter information and the production equipment information, the information acquisition efficiency can be improved, thereby also improving the overall processing efficiency of the product information analysis process.

[0167] The following is combined with Figure 6 and Figure 7 The system architecture and interaction sequence of the product information analysis in this application are described below:

[0168] like Figure 6As shown, the product information analysis method of this application can be applied to the server side, that is... Figure 6 The server module shown is used in real-world scenarios. Responding to instructions from the client to perform product information analysis, the server module can retrieve product parameter information and production equipment information from a distributed database (such as HBase), and perform product information analysis based on this information. Specifically, the server module can determine the target product with a first product defect and the target process in which the first product defect occurs based on the product parameter information; it can also determine the target production equipment corresponding to the first product defect based on the production equipment information of the target product; and it feeds back the results of the product information analysis, i.e., the determined target production equipment, to the client.

[0169] In this process, product parameter information and production equipment information stored in the distributed database are extracted from the manufacturing system using ETL (Extract-Transform-Load) tools (such as Kettle, Pentaho, and Sqoop) and stored in a Hive data warehouse within a distributed database (such as Hadoop). The data in the Hive data warehouse is then written into the HBase database. In the embodiments of this application, the manufacturing system may include systems such as YMS (Yield Management System), FDC (Fault Detection & Classification), and MES (Manufacturing Execution System). HBase (Hadoop Database) is a Hadoop database, a highly reliable, high-performance, column-oriented, scalable distributed open-source database. HBase allows for the construction of large-scale structured storage clusters on ordinary servers, enabling the processing of massive data tables. Thus, during product information analysis, the server module can directly obtain product parameter information and production equipment information from HBase, improving the efficiency of information retrieval.

[0170] This application provides a product information analysis device, such as... Figure 8 As shown, the device 80 may include: an acquisition module 801, a first determination module 802, and a second determination module 803, wherein,

[0171] The acquisition module is used to acquire product parameter information of the product under test.

[0172] The first determining module is used to determine, based on the product parameter information, a target product with a first product defect, and to determine the target process in which the first product defect occurs in the target product.

[0173] The second determining module is used to determine the target production equipment corresponding to the defect of the first product based on the production equipment information corresponding to the target product.

[0174] The target production equipment includes the production equipment in the target process.

[0175] The product parameter information and the production equipment information are stored in the same distributed database.

[0176] In another embodiment of this application, the first determining module is specifically used for:

[0177] The product parameter information is parsed to determine the first defect information, which includes the defect type.

[0178] A target product is identified as having the first product defect, wherein the defect type includes the first product defect.

[0179] In another embodiment of this application, the first determining module is specifically used for:

[0180] The product parameter information is compared with a preset product parameter threshold to determine defective product parameters that do not meet the preset product parameter threshold.

[0181] The first defect information is determined based on the parameters of the defective product.

[0182] In another embodiment of this application, the product parameter information includes product image information.

[0183] The first determining module is specifically used to: identify the product image information through an image processing algorithm to determine the first defect information.

[0184] In another embodiment of this application, the product parameter information includes second defect information.

[0185] The first determining module is specifically used to: determine the target product that has the first product defect based on the second defect information;

[0186] The second defect information includes product defect information identified based on a neural network model.

[0187] In another embodiment of this application, the device further includes a correction module for comparing the first defect information with the second defect information to obtain a comparison result;

[0188] The comparison results are fed back to the neural network model so that the model parameters of the neural network model can be adjusted according to the comparison results.

[0189] In another embodiment of this application, the second defect information includes product defect information of the target product in the first process.

[0190] The first defect information includes product defect information of the target product in the second process.

[0191] The second process is a production process that follows the first process in the product manufacturing process.

[0192] In another embodiment of this application, the first determining module is specifically used for: a decision tree algorithm based on the production equipment information corresponding to the target product;

[0193] Determine the target production equipment corresponding to the root node in the decision tree.

[0194] In another embodiment of this application, the first determining module is specifically used to: obtain a first position parameter of the defect location of the first product defect on the target product;

[0195] Determine the first quantity of the target products whose first position parameters satisfy the preset position requirements;

[0196] If the first quantity accounts for a first proportion of the number of products to be tested that is greater than a first preset threshold, a third process is determined to be the first occurrence of the first product defect in the target product, and the target process includes the third process.

