An intelligent seal quality assessment system based on big data

Abstract

The present invention relates to the technical field of seal quality assessment, and in particular to a seal quality intelligent assessment system based on big data, including a quality assessment platform, an image verification unit, an information collection unit, an appearance detection unit, a condition assessment unit, an integrated assessment unit and a feedback response unit; the present invention analyzes from two perspectives of the appearance and stability performance of the seal to improve the accuracy and comprehensiveness of the seal quality assessment, and analyzes the image processing operation in a progressive manner to reduce the influence of the preprocessing operation of the appearance feature image on the appearance quality detection, and performs sampling quality assessment feedback operation through information feedback and information fusion to understand the overall quality of the current batch of seals, so as to intuitively understand the overall quality of the current matching seals based on the text feedback, so as to manage the processing technology of the seals to improve the overall quality of the seals.

Description

Technical Field

[0001] The present invention relates to the technical field of seal quality assessment, and in particular to an intelligent seal quality assessment system based on big data. Background Art

[0002] ‌Seals are components or materials designed to prevent fluids or solid particles from leaking from adjacent mating surfaces. The main function of these components is to prevent external impurities such as dust and moisture from invading the interior of the machine and equipment, and to prevent internal substances from leaking into the external environment. Seals can be divided into two categories: static seals and dynamic seals according to their movement state;

[0003] Seal manufacturers need to ensure double protection: ensure product quality and service quality. High-quality seals can prevent leakage, protect equipment and personal safety, and improve equipment efficiency and performance. However, in the existing technology, it is impossible to accurately evaluate the quality of seals, which is not conducive to optimizing the processing technology of seals. At the same time, it is impossible to supervise the appearance quality and appearance image processing of seals, which affects the accuracy of appearance quality evaluation and is not conducive to the rational management of seals.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent seal quality assessment system based on big data to solve the above-mentioned technical defects. The present invention analyzes from the two perspectives of seal appearance and stability performance to improve the accuracy and comprehensiveness of seal quality assessment, and analyzes image processing operations in a progressive manner to reduce the impact of preprocessing operations of appearance feature images on appearance quality detection, and performs sampling quality assessment feedback operations through information feedback and information fusion to understand the overall quality of the current batch of seals, so as to intuitively understand the overall quality of the current matching seals based on text feedback, so as to manage the processing technology of the seals and improve the overall quality of the seals.

[0006] The object of the present invention can be achieved by the following technical solutions: A seal quality intelligent assessment system based on big data, comprising a quality assessment platform, an image verification unit, an information acquisition unit, an appearance detection unit, a condition assessment unit, an integrated assessment unit and a feedback response unit;

[0007] The information collection unit is used to collect the appearance data and climate detection parameters of the seal, and send the appearance data and climate detection parameters to the appearance detection unit and the condition evaluation unit respectively. After receiving the appearance data, the appearance detection unit immediately performs appearance quality evaluation feedback analysis on the appearance data, compares and analyzes the obtained difference risk identification string and size verification string, and obtains a qualified signal and a unqualified signal;

[0008] The image verification unit retrieves the appearance feature image from the appearance detection unit, processes the appearance feature image, tracks, supervises, evaluates and analyzes the obtained image interference evaluation coefficient G, and obtains a deviation signal and a standard signal;

[0009] After receiving the climate detection parameters, the condition evaluation unit immediately performs condition control detection feedback analysis on the climate detection parameters, and further compares and analyzes the obtained climate performance evaluation coefficients to obtain stable signals and fluctuation signals;

[0010] The integrated evaluation unit is used to perform sampling quality evaluation feedback operations on the seals to be tested, compare and analyze the obtained quality stability evaluation index, and obtain defective signals and compliant signals.

