Product testing methods, apparatus, computer devices, and storage media

Abstract

This invention provides a product testing method, a product testing apparatus, a computer device, and a computer storage medium. The method includes: acquiring testing data of a product to be tested and generating a product testing data distribution map based on the testing data, wherein the testing data includes the product's external dimensions; determining whether the testing data distribution map is consistent with the feature information of a standard testing data distribution map in a database; if consistent, generating a prompt message indicating that the testing data of the product to be tested is qualified; if inconsistent, generating a prompt message indicating that the testing data of the product to be tested is abnormal. This method enables product testing to be performed in a more efficient and intelligent manner.

Description

Technical Field

[0001] This invention relates to the field of product testing, specifically to a product testing method, a product testing device, a computer device, and a computer storage medium. Background Technology

[0002] In the manufacturing process of electronic products, it is necessary to analyze the flatness of the product casing. The flatness of the casing directly affects the lifespan of the internal electronic components, and the higher the flatness, the better the user experience. Existing methods for detecting the flatness of product casings are inefficient and lack intelligence. Summary of the Invention

[0003] In view of the above, it is necessary to propose a product testing method, a product testing device, a computer device, and a computer storage medium, so that product testing can be carried out in a more efficient and intelligent manner.

[0004] A first aspect of this application provides a method for use in a product testing apparatus, the method comprising:

[0005] Acquire the test data of the product to be tested and generate a product test data distribution map based on the test data, wherein the test data includes the product's external dimensions;

[0006] Determine whether the distribution map of the detected data is consistent with the feature information of the standard distribution map of detected data in the database;

[0007] If they match, a notification message indicating that the test data of the product under test is qualified will be generated;

[0008] If there is a discrepancy, a message indicating that the test data of the product under test is abnormal will be generated.

[0009] Preferably, the method further includes:

[0010] The product detection data distribution map is compared with the feature information of the abnormal detection data distribution map in the database. Based on the comparison results, the abnormal cause corresponding to the feature information of the abnormal product is found and output.

[0011] Preferably, the product testing device includes at least one sensor, and the step of acquiring the testing data of the product to be tested and generating a product testing data distribution map based on the testing data includes:

[0012] Acquire the detection data of the product sent by the sensor, wherein the detection data includes the location information of the measurement point and the product appearance size data corresponding to the measurement point;

[0013] Based on the detection location information of the product to be tested, retrieve the product appearance dimension data of the measurement point corresponding to the detection location information from the detection data;

[0014] The detection location information and the corresponding product appearance size data are classified according to preset rules, and the same type of data is displayed in the product detection data distribution map according to the first preset symbol.

[0015] Preferably, the preset rules include:

[0016] The classification algorithm can be any one or more of the following: average-based classification algorithm, Bayesian-based classification algorithm, vector basis-based classification algorithm, and logistic regression-based classification algorithm.

[0017] Preferably, the method further includes:

[0018] The product detection data sent by the sensor is cleaned to remove abnormal product detection data;

[0019] The data cleaning process includes comparing the acquired product appearance size data with a first threshold. If the acquired appearance size data exceeds the range of the first threshold, the data is considered abnormal.

[0020] Preferably, the method for determining whether the detection data distribution map is consistent with the feature information of the standard detection data distribution map in the database includes:

[0021] Compare the first threshold corresponding to the detection data distribution map with the first threshold corresponding to the standard detection data distribution map;

[0022] If the absolute value of the difference between the first threshold corresponding to the detection data distribution map and the first threshold corresponding to the standard detection data distribution map is less than the first preset tolerance, then it is determined that the feature information of the detection data distribution map is consistent with that of the standard detection data distribution map in the database.

[0023] Preferably, the method for determining whether the detection data distribution map is consistent with the feature information of the standard detection data distribution map in the database further includes:

[0024] Obtain the distribution map of the data to be detected, and use image recognition methods to extract feature information from the distribution map of the data to be detected;

[0025] The feature information is compared with the feature information of the standard detection data distribution map stored in the database;

[0026] If they match, then the feature information of the detection data distribution map is determined to be consistent with that of the standard detection data distribution map.

[0027] A second aspect of this application provides a product testing apparatus, the apparatus comprising:

[0028] The acquisition module is used to acquire the test data of the product to be tested and generate a product test data distribution map based on the test data, wherein the test data includes the product's appearance dimensions;

[0029] The judgment module is used to determine whether the detection data distribution map is consistent with the feature information of the standard detection data distribution map in the database;

[0030] The first execution module is used to generate a prompt message indicating that the test data of the product to be tested is qualified when the test data distribution map is consistent with the feature information of the standard test data distribution map in the database.

