Machine vision-based production line quality management system

Abstract

The application relates to the technical field of production line quality management, and discloses a production line quality management system based on machine vision, which comprises a plurality of production equipment thermal imaging data acquisition modules, which are in one-to-one correspondence with the production equipment and are used for collecting thermal imaging image data of the corresponding production equipment; an analysis module which analyzes the thermal imaging image data of each production equipment; an acquisition module which acquires production quality influence indexes of each production equipment; an abnormality grade judgment module which judges the abnormality grade of the production equipment according to the production quality influence indexes; and a processing module which processes the production equipment according to the abnormality grade. The real-time temperature of the whole production equipment is monitored in real time through the thermal imaging image data, a worker can directly judge whether the temperature of the current production equipment will affect the product quality through the production quality influence indexes, and the production equipment is processed according to the abnormality grade, so that downtime is reduced, and production efficiency and product quality are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of production line quality management, in particular to a production line quality management system based on machine vision. BACKGROUND

[0002] The production line is an important production mode in modern industry, which divides the complex production process into a series of simple and standardized operations, thus reducing the operation difficulty of workers and improving the production efficiency.

[0003] The production line is usually composed of several production devices, different production devices have different temperature requirements, some production devices need to strictly control the temperature, and the temperature flexibility of some production devices is larger; in the production device which needs to strictly control the temperature, a temperature sensor is usually installed in the production device to monitor the temperature of the production device; when the temperature is abnormal, a warning is given, and the worker stops and maintains.

[0004] However, when stopping and maintaining, the worker can hardly identify the severity and fault position of the production device according to the warning, and the worker may need to spend a lot of time in full machine maintenance; thus causing long downtime and reducing production efficiency. SUMMARY

[0005] The purpose of the present application is to provide a production line quality management system based on machine vision to solve the above technical problems:

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] The production line quality management system based on machine vision is suitable for a production line, the production line includes several production devices, and the production line quality management system includes:

[0008] A plurality of production device thermal imaging data acquisition modules correspond to the production devices one by one, and are used for acquiring thermal imaging image data of the corresponding production device;

[0009] An analysis module is used for analyzing the thermal imaging image data of each production device, obtaining a production quality influence index of each production device, judging an abnormality level of the production device according to the production quality influence index, and processing according to the abnormality level of the production device.

[0010] As a further scheme of the present application, the analysis process of the analysis module is:

[0011] S1: numbering the production device, denoted as number i;

[0012] S2: collecting the thermal imaging image data of the number i production device through the thermal imaging data acquisition module corresponding to the number i production device.

[0013] S4: obtaining the production quality influence index of the production equipment numbered i by analyzing the real-time thermal imaging image data of the production equipment numbered i in the current working time;

[0014] S5: judging the abnormal level of the production equipment according to the production quality influence index; and processing according to the abnormal level of the production equipment.

[0015] As a further scheme of the present application, the obtaining process of the production quality influence index of the production equipment numbered i comprises the following processes:

[0016] S10: dividing the thermal imaging image data of the production equipment numbered i into K monitoring area units;

[0017] S20: obtaining the real-time temperature change curve of the highest temperature in the kth monitoring area unit of the production equipment numbered i with the current working time;

[0018] S30: obtaining the preset temperature change curve of the preset temperature in the kth monitoring area unit of the production equipment numbered i with the working time through the preset information module;

[0019] S40: obtaining the temperature difference change curve of the temperature deviation absolute value in the kth monitoring area unit of the production equipment numbered i with the working time by analyzing the real-time temperature change curve and the preset temperature change curve;

[0020] S50: obtaining the temperature abnormality index of the kth monitoring area unit of the production equipment numbered i by analyzing the temperature difference change curve of the temperature deviation absolute value in the kth monitoring area unit of the production equipment numbered i with the working time;

[0021] S60: obtaining the production quality influence index of the production equipment numbered i by analyzing the temperature abnormality indexes of all monitoring area units of the production equipment numbered i.