[0011] Preferably, the appearance quality assessment feedback analysis process of the appearance inspection unit is as follows:

[0012] Collect the number of seal samples in the current batch, randomly select a part of them as samples to be tested, and then collect the test time period of the samples to be tested, and set it as the time threshold, and at the same time set the samples to be tested as seals to be tested, and obtain the appearance data of the seals to be tested within the time threshold, the appearance data represents the appearance feature image, perform a preprocessing operation on the appearance feature image, the preprocessing operation includes scaling and cropping, obtain the appearance feature image after the preprocessing operation, obtain the difference features of the seal from the appearance feature image after the preprocessing operation, the difference features include cracks and bubbles, and then obtain the number corresponding to each difference feature, and perform digital extraction on the number corresponding to each difference feature, and set the difference risk identification string as the character string composed of the digital extraction of the number corresponding to each difference feature;

[0013] At the same time, a standard appearance feature image of the seal to be inspected is obtained, and then the size difference value between the seal in the appearance feature image after the preprocessing operation and the seal in the standard appearance feature image is obtained, and the size difference value is digitally extracted and composed, and the character string composed of the digital extraction of the size difference value is set as a size verification character string.

[0014] Preferably, the difference risk identification string and the size verification string are compared and analyzed one by one with the preset difference risk identification string and the preset size verification string recorded and stored internally:

[0015] If the difference risk identification string corresponds to the preset difference risk identification string one-to-one, and the size verification string corresponds to the preset size verification string one-to-one, then a qualified signal is generated;

[0016] If the one-to-one correspondence between the difference risk identification string and the preset difference risk identification string is not met, or the one-to-one correspondence between the size verification string and the preset size verification string is not met, an unqualified signal is generated.

[0017] Preferably, the processing tracking supervision evaluation analysis process of the image verification unit is as follows:

[0018] The appearance feature image after the preprocessing operation is obtained, and the appearance feature image after the preprocessing operation is set as the verification feature image. The basic information of the seal is obtained from the verification feature image. The basic information includes image clarity, image size difference and color difference deviation value. The image clarity, image size difference and color difference deviation value are labeled as HQ, TC and SP respectively. The image clarity HQ, image size difference TC and color difference deviation value SP are substituted into the formula The image interference assessment coefficient is obtained, wherein a1, a2 and a3 are respectively the preset proportional factor coefficients of the picture clarity HQ, the image size difference TC and the color difference deviation value SP, a1, a2 and a3 are all greater than zero, G is the image interference assessment coefficient, and the image interference assessment coefficient G is compared and analyzed with the preset image interference assessment coefficient threshold value recorded and stored internally:

[0019] If the ratio between the image interference assessment coefficient G and the preset image interference assessment coefficient threshold is less than 1, a deviation signal is generated;

[0020] If the ratio between the image interference evaluation coefficient G and the preset image interference evaluation coefficient threshold is greater than or equal to 1, a standard signal is generated.

[0021] Preferably, the image size difference represents the difference between the verification feature image size and the standard appearance feature image size; the analysis process of the color difference deviation value is as follows: the verification feature image is divided into g sub-region blocks, g is a natural number greater than zero, the color difference value of each sub-region block is obtained, the color difference value is compared and analyzed with the preset color difference value, and the ratio of the number of sub-region blocks corresponding to the color difference value greater than the preset color difference value threshold to the total number of sub-region blocks is set as the color difference deviation value.

[0022] Preferably, the condition control detection feedback analysis process of the condition evaluation unit is as follows:

[0023] S1: m climate detection parameters are set, where m is a natural number greater than zero, and the climate detection parameters include high temperature and low temperature. n set climate values ​​of each climate detection parameter are set, where n is a natural number greater than zero. The proportion of the number of set climate values ​​corresponding to the seal to be detected being in a sealed state under each set climate value is obtained, and the proportion is set as the climate performance stability value;

[0024] S2: Obtain the climate performance stability value of each climate detection parameter, compare and analyze the climate performance stability value with the stored preset climate performance stability value threshold, set the number of climate detection parameters corresponding to the climate performance stability value greater than or equal to the preset climate performance stability value threshold as the conditional stability value, and then obtain the ratio between the conditional stability value and m, and set the ratio between the conditional stability value and m as the climate performance evaluation coefficient, and compare and analyze the climate performance evaluation coefficient with the preset climate performance evaluation coefficient threshold stored internally:

[0025] If the ratio between the climate performance evaluation coefficient and the preset climate performance evaluation coefficient threshold is greater than or equal to 1, a stable signal is generated;

[0026] If the ratio between the climate performance evaluation coefficient and the preset climate performance evaluation coefficient threshold is less than 1, a fluctuation signal is generated.

