Production workshop product quality detection method and system

Abstract

The invention discloses a production workshop product quality detection method and system, relates to the field of production workshops, and solves the problem that the accuracy degree of a detection result cannot be guaranteed during production workshop product quality detection. The detection preparation module analyzes the detection precision of the target product to obtain the feature complexity level of the target product; the configuration terminal adaptively adjusts the configuration parameters of the quality inspection equipment, and the production workshop transfers the produced target product to the quality inspection equipment; the image acquisition module acquires pixel capture data of a target product; the image analysis module performs image analysis on the captured image of the target product to obtain a suspected flaw area of the target product, and the surface detection module performs flaw detection on the suspected flaw area of the target product; and the flaw evaluation module comprehensively evaluates the surface flaws of the target product. According to the invention, customized deployment of product quality detection is realized.

Description

Technical Field

[0001] The present invention belongs to the field of production workshops and relates to product quality detection technology, in particular to a production workshop product quality detection method and system. Background Art

[0002] Product quality inspection in the production workshop is a vital part of the manufacturing industry. It is used to ensure that the products produced meet specific quality standards and requirements. Various testing equipment is used to test the product's size, weight, material composition, appearance and other properties to ensure that the product quality meets the requirements. Through product quality inspection, manufacturers can promptly discover and solve problems in the production process, thereby achieving the effect of improving product qualification rate, reducing defective rate, improving production efficiency and reducing production costs.

[0003] Under the current technical background, when using image recognition technology to conduct quality inspection on products in production workshops, fixed image capture solutions are often used. In the image capture process, product specifications and product features are not considered, making it impossible to guarantee the accuracy of the inspection results. Therefore, the problem lies in how to implement customized solutions for product quality inspection while ensuring quality inspection efficiency. To this end, we propose a product quality detection method and system for production workshops. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention aims to provide a method and system for detecting product quality in a production workshop.

[0005] The technical problems to be solved by the present invention are: How to implement customized solution deployment for product quality inspection while ensuring quality inspection efficiency.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, a method for detecting product quality in a production workshop, the method steps comprising: Step S101, the production terminal sends the product specification data of the target product to the detection preparation module, and the detection preparation module analyzes the detection accuracy of the target product, and sends the characteristic complexity level of the target product obtained by the analysis to the configuration terminal; Step S102, the configuration terminal adaptively adjusts the configuration parameters of the quality inspection equipment. After the debugging of the quality inspection equipment is completed, the production workshop performs the production operation of the target product and transports the completed target product to the quality inspection equipment for quality inspection; Step S103, the image acquisition module acquires pixel capture data of the target product and sends it to the image analysis module. Meanwhile, the target product of the same batch and the same specification that has passed the inspection is preferably selected as a sample product, and the pixel capture data of the sample product is acquired as pixel reference data; Step S104: the image analysis module performs image analysis on the captured image of the target product, obtains the suspected defect area of ​​the target product through analysis, and sends it to the surface detection module; the surface detection module performs defect detection on the suspected defect area of ​​the target product, obtains the defect impact range and defect peak-to-valley difference of the target product in the suspected defect area through detection, and sends them to the defect assessment module; In step S105, the defect assessment module performs a comprehensive assessment on the surface defects of the target product and sends the assessment results of the suspected defective areas to the sorting terminal, and then the sorting terminal sorts the target product.

[0007] Further, the product specification data includes the number of standard product faces, the number of characteristic corners and the number of characteristic concavities and convexities of the target product; The pixel capture data includes the number of captured images of the target product, the captured image specifications, and the coordinate color array of each captured image; The pixel reference data includes the number of captured images of the sample product, the captured image specifications, and a comparison color array for each captured image.

