Intelligent electronic product risk early warning system and method based on artificial intelligence

Abstract

The invention relates to the technical field of risk early warning, and discloses an intelligent electronic product risk early warning system and method based on artificial intelligence, and the system comprises an acquisition module which determines a target product image of a to-be-detected intelligent electronic product, and an analysis module which determines the target product image when identifying that the target product image is suspected to accord with calibration; a target product image is divided into a plurality of sub-quality detection areas, each sub-quality detection area is analyzed, the sub-quality detection areas are deleted according to an analysis result, a processing module extracts all quality pixel values of the remaining sub-quality detection areas after deletion, and the quality pixel values of the remaining sub-quality detection areas are calculated. Each two quality pixel values on the first quality sequence or the second quality sequence are used as to-be-associated quality pixels, whether the to-be-associated quality pixels are associated is verified based on the quality evaluation model, and the calculation module determines whether to perform early warning on the to-be-detected intelligent electronic product according to the comprehensive quality value. According to the invention, risk early warning is carried out on the intelligent electronic product through the artificial intelligence learning model, and the early warning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of risk warning technology, and in particular to an intelligent electronic product risk warning system and method based on artificial intelligence. Background Art

[0002] With the continuous advancement of science and technology, artificial intelligence (AI) technology has been widely used in various fields. The deep learning models and data analysis in artificial intelligence technology can effectively monitor and warn electronic products in real time, thereby assisting personnel to promptly detect potential problems and deal with them.

[0003] Chinese Patent Publication No.: CN118966893A discloses an intelligent electronic product quality safety risk monitoring and control system, including: a data acquisition unit, used to obtain the quality inspection data of the electronic product; a data processing unit, used to calculate the quality inspection score according to the quality inspection data, and determine the safety risk level according to the quality inspection score; a risk control unit, used to compare the safety risk level with the preset highest safety risk level threshold, when the safety risk level of the electronic product is higher than the highest safety risk level threshold, the risk control unit determines that the electronic product is unqualified and issues an early warning. It can be seen that when obtaining the quality inspection data of the electronic product, if the obtained quality inspection data is inaccurate, there is a risk of error in issuing an early warning, and whether there is a correlation between the data is not considered, which leads to the accumulation of early warning errors.

[0004] Therefore, it is necessary to design an intelligent electronic product risk warning system and method based on artificial intelligence to solve the problems existing in current technology. Summary of the invention

[0005] In view of this, the present invention proposes an intelligent electronic product risk warning system and method based on artificial intelligence, aiming to solve the risk of inaccurate detection data and erroneous warnings, and does not consider whether there is correlation between data, thereby leading to the problem of accumulation of warning errors.

[0006] In one aspect, the present invention proposes an intelligent electronic product risk early warning system based on artificial intelligence, comprising: An acquisition module is configured to acquire an initial product image of the intelligent electronic product to be detected, and analyze the initial product image to determine a target product image of the intelligent electronic product to be detected; an analysis module configured to analyze the target product image, generate a quality calibration for the intelligent electronic product to be detected according to the analysis result, divide the target product image into a plurality of sub-quality detection areas when a suspected conformity with the calibration is identified, analyze each sub-quality detection area, and delete the sub-quality detection area according to the analysis result; The processing module is configured to extract all quality pixel values ​​of the remaining sub-quality detection area after deletion, and compare the quality pixel values ​​with the corresponding quality standard pixel values, divide all the quality pixel values ​​according to the comparison result, construct a first quality number series and a second quality number series according to the division result, numerically sort all the quality pixel values ​​in the first quality number series and the second quality number series, and use every two quality pixel values ​​in the first quality number series or the second quality number series as quality pixels to be associated, and verify whether the quality pixels to be associated are associated based on the quality evaluation model, and count the number of associations according to the verification result; The calculation module is configured to determine the comprehensive quality value of the intelligent electronic product to be detected according to the associated quantity and the quality pixel value without association, and determine whether to issue an early warning for the intelligent electronic product to be detected according to the comprehensive quality value.

[0007] Furthermore, when parsing the initial product image to determine the target product image of the intelligent electronic product to be detected, it includes: The acquisition module preprocesses the initial product image, wherein the preprocessing includes image denoising, contrast adjustment, and image edge sharpening; Substituting the preprocessed initial product image into the convolutional neural network model, and outputting the image effective value of the preprocessed initial product image based on the convolutional neural network model; When the image validity value is greater than or equal to a preset image validity value, the initial product image is determined as the target product image of the intelligent electronic product to be detected; When the image validity value is less than the preset image validity value, the initial product image of the to-be-detected intelligent electronic product is reacquired.

[0008] Furthermore, when analyzing the target product image and generating a quality calibration for the intelligent electronic product to be inspected according to the analysis result, it includes: The analysis module extracts all target image pixels of the target product image, extracts all standard target image pixels corresponding to the target image pixels in the standard target product image, determines the target pixel values ​​corresponding to all target image pixels, and determines the standard target pixel values ​​corresponding to all standard target image pixels; When the target pixel values ​​are all equal to the standard target pixel values, a quality compliance calibration is generated for the intelligent electronic product to be inspected; When the target pixel values ​​are not equal to the standard target pixel values, a quality non-conformity calibration is generated for the intelligent electronic product to be detected; When one or more target pixel values ​​are equal to the standard target pixel value, and one or more target pixel values ​​are not equal to the standard target pixel value, a suspected compliance calibration is generated for the intelligent electronic product to be detected.