[0027] Preferably, the sampling quality assessment feedback operation process of the integrated assessment unit is as follows:

[0028] The total number of seals to be detected is obtained, and the total number of seals to be detected is set as the total sample value, and the seals to be detected corresponding to the qualified signal and the seals to be detected corresponding to the stable signal are obtained at the same time, and then the seals to be detected that overlap with the seals to be detected corresponding to the qualified signal and the seals to be detected corresponding to the stable signal are obtained, and the seals to be detected that overlap with the seals to be detected corresponding to the qualified signal and the seals to be detected corresponding to the stable signal are set as the number of efficient seals, and the ratio between the number of efficient seals and the total sample value is set as the quality stability assessment index, and the quality stability assessment index is compared and analyzed with the preset quality stability assessment index threshold value recorded and stored internally:

[0029] If the ratio between the quality stability assessment index and the preset quality stability assessment index threshold is less than 1, a defective signal is generated;

[0030] If the ratio between the quality stability assessment index and the preset quality stability assessment index threshold is greater than or equal to 1, a compliance signal is generated.

[0031] The beneficial effects of the present invention are as follows:

[0032] The present invention improves the accuracy and comprehensiveness of seal quality assessment by analyzing from two perspectives: appearance and stability performance of seals, that is, performing appearance quality assessment feedback analysis on appearance data and conditional control test feedback analysis on climate test parameters, so as to understand the appearance quality of the seals to be tested and the performance stability effect under different climate conditions;

[0033] The present invention analyzes the image processing operation in a progressive manner to reduce the impact of the preprocessing operation of the appearance feature image on the appearance quality detection, which helps to improve the accuracy of the appearance quality detection results, and performs sampling quality assessment feedback operations through information feedback and information fusion to understand the overall quality of the current batch of seals, so as to intuitively understand the overall quality of the current matching seals based on text feedback, so as to manage the processing technology of the seals and improve the overall quality of the seals. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below in conjunction with the accompanying drawings;

[0035] Figure 1 It is a flowchart of the system of the present invention;

[0036] Figure 2 It is a local analysis reference diagram of the first embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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. Embodiment 1

[0038] See also Figure 1 to Figure 2 As shown, the present invention is a seal quality intelligent assessment system based on big data, including a quality assessment platform, an image verification unit, an information collection unit, an appearance inspection unit, a condition assessment unit, an integrated assessment unit and a feedback response unit, the quality assessment platform is connected to the image verification unit and the information collection unit in a one-way communication, the information collection unit is connected to the appearance inspection unit and the condition assessment unit in a one-way communication, the appearance inspection unit and the condition assessment unit are connected to the integrated assessment unit and the feedback response unit in a one-way communication, the integrated assessment unit is connected to the feedback response unit in a one-way communication, and the image verification unit is connected to the feedback response unit in a one-way communication;

[0039] When the quality assessment platform monitors any seal detection, a supervision instruction is generated and sent to the information collection unit and the image verification unit. When the information collection unit receives the supervision instruction, it immediately collects the appearance data and climate detection parameters of the seal, and sends the appearance data and climate detection parameters to the appearance detection unit and the condition evaluation unit respectively. After receiving the appearance data, the appearance detection unit immediately performs appearance quality assessment feedback analysis on the appearance data to understand the appearance quality of the seal. The specific appearance quality assessment feedback analysis process is as follows:

[0040] The number of seal samples in the current batch is collected, and a part of them is randomly selected as samples to be tested, and then the detection time period of the samples to be tested is collected and set as the time threshold, and the samples to be tested are set as seals to be tested, and the appearance data of the seals to be tested within the time threshold is obtained, and the appearance data represents the appearance feature image, and the appearance feature image is preprocessed, and the preprocessing operation includes scaling, cropping, etc., and the appearance feature image after the preprocessing operation is obtained, and the difference features of the seal are obtained from the appearance feature image after the preprocessing operation, and the difference features include cracks, bubbles, etc., and then the number corresponding to each difference feature is obtained, and the number corresponding to each difference feature is digitally extracted and composed, and the character string composed of the number corresponding to each difference feature is set as a difference risk identification string. It should be noted that the difference risk identification string is an influencing parameter reflecting the appearance quality of the seal to be tested;

[0041] At the same time, a standard appearance feature image of the seal to be detected is obtained, and then the size difference value between the seal in the appearance feature image after the preprocessing operation and the seal in the standard appearance feature image is obtained, and the size difference value is digitally extracted and composed, and the character string composed of the digital extraction of the size difference value is set as a size verification character string. It should be noted that the size verification character string is an influencing parameter reflecting the appearance quality of the seal to be detected;

[0042] The difference risk identification string and the dimension verification string are compared and analyzed one by one with the preset difference risk identification string and the preset dimension verification string stored in the internal storage:

[0043] If the difference risk identification string corresponds to the preset difference risk identification string one-to-one, and the size verification string corresponds to the preset size verification string one-to-one, then a qualified signal is generated;

[0044] If the difference risk identification string does not correspond to the preset difference risk identification string one-to-one, or the size verification string does not correspond to the preset size verification string one-to-one, an unqualified signal is generated, and the qualified signal and the unqualified signal are sent to the feedback response unit. After receiving the qualified signal and the unqualified signal, the feedback response unit immediately displays the preset warning text corresponding to the qualified signal and the unqualified signal, so as to intuitively understand the appearance quality of the seal to be tested;

[0045] When a supervision instruction is generated, the image verification unit retrieves the appearance feature image from the appearance inspection unit, and processes, tracks, supervises, evaluates and analyzes the appearance feature image to determine whether the preprocessing operation of the appearance feature image interferes with the appearance quality inspection, so as to manage it in a timely manner and reduce the impact of the preprocessing operation of the appearance feature image on the appearance quality inspection, which helps to improve the accuracy of the appearance quality inspection results. The specific processing, tracking, supervision, evaluation and analysis process is as follows:

[0046] The appearance feature image after the preprocessing operation is obtained, and the appearance feature image after the preprocessing operation is set as the verification feature image. The basic information of the seal is obtained from the verification feature image. The basic information includes image clarity, image size difference, and color difference deviation value. The image clarity, image size difference, and color difference deviation value are labeled as HQ, TC, and SP, respectively. The image clarity HQ, image size difference TC, and color difference deviation value SP are substituted into the formula The image interference assessment coefficient is obtained, wherein a1, a2 and a3 are respectively the preset proportional factor coefficients of the picture clarity HQ, the image size difference TC and the color difference deviation value SP, a1, a2 and a3 are all greater than zero, G is the image interference assessment coefficient, and the image interference assessment coefficient G is compared and analyzed with the preset image interference assessment coefficient threshold value recorded and stored internally:

[0047] If the ratio between the image interference assessment coefficient G and the preset image interference assessment coefficient threshold is less than 1, a deviation signal is generated;

[0048] If the ratio between the image interference evaluation coefficient G and the preset image interference evaluation coefficient threshold is greater than or equal to 1, a standard signal is generated, and the deviation signal and the standard signal are sent to the feedback response unit. After receiving the deviation signal and the standard signal, the feedback response unit immediately displays the preset warning text corresponding to the deviation signal and the standard signal, so as to intuitively understand the image processing effect of the seal to be detected, so as to ensure the accuracy of the analysis result, and at the same time, the feedback result of the appearance detection unit is reasonably adopted according to the feedback information to ensure the accuracy of the detection result;

[0049] In the embodiment of the present invention, the image size difference represents the difference between the size of the verification feature image and the size of the standard appearance feature image. It should be noted that the image size difference is an influencing parameter reflecting the effect of the preprocessing operation of the verification feature image, and is also an influencing parameter for subsequent comparison;