[0008] Furthermore, in step S101, the analysis process of the detection preparation module is specifically as follows: Obtain the standard product face number, characteristic corner number and characteristic concave-convex number of the target product, and calculate the product complexity index of the target product; Compare the product complexity index of the target product with the standard complexity index; If the product complexity index is less than or equal to the first standard complexity index, the feature complexity level of the target product is determined to be the first feature complexity level; If the product complexity index is greater than the first standard complexity index and less than or equal to the second standard complexity index, the feature complexity level of the target product is determined to be the second feature complexity level; If the product complexity index is greater than the second standard complexity index, the feature complexity level of the target product is determined to be the third feature complexity level.

[0009] Furthermore, the values ​​of the first standard complexity index and the second standard complexity index are both greater than zero, the first standard complexity index is smaller than the second standard complexity index, the complexity of products corresponding to the first characteristic complexity level is lower than the complexity of products corresponding to the second characteristic complexity level, and the complexity of products corresponding to the second characteristic complexity level is lower than the complexity of products corresponding to the third characteristic complexity level.

[0010] Furthermore, in step S102, the parameter configuration process of the configuration terminal is specifically as follows: Obtain the feature complexity level of the target product and set the configuration parameters of the quality inspection equipment according to the feature complexity level; The corresponding relationship between configuration parameters and feature complexity level is as follows: If the feature complexity level of the target product is the first feature complexity level, the configuration parameters of the corresponding quality inspection equipment are the third camera distance and the third viewing range; If the feature complexity level of the target product is the second feature complexity level, the configuration parameters of the corresponding quality inspection equipment are the second camera distance and the second viewing range; If the feature complexity level of the target product is the third feature complexity level, the configuration parameters of the corresponding quality inspection equipment are the first camera distance and the first viewing range; Among them, the value of the first camera distance is smaller than the value of the second camera distance, the value of the second camera distance is smaller than the value of the third camera distance, the value of the first viewing range is smaller than the value of the second viewing range, and the value of the second viewing range is smaller than the value of the third viewing range.

[0011] Furthermore, in step S104, the analysis process of the image analysis module is specifically as follows: Get the number of captured images of the target product, the captured image specifications, and the coordinate color array of each captured image; Then, pixel reference data including a comparison color array of each captured image of the sample product is obtained, and a color deviation value of the corresponding captured image of the target product is calculated; comparing the color deviation value of the captured image with the deviation threshold value; If the color deviation value is less than the deviation threshold, no additional operation is performed; If the color deviation value is greater than or equal to the deviation threshold value, the position area of ​​the captured image corresponding to the target product is defined as a suspected defect area.

[0012] Further, in step S104, the defect detection process of the surface detection module specifically includes: By scanning the surface of the target product, the defect impact range and defect peak-to-valley difference of the target product in the suspected defect area are obtained; The suspected defective area of ​​the target product is scanned by a laser rangefinder, and the defect impact area is constructed according to the defect location. The total area of ​​the defect impact area is taken as the defect impact range of the target product. The highest and lowest points of the defect location are obtained by scanning, and the height difference between the highest and lowest points of the defect location is calculated as the defect peak-to-valley height difference of the target product.

[0013] Furthermore, in step S105, the evaluation process of the defect evaluation module is specifically as follows: Obtain the defect impact range, defect peak-to-valley difference, and captured image specifications of the target product in the suspected defect area, and calculate the defect impact value of the suspected defect area; Compare the defect impact value of the suspected defect area with the defect impact interval; If the defect impact value belongs to the first defect impact interval, the evaluation result of the suspected defect area is determined to be a slight defect; If the defect impact value belongs to the second defect impact interval, the evaluation result of the suspected defect area is determined to be a severe defect; The specific values ​​of the defect impact interval are pre-stored in the database, the values ​​of the first defect impact interval and the second defect impact interval are both greater than zero, and the values ​​of the first defect impact interval are both less than the values ​​of the second defect impact interval.

[0014] Furthermore, in step S105, the sorting process of the sorting terminal includes: If the target product does not have any suspected defective area, the target product will be transported to the product storage area; If the target product has suspected defective areas and the evaluation results of the suspected defective areas are all slight defects, the target product will be transported to the rework product area; If the target product has suspected defective areas and the evaluation results of the suspected defective areas are all severe defects, the target product will be transferred to the waste product area.