[0031] The second execution module is used to generate a prompt message indicating that the test data of the product to be tested is abnormal when the feature information of the test data distribution map is inconsistent with that of the standard test data distribution map in the database.

[0032] A third aspect of this application provides a computer device including a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the product testing method as described above.

[0033] A fourth aspect of this application provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the product testing method as described above.

[0034] The product testing method, device, computer, and storage medium of this invention generate a product testing data distribution map by acquiring testing data of the product to be tested. The method then determines whether the distribution map matches the characteristic information of a standard testing data distribution map in a database. If they match, a message indicating that the product's testing data is qualified is generated; otherwise, a message indicating that the product's testing data is abnormal is generated. This method can quickly and accurately detect abnormal product testing data. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the application environment architecture of the product testing method provided in Embodiment 1 of the present invention.

[0036] Figure 2 This is a flowchart of the product testing method provided in Embodiment 2 of the present invention.

[0037] Figure 3 This is an example diagram of a detection data distribution map provided in Embodiment 2 of the present invention.

[0038] Figure 4 This is an example diagram of another detection data distribution map provided in Embodiment 2 of the present invention.

[0039] Figure 5This is a standard detection data distribution diagram provided in Embodiment 2 of the present invention.

[0040] Figure 6 This is a schematic diagram of the product testing device provided in Embodiment 3 of the present invention.

[0041] Figure 7 This is a schematic diagram of a computer device provided in Embodiment 4 of the present invention. Detailed Implementation

[0042] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0043] Numerous specific details are set forth in the following description to provide a thorough understanding of the invention. The described embodiments are merely some, not all, of the embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0045] Example 1

[0046] See Figure 1 The diagram shown illustrates the application environment architecture of the product testing method provided in Embodiment 1 of the present invention. The product testing method of the present invention is applied in a computer device 1, and the computer device 1 and at least one production device 2 establish a communication connection via a network.

[0047] The production equipment 2 is used to acquire testing data of the product during the production process. This testing data includes, but is not limited to, the product's external dimensions and key processing parameters. The production equipment 2 can be multiple pieces of equipment located in different production workshops and processes, allowing the computer device 1 to acquire different testing data from each workshop and process. The production equipment 2 can be a testing machine specifically used by the manufacturer to test product quality, or it can be a production machine used to manufacture the product. In other embodiments, the production equipment can also be specific testing equipment used by a third-party testing organization to test product quality.

[0048] The computer device 1 is used to determine whether the acquired test data is normal. If the test data is abnormal, the computer device 1 queries and outputs the cause of the abnormality. The computer device 1 can be an electronic device with product testing software installed, such as a personal computer or an online testing machine with computing and storage functions.

[0049] The network can be a wired network or a wireless network, such as radio, Wireless Fidelity (WIFI), cellular, satellite, broadcast, etc.

[0050] In another embodiment of the present invention, the computer device 1, at least one production device 2, and the cloud server 3 establish a communication connection via a network. The production device 2 uploads the acquired testing data to the cloud server 3, and the computer device 1 retrieves the required testing data from the cloud server 3 according to preset query conditions.

[0051] Example 2

[0052] Please see Figure 2 The diagram shown is a flowchart of a product testing method provided in the second embodiment of the present invention. The order of the steps in the flowchart can be changed, and some steps can be omitted, depending on different requirements.

[0053] Step S1: Obtain the test data of the product to be tested and generate a product test data distribution map, wherein the test data is the appearance dimensions of the product.

[0054] The specific steps of step S1 include:

[0055] (1) Acquire the detection data of the product sent by the sensor, wherein the detection data includes the location information of the measurement point and the product appearance dimension data corresponding to the measurement point. The product appearance dimension data includes the processing dimensions of the product, the flatness information of the product surface, etc. The flatness information is obtained by measuring the distance of the product surface from the specified position. If the distance of multiple detection positions on the product surface from the specified position is within the standard distance range, it indicates that the flatness of the product surface meets the design requirements.

[0056] The method for acquiring detection data of the product to be tested includes: acquiring detection data sent by sensors located at preset positions in the production equipment 2. Computer device 1 receives the detection data sent by the sensors and stores the detection data and measurement positions corresponding to the sensor's measurement position. For example, if the sensor is a photosensitive sensor, the sensor calculates the distance from the sensor to the product surface by receiving the reflection time of reflected light and the speed of light propagation, and sends the acquired distance information to computer device 1. There is a correspondence between the number of sensors and the detection positions. This correspondence can be one sensor measuring detection data at one detection position, or one sensor measuring detection data at multiple detection positions. For example, if the surface of the product to be tested has 24 detection positions, one sensor can sense the detection data of three adjacent detection positions.