[0022] As a further scheme of the present application, in step S50, the temperature abnormality index P of the kth monitoring area unit of the production equipment numbered i is calculated by the formula:

[0023]

[0024] ik ;

[0025] Wherein, f(X) is the first judgment function, f(X)=X when X>0; f(X)=0 when X≤0; T ikc (t) is the temperature difference change curve of the temperature deviation absolute value in the kth monitoring area unit of the production equipment numbered i with the working time; t i ​The total duration of the current work of the production equipment numbered i from the start time to the current time; W1 is the first allowable error; θ ik The adjustment coefficient of the kth monitoring area unit of the production equipment numbered i, 0≤θ ik ≤1; W2 is the second allowable error; T ikcmax The maximum temperature deviation value of the kth monitoring area unit of the production equipment numbered i from the start time to the current time; C1 is the first preset constant; C2 is the second preset constant; δ1 is the first preset weight coefficient; δ2 is the second preset weight coefficient; C3 is the third preset constant.

[0026] As a further scheme of the application: the adjustment coefficient θ ik of the kth monitoring area unit of the production equipment numbered i is obtained by the following method:

[0027] S100: obtaining the production area of the corresponding production equipment of the kth monitoring area unit of the production equipment numbered i;

[0028] S200: determining the labeled parts in the production area;

[0029] S300: determining the adjustment coefficient θ ik of the kth monitoring area unit of the production equipment numbered i according to the labeled parts.

[0030] As a further scheme of the application: in step S60, the production quality influence index Z i of the production equipment numbered i is calculated by the formula:

[0031]

[0032] iy ;

[0033] Wherein, K is the total number of monitoring area units of the production equipment numbered i, k∈K; Q iy is the total number of temperature abnormalities in the K monitoring area units of the production equipment numbered i; is the first comprehensive weight coefficient; is the second comprehensive weight coefficient.

[0034] As a further scheme of the application: the abnormality level includes no abnormality, slight abnormality and serious abnormality.

[0035] As a further scheme of the application: the judgment process of the abnormality level is: comparing the production quality influence index Z i with the preset threshold [R1, R2];

[0036] When Z i = 0, the abnormality level of the production equipment numbered i is no abnormality;

[0037] When 0 < Z < R1, the abnormal level of the production equipment numbered i is slight abnormality; i When 0 < Z < R1, the abnormal level of the production equipment numbered i is slight abnormality;

[0038] When R1 < Z < R2, the abnormal level of the production equipment numbered i is serious abnormality. i When R1 < Z < R2, the abnormal level of the production equipment numbered i is serious abnormality.

[0039] Advantages of the present application:

[0040] The present application sets a production equipment thermal imaging data acquisition module near each production equipment, acquires thermal imaging image data of the corresponding production equipment, and then analyzes the thermal imaging image data of each production equipment through an analysis module; obtains a production quality influence index of each production equipment; judges the abnormal level of the production equipment according to the production quality influence index; and processes according to the abnormal level of the production equipment; the real-time temperature of the entire production equipment is monitored in real time through the thermal imaging image data, and the staff can directly judge whether the temperature of the current production equipment will affect the product quality through the production quality influence index, and the production equipment is processed according to the abnormal level, thereby reducing downtime, improving production efficiency and product quality. BRIEF DESCRIPTION OF DRAWINGS

[0041] The present application will be further described below in conjunction with the drawings.

[0042] Figure 1 The system module framework of an embodiment of the present application. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0044] Please refer to Figure 1 As shown in the figure, in one embodiment, a machine vision-based production line quality management system is provided, which is suitable for a production line including a plurality of production equipment, and the production line quality management system includes:

[0045] A plurality of production equipment thermal imaging data acquisition modules correspond to the production equipment one by one, and are used to acquire thermal imaging image data of the corresponding production equipment;

[0046] An analysis module is used to analyze the thermal imaging image data of each production equipment; obtain a production quality influence index of each production equipment; judge the abnormal level of the production equipment according to the production quality influence index; and process according to the abnormal level of the production equipment;

[0047] Through the technical scheme, the embodiment sets a production equipment thermal imaging data acquisition module near each production equipment, and the specific position is placed according to experience, so that there is no external interference; thermal imaging image data of the corresponding production equipment is collected, and then the thermal imaging image data of each production equipment is analyzed through an analysis module; a production quality influence index of each production equipment is obtained; an abnormality level of the production equipment is judged according to the production quality influence index; and the production equipment is processed according to the abnormality level; the real-time temperature of the entire production equipment is monitored in real time through the thermal imaging image data, and the production quality influence index can be directly used by a worker to judge whether the temperature of the current production equipment will affect the product quality, and the production equipment is processed according to the abnormality level, so that downtime is reduced, and production efficiency and product quality are improved.

