Production line quality management system based on machine vision

Abstract

The invention relates to the technical field of production line quality management, and discloses a production line quality management system based on machine vision, and the system comprises a plurality of production equipment thermal imaging data collection modules which are in one-to-one correspondence with production equipment and are used for collecting the thermal imaging data of the corresponding production equipment; the analysis module is used for analyzing the thermal imaging image data of each piece of production equipment; obtaining a production quality influence index of each piece of production equipment; judging the abnormal grade of the production equipment according to the production quality influence index; the real-time temperature of the whole production equipment is monitored in real time through thermal imaging image data, so that a worker can directly judge whether the temperature of the current production equipment affects the product quality or not through the production quality influence index and correspondingly process the production equipment according to the abnormal grade; downtime is reduced, and production efficiency and product quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production line quality management, and particularly to a production line quality management system based on machine vision. Background Art

[0002] A production line is an important production method in modern industry. By decomposing the complex production process into a series of simple and standardized operations through the production line, this method reduces the operation difficulty of workers and improves production efficiency.

[0003] A production line usually consists of several production devices. Different production devices have different temperature requirements. Some production devices need to strictly control the temperature, while the temperature of some production devices has a greater elasticity; among the production devices that need to strictly control the temperature, the temperature of the production device is usually monitored by installing a temperature sensor inside the production device; when the temperature is abnormal, a warning is given and the staff stops the machine for maintenance.

[0004] However, when stopping the machine for maintenance, it is very difficult for the staff to distinguish the severity of the production device abnormality and the fault location only based on the warning. If the staff performs a full-machine inspection on the machine, it may take a lot of time; resulting in a long downtime and reducing production efficiency. Summary of the Invention

[0005] The purpose of the present invention 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 invention can be achieved by the following technical solutions:

[0007] A production line quality management system based on machine vision is applicable to a production line. The production line includes several production devices. The production line quality management system includes:

[0008] Several production device thermal imaging data acquisition modules, corresponding to the production devices one by one, for acquiring thermal imaging image data of the corresponding production devices;

[0009] An analysis module, by analyzing the thermal imaging image data of each production device, obtains the production quality influence index of each production device; judges the abnormality level of the production device according to the production quality influence index; and processes according to the abnormality level of the production device.

[0010] As a further solution of the present invention: The analysis process of the analysis module is as follows:

[0011] S1: Number the production devices, denoted as number i;

[0012] S2: Acquire the thermal imaging image data of the production device numbered i through the thermal imaging data acquisition module corresponding to the production device numbered i;

[0013] S4: Analyze the real-time thermal imaging image data of the production equipment numbered i during the current working time to obtain the production quality impact index of the production equipment numbered i;

[0014] S5: Judge the abnormal level of the production equipment according to the production quality impact index; and process it according to the abnormal level of the production equipment.

[0015] As a further solution of the present invention: The process of obtaining the production quality impact index of the production equipment numbered i includes the following processes:

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

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

[0018] S30: Obtain the preset temperature change curve of the k-th monitoring area unit of the production equipment numbered i with the working duration through the preset information module;

[0019] S40; Analyze the real-time temperature change curve and the preset temperature change curve of the k-th monitoring area unit of the production equipment numbered i to obtain the temperature difference change curve of the absolute value of the temperature deviation in the k-th monitoring area unit of the production equipment numbered i with the working duration;

[0020] S50: Analyze the temperature difference change curve of the absolute value of the temperature deviation in the k-th monitoring area unit of the production equipment numbered i with the working duration to obtain the temperature anomaly index of the k-th monitoring area unit of the production equipment numbered i;

[0021] S60: Analyze the temperature anomaly indexes of all monitoring area units of the production equipment numbered i to obtain the production quality impact index of the production equipment numbered i.