[0009] Furthermore, when each sub-quality detection area is parsed and the sub-quality detection area is deleted according to the parsing result, it includes: When there is a sub-quality detection region whose target pixel values ​​are all equal to the standard target pixel value, the sub-quality detection region is deleted, and each target pixel value of the remaining sub-quality detection region is used as a quality pixel value.

[0010] Further, when all quality pixel values ​​of the remaining sub-quality detection area after deletion are extracted, and the quality pixel values ​​are compared with corresponding quality standard pixel values, all quality pixel values ​​are divided according to the comparison result, and the first quality number series and the second quality number series are constructed according to the division result, it includes: The processing module extracts all quality pixel values ​​of the remaining sub-quality detection areas after deletion, and compares each quality pixel value with the corresponding quality standard pixel value; When the quality pixel value is greater than or equal to the quality standard pixel value, a first pixel difference between the quality pixel value and the quality standard pixel value is obtained, and the first quality sequence is constructed; When the quality pixel value is less than the quality standard pixel value, a second pixel difference between the quality pixel value and the quality standard pixel value is obtained, and the second quality sequence is constructed; Dividing first pixel differences of the same sub-mass detection area in the first mass number sequence into a first sub-mass number sequence, and dividing the remaining first pixel differences into a second sub-mass number sequence, wherein the first mass number sequence includes the second sub-mass number sequence and a plurality of the first sub-mass number sequences; The second pixel differences of the same sub-mass detection area in the second mass number sequence are divided into a third sub-mass number sequence, and the remaining second pixel differences are divided into a fourth sub-mass number sequence, wherein the second mass number sequence includes the fourth sub-mass number sequence and a plurality of the third sub-mass number sequences.

[0011] Furthermore, when all the quality pixel values ​​in the first quality number sequence and the second quality number sequence are numerically sorted, and every two quality pixel values ​​in the first quality number sequence or the second quality number sequence are used as quality pixels to be associated, the method includes: The processing module sorts the first pixel difference values ​​in each of the first sub-mass sequence by numerical value, and uses adjacent first pixel difference values ​​as first mass pixels to be associated; The processing module sorts the second pixel differences in each of the third sub-quality series by numerical value, and uses adjacent second pixel differences as second quality pixels to be associated.

[0012] Further, when verifying whether the quality pixels to be associated are associated based on the quality evaluation model and counting the number of associations according to the verification result, it includes: The processing module obtains historical quality pixel values ​​of historical target product images, quality standard pixel values ​​of standard target product images and historical pixel difference values, and constructs a pixel data set, and samples the pixel data set according to a preset ratio to obtain a pixel training set and a pixel test set; The processing module preselects a twin neural network model, iteratively trains the twin neural network model according to the pixel training set, and evaluates the iteratively trained twin neural network model according to the test subset; If the ternary loss of the twin neural network model after the current iterative training is greater than or equal to the ternary loss of the twin neural network model after the previous iterative training, the learning rate is adjusted using piecewise decay, and iterative training continues until the preset number of iterations is reached; If the ternary loss of the twin neural network model after the current iterative training is less than the ternary loss of the twin neural network model after the previous iterative training, the iterative training is stopped, and the twin neural network model after the current iterative training is used as the quality evaluation model, and the first quality pixel to be associated and the second quality pixel to be associated are respectively substituted into the quality evaluation model, and the model verification result is output; When the model verification result meets the preset model verification result, it is determined that the to-be-associated first quality pixels or the to-be-associated second quality pixels have pixel associations, and the number of associations with pixel associations is counted.

[0013] Further, when determining the comprehensive quality value of the intelligent electronic product to be detected according to the associated quantity and the unassociated quality pixel value, it includes: The calculation module determines the comprehensive quality value by the following formula: ; ; ; in, represents the quality of association, Indicates the number of associations, represents the number of the first sub-mass series, Indicates The largest first pixel difference in the first sub-mass sequence, Indicates The smallest first pixel difference in the first sub-mass sequence, represents the number of the third sub-mass series, Indicates The largest second pixel difference in the third sub-mass sequence, Indicates The smallest second pixel difference in the third sub-mass sequence, represents non-associated quality, represents the number of first pixel difference values ​​in the second sub-mass sequence, represents the maximum first pixel difference in the second sub-mass sequence, Indicates the number of the second sub-mass sequence The first pixel difference, represents the number of second pixel differences in the fourth sub-mass sequence, represents the maximum second pixel difference in the fourth sub-mass sequence, Indicates the fourth sub-mass sequence The second pixel difference, Represents the comprehensive quality value, It represents the weight coefficient, and the weight coefficient is determined according to the group of smart electronic products to be detected.

[0014] Furthermore, when determining whether to issue an early warning to the intelligent electronic product to be detected according to the comprehensive quality value, it includes: The comprehensive quality value and a pre-set comprehensive quality value threshold Performing a comparison, and determining whether to issue an early warning for the intelligent electronic product to be detected according to the comparison result; when Greater than or equal to When the detected intelligent electronic product is detected, it is determined to issue an early warning; when Less than When , it is determined that no warning is issued for the intelligent electronic product to be detected.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: by dividing the target product image into multiple sub-quality detection areas and performing analysis, comparison and division of quality pixel values, the quality problems of the intelligent electronic products to be detected are effectively identified, and the accuracy of risk identification is ensured; by extracting and screening out the sub-quality detection areas with problems, manual intervention is reduced, and at the same time, the intelligence and automation of risk warnings are improved, and the focus is effectively on the sub-quality detection areas with problems, and the analysis and processing processes are optimized; and, by utilizing the quality evaluation model in artificial intelligence to analyze the associated quality pixels, the comprehensive quality value can be accurately calculated, thereby issuing warnings in a timely manner, reducing the risk of non-compliant intelligent electronic products to be detected entering the market, and improving the accuracy and reliability of risk warnings for intelligent electronic products.