[0048] It should be noted that real-time monitoring of the real-time temperature of the entire production equipment according to the thermal imaging image data is identified through machine vision, which is prior art and will not be described in detail here.

[0049] As an embodiment of the application, the analysis process of the analysis module is as follows:

[0050] S1: numbering the production equipment, denoted as number i;

[0051] S2: collecting thermal imaging image data of the production equipment numbered i through the thermal imaging data acquisition module corresponding to the production equipment numbered i;

[0052] S4: obtaining a production quality influence index of the production equipment numbered i by analyzing the real-time thermal imaging image data of the production equipment numbered i in the current working time;

[0053] S5: judging an abnormality level of the production equipment according to the production quality influence index; and processing the production equipment according to the abnormality level.

[0054] Through the technical scheme, the embodiment first numbers the production equipment, denoted as number i; then collects thermal imaging image data of the production equipment numbered i through the thermal imaging data acquisition module corresponding to the production equipment numbered i; then obtains a production quality influence index of the production equipment numbered i by analyzing the real-time thermal imaging image data of the production equipment numbered i in the current working time; finally, judges an abnormality level of the production equipment according to the production quality influence index; and processes the production equipment according to the abnormality level; the abnormality of the production equipment numbered i is found in time, the product quality is prevented from being affected due to the abnormality of the production equipment numbered i, and the product qualification rate is improved.

[0055] As an embodiment of the application, the process of obtaining the production quality influence index of the production equipment numbered i includes the following processes:

[0056] S10: dividing the thermal imaging image data of the production equipment numbered i into K monitoring area units;

[0057] S20: obtaining a real-time temperature change curve of the highest temperature in the kth monitoring area unit of the production equipment numbered i with respect to the current working time length;

[0058] S30: obtaining a preset temperature change curve of the preset temperature of the kth monitoring area unit of the production equipment numbered i with respect to the working time length through a preset information module;

[0059] S40: obtaining a temperature difference change curve of the temperature deviation absolute value of the kth monitoring area unit of the production equipment numbered i with respect to the working time length by analyzing the real-time temperature change curve and the preset temperature change curve of the kth monitoring area unit of the production equipment numbered i;

[0060] S50: obtaining the temperature anomaly index of the kth monitoring area unit of the production equipment numbered i by analyzing the temperature difference change curve of the temperature deviation absolute value of the kth monitoring area unit of the production equipment numbered i with respect to the working time length;

[0061] S60: obtaining the production quality influence index of the production equipment numbered i by analyzing the temperature anomaly indexes of all the monitoring area units of the production equipment numbered i.

[0062] Through the above technical solution, the thermal imaging image data of the production equipment numbered i is first divided into K monitoring area units, the overall temperature of the production equipment numbered i is monitored, then a real-time temperature change curve of the highest temperature in the kth monitoring area unit of the production equipment numbered i with respect to the current working time length is obtained, then a preset temperature change curve of the preset temperature of the kth monitoring area unit of the production equipment numbered i with respect to the working time length is obtained through a preset information module, then a temperature difference change curve of the temperature deviation absolute value of the kth monitoring area unit of the production equipment numbered i with respect to the working time length is obtained by analyzing the real-time temperature change curve and the preset temperature change curve of the kth monitoring area unit of the production equipment numbered i, then the temperature anomaly index of the kth monitoring area unit of the production equipment numbered i is obtained by analyzing the temperature difference change curve of the temperature deviation absolute value of the kth monitoring area unit of the production equipment numbered i with respect to the working time length, and finally the production quality influence index of the production equipment numbered i is obtained by analyzing the temperature anomaly indexes of all the monitoring area units of the production equipment numbered i, so that the abnormality of the production equipment numbered i can be found in time, the product quality can be prevented from being affected due to the abnormality of the production equipment numbered i, and the product qualification rate is improved.

[0063] As an embodiment of the present application, in step S50, the temperature anomaly index is obtained by the formula:

[0064]

[0065] The temperature abnormality index P of the kth monitoring area unit of the i th production equipment is calculated ik ;

[0066] Wherein, f(X) is a first judgment function, f(X)=X when X>0; f(X)=0 when X≤0; T ikc (t) is the temperature difference curve of the absolute value of the temperature deviation of the kth monitoring area unit of the i th production equipment with the working time; t i is the total duration from the start time of the current work of the i th production equipment to the current time; W1 is a first allowable error; θ ik is the adjustment coefficient of the kth monitoring area unit of the i th production equipment, 0≤θ ik ≤1; W2 is a second allowable error; T ikcmax is the maximum temperature deviation value of the kth monitoring area unit of the i th production equipment from the start time of the current work to the current time; C1 is a first preset constant; C2 is a second preset constant; δ1 is a first preset weight coefficient; δ2 is a second preset weight coefficient; C3 is a third preset constant.