[0022] As a further solution of the present invention: In step S50, through the formula:

[0023]

[0024] Calculate the temperature anomaly index P of the k-th monitoring area unit of the production equipment numbered i ik ;

[0025] where f(X) is the first judgment function, when X>0, f(X)=X; when X≤0, f(X)=0; T ikc (t) is the temperature difference change curve of the absolute value of the temperature deviation in the k-th monitoring area unit of the production equipment numbered i with the working duration; t iThe total duration from the start time to the current time of the production equipment numbered i for this work; W 1 The first allowable error; θ ik The adjustment coefficient of the k-th monitoring area unit of the production equipment numbered i, 0 ≤ θ ik ≤ 1; W 2 The second allowable error; T ikcmax The maximum temperature deviation value from the start time to the current time of the k-th monitoring area unit of the production equipment numbered i; C 1 The first preset constant; C 2 The second preset constant; δ 1 The first preset weight coefficient; δ 2 The second preset weight coefficient; C 3 The third preset constant.

[0026] As a further solution of the present invention: The method for obtaining the adjustment coefficient θ ik of the k-th monitoring area unit of the production equipment numbered i is as follows:

[0027] S100: Obtain the production area of the production equipment corresponding to the k-th monitoring area unit of the production equipment numbered i;

[0028] S200: Determine the marked parts within this production area;

[0029] S300: Determine the adjustment coefficient θ ik of the k-th monitoring area unit of the production equipment numbered i according to the marked parts.

[0030] As a further solution of the present invention: In step S60, through the formula:

[0031]

[0032] Calculate the production quality influence index Z i of the production equipment numbered i;

[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 anomalies among 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 solution of the present invention: The anomaly levels include no anomaly, minor anomaly, and severe anomaly.

[0035] As a further solution of the present invention: The process for judging the anomaly levels is as follows: Compare the production quality influence index Z i with the preset thresholds [R 1 、R2 for comparison;

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

[0037] When 0 < Z i ≤ R 1 at this time, the abnormal level of the production equipment numbered i is minor abnormality;

[0038] When R 1 < Z i ≤ R 2 at this time, the abnormal level of the production equipment numbered i is serious abnormality.

[0039] Advantages of the present invention:

[0040] In the present invention, by setting a production equipment thermal imaging data acquisition module near each production equipment, the thermal imaging image data of the corresponding production equipment is collected, and then the analysis module analyzes the thermal imaging image data of each production equipment; the production quality influence index of each production equipment is obtained; the abnormal level of the production equipment is judged according to the production quality influence index; and processing is carried out according to the abnormal level of the production equipment; by monitoring the real-time temperature of the entire production equipment through the thermal imaging image data, the staff can directly judge whether the current temperature of the production equipment will affect the product quality according to the production quality influence index, and perform corresponding processing on the production equipment according to the abnormal level, reducing the downtime and improving the production efficiency and product quality. Description of the drawings

[0041] The present invention will be further described below with reference to the drawings.

[0042] Figure 1 It is a system module framework diagram of an embodiment of the present invention. Detailed implementation manners

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0044] Please refer to Figure 1 As shown, in one embodiment, a production line quality management system based on machine vision is provided, which is applicable to a production line. The production line includes a plurality of production equipment, and the production line quality management system includes:

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

[0046] An analysis module 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;

[0047] Through the above technical solution, in this embodiment, a production equipment thermal imaging data acquisition module is set near each production equipment, and the specific position is placed according to experience to avoid external interference. The thermal imaging image data of the corresponding production equipment is acquired, and then the analysis module 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 real-time temperature of the entire production equipment is monitored in real time through the thermal imaging image data. The staff can directly judge whether the current temperature of the production equipment will affect the product quality according to the production quality influence index, and perform corresponding processing on the production equipment according to the abnormal level, reducing the downtime and improving the production efficiency and product quality;

[0048] It should be noted that monitoring the real-time temperature of the entire production equipment in real time according to the thermal imaging image data and identifying the thermal imaging image data through machine vision are prior arts and will not be elaborated here.

[0049] As an implementation manner of the present invention, the analysis process of the analysis module is as follows:

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

[0051] S2: Acquire the 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: Analyze the real-time thermal imaging image data of the production equipment numbered i during the current working time to obtain the production quality influence index of the production equipment numbered i;

[0053] 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.

[0054] Through the above technical solution, in this embodiment, the production equipment is first numbered as number i; then the thermal imaging image data of the production equipment numbered i is collected by the corresponding thermal imaging data acquisition module of the production equipment numbered i; then, by analyzing the real-time thermal imaging image data of the production equipment numbered i during the current working time, the production quality influence index of the production equipment numbered i is obtained; finally, the abnormal level of the production equipment is judged according to the production quality influence index; and the processing is carried out according to the abnormal level of the production equipment; the abnormality of the production equipment numbered i is timely detected, preventing the product quality from being affected due to the abnormality of the production equipment numbered i, and improving the product qualification rate.

