CN-121978103-A - Multi-dimensional detection method and system for product defects of stewed noodle automatic production line

Abstract

The invention discloses a multi-dimensional detection method and a system for product defects of an automatic braised noodle production line, and relates to the field of data processing, wherein the method comprises the steps of obtaining a plurality of dough products corresponding to a dough control scheme according to the automatic dough mixing line of the braised noodle automatic production line; the method comprises the steps of detecting surface variation of a plurality of dough products according to a dough surface defect index set, introducing a sanitary variation detection correction mechanism to conduct sanitary variation analysis on the plurality of dough products, detecting strength variation of the plurality of dough products according to a dough mixing control scheme, and conducting global defect assessment on the plurality of dough products according to a dough variation detection first matrix, a dough variation detection second matrix and a dough variation detection third matrix to obtain a dough defect detection report. The technical problems of inaccurate and incomplete detection results in the defect detection of the existing stewed noodle automatic production line are solved, and the technical effects of improving the accuracy and comprehensiveness of the defect detection are achieved.

Inventors

• PAN GUOQIANG
• ZHAN SHAOWEI

Assignees

• 南通昶昊机电制造有限公司

Dates

Publication Date

20260505

Application Date

20251128

Claims (10)

1. 1. The multi-dimensional detection method for the defects of the products of the stewed noodle automatic production line is characterized by comprising the following steps of: obtaining a plurality of dough products corresponding to the dough control scheme according to the automation of the stewed noodle automatic production line and the upper threads; performing surface mutation detection on the dough products according to the dough surface defect index set to obtain a dough mutation detection first matrix; Introducing a sanitary mutation detection and correction mechanism to perform sanitary mutation analysis on the dough products to obtain a dough mutation detection second matrix; Performing muscle power variation detection on the dough products according to the dough kneading control scheme to obtain a dough variation detection third matrix; And performing global defect evaluation on the dough products according to the first dough deformation detection matrix, the second dough deformation detection matrix and the third dough deformation detection matrix to obtain a dough defect detection report.
2. 2. The method for multi-dimensional inspection of defects in an automatic braised noodle production line according to claim 1, wherein the step of performing surface variation inspection on the plurality of dough products according to the dough surface defect index set to obtain a dough variation inspection first matrix comprises: performing similarity evaluation on the dough surface defect index set to obtain a defect index similarity evaluation set; decoupling optimization is carried out on the dough surface defect index set according to the defect index similarity evaluation set, so that a plurality of independent defect indexes are obtained; Performing convolution characteristic learning according to the independent defect indexes to obtain a plurality of defect convolution capture models; And performing surface variation capture on the dough products according to the defect convolution capture models to generate a first dough variation detection matrix.
3. 3. The multi-dimensional inspection method of defects in an automatic braised noodle production line product as in claim 2, wherein the capturing of surface variations of the plurality of dough products according to the plurality of defect convolution capture models comprises: Extracting a kth dough product according to the plurality of dough products, and acquiring a kth dough image according to the industrial camera by performing multi-angle image acquisition on the kth dough product to obtain a kth dough image, wherein K is a positive integer; Synchronously calling the K-th image acquisition scene data corresponding to the K-th dough image, and carrying out anomaly identification according to the K-th image acquisition scene data to obtain K-th acquisition scene anomaly characteristics; Correcting the Kth dough image according to the Kth acquisition scene abnormal characteristics to obtain a Kth dough corrected image; Inputting the Kth dough correction image into the plurality of defect convolution capture models to obtain a plurality of dough defect capture results, and fusing the plurality of dough defect capture results to generate a Kth surface variation detection result.
4. 4. The multi-dimensional inspection method of product defects in an automatic braised noodles production line according to claim 1, wherein the sanitary variation inspection and correction mechanism comprises: Performing colony mutation detection correction on the Kth dough product according to the dough kneading control scheme to obtain a first characteristic of sanitary mutation detection; Performing pH value variation detection correction on the Kth dough product according to the dough mixing control scheme to obtain a second characteristic of sanitary variation detection; performing odor mutation detection correction on the kth dough product according to the dough mixing control scheme to generate a third characteristic of sanitary mutation detection; generating a K dough sanitary mutation detection result according to the sanitary mutation detection first characteristic, the sanitary mutation detection second characteristic and the sanitary mutation detection third characteristic, and adding the K dough sanitary mutation detection result to the dough mutation detection second matrix.