[0057] In another embodiment of the present invention, the step further includes cleaning the product detection data sent by the sensor and removing abnormal product detection data;

[0058] The cleaning method includes: comparing the obtained product appearance size data with a first threshold; if the obtained appearance size data is not within the range of the first threshold, then the data is abnormal data.

[0059] (2) The computer device 1 searches for the product appearance size data of the measurement point corresponding to the detection location information from the detection data based on the detection location information of the product to be detected.

[0060] For example, if there are 100 detection points on the product surface, it is necessary to analyze the measurement data from the 20th to the 80th detection points. Computer device 1 queries the corresponding sensor's detection data based on the measurement point and stores the detection data corresponding to the measurement point.

[0061] (3) Classify the detection location information and the product appearance size data corresponding to the detection location information according to the preset rules, and display the same type of data in the product detection data distribution map according to the first preset symbol.

[0062] The preset rules include any one or more of the following: classification algorithms based on average values, classification algorithms based on Bayes' theorem, classification algorithms based on vector basis, and classification algorithms based on logistic regression.

[0063] For example, in one embodiment, the acquired detection data is classified according to different detection locations. At least two detection data points per unit area are grouped into one category, and the average value of the at least two detection data points is taken. Data in the same category are represented by the same color in the product detection data distribution map. Figure 3As shown in the figure, the same interval represents the measurement data of the same interval.

[0064] In another embodiment, the measurement data is divided into different intervals according to the data range, data within the same interval are grouped together, and the distribution is described in the form of contour lines on the product inspection data distribution map, such as... Figure 4 As shown in the figure, the contour lines represent different measurement data for the same interval.

[0065] Step S2: Determine whether the distribution map of the detection data is consistent with the feature information of the standard distribution map of the detection data in the database.

[0066] The method for determining whether the detection data distribution map is consistent with the feature information of the standard detection data distribution map in the database may be:

[0067] The first threshold corresponding to the detection data distribution map is compared with the first threshold corresponding to the standard detection data distribution map. The first threshold corresponding to the detection data distribution map is the difference between the maximum and minimum values ​​in the detection data distribution map. The first threshold corresponding to the standard detection data distribution map is also the difference between the maximum and minimum values ​​in the standard detection data distribution map.

[0068] If the absolute value of the difference between the first threshold corresponding to the detection data distribution map and the first threshold corresponding to the standard detection data distribution map is less than the first preset tolerance, then the feature information of the detection data distribution map is consistent with that of the standard detection data distribution map in the database.

[0069] The method for determining whether the detection data distribution map is consistent with the feature information of the standard detection data distribution map in the database can also be:

[0070] Obtain the distribution map of the data to be detected, and extract the feature information from the distribution map using an image recognition method. The image recognition method includes any one of the following: a neural network-based image recognition algorithm, a wavelet operation-based image recognition algorithm, or a fractal feature-based image recognition algorithm.

[0071] The feature information is compared with the feature information of the standard detection data distribution map stored in the database;

[0072] If they match, then the feature information of the detection data distribution map is consistent with that of the standard detection data distribution map.

[0073] like Figure 5 The standard test data distribution map shown illustrates how different contour line distributions represent measurement characteristics of a product dimension. For example... Figure 5The "tile-shaped" standard test data distribution chart in the diagram represents the movement trajectory of the cutting tool during the product manufacturing process under normal circumstances. Each standard test data distribution chart is related to the product's manufacturing process, such as the surface flatness of the product under different cutting tool models and different surface flatness of the product under different cutting tool speeds.

[0074] Step S3: If they match, a notification message indicating that the test data of the product to be tested is qualified is generated.

[0075] In one embodiment of the present invention, the notification message can be sent via text notification messages such as email, instant reminder messages, or SMS. In another embodiment, the notification message can be sent via voice notification messages such as microphone or ringtone.

[0076] Step S4: If there is a discrepancy, a prompt message indicating that the test data of the product to be tested is abnormal will be generated.

[0077] In one embodiment of the present invention, the product testing method further includes, when abnormal testing data occurs, comparing the product testing data distribution map with the feature information of the abnormal testing data distribution map in the database, and finding and outputting the abnormal cause corresponding to the feature information of the abnormal product based on the comparison result.

[0078] The above Figure 2-5 The product testing method of the present invention has been described in detail. The functional modules of the software device for implementing the product testing method and the hardware device architecture for implementing the product testing method are described below with reference to Figures 6-7.