[0055] As an implementation manner of the present invention, the obtaining process of the production quality influence index of the production equipment numbered i includes the following process:

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

[0057] S20: Obtain the real-time temperature change curve of the highest temperature in the k-th monitoring area unit of the production equipment numbered i with the current working duration;

[0058] S30: Obtain the preset temperature change curve of the k-th monitoring area unit of the production equipment numbered i with the working duration through the preset information module;

[0059] S40; Analyze the real-time temperature change curve and the preset temperature change curve of the k-th monitoring area unit of the production equipment numbered i to obtain the temperature difference change curve of the absolute value of the temperature deviation in the k-th monitoring area unit of the production equipment numbered i with the working duration;

[0060] S50: Analyze the temperature difference change curve of the absolute value of the temperature deviation in the k-th monitoring area unit of the production equipment numbered i with the working duration to obtain the temperature abnormality index of the k-th monitoring area unit of the production equipment numbered i;

[0061] S60: Analyze the temperature abnormality indexes of all monitoring area units of the production equipment numbered i to obtain the production quality influence index of the production equipment numbered i.

[0062] Through the above technical solution, in this embodiment, 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, by obtaining the real-time temperature change curve of the highest temperature in the k-th monitoring area unit of the production equipment numbered i with the current working duration; afterwards, through the preset information module, the preset temperature change curve of the k-th monitoring area unit of the production equipment numbered i with the working duration is obtained; subsequently, by analyzing the real-time temperature change curve and the preset temperature change curve of the k-th monitoring area unit of the production equipment numbered i, the temperature difference change curve of the absolute value of the temperature deviation in the k-th monitoring area unit of the production equipment numbered i with the working duration is obtained; then, by analyzing the temperature difference change curve of the absolute value of the temperature deviation in the k-th monitoring area unit of the production equipment numbered i with the working duration, the temperature anomaly index of the k-th monitoring area unit of the production equipment numbered i is obtained; finally, by analyzing the temperature anomaly indexes of all monitoring area units of the production equipment numbered i, the production quality influence index of the production equipment numbered i is obtained; the abnormality of the production equipment numbered i is detected in a timely manner, preventing the product quality from being affected due to the abnormality of the production equipment numbered i, and improving the product qualification rate.

[0063] As an implementation manner of the present invention, in step S50, through the formula:

[0064]

[0065] Calculate the temperature anomaly index P of the k-th monitoring area unit of the production equipment numbered i ik ;

[0066] wherein, f(X) is the first judgment function. When X>0, f(X)=X; when X≤0, f(X)=0; T ikc (t) is the temperature difference change curve of the absolute value of the temperature deviation in the k-th monitoring area unit of the production equipment numbered i with the working duration; t i is the total duration from the start time of the current work of the production equipment numbered i to the current time; W 1 is the first allowable error; θ ik is the adjustment coefficient of the k-th monitoring area unit of the production equipment numbered i, 0≤θ ik ≤1; W 2 is the second allowable error; T ikcmax is the maximum temperature deviation value from the start time of the current work to the current time of the k-th monitoring area unit of the production equipment numbered i; C 1 is the first preset constant; C 2 is the second preset constant; δ 1 is the first preset weight coefficient; δ 2 is the second preset weight coefficient; C 3 is the third preset constant.