[0067] Through the above technical solution, the embodiment is the cumulative amount of the absolute value of the temperature deviation of the kth monitoring area unit from the start time of the current work of the i th production equipment to the current time; θ ik W1 is the allowable error value of the kth monitoring area unit of the i th production equipment; is the difference between the cumulative amount of the absolute value of the temperature deviation of the kth monitoring area unit from the start time of the current work of the i th production equipment to the current time and the allowable error value of the kth monitoring area unit of the i th production equipment; in the formula , X in the first judgment function f(X) refers to When , it indicates that the cumulative amount of the absolute value of the temperature deviation of the kth monitoring area unit from the start time of the current work of the i th production equipment to the current time is within the allowable error value of the kth monitoring area unit of the i th production equipment, so the cumulative amount of the absolute value of the temperature deviation of the kth monitoring area unit from the start time of the current work of the i th production equipment to the current time has no effect on the product quality; When , it indicates that the cumulative amount of the absolute value of the temperature deviation of the kth monitoring area unit from the start time of the current work of the i th production equipment to the current time exceeds the allowable error value of the kth monitoring area unit of the i th production equipment, so the cumulative amount of the absolute value of the temperature deviation of the kth monitoring area unit from the start time of the current work of the i th production equipment to the current time has a greater impact on the product quality; The total length of time from the start of the current work to the current time of the kth monitoring area unit of the i th production equipment The difference between the cumulative amount of the absolute value of the temperature deviation of the kth monitoring area unit of the i th production equipment and the allowable error value of the kth monitoring area unit of the i th production equipment The greater, the greater the impact on product quality, the temperature anomaly index P of the kth monitoring area unit of the i th production equipment ik The greater, the greater the impact on product quality, the temperature anomaly index P of the kth monitoring area unit of the i th production equipment ik W2 is the allowable error value of the kth monitoring area unit of the i th production equipment; T ikcmax -θ ik W2 is the difference between the maximum temperature deviation value of the kth monitoring area unit of the i th production equipment from the start of the current work to the current time and the allowable error value of the kth monitoring area unit of the i th production equipment; In the formula f(T ikcmax -θ ik W2), X in the first judgment function f(X) refers to T ikcmax -θ ik W2; when T ikcmax -θ ik W2≤0, the maximum temperature deviation value of the kth monitoring area unit of the i th production equipment from the start of the current work to the current time is within the allowable error value of the kth monitoring area unit of the i th production equipment, indicating that the maximum temperature deviation value of the kth monitoring area unit of the i th production equipment from the start of the current work to the current time has no impact on product quality, f(T ikcmax -θ ik W2) = 0; when T ikcmax -θ ik W2>0, the maximum temperature deviation value of the kth monitoring area unit of the i th production equipment from the start of the current work to the current time exceeds the allowable error value of the kth monitoring area unit of the i th production equipment, indicating that the maximum temperature deviation value of the kth monitoring area unit of the i th production equipment from the start of the current work to the current time has an impact on product quality; f(T ikcmax -θ ik W2) = T ikcmax -θ ik W2 The difference between the maximum temperature deviation value of the kth monitoring area unit of the i th production equipment from the start of the current work to the current time and the allowable error value of the kth monitoring area unit of the i th production equipment T ikcmax -θ ik The greater, the greater the impact on product quality, the temperature anomaly index P of the kth monitoring area unit of the i th production equipment ik The greater, the greater the impact on product quality, the temperature anomaly index P of the kth monitoring area unit of the i th production equipment And f(T ikcmax -θ ik W2) = 0, indicating that the kth monitoring area unit of the i th production equipment has no impact on product quality, so when Pik = 0, the numbered i production equipment k monitoring area unit has no abnormality; when P ik > 0, the numbered i production equipment k monitoring area unit has abnormality;

[0068] It should be noted that the first allowable error W1, the second allowable error W2, the first preset constant C1, the second preset constant C2, the first preset weight coefficient δ1, the second preset weight coefficient δ2 and the third preset constant C3 are preset values, which are obtained according to experience and will not be described here.