5. 5. The multi-dimensional inspection method of defects in an automated braised noodle production line according to claim 4, wherein the performing colony mutation detection correction on the kth dough product according to the dough kneading control scheme to obtain a first characteristic of sanitary mutation detection comprises: performing colony detection on the Kth dough product to obtain Kth colony detection data; according to the dough mixing control scheme, normal sample retrieval of dough colony detection is carried out, and a dough colony detection space is constructed; performing mutation capture on the Kth colony detection data according to the dough colony detection space to obtain a Kth colony mutation capture result; according to colony detection operation data and colony detection environment data corresponding to the Kth colony detection data, detection interference analysis is carried out, and the Kth colony detection interference characteristics are determined; correcting the K colony mutation capturing result according to the K colony detection interference characteristic to generate the sanitary mutation detection first characteristic.
6. 6. The multi-dimensional inspection method of product defects in an automated braised noodle production line according to claim 1, wherein the performing the detection of the gluten-induced variability of the plurality of dough products according to the dough kneading control scheme to obtain a third matrix of dough-induced variability detection comprises: Performing muscle force detection on the dough products to obtain muscle force detection data of each dough, and synchronously recording the muscle force detection scene data of each dough; performing normal sample retrieval for dough muscle force detection according to the dough kneading control scheme, and constructing a muscle force detection space; Performing mutation capture on the dough muscle strength detection data according to the muscle strength detection space to obtain dough muscle strength mutation characteristics; And performing interference compensation on the dough muscle strength variation characteristics according to the muscle strength detection scene data to generate the dough variation detection third matrix.
7. 7. The multi-dimensional inspection method of product defects in an automatic braised noodles production line according to claim 6, wherein the step of performing interference compensation on the dough strength variation characteristics according to the strength inspection scene data to generate the dough strength variation inspection third matrix comprises the following steps: Performing anomaly detection according to the muscle force detection scene data to obtain anomaly characteristics of the muscle force detection scenes; performing integrated fusion training according to the muscle force variation detection compensation historical set to obtain a muscle force variation detection compensation model; inputting the abnormal characteristics of each muscle force detection scene and the dough muscle force variation characteristics into the muscle force variation detection compensation model to obtain each muscle force variation compensation result; And constructing a third matrix for detecting dough deformation according to the result of the compensation of the variation of each muscle force.
8. 8. The multi-dimensional inspection method of defects in an automatic braised noodle production line according to claim 1, wherein the global defect evaluation is performed on the plurality of dough products according to the first matrix for detecting dough variation, the second matrix for detecting dough variation and the third matrix for detecting dough variation to obtain a report of detecting the defects of the dough, comprising: Performing surface defect evaluation according to the dough deformation detection first matrix to obtain the surface defect coefficients of each dough; Performing sanitary defect evaluation according to the dough change detection second matrix to obtain sanitary defect coefficients of each dough; Performing reinforcement defect evaluation according to the dough deformation detection third matrix to obtain reinforcement defect coefficients of each dough; And respectively carrying out weighted fusion on the surface defect coefficients of each dough, the sanitary defect coefficients of each dough and the gluten defect coefficients of each dough according to the multidimensional defect weights of the dough, and generating a dough defect detection report, wherein the dough defect detection report comprises the global defect coefficients of each dough.
9. 9. The multi-dimensional inspection method of defects of an automatic braised noodle production line according to claim 8, wherein whether the global defect coefficient of each dough is larger than or equal to a global defect threshold of the dough is judged, each global defect judgment result is obtained, and a dough defect early warning signal is generated according to each global defect judgment result.
10. 10. A multi-dimensional inspection system for product defects of an automatic braised noodle production line, characterized in that the system is used for implementing the multi-dimensional inspection method for product defects of an automatic braised noodle production line according to any one of claims 1 to 9, and the system comprises: A dough product obtaining module for obtaining a plurality of dough products corresponding to the dough control scheme according to the automatic dough mixing and noodle line of the stewed noodle automatic production line; The surface variation detection module is used for carrying out surface variation detection on the dough products according to the dough surface defect index set to obtain a dough variation detection first matrix; The sanitary mutation analysis module is used for introducing a sanitary mutation detection and correction mechanism to perform sanitary mutation analysis on the dough products so as to obtain a dough mutation detection second matrix; the dough kneading control scheme is used for controlling dough kneading of the dough products according to the dough kneading control scheme, and the dough kneading control scheme is used for controlling dough kneading of the dough products according to the dough kneading control scheme; And the global defect evaluation module is used for performing global defect evaluation on the dough products according to the first dough deformation detection matrix, the second dough deformation detection matrix and the third dough deformation detection matrix to obtain a dough defect detection report.