[0015] In a second aspect, a production workshop product quality inspection system includes: a production terminal, an inspection preparation module, a configuration terminal, an image acquisition module, an image analysis module, a surface inspection module, a defect assessment module, a sorting terminal and a server; The production terminal is used to upload product specification data of the target product, the detection preparation module is used to analyze the detection accuracy of the target product to obtain the feature complexity level of the target product, the configuration terminal is used to adaptively adjust the configuration parameters of the quality inspection equipment, the image acquisition module is used to acquire pixel capture data of the target product, the image analysis module is used to perform image analysis on the captured image of the target product, the surface detection module is used to perform defect detection on suspected defect areas of the target product, the defect assessment module is used to comprehensively assess the surface defects of the target product, and the sorting terminal is used to sort the target product.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The present invention firstly uploads the product specification data of the target product by the production terminal, and then uses the detection preparation module to analyze the detection accuracy of the target product to obtain the characteristic complexity level of the target product, and then uses the configuration terminal to adaptively adjust the configuration parameters of the quality inspection equipment, and transports the completed target product to the quality inspection equipment for quality inspection, and then obtains the pixel capture data of the target product through the image acquisition module, and then uses the image analysis module to perform image analysis on the captured image of the target product to obtain the suspected defective area of ​​the target product, and also uses the surface detection module to perform defect detection on the suspected defective area of ​​the target product to obtain the defect influence range and defect peak-to-valley difference of the target product in the suspected defective area, and finally uses the defect evaluation module to comprehensively evaluate the surface defects of the target product. The present invention realizes the deployment of customized solutions for product quality inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0018] Figure 1 is a flow chart of the method of the present invention; Figure 2 is the overall system block diagram of the present invention; Figure 3 It is a structural schematic diagram of the target product in the present invention; Figure 4 It is a schematic diagram of the principle of the image acquisition module in the present invention; Figure 5 It is a schematic diagram of the application of the surface detection module in the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1: See also Figure 1-5 As shown, the technical solution provided by the present invention is: a method for detecting product quality in a production workshop, comprising the following steps: Step S101, the production terminal sends the product specification data of the target product to the detection preparation module, and the detection preparation module analyzes the detection accuracy of the target product, and sends the characteristic complexity level of the target product obtained by the analysis to the configuration terminal; Step S102, the configuration terminal adaptively adjusts the configuration parameters of the quality inspection equipment. After the debugging of the quality inspection equipment is completed, the production workshop performs the production operation of the target product and transports the completed target product to the quality inspection equipment for quality inspection; Step S103, the image acquisition module acquires pixel capture data of the target product and sends it to the image analysis module. Meanwhile, the target product of the same batch and the same specification that has passed the inspection is preferably selected as a sample product, and the pixel capture data of the sample product is acquired as pixel reference data; Step S104: the image analysis module performs image analysis on the captured image of the target product, obtains the suspected defect area of ​​the target product through analysis, and sends it to the surface detection module; the surface detection module performs defect detection on the suspected defect area of ​​the target product, obtains the defect impact range and defect peak-to-valley difference of the target product in the suspected defect area through detection, and sends them to the defect assessment module; In step S105, the defect assessment module performs a comprehensive assessment on the surface defects of the target product and sends the assessment results of the suspected defective areas to the sorting terminal, and then the sorting terminal sorts the target product.