[0069] As an embodiment of the present application, the adjustment coefficient θ ik of the numbered i production equipment k monitoring area unit is obtained by the following method:

[0070] S100: obtaining the production area of the numbered i production equipment k monitoring area unit corresponding production equipment;

[0071] S200: determining the labeled parts in the production area;

[0072] S300: determining the adjustment coefficient θ ik of the numbered i production equipment k monitoring area unit according to the labeled parts.

[0073] Through the above technical solution, the present embodiment obtains the production area of the numbered i production equipment k monitoring area unit corresponding production equipment, and there are several production parts participating in operation in the production area. The production part with the greatest impact on product quality under the preset temperature difference change in the production area is selected as the labeled part of the numbered i production equipment k monitoring area unit. The adjustment coefficient θ ik of the numbered i production equipment k monitoring area unit is determined according to the impact of the labeled part of the numbered i production equipment k monitoring area unit on product quality under the preset temperature difference change. The greater the impact of the labeled part of the numbered i production equipment k monitoring area unit on product quality under the preset temperature difference change, the smaller the adjustment coefficient θ ik of the numbered i production equipment k monitoring area unit, and the smaller the first allowable error value θ ik W1 of the numbered i production equipment k monitoring area unit. The smaller the impact of the labeled part of the numbered i production equipment k monitoring area unit on product quality under the preset temperature difference change, the greater the adjustment coefficient θ ik of the numbered i production equipment k monitoring area unit, and the greater the first allowable error value θ ik W1 of the numbered i production equipment k monitoring area unit. The specific value of the adjustment coefficient θ ik of the numbered i production equipment k monitoring area unit is determined according to experience and will not be described here.

[0074] As one embodiment of the present invention, in step S60, the formula is:

[0075]

[0076] Calculate the production quality impact index Z of production equipment number i. i ;

[0077] Where K is the total number of monitoring area units for production equipment numbered i, k∈K; Q iy The total number of temperature anomalies in the K monitoring area units of production equipment numbered i; This is the first comprehensive weighting coefficient; This is the second comprehensive weighting coefficient.

[0078] Through the above technical solution, this embodiment This refers to the cumulative value of the temperature anomaly index in the K monitoring area units of production equipment numbered i. The larger the value, the more severe the malfunction of production equipment number i, and the greater the impact on product quality. The production quality impact index Z for production equipment number i is... i The larger the number; the total number of temperature anomalies in the K monitoring area units of production equipment numbered i, Q iy The larger the value, the more severe the malfunction of production equipment number i, and the greater the impact on product quality. The production quality impact index Z for production equipment number i is... i The larger;

[0079] It should be noted that the first comprehensive weighting coefficient Second comprehensive weighting coefficient These are preset values, obtained based on experience, and will not be detailed here.

[0080] As one embodiment of the present invention, the process for determining the anomaly level is as follows:

[0081] The production quality impact index Z i Compare with preset thresholds [R1, R2];

[0082] When Z i When = 0, the anomaly level of production equipment number i is no anomaly;

[0083] When 0 <Z i When R1 is less than or equal to 1, the anomaly level of production equipment number i is minor.

[0084] When R1 <Z i When R2 is less than or equal to 2, the anomaly level of production equipment number i is severe anomaly.

[0085] Through the technical solution, when Z i =0, it indicates that the total number of temperature abnormality Q in the K monitoring area units of the production equipment numbered i iy and the cumulative value of the temperature abnormality index in the K monitoring area units of the production equipment numbered i are both zero; the abnormality level of the production equipment numbered i is no abnormality; when 0 i ≤R1, it indicates that the production quality influence index Z i of the production equipment numbered i is small, and the abnormality level of the production equipment numbered i is slight abnormality; the production equipment numbered i is repaired according to the monitoring area units of temperature abnormality; when the abnormality is slight, the staff can directly determine the position of abnormality according to the temperature abnormality index of each monitoring area unit, speed up the repair speed, and reduce the downtime; when R1 i ≤R2, the abnormality level of the production equipment numbered i is serious abnormality; it indicates that the production quality influence index Z i of the production equipment numbered i is large, and the abnormality level of the production equipment numbered i is serious abnormality; the production equipment numbered i is overhauled;

[0086] It should be noted that the preset threshold [R1, R2] is a preset value, which is obtained according to experience and will not be described here.