[0067] Through the above technical solution, in this embodiment is the cumulative amount of the absolute value of the temperature deviation of the k-th monitoring area unit for the total duration from the start time to the current time of the production equipment numbered i; θ ik W 1 is the allowable error value of the k-th monitoring area unit of the production equipment numbered i for the first time; is the difference between the cumulative amount of the absolute value of the temperature deviation of the k-th monitoring area unit for the total duration from the start time to the current time of the production equipment numbered i and the allowable error value of the k-th monitoring area unit of the production equipment numbered i for the first time; in the formula in, 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 k-th monitoring area unit for the total duration from the start time to the current time of the production equipment numbered i is within the allowable error value of the k-th monitoring area unit of the production equipment numbered i for the first time. Therefore, the cumulative amount of the absolute value of the temperature deviation of the k-th monitoring area unit for the total duration from the start time to the current time of the production equipment numbered i has no impact on the product quality; When , it indicates that the cumulative amount of the absolute value of the temperature deviation of the k-th monitoring area unit for the total duration from the start time to the current time of the production equipment numbered i exceeds the allowable error value of the k-th monitoring area unit of the production equipment numbered i for the first time. Therefore, the cumulative amount of the absolute value of the temperature deviation of the k-th monitoring area unit for the total duration from the start time to the current time of the production equipment numbered i has a greater impact on the product quality; The difference between the cumulative amount of the absolute value of the temperature deviation of the k-th monitoring area unit for the total duration from the start time to the current time of the production equipment numbered i and the allowable error value of the k-th monitoring area unit of the production equipment numbered i for the first time The greater the value, the greater the impact on the product quality, and the temperature anomaly index P of the k-th monitoring area unit of the production equipment numbered i ik The greater the value; θ ik W 2 is the allowable error value of the k-th monitoring area unit of the production equipment numbered i for the second time; T ikcmax -θ ik W 2 is the difference between the maximum temperature deviation value of the k-th monitoring area unit of the production equipment numbered i from the start time to the current time and the allowable error value of the k-th monitoring area unit of the production equipment numbered i for the second time; in the formula f(T ikcmax -θ ik W 2 ), X in the first judgment function f(X) refers to T ikcmax -θ ik W 2 ; when Tikcmax -θ ik W 2 When ≤ 0, the maximum temperature deviation value from the start time to the current time of the k-th monitoring area unit of the production equipment numbered i is within the allowable error value of the k-th monitoring area unit of the second production equipment numbered i, indicating that the maximum temperature deviation value from the start time to the current time of the k-th monitoring area unit of the production equipment numbered i has no impact on product quality, f(T ikcmax -θ ik W 2 ) = 0; when T ikcmax -θ ik W 2 > 0, the maximum temperature deviation value from the start time to the current time of the k-th monitoring area unit of the production equipment numbered i exceeds the allowable error value of the k-th monitoring area unit of the second production equipment numbered i, indicating that the maximum temperature deviation value from the start time to the current time of the k-th monitoring area unit of the production equipment numbered i has an impact on product quality; f(T ikcmax -θ ik W 2 ) = T ikcmax -θ ik W 2 The difference T between the maximum temperature deviation value from the start time to the current time of the k-th monitoring area unit of the production equipment numbered i and the allowable error value of the k-th monitoring area unit of the second production equipment numbered i ikcmax -θ ik W 2 The greater the value, the greater the impact on product quality, and the temperature anomaly index P of the k-th monitoring area unit of the production equipment numbered i ik is greater; when and f(T ikcmax -θ ik W 2 ) = 0, it indicates that the k-th monitoring area unit of the production equipment numbered i has no impact on product quality. Therefore, when P ik = 0, the k-th monitoring area unit of the production equipment numbered i has no anomaly; when P ik > 0, the k-th monitoring area unit of the production equipment numbered i has an anomaly;

[0068] It should be noted that the first allowable error W 1 , the second allowable error W 2 , the first preset constant C 1 , the second preset constant C 2 , the first preset weight coefficient δ 1 , the second preset weight coefficient δ 2 and the third preset constant C 3 are preset values obtained based on experience and will not be elaborated here.

[0069] As an implementation manner of the present invention, the adjustment coefficient θ of the k-th monitoring area unit of the production equipment numbered i ik is obtained as follows:

[0070] S100: Obtain the production area of the production equipment corresponding to the k-th monitoring area unit of the production equipment numbered i;

[0071] S200: Determine the marked parts within this production area;

[0072] S300: Determine the adjustment coefficient θ of the k-th monitoring area unit of the production equipment numbered i according to the marked parts ik .