[0021] The method involves a server, which is connected to a production terminal, a detection preparation module, a configuration terminal, an image acquisition module, an image analysis module, a surface detection module, a defect assessment module and a sorting terminal; In this embodiment, several products of the same batch, the same specifications and produced by the same production workshop are preferably used as target products. In the actual working process, the target product includes any entity with characteristics such as shape, size, color, etc. In this embodiment, the hardware module is preferably used as the target product; See also Figure 3 As shown, the production terminal sends the product specification data of the target product to the server, and the server sends the product specification data of the target product to the detection preparation module. The product specification data includes the number of standard product faces, the number of characteristic corners, and the number of characteristic concave-convex parts of the target product. The number of characteristic corners is the number of special angles formed between adjacent faces of the target product. In this embodiment, the value range of the special angle is (0°, 90°) ∪ (90°, 180°). The number of characteristic concave-convex parts is the total number of convex parts and concave parts on the surface of the target product, as shown in FIG. Figure 3 As shown, after the raised parts on the surface of the target product are cut off and the concave parts are filled, the number of faces of the target product at this time is recorded as the number of faces of the standard product.

[0022] In this embodiment, the detection preparation module is used to analyze the detection accuracy of the target product. The analysis process is as follows: Obtain the standard product face number BM, characteristic corner number BJ and characteristic concave-convex number UT of the target product, and calculate the product complexity index FZ of the target product according to the formula. The specific formula is as follows: ; Among them, e is a natural constant, a1, a2 and a3 are weight coefficients with fixed values, and the values ​​of a1, a2 and a3 are all greater than zero, a1 is the weight of the number of product faces, a2 is the weight of the characteristic corners, and a3 is the weight of the characteristic concave-convex. It can be understood that, under the same product specifications, the more product faces, the more characteristic corners, the more characteristic concave-convex the target product is, the more complex the surface details of the target product are. Under the same product face number, characteristic corner number and characteristic concave-convex number, the smaller the product specifications, the more complex the surface details of the target product are. Compare the product complexity index of the target product with the standard complexity index; If the product complexity index is less than or equal to the first standard complexity index, the feature complexity level of the target product is determined to be the first feature complexity level; If the product complexity index is greater than the first standard complexity index and less than or equal to the second standard complexity index, the feature complexity level of the target product is determined to be the second feature complexity level; If the product complexity index is greater than the second standard complexity index, the characteristic complexity level of the target product is determined to be the third characteristic complexity level; Among them, the values ​​of the first standard complexity index and the second standard complexity index are both greater than zero, the first standard complexity index is less than the second standard complexity index, the complexity of the product corresponding to the first characteristic complexity level is lower than the complexity of the product corresponding to the second characteristic complexity level, and the complexity of the product corresponding to the second characteristic complexity level is lower than the complexity of the product corresponding to the third characteristic complexity level; The detection preparation module sends the feature complexity level of the target product to the server, and the server sends the feature complexity level of the target product to the configuration terminal.

[0023] Furthermore, the configuration terminal is used to adaptively adjust the configuration parameters of the quality inspection equipment. The parameter configuration process is as follows: In this embodiment, the quality inspection device is a device capable of capturing images at multiple points, and preferably a high-definition camera is used as the quality inspection device; Obtain the feature complexity level of the target product and set the configuration parameters of the quality inspection equipment according to the feature complexity level; The corresponding relationship between configuration parameters and feature complexity level is as follows: If the feature complexity level of the target product is the first feature complexity level, the configuration parameters of the corresponding quality inspection equipment are the third camera distance and the third viewing range; If the feature complexity level of the target product is the second feature complexity level, the configuration parameters of the corresponding quality inspection equipment are the second camera distance and the second viewing range; If the feature complexity level of the target product is the third feature complexity level, the configuration parameters of the corresponding quality inspection equipment are the first camera distance and the first viewing range; The value of the first camera distance is smaller than the value of the second camera distance, the value of the second camera distance is smaller than the value of the third camera distance, the value of the first framing range is smaller than the value of the second framing range, and the value of the second framing range is smaller than the value of the third framing range; It can be understood that the camera distance is the distance between the shooting lens of the quality inspection equipment and the target product, and the viewing range is the size of the viewfinder of the corresponding shooting lens. The closer the camera distance, the smaller the viewing range, and the finer the image captured by the quality inspection equipment.