[0087] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application, and cannot be considered as limiting the scope of the present application. Any equivalent changes and improvements made according to the scope of the present application should still belong to the scope of the present application.

Claims

1. A machine vision-based production line quality management system suitable for a production line including a plurality of production devices, characterized by, The production line quality management system comprises: A plurality of production equipment thermal imaging data acquisition modules correspond to the production equipment one by one, and are used for collecting thermal imaging image data of the corresponding production equipment; An analysis module, which analyzes the thermal imaging image data of each production equipment, obtains the production quality influence index of each production equipment, judges the abnormal level of the production equipment according to the production quality influence index, and processes according to the abnormal level of the production equipment; The analysis process of the analysis module is: S1: number the production equipment, denoted as number i; S2: collect the thermal imaging image data of the number i production equipment through the thermal imaging data acquisition module corresponding to the number i production equipment; S4: analyze the real-time thermal imaging image data of the number i production equipment in this working time to obtain the production quality influence index of the number i production equipment; S5: judge the abnormal level of the production equipment according to the production quality influence index, and process according to the abnormal level of the production equipment; The obtaining process of the production quality influence index of the number i production equipment includes the following processes: S10: divide the thermal imaging image data of the number i production equipment into K monitoring area units; S20: obtain the real-time temperature change curve of the highest temperature in the kth monitoring area unit of the number i production equipment with the working time; S30: obtain the preset temperature change curve of the preset temperature of the kth monitoring area unit of the number i production equipment with the working time through the preset information module; S40: analyze the real-time temperature change curve and the preset temperature change curve of the kth monitoring area unit of the number i production equipment to obtain the temperature difference change curve of the temperature deviation absolute value of the kth monitoring area unit of the number i production equipment with the working time; S50: analyze the temperature difference change curve of the temperature deviation absolute value of the kth monitoring area unit of the number i production equipment with the working time to obtain the temperature abnormality index of the kth monitoring area unit of the number i production equipment; S60: analyze the temperature abnormality index of all monitoring area units of the number i production equipment to obtain the production quality influence index of the number i production equipment.

2. The machine vision-based production line quality management system according to claim 1, characterized by, In step S50, the formula is: calculating a temperature anomaly index P of the kth monitoring area unit of the i th production device ik ; Wherein, f(X) is a first judgment function, f(X)=X when X>0; f(X)=0 when X≤0; T ikc (t) is the temperature difference curve of the absolute value of the temperature deviation of the kth monitoring area unit of the production equipment numbered i with the working time; t i is the total length of time from the start time of this work of the production equipment numbered i to the current time; W1 is a first allowable error; θ ik is the adjustment coefficient of the kth monitoring area unit of the production equipment numbered i, 0≤θ ik ≤1; W2 is a second allowable error; T ikcmax is the maximum temperature deviation value of the kth monitoring area unit of the production equipment numbered i from the start time of this work to the current time; C1 is a first preset constant; C2 is a second preset constant; δ1 is a first preset weight coefficient; δ2 is a second preset weight coefficient; C3 is a third preset constant.

3. The machine vision-based production line quality management system according to claim 2, characterized by, the adjustment coefficient θ of the i-th production device k-th monitoring area unit ik The method for obtaining the adjustment coefficient θ is: S100: obtain the production area of the kth monitoring area unit of the number i production equipment corresponding to the production equipment; S200: determine the labeled parts in the production area; S300: Determine the adjustment coefficient θ of the i-th monitoring area unit of the k-th production equipment according to the labeled parts ik .

4. The machine vision-based production line quality management system according to claim 3, characterized by, In step S60, the formula is: calculating a production quality influence index Z of the production facility i i ; wherein K is the total number of production equipment monitoring area units numbered i, k∈K; Q iy is the total number of temperature abnormalities in the K monitoring area units of production equipment numbered i; is the first comprehensive weight coefficient; is the second comprehensive weight coefficient.

5. The machine vision-based production line quality management system according to claim 4, characterized by, The abnormal level includes no abnormality, slight abnormality and serious abnormality.

6. The machine vision-based production line quality management system according to claim 5, characterized by, The judgment process of the abnormal level is: The production quality influence index Z i is compared with a preset threshold [R1, R2]. When Z i = 0, the anomaly level of the production device numbered i is no anomaly; When 0 <Z i When R1 is less than or equal to 1, the anomaly level of production equipment number i is minor. When R1< Z i When R2, the anomaly level of the numbered i production device is a severe anomaly.

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