[0079] As you may understand, the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0080] Example 3

[0081] Figure 6 This is a structural diagram of a preferred embodiment of the product testing device of the present invention.

[0082] In some embodiments, the product testing device 10 operates in a computer device. The computer device is connected to multiple user terminals via a network. The product testing device 10 may include multiple functional modules composed of program code segments. The program code of each program segment in the product testing device 10 may be stored in the memory of the computer device and executed by the at least one processor to implement the product testing function.

[0083] In this embodiment, the product testing device 10 can be divided into multiple functional modules according to the functions it performs. (See also...) Figure 6As shown, the functional modules may include: an acquisition module 101, a judgment module 102, a first execution module 103, and a second execution module 104. The module referred to in this invention is a series of computer program segments that can be executed by at least one processor and perform a fixed function, and which are stored in memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0084] The acquisition module 101 is used to acquire the test data of the product to be tested and generate a product test data distribution map based on the test data, wherein the test data includes the appearance dimensions of the product.

[0085] The judgment module 102 is used to determine whether the detection data distribution map is consistent with the feature information of the standard detection data distribution map in the database.

[0086] The first execution module 103 is used to generate a prompt message indicating that the test data of the product to be tested is qualified when the feature information of the test data distribution map is consistent with the feature information of the standard test data distribution map in the database.

[0087] The second execution module 104 is used to generate a prompt message indicating that the test data of the product to be tested is abnormal when the feature information of the test data distribution map is inconsistent with that of the standard test data distribution map in the database.

[0088] Example 4

[0089] Figure 7 This is a schematic diagram of a preferred embodiment of the computer device of the present invention.

[0090] The computer device 1 includes a memory 20, a processor 30, and a computer program 40 stored in the memory 20 and executable on the processor 30, such as a product testing program. When the processor 30 executes the computer program 40, it implements the steps described in the product testing method embodiments above, for example... Figure 2 Steps S1 to S4 are shown. Alternatively, when the processor 30 executes the computer program 40, it implements the functions of each module / unit in the above-described product testing device embodiment, for example... Figure 6 Units 101-104 in the text.

[0091] For example, the computer program 40 can be divided into one or more modules / units, which are stored in the memory 20 and executed by the processor 30 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, the instruction segments describing the execution process of the computer program 40 in the computer device 1. For example, the computer program 40 can be divided into... Figure 6 The module consists of an acquisition module 101, a judgment module 102, a first execution module 103, and a second execution module 104.

[0092] The computer device 1 may be a desktop computer, laptop, handheld computer, or cloud server, etc. Those skilled in the art will understand that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation on the computer device 1. It may include more or fewer components than shown, or combine certain components, or use different components. For example, the computer device 1 may also include input / output devices, network access devices, buses, etc.

[0093] The processor 30 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor 30 may be any conventional processor. The processor 30 is the control center of the computer device 1, connecting various parts of the computer device 1 via various interfaces and lines.

[0094] The memory 20 can be used to store the computer program 40 and / or modules / units. The processor 30 implements various functions of the computer device 1 by running or executing the computer program and / or modules / units stored in the memory 20 and calling the data stored in the memory 20. The memory 20 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device 1 (such as audio data, telephone book, etc.). In addition, the memory 20 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0095] If the modules / units integrated in the computer device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0096] In the several embodiments provided by this invention, it should be understood that the disclosed computer apparatus and method can be implemented in other ways. For example, the computer apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and other division methods may be used in actual implementation.

[0097] Furthermore, the functional units in the various embodiments of the present invention can be integrated into the same processing unit, or each unit can exist physically separately, or two or more units can be integrated into the same unit. The integrated units described above can be implemented in hardware or in the form of hardware plus software functional modules.