[0016] On the other hand, the present application also provides an intelligent electronic product risk warning method based on artificial intelligence, which is applied to the above-mentioned intelligent electronic product risk warning system based on artificial intelligence, including: Acquire an initial product image of the intelligent electronic product to be detected, and analyze the initial product image to determine a target product image of the intelligent electronic product to be detected; Analyze the target product image, generate a quality calibration for the intelligent electronic product to be detected according to the analysis result, divide the target product image into a plurality of sub-quality detection areas when a suspected product that meets the calibration is identified, analyze each sub-quality detection area, and delete the sub-quality detection area according to the analysis result; Extracting all quality pixel values ​​of the remaining sub-quality detection area after deletion, and comparing the quality pixel values ​​with the corresponding quality standard pixel values, dividing all quality pixel values ​​according to the comparison result, constructing a first quality number series and a second quality number series according to the division result, numerically sorting all quality pixel values ​​in the first quality number series and the second quality number series, and taking every two quality pixel values ​​in the first quality number series or the second quality number series as quality pixels to be associated, and verifying whether the quality pixels to be associated are associated based on the quality evaluation model, and counting the number of associations according to the verification result; The comprehensive quality value of the intelligent electronic product to be detected is determined according to the number to be associated and the quality pixel value that has not been associated, and whether to issue an early warning to the intelligent electronic product to be detected is determined according to the comprehensive quality value.

[0017] It is understandable that the above-mentioned artificial intelligence-based smart electronic product risk warning system and method have the same beneficial effects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A functional block diagram of an intelligent electronic product risk warning system based on artificial intelligence provided by an embodiment of the present invention; Figure 2 A flowchart of an intelligent electronic product risk warning method based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] In some embodiments of the present application, see Figure 1 As shown, an intelligent electronic product risk early warning system based on artificial intelligence includes: The acquisition module is configured to obtain an initial product image of the intelligent electronic product to be detected, and analyze the initial product image to determine a target product image of the intelligent electronic product to be detected.

[0021] The analysis module is configured to analyze the target product image, generate quality calibration for the intelligent electronic product to be inspected according to the analysis results, and when a suspected product that meets the calibration is identified, divide the target product image into multiple sub-quality inspection areas, analyze each sub-quality inspection area, and delete the sub-quality inspection area according to the analysis results.

[0022] The processing module is configured to extract all quality pixel values ​​of the remaining sub-quality detection area after deletion, and compare the quality pixel values ​​with the corresponding quality standard pixel values, divide all the quality pixel values ​​according to the comparison result, construct a first quality sequence and a second quality sequence according to the division result, numerically sort all the quality pixel values ​​in the first quality sequence and the second quality sequence, and use every two quality pixel values ​​in the first quality sequence or the second quality sequence as quality pixels to be associated, and verify whether the quality pixels to be associated are associated based on the quality evaluation model, and count the number of associations according to the verification result.

[0023] The calculation module is configured to determine the comprehensive quality value of the intelligent electronic product to be detected according to the associated quantity and the quality pixel value without association, and determine whether to issue an early warning for the intelligent electronic product to be detected according to the comprehensive quality value.

[0024] Specifically, the acquisition module obtains the initial product image of the smart electronic product to be tested through the image acquisition device, and uses the image algorithm and deep learning model to analyze it to obtain the target product image. This avoids the accumulation of errors in the target product image that lead to subsequent analysis errors, and ensures the accuracy and effectiveness of the target product image. The analysis module is responsible for analyzing the target product image to generate a quality calibration to determine whether the smart electronic product to be tested has risks. Risks include cracks or cracks on the surface of the smart electronic product to be tested. If there is a suspected compliance with the calibration, it is considered that the smart electronic product to be tested may have risks and further analysis is required. The target product image is divided into multiple sub-quality detection areas, preferably 20 sub-quality detection areas, which can be divided according to the actual size of the smart electronic product to be detected. The sub-quality detection areas are intelligently divided and deleted, which helps to optimize computing resources, and each sub-quality detection area is analyzed. The sub-quality detection area is deleted according to the analysis results, which avoids irrelevant areas from affecting the results of risk warning, and further improves the detection accuracy. The processing module extracts all quality pixel values ​​in the remaining sub-quality detection areas after deletion, and compares them with the corresponding standard quality pixel values, avoiding errors caused by human factors in the smart electronic products to be detected, and improving the accuracy of risk warning. All quality pixel values ​​are divided, and the first quality series and the second quality series are constructed respectively. The hierarchical division and numerical sorting make the quality pixel value distribution of the first quality series and the second quality series clearer, laying a foundation for the subsequent capture of the correlation between quality pixel values. Generally, when there are cracks or fissures on the surface of the smart electronic product to be tested, they may not be completely independent random fractures, but extend along the direction of stress concentration of the smart electronic product to be tested. Therefore, the quality pixel values ​​at the cracks or fissures will maintain a certain correlation. The quality evaluation model verifies whether there is a correlation between the quality pixels to be associated, which improves the intelligence and accuracy of the risk judgment of the smart electronic product to be tested. The calculation module calculates the comprehensive quality value of the smart electronic product to be tested based on the number of associations and the unassociated quality pixel values, so as to determine whether there is a risk in the smart electronic product to be tested. It not only pays attention to a single possible defect, but also considers the defects of the smart electronic product to be tested as a whole, ensuring the accuracy and comprehensiveness of the risk warning.