[0073] Through the above technical solution, in this embodiment, the production area of the production equipment corresponding to the k-th monitoring area unit of the production equipment numbered i is obtained. There are several production parts participating in the operation within this production area. The production part that has the greatest influence on the product quality under the preset temperature difference change within this production area is selected as the marked part of the k-th monitoring area unit of the production equipment numbered i; the adjustment coefficient θ of the k-th monitoring area unit of the production equipment numbered i is determined according to the degree of influence of the marked part of the k-th monitoring area unit of the production equipment numbered i on the product quality under the preset temperature difference change ik , the greater the degree of influence of the marked part of the k-th monitoring area unit of the production equipment numbered i on the product quality under the preset temperature difference change, the adjustment coefficient θ of the k-th monitoring area unit of the production equipment numbered i ik is smaller, and the allowable error value θ ik W 1 of the k-th monitoring area unit of the first production equipment numbered i is smaller; the smaller the degree of influence of the marked part of the k-th monitoring area unit of the production equipment numbered i on the product quality under the preset temperature difference change, the adjustment coefficient θ of the k-th monitoring area unit of the production equipment numbered i ik is larger, and the allowable error value θ ik W 1 of the k-th monitoring area unit of the production equipment numbered i is larger; the specific value of the adjustment coefficient θ of the k-th monitoring area unit of the production equipment numbered i ik is determined according to experience and will not be elaborated here.

[0074] As an implementation manner of the present invention, in step S60, through the formula:

[0075]

[0076] calculate the production quality influence index Z of the production equipment numbered i i ;

[0077] wherein, K is the total number of monitoring area units of the production equipment numbered i, k ∈ K; Qiy The total number of temperature anomalies in the K monitoring area units of the production equipment numbered i; Is the first comprehensive weight coefficient; Is the second comprehensive weight coefficient.

[0078] Through the above technical solution, in this embodiment Is the cumulative value of the temperature anomaly index in the K monitoring area units of the monitoring area unit of the production equipment numbered i; the cumulative value of the temperature anomaly index in the K monitoring area units of the monitoring area unit of the production equipment numbered i The larger it is, the more serious the abnormal situation of the production equipment numbered i, the greater the impact on product quality, and the production quality impact index Z of the production equipment numbered i i The larger; the total number Q of temperature anomalies in the K monitoring area units of the production equipment numbered i iy The larger, the more serious the abnormal situation of the production equipment numbered i, the greater the impact on product quality, and the production quality impact index Z of the production equipment numbered i i The larger;

[0079] It should be noted that the first comprehensive weight coefficient And the second comprehensive weight coefficient Are preset values, obtained based on experience, and will not be elaborated here.

[0080] As an implementation manner of the present invention, the process of judging the abnormal level is:

[0081] Compare the production quality impact index Z i With the preset threshold [R 1 、R 2 ;

[0082] When Z i = 0, the abnormal level of the production equipment numbered i is no abnormality;

[0083] When 0 < Z i ≤ R 1 At this time, the abnormal level of the production equipment numbered i is minor abnormality;

[0084] When R 1 < Z i ≤ R 2 At this time, the abnormal level of the production equipment numbered i is severe abnormality.

[0085] Through the above technical solution, in this embodiment when Z i = 0, it indicates that the total number Q of temperature anomalies in the K monitoring area units of the production equipment numbered i iy And the cumulative value of the temperature anomaly index in the K monitoring area units of the monitoring area unit of the production equipment numbered i are both zero; the abnormal level of the production equipment numbered i is no abnormality; when 0 < Zi ≤R 1 When it is, it indicates that the production quality influence index Z of the production equipment with serial number i i is relatively small, and the abnormal level of the production equipment with serial number i is a minor abnormality; perform maintenance according to the monitoring area unit of temperature abnormality in the production equipment with serial number i; in the case of minor abnormality, the staff can directly judge the location where the abnormality occurs based on the temperature abnormality index of each monitoring area unit, accelerate the maintenance speed, and reduce the downtime; when R 1 <Z i ≤R 2 When it is, the abnormal level of the production equipment with serial number i is a serious abnormality; it indicates that the production quality influence index Z of the production equipment with serial number i i is relatively large, and the abnormal level of the production equipment with serial number i is a serious abnormality; perform a complete machine maintenance on the production equipment with serial number i;

[0086] It should be noted that the preset thresholds [R 1 、R 2 are preset values, obtained based on experience, and will not be elaborated here.