[0050] In an embodiment of the present invention, the analysis process of the color difference deviation value is as follows: the verification feature image is divided into g sub-region blocks, g is a natural number greater than zero, the color difference value of each sub-region block is obtained, the color difference value is compared and analyzed with the preset color difference value, and the ratio of the number of sub-region blocks corresponding to the color difference value greater than the preset color difference value threshold to the total number of sub-region blocks is set as the color difference deviation value. It should be noted that the color difference deviation value is an influencing parameter reflecting the effect of the preprocessing operation of the verification feature image. At the same time, the larger the value of the color difference deviation value, the greater the risk of error in the subsequent analysis of the verification feature image. Embodiment 2

[0051] After receiving the climate detection parameters, the condition evaluation unit immediately performs condition control detection feedback analysis on the climate detection parameters to understand the performance stability of the seal to be tested under different climate conditions. The specific condition control detection feedback analysis process is as follows:

[0052] m climate detection parameters are set, where m is a natural number greater than zero, and the climate detection parameters include high temperature, low temperature, etc. n set climate values ​​are set for each climate detection parameter, where n is a natural number greater than zero, and the proportion of the number of set climate values ​​corresponding to the seal to be detected being in a sealed state under each set climate value is obtained, and the proportion is set as the climate performance stability value. It should be noted that the larger the value of the climate performance stability value, the better the performance stability effect of the seal under different climate conditions;

[0053] Obtain the climate performance stability value of each climate detection parameter, compare and analyze the climate performance stability value with the stored preset climate performance stability value threshold, set the number of climate detection parameters corresponding to the climate performance stability value greater than or equal to the preset climate performance stability value threshold as the conditional stability value, and then obtain the ratio between the conditional stability value and m, and set the ratio between the conditional stability value and m as the climate performance evaluation coefficient, and compare and analyze the climate performance evaluation coefficient with the preset climate performance evaluation coefficient threshold stored internally:

[0054] If the ratio between the climate performance evaluation coefficient and the preset climate performance evaluation coefficient threshold is greater than or equal to 1, a stable signal is generated;

[0055] If the ratio between the climate performance evaluation coefficient and the preset climate performance evaluation coefficient threshold is less than 1, a fluctuation signal is generated, and the stable signal and the fluctuation signal are sent to the feedback response unit. After receiving the stable signal and the fluctuation signal, the feedback response unit immediately displays the preset warning text corresponding to the stable signal and the fluctuation signal, so as to intuitively understand the climate stability of the seal to be tested;

[0056] The integrated evaluation unit is used to perform sampling quality evaluation feedback operations on the seals to be tested, so as to understand the overall quality of the current batch of seals, so as to provide intuitive text feedback and rationalize the management of the current batch of seals. The specific sampling quality evaluation feedback operation process is as follows:

[0057] The total number of seals to be detected is obtained, and the total number of seals to be detected is set as the total sample value, and the seals to be detected corresponding to the qualified signal and the seals to be detected corresponding to the stable signal are obtained at the same time, and then the seals to be detected that overlap with the seals to be detected corresponding to the qualified signal and the seals to be detected corresponding to the stable signal are obtained, and the seals to be detected that overlap with the seals to be detected corresponding to the qualified signal and the seals to be detected corresponding to the stable signal are set as the number of efficient seals, and the ratio between the number of efficient seals and the total sample value is set as the quality stability assessment index, and the quality stability assessment index is compared and analyzed with the preset quality stability assessment index threshold value recorded and stored internally:

[0058] If the ratio between the quality stability assessment index and the preset quality stability assessment index threshold is less than 1, a defective signal is generated;