[0025] It can be understood that the automation level of risk warning is improved by dividing the quality pixel values ​​and verifying the quality evaluation model. The accuracy of risk warning is improved by analyzing the initial product image, dividing the sub-quality inspection areas and verifying the quality evaluation model, thereby avoiding the accumulation of errors caused by human factors or incorrect data, and thus improving the intelligence level of risk warning.

[0026] In some embodiments of the present application, when parsing the initial product image to determine the target product image of the smart electronic product to be detected, it includes: the acquisition module preprocesses the initial product image, the preprocessing includes image denoising, adjusting contrast and sharpening image edges, substituting the preprocessed initial product image into a convolutional neural network model, and outputting the image effective value of the preprocessed initial product image based on the convolutional neural network model. When the image effective value is greater than or equal to the preset image effective value, the initial product image is determined as the target product image of the smart electronic product to be detected; when the image effective value is less than the preset image effective value, the initial product image of the smart electronic product to be detected is reacquired.

[0027] Specifically, the acquisition module performs image denoising, contrast adjustment and edge sharpening on the initial product image, reducing the interference of noise on the subsequent determination of the target product image, enhancing the details of the initial product image, and enabling the convolutional neural network (CNN) model to extract image features and improve classification accuracy. The preset image effective value is preferably 0.7. The convolutional neural network model is trained in advance. During the training process, a large number of pre-processed initial product images are used for training. The convolutional neural network model includes multiple convolutional layers, pooling layers and fully connected layers. By training the convolutional neural network model, it can learn and capture the image features of the initial product image. According to the softmax function of the convolutional neural network model, the probability of the validity or invalidity of the image is output, that is, the image effective value, which lays the foundation for subsequent analysis, avoids the deviation of risk warning caused by errors in the target product image, and improves the accuracy of the target product image.

[0028] In some embodiments of the present application, when analyzing the target product image and generating quality calibration for the smart electronic product to be inspected based on the analysis results, it includes: the analysis module extracts all target image pixels of the target product image, and extracts all standard target image pixels corresponding to the target image pixels in the standard target product image, determines the target pixel values ​​corresponding to all target image pixels, and determines the standard target pixel values ​​corresponding to all standard target image pixels; when the target pixel values ​​are all equal to the standard target pixel values, the quality of the smart electronic product to be inspected meets the calibration; when the target pixel values ​​are all not equal to the standard target pixel values, the quality of the smart electronic product to be inspected does not meet the calibration; when there are one or more target pixel values ​​equal to the standard target pixel values ​​and there are one or more target pixel values ​​not equal to the standard target pixel values, the smart electronic product to be inspected is suspected to meet the calibration.

[0029] Specifically, the standard target product image is obtained based on the standard smart electronic product, and the target image pixels correspond to the standard target image pixels one-to-one, ensuring the correspondence between the target pixel value and the standard target pixel value. The quality compliance calibration indicates that the smart electronic product to be tested meets the standard, and the quality non-compliance calibration indicates that the smart electronic product to be tested does not meet the standard, and an early warning is directly issued. When a suspected compliance calibration is generated, it indicates that the smart electronic product to be tested may or may not meet the standard, and further analysis and processing are required to avoid missed judgments.

[0030] In some embodiments of the present application, when each sub-quality detection area is parsed and the sub-quality detection area is deleted according to the parsing result, it includes: when there are target pixel values ​​in the sub-quality detection area that are all equal to the standard target pixel value, the sub-quality detection area is deleted, and each target pixel value of the remaining sub-quality detection area is used as a quality pixel value.

[0031] Specifically, when all target pixel values ​​in a sub-quality detection area are consistent with the standard target pixel values, it means that the sub-quality detection area meets the standards in the standard target product image, and no further analysis is required. The sub-quality detection area is directly deleted, and only the sub-quality detection area with differences is retained for subsequent processing, which reduces the impact of regional redundancy on subsequent analysis.

[0032] In some embodiments of the present application, when extracting all quality pixel values ​​of the remaining sub-quality detection area after deletion, and comparing the quality pixel values ​​with the corresponding quality standard pixel values, dividing all the quality pixel values ​​according to the comparison results, and constructing the first quality sequence and the second quality sequence according to the division results, it includes: a processing module extracts all quality pixel values ​​of the remaining sub-quality detection area after deletion, and compares each quality pixel value with the corresponding quality standard pixel value, when the quality pixel value is greater than or equal to the quality standard pixel value, then obtaining a first pixel difference between the quality pixel value and the quality standard pixel value, and constructing the first quality sequence, when the quality pixel value is greater than or equal to the quality standard pixel value When the pixel value is less than the quality standard pixel value, a second pixel difference between the quality pixel value and the quality standard pixel value is obtained, and a second mass number sequence is constructed, the first pixel difference in the same sub-mass detection area in the first mass number sequence is divided into a first sub-mass number sequence, the remaining first pixel difference is divided into a second sub-mass number sequence, the first mass number sequence includes a second sub-mass number sequence and a plurality of first sub-mass number sequences, the second pixel difference in the same sub-mass detection area in the second mass number sequence is divided into a third sub-mass number sequence, the remaining second pixel difference is divided into a fourth sub-mass number sequence, the second mass number sequence includes a fourth sub-mass number sequence and a plurality of third sub-mass number sequences.