[0024] As a further solution of the present invention, after the debugging of the quality inspection equipment is completed, the production workshop performs the production operation of the target product, and transports the completed target product to the quality inspection equipment for quality inspection; See also Figure 4 As shown, the image acquisition module is used to acquire pixel capture data of the target product and send the pixel capture data to the server. The server sends the pixel capture data to the image analysis module. The pixel capture data includes the number of captured images of the target product, the captured image specifications, and the coordinate color array of each captured image. The coordinate color array is specifically the RGB value of each pixel point in the captured image. It should be noted that the captured image specification is the area size of the target product mapped by the viewing range of the quality inspection equipment. The unit of the captured image specification is pixel. Please refer to Figure 4 As shown, the larger the value of the camera distance of the quality inspection device is, the larger the viewing range is, and the larger the mapping range of the viewing range of the quality inspection device corresponding to the target product is, that is, the larger the specification of the captured image is; Similarly, it is preferred to use qualified target products of the same batch and specifications as sample products, and obtain pixel capture data of the sample products as pixel reference data. The pixel reference data includes the number of captured images of the sample products, the captured image specifications, and the comparison color array of each captured image.

[0025] Furthermore, the image analysis module is used to perform image analysis on the captured image of the target product. The specific analysis process is as follows: Obtain the number of captured images N, the captured image specification TG, and the coordinate color array SCn (Rni, Gni, Bni) of each captured image of the target product, where n is the number of the captured image, the upper limit of n is the number of captured images N, and i is the sequence number of the pixel point, the upper limit of i is equal to the captured image specification TG; Then, pixel reference data including a comparison color array BSC (BRi, BGi, BBi) of each captured image of the sample product is obtained; The color deviation value PCn of the target product corresponding to the captured image is calculated according to the formula. The specific formula is as follows: ; comparing the color deviation value of the captured image with the deviation threshold value; If the color deviation value is less than the deviation threshold, no additional operation is performed; If the color deviation value is greater than or equal to the deviation threshold value, the position area corresponding to the target product in the captured image is defined as a suspected defect area; The image analysis module sends the suspected defective area of ​​the target product to the server, and the server sends the suspected defective area of ​​the target product to the surface detection module.

[0026] See also Figure 5 As shown, the surface detection module is used to perform defect detection on suspected defect areas of the target product; By scanning the surface of the target product, the defect impact range and defect peak-to-valley difference of the target product in the suspected defect area are obtained; In this embodiment, a laser rangefinder is used to scan the suspected defective area of ​​the target product. Figure 5 As shown in the figure, the defect impact range is specifically the total area of ​​the defect part and the defect impact area, and the defect peak-to-valley difference is the height difference between the highest point and the lowest point of the defect part; The surface detection module sends the defect impact range and defect peak-to-valley difference of the target product in the suspected defect area to the server, and the server sends the defect impact range and defect peak-to-valley difference of the target product in the suspected defect area to the defect assessment module.

[0027] Furthermore, the defect assessment module is used to comprehensively assess the surface defects of the target product. The specific assessment process is as follows: Obtain the defect impact range XS, defect peak-to-valley difference LC, and captured image specification TG of the target product in the suspected defect area, and calculate the defect impact value YX of the suspected defect area according to the formula. The specific formula is as follows: ; Among them, b1 and b2 are weight coefficients with fixed values, the values ​​of b1 and b2 are both greater than zero, b1 is the defect proportion weight, and b2 is the defect depth weight; Compare the defect impact value of the suspected defect area with the defect impact interval; If the defect impact value belongs to the first defect impact interval, the evaluation result of the suspected defect area is determined to be a slight defect; If the defect impact value belongs to the second defect impact interval, the evaluation result of the suspected defect area is determined to be a severe defect; The specific values ​​of the defect impact interval are pre-stored in the database, the values ​​of the first defect impact interval and the second defect impact interval are both greater than zero, and the values ​​of the first defect impact interval are both less than the values ​​of the second defect impact interval; The defect assessment module sends the assessment results of the suspected defect areas to the server, and the server sends the assessment results of the suspected defect areas to the sorting terminal.