[0098] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or computer devices recited in the computer device claims may also be implemented by the same unit or computer device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A product inspection method applied to a product inspection apparatus, characterized by, The product testing device includes at least one sensor, and the method includes: Acquire detection data of the product to be tested and generate a product detection data distribution map based on the detection data, wherein the detection data includes the appearance dimensions of the product, including: acquiring the detection data of the product sent by the sensor, wherein the detection data includes the location information of the measurement point and the appearance dimension data of the product corresponding to the measurement point; Based on the detection location information of the product to be tested, retrieve the product appearance dimension data of the measurement point corresponding to the detection location information from the detection data; The detection location information and the corresponding product appearance size data are classified according to preset rules, and the same type of data is displayed in the product detection data distribution map according to a first preset symbol. This includes: classifying the acquired detection data according to different detection location information, grouping at least two detection data within a unit area into one category, averaging the at least two detection data, and representing the same type of data with the same color in the product detection data distribution map; dividing the measurement data into different intervals according to the data range, grouping the data in the same interval into one category, and describing it in the form of contour lines in the product detection data distribution map. Determining whether the detection data distribution map is consistent with the feature information of the standard detection data distribution map in the database includes: comparing a first threshold corresponding to the detection data distribution map with a first threshold corresponding to the standard detection data distribution map, wherein the first threshold corresponding to the detection data distribution map is the difference between the maximum and minimum values ​​in the detection data distribution map, and the first threshold corresponding to the standard detection data distribution map is the difference between the maximum and minimum values ​​in the standard detection data distribution map; if the absolute value of the difference between the first threshold corresponding to the detection data distribution map and the first threshold corresponding to the standard detection data distribution map is less than a first preset tolerance, then it is determined that the detection data distribution map is consistent with the feature information of the standard detection data distribution map in the database. If they match, a notification message indicating that the test data of the product under test is qualified will be generated; If there is a discrepancy, a message indicating that the test data of the product under test is abnormal will be generated.

2. The product inspection method according to claim 1, characterized by, The method further includes: The product detection data distribution map is compared with the feature information of the abnormal detection data distribution map in the database. Based on the comparison results, the abnormal cause corresponding to the feature information of the abnormal product is found and output.

3. The product testing method as described in claim 1, characterized in that, The preset rules include: The classification algorithm can be any one or more of the following: average-based classification algorithm, Bayesian-based classification algorithm, vector basis-based classification algorithm, and logistic regression-based classification algorithm.

4. The product testing method as described in claim 1, characterized in that, The method further includes: The product detection data sent by the sensor is cleaned to remove abnormal product detection data; The data cleaning process includes comparing the acquired product appearance size data with a first threshold. If the acquired appearance size data exceeds the range of the first threshold, the data is considered abnormal.

5. The product testing method as described in claim 1, characterized in that, The method for determining whether the detection data distribution map is consistent with the feature information of the standard detection data distribution map in the database further includes: Obtain the distribution map of the data to be detected, and use image recognition methods to extract feature information from the distribution map of the data to be detected; The feature information is compared with the feature information of the standard detection data distribution map stored in the database; If they match, then the feature information of the detection data distribution map is determined to be consistent with that of the standard detection data distribution map.

6. A product testing device, characterized in that, The product testing device includes at least one sensor, and the device includes: The acquisition module is used to acquire the detection data of the product to be tested and generate a product detection data distribution map based on the detection data. The detection data includes the appearance dimensions of the product and includes: acquiring the detection data of the product sent by the sensor, wherein the detection data includes the location information of the measurement point and the appearance dimension data of the product corresponding to the measurement point. Based on the detection location information of the product to be tested, retrieve the product appearance dimension data of the measurement point corresponding to the detection location information from the detection data; The detection location information and the corresponding product appearance size data are classified according to preset rules, and the same type of data is displayed in the product detection data distribution map according to a first preset symbol. This includes: classifying the acquired detection data according to different detection location information, grouping at least two detection data within a unit area into one category, averaging the at least two detection data, and representing the same type of data with the same color in the product detection data distribution map; dividing the measurement data into different intervals according to the data range, grouping the data in the same interval into one category, and describing it in the form of contour lines in the product detection data distribution map. The judgment module is used to determine whether the detection data distribution map is consistent with the feature information of the standard detection data distribution map in the database, including: comparing a first threshold corresponding to the detection data distribution map with a first threshold corresponding to the standard detection data distribution map, wherein the first threshold corresponding to the detection data distribution map is the difference between the maximum and minimum values ​​in the detection data distribution map, and the first threshold corresponding to the standard detection data distribution map is the difference between the maximum and minimum values ​​in the standard detection data distribution map; if the absolute value of the difference between the first threshold corresponding to the detection data distribution map and the first threshold corresponding to the standard detection data distribution map is less than a first preset tolerance, then it is determined that the detection data distribution map is consistent with the feature information of the standard detection data distribution map in the database; The first execution module is used to generate a prompt message indicating that the test data of the product to be tested is qualified when the test data distribution map is consistent with the feature information of the standard test data distribution map in the database. The second execution module is used to generate a prompt message indicating that the test data of the product to be tested is abnormal when the feature information of the test data distribution map is inconsistent with that of the standard test data distribution map in the database.

7. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the product testing method as described in any one of claims 1-5.

8. A computer storage medium storing a computer program thereon, characterized in that: When the computer program is executed by the processor, it implements the product testing method as described in any one of claims 1-5.

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