[0033] Specifically, the target pixel values ​​corresponding to the remaining sub-quality detection areas are used as quality pixel values, and the corresponding standard target pixel values ​​in the standard target product image are used as quality standard pixel values. The first pixel difference value indicates that it is greater than or equal to the quality standard pixel value, and the second pixel difference value indicates that it is less than the quality standard pixel value. They are separated and further refined for the same sub-quality detection area, which lays the foundation for capturing the correlation between quality pixel values ​​in the same sub-quality detection area, and divides the quality pixel values ​​of the remaining different sub-quality detection areas, avoiding misjudgment caused by ignoring a single difference and the overall situation, thereby improving the accuracy of risk warning.

[0034] In some embodiments of the present application, when all quality pixel values ​​in the first quality sequence and the second quality sequence are numerically sorted, and every two quality pixel values ​​in the first quality sequence or the second quality sequence are used as quality pixels to be associated, the method includes: the processing module numerically sorts the first pixel difference values ​​in each first sub-quality sequence, and uses adjacent first pixel difference values ​​as first quality pixels to be associated, and the processing module numerically sorts the second pixel difference values ​​in each third sub-quality sequence, and uses adjacent second pixel difference values ​​as second quality pixels to be associated.

[0035] Specifically, the first sub-mass series may exist according to the first pixel difference in the same sub-mass detection area, and the third sub-mass series may exist in multiple ways. Therefore, the first pixel difference in each first sub-mass series is numerically sorted, and the second pixel difference in each third sub-mass series is numerically sorted. When there are cracks or fissures on the surface of the smart electronic product to be detected, the cracks or fissures will expand along the direction of stress concentration of the smart electronic product to be detected, resulting in a certain correlation between the first pixel difference or the second pixel difference. The first pixel difference or the second pixel difference can be aggregated through numerical sorting, thereby avoiding the influence of disordered data on the analysis results, and laying a foundation for the subsequent quality evaluation model.

[0036] In some embodiments of the present application, when verifying whether the quality pixels to be associated are associated based on the quality evaluation model and counting the number of associations according to the verification results, it includes: a processing module obtains the historical quality pixel values ​​of the historical target product images, the quality standard pixel values ​​of the standard target product images and the historical pixel difference values, and constructs a pixel data set, samples the pixel data set according to a preset ratio to obtain a pixel training set and a pixel test set, the processing module pre-selects a twin neural network model, iteratively trains the twin neural network model according to the pixel training set, and evaluates the iteratively trained twin neural network model according to the test subset; if the ternary loss of the twin neural network model after the current iterative training is greater than or equal to the ternary loss of the previous iterative training If the ternary loss of the twin neural network model after training is smaller than the ternary loss of the twin neural network model after the previous iterative training, the iterative training is stopped, and the twin neural network model after the current iterative training is used as the quality evaluation model. The first quality pixel to be associated and the second quality pixel to be associated are respectively substituted into the quality evaluation model, and the model verification result is output. When the model verification result meets the preset model verification result, it is determined that the first quality pixel to be associated or the second quality pixel to be associated has pixel association, and the number of pixel associations is counted.

[0037] Specifically, the historical quality pixel values ​​of the historical target product images, the quality standard pixel values ​​of the standard target product images, and the historical pixel differences record all the data of the smart electronic products in different periods. The pixel training set and the pixel test set are obtained by constructing a pixel data set and sampling according to a preset ratio. The preset ratio is preferably 3:2 sampling, so that the pixel training set and the pixel test set contain a variety of data from different periods. The pixel training set is used to train the twin neural network model, and the pixel test set is used to evaluate the performance of the trained model. The twin neural network model contains multiple levels, each layer is responsible for extracting features at different levels. In the twin neural network model, by sharing the same hierarchical structure, features are continuously extracted to capture the similarity between input data. The activation function is used for nonlinear transformation, thereby helping the twin neural network model capture complex patterns in the data. By training the twin neural network model, it is possible to accurately determine whether there is a correlation between the first quality pixel to be associated or the second quality pixel to be associated, thereby outputting the verification result, avoiding judgment errors caused by simple calculations.

[0038] It can be understood that the ternary loss enables the twin neural network to bring similar data closer and push dissimilar data farther away. If the ternary loss of the twin neural network model after the current iterative training is less than the ternary loss of the twin neural network model after the previous iterative training, the learning rate of the model is adjusted by piecewise attenuation, and the iterative training continues. In each iteration, the model will try to learn the patterns and relationships in the input data to improve its prediction or verification capabilities until the preset number of iterations is reached. If the ternary loss of the twin neural network model after the current iterative training is less than the ternary loss of the twin neural network model after the previous iterative training, the iterative training is stopped. At this time, the twin neural network model has become stable and can be close to the global optimal solution. Using it as a quality evaluation model can effectively derive whether there is a pixel association between the first quality pixels to be associated or the second quality pixels to be associated, thereby deriving the number of associations with pixel associations based on all the first quality pixels to be associated and all the second quality pixels to be associated.