[0028] In this embodiment, the sorting terminal is used to sort the target products, and the sorting process includes: If the target product does not have any suspected defective area, the target product will be transported to the product storage area; If the target product has suspected defective areas and the evaluation results of the suspected defective areas are all slight defects, the target product will be transported to the rework product area; If the target product has suspected defective areas and the evaluation results of the suspected defective areas are all severe defects, the target product will be transported to the waste product area; In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0029] Embodiment 2: See also Figure 2 As shown, based on another concept of the same invention, a production workshop product quality detection system is now proposed, including: Production terminal, used to upload product specification data of target products; The detection preparation module is used to analyze the detection accuracy of the target product and obtain the feature complexity level of the target product; Configuration terminal, used to adaptively adjust the configuration parameters of quality inspection equipment; Further, an image acquisition module is used to acquire pixel capture data of the target product; An image analysis module, for performing image analysis on a captured image of a target product; A surface inspection module is used to perform defect inspection on suspected defect areas of the target product; Defect assessment module, used to comprehensively evaluate the surface defects of the target product; The sorting terminal is used to sort the target products.

[0030] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for detecting product quality in a production workshop, characterized in that: The method steps include: Step S101, the production terminal sends the product specification data of the target product to the detection preparation module, and the detection preparation module analyzes the detection accuracy of the target product, and sends the characteristic complexity level of the target product obtained by the analysis to the configuration terminal; Step S102, the configuration terminal adaptively adjusts the configuration parameters of the quality inspection equipment. After the debugging of the quality inspection equipment is completed, the production workshop performs the production operation of the target product and transports the completed target product to the quality inspection equipment for quality inspection; Step S103, the image acquisition module acquires pixel capture data of the target product and sends it to the image analysis module. Meanwhile, the target product of the same batch and the same specification that has passed the inspection is preferably selected as a sample product, and the pixel capture data of the sample product is acquired as pixel reference data; Step S104: the image analysis module performs image analysis on the captured image of the target product, obtains the suspected defect area of ​​the target product through analysis, and sends it to the surface detection module; the surface detection module performs defect detection on the suspected defect area of ​​the target product, obtains the defect impact range and defect peak-to-valley difference of the target product in the suspected defect area through detection, and sends them to the defect assessment module; In step S105, the defect assessment module performs a comprehensive assessment on the surface defects of the target product and sends the assessment results of the suspected defective areas to the sorting terminal, and then the sorting terminal sorts the target product.

2. A method for detecting product quality in a production workshop according to claim 1, characterized in that: The product specification data includes the standard product face number, characteristic corner number and characteristic concavity and convexity number of the target product; The pixel capture data includes the number of captured images of the target product, the captured image specifications, and the coordinate color array of each captured image; The pixel reference data includes the number of captured images of the sample product, the captured image specifications, and a comparison color array for each captured image.

3. A method for detecting product quality in a production workshop according to claim 2, characterized in that: The analysis process of the detection preparation module in step S101 is specifically as follows: Obtain the standard product face number, characteristic corner number and characteristic concave-convex number of the target product, and calculate the product complexity index of the target product; Compare the product complexity index of the target product with the standard complexity index; If the product complexity index is less than or equal to the first standard complexity index, the characteristic complexity level of the target product is determined to be the first characteristic complexity level; If the product complexity index is greater than the first standard complexity index and less than or equal to the second standard complexity index, the feature complexity level of the target product is determined to be the second feature complexity level; If the product complexity index is greater than the second standard complexity index, the feature complexity level of the target product is determined to be the third feature complexity level.

4. A method for detecting product quality in a production workshop according to claim 3, characterized in that: The values ​​of the first standard complexity index and the second standard complexity index are both greater than zero, the first standard complexity index is smaller than the second standard complexity index, the complexity of products corresponding to the first characteristic complexity level is lower than the complexity of products corresponding to the second characteristic complexity level, and the complexity of products corresponding to the second characteristic complexity level is lower than the complexity of products corresponding to the third characteristic complexity level.