[0059] If the ratio between the quality stability assessment index and the preset quality stability assessment index threshold is greater than or equal to 1, a compliance signal is generated, and the defective signal and the compliance signal are sent to the feedback response unit. After receiving the defective signal and the compliance signal, the feedback response unit immediately displays the preset warning texts corresponding to the defective signal and the compliance signal, that is, the preset warning text corresponding to the defective signal is "the current match is unqualified", and the preset warning text corresponding to the compliance signal is "the current match is qualified", so that the overall quality of the currently matched seal can be intuitively understood according to the text feedback, so as to manage the processing process of the seal and improve the quality of the seal;

[0060] In summary, the present invention improves the accuracy and comprehensiveness of seal quality assessment by analyzing from two perspectives: appearance and stability performance of seals. That is, appearance quality assessment feedback analysis is performed on appearance data and conditional control detection feedback analysis is performed on climate detection parameters to understand the appearance quality of the seals to be detected and the performance stability effect under different climatic conditions. The image processing operation is analyzed in a progressive manner to reduce the impact of the preprocessing operation of the appearance feature image on the appearance quality detection, which helps to improve the accuracy of the appearance quality detection results. Sampling quality assessment feedback operations are performed through information feedback and information fusion to understand the overall quality of the current batch of seals, so that the overall quality of the current matching seals can be intuitively understood based on text feedback, so as to manage the processing technology of the seals and improve the overall quality of the seals.

[0061] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0062] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A seal quality intelligent evaluation system based on big data, characterized in that: It includes a quality assessment platform, an image verification unit, an information collection unit, an appearance inspection unit, a condition assessment unit, an integrated assessment unit, and a feedback response unit; The information collection unit is used to collect the appearance data and climate detection parameters of the seal, and send the appearance data and climate detection parameters to the appearance detection unit and the condition evaluation unit respectively. After receiving the appearance data, the appearance detection unit immediately performs appearance quality evaluation feedback analysis on the appearance data, compares and analyzes the obtained difference risk identification string and size verification string, and obtains a qualified signal and a unqualified signal; The image verification unit retrieves the appearance feature image from the appearance detection unit, processes the appearance feature image, tracks, supervises, evaluates and analyzes the obtained image interference evaluation coefficient G, and obtains a deviation signal and a standard signal; After receiving the climate detection parameters, the condition evaluation unit immediately performs condition control detection feedback analysis on the climate detection parameters, and further compares and analyzes the obtained climate performance evaluation coefficients to obtain stable signals and fluctuation signals; The integrated evaluation unit is used to perform sampling quality evaluation feedback operation on the seal to be tested, compare and analyze the obtained quality stability evaluation index, and obtain defective signals and compliant signals; The appearance quality evaluation feedback analysis process of the appearance inspection unit is as follows: Collect the number of seal samples in the current batch, randomly select a part of them as samples to be tested, and then collect the test time period of the samples to be tested, and set it as the time threshold, and at the same time set the samples to be tested as seals to be tested, and obtain the appearance data of the seals to be tested within the time threshold, the appearance data represents the appearance feature image, and perform preprocessing operations on the appearance feature image, the preprocessing operations include scaling and cropping, and obtain the appearance feature image after the preprocessing operation, and obtain the difference features of the seal from the appearance feature image after the preprocessing operation, the difference features include cracks and bubbles, and then obtain the number corresponding to each difference feature, and perform digital extraction on the number corresponding to each difference feature, and set the difference risk identification string as the character string composed of the digital extraction of the number corresponding to each difference feature; At the same time, a standard appearance feature image of the seal to be inspected is obtained, and then the size difference value between the seal in the appearance feature image after the preprocessing operation and the seal in the standard appearance feature image is obtained, and the size difference value is digitally extracted and composed, and the character string composed of the digital extraction of the size difference value is set as a size verification character string; The difference risk identification string and the size verification string are compared and analyzed one by one with the preset difference risk identification string and the preset size verification string stored in the internal input: If the difference risk identification string corresponds to the preset difference risk identification string one-to-one, and the size verification string corresponds to the preset size verification string one-to-one, then a qualified signal is generated; If the difference risk identification string does not correspond to the preset difference risk identification string one-to-one, or the size verification string does not correspond to the preset size verification string one-to-one, a failure signal is generated; The processing, tracking, supervision, evaluation and analysis process of the image verification unit is as follows: The appearance feature image after the preprocessing operation is obtained, and the appearance feature image after the preprocessing operation is set as the verification feature image. The basic information of the seal is obtained from the verification feature image. The basic information includes image clarity, image size difference and color difference deviation value. The image clarity, image size difference and color difference deviation value are labeled as HQ, TC and SP respectively. The image clarity HQ, image size difference TC and color difference deviation value SP are substituted into the formula The image interference assessment coefficient is obtained, wherein a1, a2 and a3 are respectively the preset proportional factor coefficients of the picture clarity HQ, the image size difference TC and the color difference deviation value SP, a1, a2 and a3 are all greater than zero, G is the image interference assessment coefficient, and the image interference assessment coefficient G is compared and analyzed with the preset image interference assessment coefficient threshold value recorded and stored internally: If the ratio between the image interference assessment coefficient G and the preset image interference assessment coefficient threshold is less than 1, a deviation signal is generated; If the ratio between the image interference assessment coefficient G and the preset image interference assessment coefficient threshold is greater than or equal to 1, a standard signal is generated; The image size difference represents the difference between the verification feature image size and the standard appearance feature image size; the analysis process of the color difference deviation value is as follows: the verification feature image is divided into g sub-region blocks, g is a natural number greater than zero, the color difference value of each sub-region block is obtained, the color difference value is compared and analyzed with the preset color difference value, and the ratio of the number of sub-region blocks corresponding to the color difference value greater than the preset color difference value threshold to the total number of sub-region blocks is set as the color difference deviation value.