[0039] In some embodiments of the present application, when determining the comprehensive quality value of the intelligent electronic product to be detected according to the associated quantity and the quality pixel value without association, it includes: The calculation module determines the comprehensive quality value by the following formula: ; ; ; in, represents the quality of association, Indicates the number of associations, represents the number of the first sub-mass series, Indicates The largest first pixel difference in the first sub-mass sequence, Indicates The smallest first pixel difference in the first sub-mass sequence, represents the number of the third sub-mass series, Indicates The largest second pixel difference in the third sub-mass sequence, Indicates The smallest second pixel difference in the third sub-mass sequence, represents non-associated quality, represents the number of first pixel difference values ​​in the second sub-mass sequence, represents the maximum first pixel difference in the second sub-mass sequence, Indicates the number of the second sub-mass sequence The first pixel difference, represents the number of second pixel differences in the fourth sub-mass sequence, represents the maximum second pixel difference in the fourth sub-mass sequence, Indicates the fourth sub-mass sequence The second pixel difference, Represents the comprehensive quality value, It represents the weight coefficient, and the weight coefficient is determined according to the group of smart electronic products to be detected.

[0040] Specifically, the weight coefficient is determined according to the group of the smart electronic products to be tested. When the smart electronic products to be tested are used by general consumers (except the elderly and young children), the weight coefficient is selected as 1.2; when the smart electronic products to be tested are used by the elderly and young children (children over 36 months to 14 years old), the weight coefficient is 1.6; when the smart electronic products to be tested are used for infants (0 to 36 months), the weight coefficient is 1.8; when the smart electronic products to be tested are used in all age groups, the weight coefficient is 1.4. The comprehensive quality value is determined by calculation based on the audience group, associated quality and non-associated quality, thereby comprehensively measuring the risks of the smart electronic products to be tested and improving the flexibility and adaptability of risk warnings for smart electronic products to be tested.

[0041] In some embodiments of the present application, when determining whether to issue an early warning to the intelligent electronic product to be detected according to the comprehensive quality value, the process includes: and a pre-set comprehensive quality value threshold Compare and determine whether to issue an early warning to the intelligent electronic product to be detected according to the comparison result. Greater than or equal to When , it is determined that the intelligent electronic product to be detected should be warned. Less than When , it is determined that no warning is issued for the intelligent electronic product to be detected.

[0042] Specifically, by comparing the comprehensive quality value of the smart electronic product to be tested with the pre-set comprehensive quality value threshold, the status of the smart electronic product to be tested can be accurately judged. When the comprehensive quality value is greater than or equal to the comprehensive quality value threshold, the smart electronic products that do not meet the standards can be accurately identified, thereby achieving effective early warning. Analysis and verification are performed based on the convolutional neural network model and the quality evaluation model, ensuring the accuracy of the analysis process, thereby improving the accuracy of risk early warning.

[0043] In summary, the beneficial effects of the present invention are as follows: by dividing the target product image into multiple sub-quality detection areas and performing analysis, comparing and dividing the quality pixel values, the quality problems of the intelligent electronic products to be detected are effectively identified, and the accuracy of risk identification is ensured; by extracting and screening out the sub-quality detection areas with problems, manual intervention is reduced, and at the same time, the intelligence and automation of risk warnings are improved, and the focus is effectively on the sub-quality detection areas with problems, and the analysis and processing processes are optimized. Moreover, by using the quality evaluation model in artificial intelligence to analyze the associated quality pixels, the comprehensive quality value can be accurately calculated, thereby issuing warnings in a timely manner, reducing the risk of non-standard intelligent electronic products to be detected entering the market, and improving the accuracy and reliability of risk warnings for intelligent electronic products.

[0044] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides an intelligent electronic product risk warning method based on artificial intelligence, which is applied to the above-mentioned intelligent electronic product risk warning system based on artificial intelligence, including: S100: Acquire an initial product image of the intelligent electronic product to be inspected, and analyze the initial product image to determine a target product image of the intelligent electronic product to be inspected.

[0045] S200: Analyze the target product image, generate quality calibration for the intelligent electronic product to be inspected according to the analysis result, divide the target product image into multiple sub-quality inspection areas when it is identified as suspected to meet the calibration, and analyze each sub-quality inspection area, and delete the sub-quality inspection area according to the analysis result.

[0046] S300: extracting all quality pixel values ​​of the remaining sub-quality detection area after deletion, and comparing the quality pixel values ​​with the corresponding quality standard pixel values, dividing all the quality pixel values ​​according to the comparison result, constructing a first quality sequence and a second quality sequence according to the division result, numerically sorting all the quality pixel values ​​in the first quality sequence and the second quality sequence, and taking every two quality pixel values ​​in the first quality sequence or the second quality sequence as quality pixels to be associated, and verifying whether the quality pixels to be associated are associated based on the quality evaluation model, and counting the number of associations according to the verification result.

[0047] S400: Determine a comprehensive quality value of the intelligent electronic product to be detected according to the number of pixels to be associated and the quality pixel values ​​that have not been associated, and determine whether to issue an early warning for the intelligent electronic product to be detected according to the comprehensive quality value.