5. A method for detecting product quality in a production workshop according to claim 3, characterized in that: The parameter configuration process of the terminal in step S102 is specifically as follows: Obtain the feature complexity level of the target product and set the configuration parameters of the quality inspection equipment according to the feature complexity level; The corresponding relationship between configuration parameters and feature complexity level is as follows: If the feature complexity level of the target product is the first feature complexity level, the configuration parameters of the corresponding quality inspection equipment are the third camera distance and the third viewing range; If the feature complexity level of the target product is the second feature complexity level, the configuration parameters of the corresponding quality inspection equipment are the second camera distance and the second viewing range; If the feature complexity level of the target product is the third feature complexity level, the configuration parameters of the corresponding quality inspection equipment are the first camera distance and the first viewing range; Among them, the value of the first camera distance is smaller than the value of the second camera distance, the value of the second camera distance is smaller than the value of the third camera distance, the value of the first viewing range is smaller than the value of the second viewing range, and the value of the second viewing range is smaller than the value of the third viewing range.

6. A method for detecting product quality in a production workshop according to claim 2, characterized in that: The analysis process of the image analysis module in step S104 is specifically as follows: Obtain the number of captured images of the target product, the captured image specifications, and the coordinate color array of each captured image; Then, pixel reference data including a comparison color array of each captured image of the sample product is obtained, and a color deviation value of the corresponding captured image of the target product is calculated; comparing the color deviation value of the captured image with the deviation threshold value; If the color deviation value is less than the deviation threshold, no additional operation is performed; If the color deviation value is greater than or equal to the deviation threshold value, the position area of ​​the captured image corresponding to the target product is defined as a suspected defect area.

7. A method for detecting product quality in a production workshop according to claim 6, characterized in that: The defect detection process of the surface detection module in step S104 specifically includes: By scanning the surface of the target product, the defect impact range and defect peak-to-valley difference of the target product in the suspected defect area are obtained; The suspected defective area of ​​the target product is scanned by a laser rangefinder, and the defect impact area is constructed according to the defect location. The total area of ​​the defect impact area is taken as the defect impact range of the target product. The highest and lowest points of the defect location are obtained by scanning, and the height difference between the highest and lowest points of the defect location is calculated as the defect peak-to-valley height difference of the target product.

8. A method for detecting product quality in a production workshop according to claim 7, characterized in that: The evaluation process of the defect evaluation module in step S105 is specifically as follows: Obtain the defect impact range, defect peak-to-valley difference, and captured image specifications of the target product in the suspected defect area, and calculate the defect impact value of the suspected defect area; Compare the defect impact value of the suspected defect area with the defect impact interval; The evaluation result of the suspected defect area is determined as a slight defect or a severe defect according to the defect impact interval to which the defect impact value belongs.

9. A method for detecting product quality in a production workshop according to claim 8, characterized in that: The sorting process of the sorting terminal in step S105 includes: If the target product does not have any suspected defective area, the target product will be transported to the product storage area; If the target product has suspected defective areas and the evaluation results of the suspected defective areas are all slight defects, the target product will be transported to the rework product area; If the target product has suspected defective areas and the evaluation results of the suspected defective areas are all severe defects, the target product will be transferred to the waste product area.

10. A production workshop product quality inspection system, characterized in that: According to a production workshop product quality detection method according to any one of claims 1 to 9, the system comprises: Production terminal, used to upload product specification data of target products; The detection preparation module is used to analyze the detection accuracy of the target product and obtain the feature complexity level of the target product; Configuration terminal, used to adaptively adjust the configuration parameters of quality inspection equipment; An image acquisition module, used to acquire pixel capture data of a target product; An image analysis module, for performing image analysis on a captured image of a target product; A surface inspection module is used to perform defect inspection on suspected defect areas of the target product; Defect assessment module, used to comprehensively evaluate the surface defects of the target product; The sorting terminal is used to sort the target products.

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