Description

Multi-dimensional detection method and system for product defects of stewed noodle automatic production line Technical Field The application relates to the field of data processing, in particular to a multi-dimensional detection method and system for product defects of a stewed noodle automatic production line. Background The stewed noodles are taken as traditional cooked wheaten food popular with the public, the product quality directly influences the eating experience and market public praise of consumers, and the defect detection of the dough products in the production process of the stewed noodles is a key link for guaranteeing the product quality, so that the accurate detection of the defects of the stewed noodle dough products is very important. The main method for solving the defect detection problem of the stewed dough products at present is to simply observe and judge by means of manual experience or adopt a plurality of single-dimension automatic detection means. The existing method has the problems of strong subjectivity, low efficiency, easy fatigue and error and the like due to manual judgment, and the single-dimension automatic detection means is difficult to fully cover the defect characteristics of the dough products in multiple aspects, so that the detection result is inaccurate and incomplete, and the quality of the stewed dough products cannot be effectively ensured. In the related technology at the present stage, the defect detection of the stewed noodle automatic production line product has the technical problem of inaccurate and incomplete detection result. Disclosure of Invention According to the multi-dimensional detection method and system for the defects of the stewed noodle automatic production line, the technical means of obtaining the dough defect detection report and the like by adopting the technical means of obtaining a plurality of dough products based on automatic dough mixing, respectively carrying out surface mutation detection (obtaining a first matrix), sanitary mutation analysis (obtaining a second matrix) and muscle mutation detection (obtaining a third matrix) on the dough, comprehensively carrying out global defect assessment on the three matrices, solving the technical problems of inaccurate and incomplete detection results of the defect detection of the existing stewed noodle automatic production line products, and achieving the technical effects of improving the accuracy and comprehensiveness of the defect detection. The application provides a multi-dimensional detection method for defects of a stewed noodle automatic production line, which comprises the steps of obtaining a plurality of dough products corresponding to a dough mixing control scheme according to the automatic dough mixing control scheme, carrying out surface mutation detection on the plurality of dough products according to a dough surface defect index set to obtain a first dough mutation detection matrix, introducing a sanitary mutation detection correction mechanism to carry out sanitary mutation analysis on the plurality of dough products to obtain a second dough mutation detection matrix, carrying out muscle mutation detection on the plurality of dough products according to the dough mixing control scheme to obtain a third dough mutation detection matrix, and carrying out global defect assessment on the plurality of dough products according to the first dough mutation detection matrix, the second dough mutation detection matrix and the third dough mutation detection matrix to obtain a dough defect detection report. In a possible implementation manner, surface variation detection is carried out on a plurality of dough products according to a dough surface defect index set to obtain a dough variation detection first matrix, and the following steps are carried out, namely, the dough surface defect index set is subjected to similarity evaluation in pairs to obtain a defect index similarity evaluation set, decoupling optimization is carried out on the dough surface defect index set according to the defect index similarity evaluation set to obtain a plurality of independent defect indexes, convolution feature learning is carried out according to the plurality of independent defect indexes to obtain a plurality of defect convolution capturing models, and surface variation capturing is carried out on the plurality of dough products according to the plurality of defect convolution capturing models to generate the dough variation detection first matrix. In a possible implementation manner, the surface variation capturing is carried out on the dough products according to the defect convolution capturing models, the processing is carried out, namely, a kth dough product is extracted according to the dough products, multi-angle image capturing is carried out on the kth dough product according to an industrial camera to obtain a kth dough image, K is a positive integer, K image capturing scene data correspo