2. The seal quality intelligent evaluation system based on big data according to claim 1 is characterized in that: The condition control detection feedback analysis process of the condition evaluation unit is as follows: S1: m climate detection parameters are set, where m is a natural number greater than zero, and the climate detection parameters include high temperature and low temperature. n set climate values ​​of each climate detection parameter are set, where n is a natural number greater than zero. The proportion of the number of set climate values ​​corresponding to the seal to be detected being in a sealed state under each set climate value is obtained, and the proportion is set as the climate performance stability value; S2: Obtain the climate performance stability value of each climate detection parameter, compare and analyze the climate performance stability value with the stored preset climate performance stability value threshold, set the number of climate detection parameters corresponding to the climate performance stability value greater than or equal to the preset climate performance stability value threshold as the conditional stability value, and then obtain the ratio between the conditional stability value and m, and set the ratio between the conditional stability value and m as the climate performance evaluation coefficient, and compare and analyze the climate performance evaluation coefficient with the preset climate performance evaluation coefficient threshold stored internally: If the ratio between the climate performance evaluation coefficient and the preset climate performance evaluation coefficient threshold is greater than or equal to 1, a stable signal is generated; If the ratio between the climate performance evaluation coefficient and the preset climate performance evaluation coefficient threshold is less than 1, a fluctuation signal is generated.

3. The seal quality intelligent evaluation system based on big data according to claim 1 is characterized in that: The sampling quality assessment feedback operation process of the integrated assessment unit is as follows: The total number of seals to be detected is obtained, and the total number of seals to be detected is set as the total sample value, and the seals to be detected corresponding to the qualified signal and the seals to be detected corresponding to the stable signal are obtained at the same time, and then the seals to be detected that overlap with the seals to be detected corresponding to the qualified signal and the seals to be detected corresponding to the stable signal are obtained, and the seals to be detected that overlap with the seals to be detected corresponding to the qualified signal and the seals to be detected corresponding to the stable signal are set as the number of efficient seals, and the ratio between the number of efficient seals and the total sample value is set as the quality stability assessment index, and the quality stability assessment index is compared and analyzed with the preset quality stability assessment index threshold value recorded and stored internally: If the ratio between the quality stability assessment index and the preset quality stability assessment index threshold is less than 1, a defective signal is generated; If the ratio between the quality stability assessment index and the preset quality stability assessment index threshold is greater than or equal to 1, a compliance signal is generated.

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