[0048] Specifically, the initial product image of the intelligent electronic product to be detected is obtained, and it is analyzed to determine the target product image. By processing the initial product image, the key features of the intelligent electronic product to be detected can be effectively extracted, laying the foundation for the analysis of subsequent risk warnings. By analyzing the target product image of the intelligent electronic product to be detected and generating a quality calibration, it is determined whether there is a suspected compliance calibration, the target product image is divided into multiple sub-quality detection areas, and each sub-quality detection area is analyzed. According to the analysis results, the sub-quality detection areas are deleted to remove irrelevant or standard sub-quality detection areas, thereby focusing on the sub-quality detection areas with problems, reducing the subsequent calculation process, thereby improving the accuracy of risk warnings. The quality pixel value is compared with the corresponding quality standard pixel value, and all the quality pixel values ​​are divided according to the comparison results, and every two quality pixel values ​​on the first quality sequence or the second quality sequence are used as quality pixels to be associated. Based on the quality evaluation model, it is verified whether the quality pixels to be associated are associated, and the deep learning model is used for training and the quality evaluation model is obtained, which further improves the accuracy of risk warnings. The comprehensive quality value of the smart electronic product to be tested is determined based on the number of quality pixels to be associated and not associated. The comprehensive quality value reflects the overall quality of the smart electronic product to be tested. Whether to issue an early warning for the smart electronic product to be tested is determined based on the comprehensive quality value. This achieves accurate evaluation of the smart electronic products to be tested, thereby identifying risks and issuing early warnings in real time, thereby improving the accuracy and reliability of risk early warnings.

[0049] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0050] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0051] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An intelligent electronic product risk warning system based on artificial intelligence, characterized in that: include: An acquisition module is configured to acquire an initial product image of the intelligent electronic product to be detected, and analyze the initial product image to determine a target product image of the intelligent electronic product to be detected; an analysis module configured to analyze the target product image, generate a quality calibration for the intelligent electronic product to be detected according to the analysis result, divide the target product image into a plurality of sub-quality detection areas when a suspected conformity with the calibration is identified, analyze each sub-quality detection area, and delete the sub-quality detection area according to the analysis result; The processing module is configured to extract all quality pixel values ​​of the remaining sub-quality detection area after deletion, and compare the quality pixel values ​​with the corresponding quality standard pixel values, divide all the quality pixel values ​​according to the comparison result, construct a first quality number series and a second quality number series according to the division result, numerically sort all the quality pixel values ​​in the first quality number series and the second quality number series, and use every two quality pixel values ​​in the first quality number series or the second quality number series as quality pixels to be associated, and verify whether the quality pixels to be associated are associated based on the quality evaluation model, and count the number of associations according to the verification result; The calculation module is configured to determine the comprehensive quality value of the intelligent electronic product to be detected according to the associated quantity and the quality pixel value without association, and determine whether to issue an early warning for the intelligent electronic product to be detected according to the comprehensive quality value.

2. The intelligent electronic product risk early warning system based on artificial intelligence according to claim 1 is characterized in that: When the initial product image is parsed to determine the target product image of the intelligent electronic product to be detected, it includes: The acquisition module preprocesses the initial product image, wherein the preprocessing includes image denoising, contrast adjustment, and image edge sharpening; Substituting the preprocessed initial product image into the convolutional neural network model, and outputting the image effective value of the preprocessed initial product image based on the convolutional neural network model; When the image validity value is greater than or equal to a preset image validity value, the initial product image is determined as the target product image of the intelligent electronic product to be detected; When the image validity value is less than the preset image validity value, the initial product image of the to-be-detected intelligent electronic product is reacquired.

3. The risk early warning system for intelligent electronic products based on artificial intelligence according to claim 2 is characterized in that: When analyzing the target product image and generating a quality calibration for the intelligent electronic product to be inspected according to the analysis result, it includes: The analysis module extracts all target image pixels of the target product image, extracts all standard target image pixels corresponding to the target image pixels in the standard target product image, determines the target pixel values ​​corresponding to all target image pixels, and determines the standard target pixel values ​​corresponding to all standard target image pixels; When the target pixel values ​​are all equal to the standard target pixel values, a quality compliance calibration is generated for the intelligent electronic product to be inspected; When the target pixel values ​​are not equal to the standard target pixel values, a quality non-conformity calibration is generated for the intelligent electronic product to be detected; When one or more target pixel values ​​are equal to the standard target pixel value, and one or more target pixel values ​​are not equal to the standard target pixel value, a suspected compliance calibration is generated for the intelligent electronic product to be detected.

4. The risk early warning system for intelligent electronic products based on artificial intelligence according to claim 3 is characterized in that: When parsing each sub-quality detection area and deleting the sub-quality detection area according to the parsing result, it includes: When there is a sub-quality detection region whose target pixel values ​​are all equal to the standard target pixel value, the sub-quality detection region is deleted, and each target pixel value of the remaining sub-quality detection region is used as a quality pixel value.

5. The risk early warning system for intelligent electronic products based on artificial intelligence according to claim 4 is characterized in that: When all quality pixel values ​​of the remaining sub-quality detection area after deletion are extracted, and the quality pixel values ​​are compared with corresponding quality standard pixel values, all quality pixel values ​​are divided according to the comparison result, and the first quality number series and the second quality number series are constructed according to the division result, it includes: The processing module extracts all quality pixel values ​​of the remaining sub-quality detection areas after deletion, and compares each quality pixel value with the corresponding quality standard pixel value; When the quality pixel value is greater than or equal to the quality standard pixel value, a first pixel difference between the quality pixel value and the quality standard pixel value is obtained, and the first quality sequence is constructed; When the quality pixel value is less than the quality standard pixel value, a second pixel difference between the quality pixel value and the quality standard pixel value is obtained, and the second quality sequence is constructed; Dividing first pixel differences of the same sub-mass detection area in the first mass number sequence into a first sub-mass number sequence, and dividing the remaining first pixel differences into a second sub-mass number sequence, wherein the first mass number sequence includes the second sub-mass number sequence and a plurality of the first sub-mass number sequences; The second pixel differences of the same sub-mass detection area in the second mass number sequence are divided into a third sub-mass number sequence, and the remaining second pixel differences are divided into a fourth sub-mass number sequence, wherein the second mass number sequence includes the fourth sub-mass number sequence and a plurality of the third sub-mass number sequences.