[0087] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be considered as used to limit the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the patent coverage scope of the present invention.

Claims

1. A production line quality management system based on machine vision, applicable to a production line, wherein the production line includes several production equipments, characterized in that: The production line quality management system includes: Several production equipment thermal imaging data acquisition modules, corresponding to production equipment one by one, used to acquire thermal imaging image data of corresponding production equipment; The analysis module analyzes the thermal imaging image data of each production equipment; obtains the production quality impact index of each production equipment; determines the abnormality level of the production equipment according to the production quality impact index; and processes the production equipment according to the abnormality level.

2. The production line quality management system based on machine vision according to claim 1, characterized in that: The analysis process of the analysis module is as follows: S1: Number the production equipment, recorded as number i; S2: Collecting thermal imaging image data of production equipment number i through the thermal imaging data acquisition module corresponding to production equipment number i; S4: by analyzing the real-time thermal imaging image data of the production equipment numbered i during the current working time, the production quality impact index of the production equipment numbered i is obtained; S5: Determine the abnormality level of the production equipment according to the production quality impact index; and handle it according to the abnormality level of the production equipment.

3. The production line quality management system based on machine vision according to claim 2 is characterized in that: The process of obtaining the production quality impact index of the production equipment No. i includes the following steps: S10: Divide the thermal imaging image data of the production equipment numbered i into K monitoring area units; S20: obtaining a real-time temperature variation curve of the maximum temperature in the kth monitoring area unit of the production equipment numbered i versus the current working time; S30: obtaining a preset temperature variation curve of the preset temperature of the kth monitoring area unit of the production equipment numbered i versus the working time through the preset information module; S40; by analyzing the real-time temperature change curve of the kth monitoring area unit of the number i production equipment and the preset temperature change curve, obtaining the temperature difference change curve of the absolute value of the temperature deviation of the kth monitoring area unit of the number i production equipment with the working time; S50: Obtaining a temperature anomaly index of the kth monitoring area unit of the numbered i production equipment by analyzing a temperature difference variation curve of the absolute value of the temperature deviation of the kth monitoring area unit of the numbered i production equipment versus the working time; S60: Analyze the temperature anomaly index of all monitoring area units of the production equipment numbered i to obtain the production quality impact index of the production equipment numbered i.

4. The production line quality management system based on machine vision according to claim 3 is characterized in that: In step S50, by formula: Calculate the temperature anomaly index P of the kth monitoring area unit of the production equipment number i ik ; Wherein, f(X) is the first judgment function. When X>0, f(X)=X; when X≤0, f(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 versus the working time; t i is the total time from the start time of this work of the production equipment number i to the current time; W1 is the 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 the second allowable error; T ikcmax is the maximum temperature deviation value from the start time of this work to the current time of the kth monitoring area unit of the production equipment numbered i; 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.

5. The production line quality management system based on machine vision according to claim 4 is characterized in that: The adjustment coefficient θ of the kth monitoring area unit of the production equipment numbered i ik The method to obtain is: S100: Obtaining the production area of ​​the production equipment corresponding to the k-th monitoring area unit of the production equipment numbered i; S200: Determine the marked parts in the production area; S300: Determine the adjustment coefficient θ of the kth monitoring area unit of the production equipment numbered i according to the marked parts ik .

6. The production line quality management system based on machine vision according to claim 5, characterized in that: In step S60, by formula: Calculate the production quality impact index Z of production equipment No. i i ; Where K is the total number of monitoring area units of production equipment numbered i, k∈K; Qi y The total number of temperature anomalies in K monitoring area units of production equipment numbered i; is the first comprehensive weight coefficient; is the second comprehensive weight coefficient.

7. The production line quality management system based on machine vision according to claim 6, characterized in that: The abnormality levels include no abnormality, slight abnormality and severe abnormality.

8. The production line quality management system based on machine vision according to claim 7, characterized in that: The abnormal level judgment process is as follows: The production quality impact index Z i Compare with the preset thresholds [R1, R2]; When Z i =0, the abnormality level of the production equipment No. i is no abnormality; When 0 <Z i When ≤R1, the abnormality level of production equipment No. i is slight abnormality; When R1 <Z i When ≤R2, the abnormality level of production equipment No. i is severe abnormality.

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