[0197] In another embodiment of this application, the second determining module is specifically used to: determine the target production line among the production lines corresponding to the third process and the fourth process;

[0198] The target production equipment is determined based on the production equipment information of the candidate production equipment on the target production line;

[0199] The fourth process includes a production process in the product manufacturing process that precedes the third process, and the number of target products corresponding to the target production line is greater than a second preset threshold.

[0200] In another embodiment of this application, the occurrence time of the first product defect of the target product includes a first time range.

[0201] The second determining module is specifically used to: obtain the operating parameters from the production equipment information of the candidate production equipment;

[0202] Identify the target production equipment whose operating parameters are abnormal within the first time range.

[0203] In another embodiment of this application, the shape information of the first product defect includes first shape information.

[0204] The second determining module is specifically used to: obtain a second position parameter of the defect location of the first product defect on the production table of the candidate production equipment;

[0205] Obtain the component shape information corresponding to the second position parameter from the production equipment information of the candidate production equipment;

[0206] Identify a target production equipment whose component shape information matches the first shape information.

[0207] The apparatus in this application embodiment can execute the method provided in this application embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0208] This application embodiment obtains product parameter information of the product under test; determines the target product with a first product defect based on the product parameter information, and determines the target process in which the first product defect occurs in the target product; and determines the target production equipment corresponding to the first product defect based on the production equipment information corresponding to the target product, thereby improving the accuracy of locating the production equipment causing the product defect. Furthermore, this application embodiment stores the product parameter information and the production equipment information in the same distributed database; therefore, when obtaining the product parameter information and the production equipment information, the information acquisition efficiency can be improved, thereby also improving the overall processing efficiency of the product information analysis process.

[0209] This application provides an electronic device comprising: a memory and a processor; at least one program stored in the memory, which, when executed by the processor, can achieve the following compared to existing technologies: This application acquires product parameter information of a product under test; determines a target product with a first product defect based on the product parameter information, and determines the target process in which the first product defect occurs in the target product; and determines the target production equipment corresponding to the first product defect based on the production equipment information corresponding to the target product, thereby improving the accuracy of locating the production equipment causing the product defect. Furthermore, this application stores the product parameter information and the production equipment information in the same distributed database; therefore, when acquiring the product parameter information and the production equipment information, the information acquisition efficiency can be improved, thereby also improving the overall processing efficiency of the product information analysis process.

[0210] In one alternative embodiment, an electronic device is provided, such as Figure 9 As shown, Figure 9 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of this application.

[0211] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0212] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0213] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0214] The memory 4003 stores application code (computer program) that executes the solution of this application, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0215] Electronic devices include, but are not limited to: mobile phones, laptops, multimedia players, desktop computers, etc.

[0216] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0217] This application embodiment obtains product parameter information of the product under test; determines the target product with a first product defect based on the product parameter information, and determines the target process in which the first product defect occurs in the target product; and determines the target production equipment corresponding to the first product defect based on the production equipment information corresponding to the target product, thereby improving the accuracy of locating the production equipment causing the product defect. Furthermore, this application embodiment stores the product parameter information and the production equipment information in the same distributed database; therefore, when obtaining the product parameter information and the production equipment information, the information acquisition efficiency can be improved, thereby also improving the overall processing efficiency of the product information analysis process.

[0218] The terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0219] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0220] The above description is only an optional implementation method for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application without departing from the technical concept of this application also fall within the protection scope of the embodiments of this application.