6. The intelligent electronic product risk early warning system based on artificial intelligence according to claim 5 is characterized in that: When all the quality pixel values ​​in the first quality number sequence and the second quality number sequence are numerically sorted, and every two quality pixel values ​​in the first quality number sequence or the second quality number sequence are used as quality pixels to be associated, the method includes: The processing module sorts the first pixel difference values ​​in each of the first sub-mass sequence by numerical value, and uses adjacent first pixel difference values ​​as first mass pixels to be associated; The processing module sorts the second pixel differences in each of the third sub-quality series by numerical value, and uses adjacent second pixel differences as second quality pixels to be associated.

7. The intelligent electronic product risk early warning system based on artificial intelligence according to claim 6 is characterized in that: When verifying whether the to-be-associated quality pixels are associated based on the quality evaluation model and counting the number of associations according to the verification result, it includes: The processing module obtains historical quality pixel values ​​of historical target product images, quality standard pixel values ​​of standard target product images and historical pixel difference values, and constructs a pixel data set, and samples the pixel data set according to a preset ratio to obtain a pixel training set and a pixel test set; The processing module preselects a twin neural network model, iteratively trains the twin neural network model according to the pixel training set, and evaluates the iteratively trained twin neural network model according to the pixel test set; If the ternary loss of the twin neural network model after the current iterative training is greater than or equal to the ternary loss of the twin neural network model after the previous iterative training, the learning rate is adjusted using piecewise decay, and iterative training continues until the preset number of iterations is reached; If the ternary loss of the twin neural network model after the current iterative training is less than the ternary loss of the twin neural network model after the previous iterative training, the iterative training is stopped, and the twin neural network model after the current iterative training is used as the quality evaluation model, and the first quality pixel to be associated and the second quality pixel to be associated are respectively substituted into the quality evaluation model, and the model verification result is output; When the model verification result meets the preset model verification result, it is determined that the to-be-associated first quality pixels or the to-be-associated second quality pixels have pixel associations, and the number of associations with pixel associations is counted.

8. The risk early warning system for intelligent electronic products based on artificial intelligence according to claim 7 is characterized in that: When determining the comprehensive quality value of the intelligent electronic product to be detected according to the associated quantity and the unassociated quality pixel value, it includes: The calculation module determines the comprehensive quality value by the following formula: ； ； ； in, represents the quality of association, Indicates the number of associations, represents the number of the first sub-mass series, Indicates The largest first pixel difference in the first sub-mass sequence, Indicates The smallest first pixel difference in the first sub-mass sequence, represents the number of the third sub-mass series, Indicates The largest second pixel difference in the third sub-mass sequence, Indicates The smallest second pixel difference in the third sub-mass sequence, represents non-associated quality, represents the number of first pixel difference values ​​in the second sub-mass sequence, represents the maximum first pixel difference in the second sub-mass sequence, Indicates the number of the second sub-mass sequence The first pixel difference, represents the number of second pixel differences in the fourth sub-mass sequence, represents the maximum second pixel difference in the fourth sub-mass sequence, Indicates the fourth sub-mass sequence The second pixel difference, Represents the comprehensive quality value, It represents the weight coefficient, and the weight coefficient is determined according to the group of smart electronic products to be detected.

9. The intelligent electronic product risk early warning system based on artificial intelligence according to claim 8 is characterized in that: When determining whether to issue an early warning to the intelligent electronic product to be detected according to the comprehensive quality value, it includes: The comprehensive quality value and a pre-set comprehensive quality value threshold Performing a comparison, and determining whether to issue an early warning for the intelligent electronic product to be detected according to the comparison result; when Greater than or equal to When the detected intelligent electronic product is detected, it is determined to issue an early warning; when Less than When , it is determined that no warning is issued for the intelligent electronic product to be detected.

10. An artificial intelligence-based smart electronic product risk warning method, applied to the artificial intelligence-based smart electronic product risk warning system as claimed in any one of claims 1 to 9, characterized in that: include: Acquire an initial product image of the intelligent electronic product to be detected, and analyze the initial product image to determine a target product image of the intelligent electronic product to be detected; Analyze the target product image, generate a quality calibration for the intelligent electronic product to be detected according to the analysis result, divide the target product image into a plurality of sub-quality detection areas when a suspected product that meets the calibration is identified, analyze each sub-quality detection area, and delete the sub-quality detection area according to the analysis result; Extracting all quality pixel values ​​of the remaining sub-quality detection area after deletion, and comparing the quality pixel values ​​with the corresponding quality standard pixel values, dividing all quality pixel values ​​according to the comparison result, constructing a first quality number series and a second quality number series according to the division result, numerically sorting all quality pixel values ​​in the first quality number series and the second quality number series, and taking every two quality pixel values ​​in the first quality number series or the second quality number series as quality pixels to be associated, and verifying whether the quality pixels to be associated are associated based on the quality evaluation model, and counting the number of associations according to the verification result; The comprehensive quality value of the intelligent electronic product to be detected is determined according to the number to be associated and the quality pixel value that has not been associated, and whether to issue an early warning to the intelligent electronic product to be detected is determined according to the comprehensive quality value.

Images