Claims

1. A product information analysis method, characterized in that, include: Obtain product parameter information of the product to be tested, wherein the product parameter information includes second defect information; The process of determining a target product with a first product defect based on the product parameter information, and determining a target process in which the first product defect occurs in the target product, includes: determining the target product with the first product defect based on second defect information; wherein the second defect information includes product defect information identified based on a neural network model; the process of determining the target process in which the first product defect occurs in the target product includes: obtaining a first position parameter of the defect location of the first product defect on the target product; determining a first number of target products whose first position parameter meets a preset position requirement; and determining a third process in which the first number accounts for a first percentage of the number of products to be tested that is greater than a first preset threshold, wherein the target process includes the third process. Based on the production equipment information corresponding to the target product, the target production equipment corresponding to the first product defect is determined. This determination includes: identifying a target production line among the production lines corresponding to the third and fourth processes; and determining the target production equipment based on the production equipment information of candidate production equipment on the target production line. The fourth process includes a production process preceding the third process in the product manufacturing flow, and the number of target products corresponding to the target production line is greater than a second preset threshold. The target production equipment includes the production equipment in the target process. The product parameter information and the production equipment information are stored in the same distributed database.

2. The product information analysis method according to claim 1, characterized in that, The step of determining the target product with a first product defect based on the product parameter information includes: The product parameter information is parsed to determine the first defect information, which includes the defect type. A target product is identified as having the first product defect, wherein the defect type includes the first product defect.

3. The product information analysis method according to claim 2, characterized in that, The step of parsing the product parameter information to determine the first defect information includes: The product parameter information is compared with a preset product parameter threshold to determine defective product parameters that do not meet the preset product parameter threshold. The first defect information is determined based on the parameters of the defective product.

4. The product information analysis method according to claim 2, characterized in that, The product parameter information includes product image information. The step of parsing the product parameter information to determine the first defect information includes: The product image information is identified using an image processing algorithm to determine the first defect information.

5. The product information analysis method according to claim 2, characterized in that, The method further includes: The first defect information is compared with the second defect information to obtain the comparison result; The comparison results are fed back to the neural network model so that the model parameters of the neural network model can be adjusted according to the comparison results.

6. The product information analysis method according to claim 2, characterized in that, The second defect information includes product defect information of the target product in the first process. The first defect information includes product defect information of the target product in the second process. The second process is a production process that follows the first process in the product manufacturing process.

7. The product information analysis method according to claim 1, characterized in that, The occurrence time of the first product defect of the target product includes a first time range. The step of determining the target production equipment based on the production equipment information of candidate production equipment on the target production line includes: Obtain the operating parameters from the production equipment information of the candidate production equipment; Identify the target production equipment whose operating parameters are abnormal within the first time range.

8. The product information analysis method according to claim 7, characterized in that, The shape information of the first product defect includes first shape information. The step of determining the target production equipment based on the production equipment information of candidate production equipment on the target production line includes: Obtain the second position parameter of the defect location of the first product on the production table of the candidate production equipment; Obtain the component shape information corresponding to the second position parameter from the production equipment information of the candidate production equipment; Identify a target production equipment whose component shape information matches the first shape information.

9. A product information analysis device, characterized in that, include: The acquisition module is used to acquire product parameter information of the product to be tested, the product parameter information including second defect information; A first determining module is configured to determine a target product with a first product defect based on the product parameter information, and to determine a target process in which the first product defect occurs in the target product. Specifically, the first determining module is configured to: determine a target product with the first product defect based on second defect information; wherein the second defect information includes product defect information identified based on a neural network model; and specifically, the first determining module is configured to: obtain a first position parameter of the defect location of the first product defect on the target product; determine a first number of target products whose first position parameter satisfies a preset position requirement; and if the first number accounts for a first percentage of the number of products to be tested that is greater than a first preset threshold, determine a third process in which the first product defect first occurs in the target product, wherein the target process includes the third process. The second determining module is used to determine the target production equipment corresponding to the defect of the first product based on the production equipment information corresponding to the target product; the second determining module is specifically used to: determine the target production line among the production lines corresponding to the third process and the production lines corresponding to the fourth process; determine the target production equipment based on the production equipment information of the candidate production equipment on the target production line; wherein, the fourth process includes a production process in the product production process that is located before the third process, and the number of target products corresponding to the target production line is greater than a second preset threshold; The target production equipment includes the production equipment in the target process. The product parameter information and the production equipment information are stored in the same distributed database.

10. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to: perform the product information analysis method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the product information analysis method according to any one of claims 1 to 8.

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