A visualization-based evaluation method for pesticide formulation quality consistency management

Abstract

The present invention belongs to the technical field of pesticide preparation quality management, and specifically discloses a pesticide preparation quality consistency management and evaluation method based on visualization. When performing appearance image acquisition on batch-produced pesticide preparations, the present invention first uses standard appearance colors to select background colors for image shooting, and performs image quality scoring after the appearance image acquisition, and then performs targeted image acquisition adjustment according to the scoring results, thereby realizing dynamic acquisition of appearance images of batch pesticide preparations, and being able to maximize the consistency of appearance image quality. Subsequently, when performing physical and chemical performance quality tests on batch pesticide preparations, test consistency evaluation is performed based on test results and test result stability time, and retesting is performed when the evaluation does not meet the standards, thereby ensuring that quality analysis results are obtained under stable and consistent conditions, and invisibly improving the accuracy and reliability of the analysis results.

Description

Technical Field

[0001] The invention belongs to the technical field of pesticide preparation quality management, and specifically discloses a pesticide preparation quality consistency management evaluation method based on visualization. Background Art

[0002] Pesticide formulations are used to prevent, control or eliminate harmful organisms in agriculture, horticulture and forestry, such as pathogens, insects and weeds. Their quality management is crucial, especially post-production quality management. Through strict production quality management, we can ensure that products are safe and effective from the source and prevent harm to users, consumers or the environment.

[0003] Since the quality management of pesticide preparations involves multiple key aspects (such as appearance, ingredient content, physical and chemical properties, etc.), in order to ensure the safety, effectiveness and reliability of the products, comprehensive multi-index testing is usually required. With the development of the agricultural and horticultural industries, the demand for pesticide preparations has increased, resulting in the use of batch mode in production. Therefore, the current quality management of pesticide preparations presents the characteristics of large batches and multiple indicators. In this context, the quality management of pesticide preparations not only needs to pay attention to the quality of individual preparations, but also needs to ensure the quality consistency of batch pesticide preparations. However, there are deficiencies in this regard in the prior art, and there is a lack of systematic attention to quality consistency, which is mainly reflected in the following two points: First, it is difficult to ensure the consistency of image quality in the existing pesticide preparation appearance quality inspection. Since the appearance quality inspection requires images, the existing method usually uses fixed camera parameters, such as background color, when collecting the appearance images of pesticide preparations, and does not take into account the individual differences in the appearance of batch pesticide preparations, especially the color changes. This leads to uneven quality of the collected images, and it is impossible to provide a unified standard basis for appearance quality evaluation.

[0004] Second, when conducting physical and chemical performance quality tests, people often pay too much attention to the test results and ignore the stability of the results, which will lead to stable test results for some pesticide preparations and unstable test results for others. Performing quality analysis under the condition of inconsistent test result stability can easily lead to inaccurate and unreliable analysis results, making the analysis results lose their practicality and even leading to invalid testing, which not only wastes quality testing resources but also reduces the efficiency of quality testing. Summary of the invention

[0005] To this end, one purpose of an embodiment of the present application is to provide a visualization-based pesticide formulation quality consistency management and evaluation method, which effectively solves the problems mentioned in the background technology by performing quality evaluation management on batch pesticide formulations based on quality consistency.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A visualization-based pesticide preparation quality consistency management and evaluation method, comprising the following steps: S1, after preparing samples of the pesticide preparations currently produced in batches, dynamically collect the appearance of each preparation sample to obtain the appearance image of each preparation sample.

[0007] S2. Extract qualified appearance images based on the specifications and models of the current batch of pesticide preparations, thereby comparing the appearance features of the appearance images of each preparation sample with the qualified appearance images to analyze the appearance conformity.

[0008] S3. Calculate the appearance qualification rate from the appearance conformity analysis results of the batch pesticide formulations, and select the formulation samples with qualified appearance as preliminary formulation samples.

[0009] S4. The preliminary preparation samples screened out are tested for ingredient content to obtain ingredient content data of each preliminary preparation sample, and compared with the qualified ingredient content standard to calculate the ingredient qualified rate. At the same time, preparation samples with qualified ingredient content are screened out from the preliminary preparation samples as test preparation samples.

[0010] S5. Group the screened test preparation samples into test groups according to the number of physical and chemical test items required, and determine the physical and chemical test items corresponding to each test group.

[0011] S6. Obtain suitable environmental conditions based on the physical and chemical test items corresponding to each test group, thereby controlling the test environmental conditions of each test group.

[0012] S7. Perform performance tests on the test preparation samples in each test group under the control of test environment conditions to obtain test result images and test stabilization time, and perform performance consistency evaluation and processing based on the intra-group comparison results of the test result images and test stabilization time to obtain the final test result images of each test group.

[0013] S8. Compare the final test result image of each test group with the qualified test image to calculate the qualified rate of physical and chemical properties.

[0014] S9. Evaluate the compliance of production quality based on the appearance qualification rate, ingredient qualification rate and physical and chemical performance qualification rate of the current batch of pesticide preparations.

[0015] In combination with all the above-mentioned technical solutions, the positive effects of the present invention are as follows: 1. When acquiring appearance images of batch-produced pesticide preparations, the present invention first uses standard appearance colors to select the background color for image shooting, and performs image quality scoring after the appearance image acquisition, and then performs targeted image acquisition adjustments based on the scoring results, thereby realizing dynamic acquisition of appearance images of batch pesticide preparations, being able to maximize the consistency of appearance image quality, providing a unified standard basis for the appearance quality evaluation of pesticide preparations, and being conducive to improving the effectiveness of appearance quality evaluation.

[0016] 2. When conducting physical and chemical performance quality tests on batch pesticide preparations, the present invention conducts test consistency evaluation based on the test results and the stability time of the test results, and re-tests when the evaluation does not meet the standards. This ensures that the quality analysis results are obtained under stable and consistent conditions, which invisibly improves the accuracy and reliability of the analysis results, not only ensuring the effectiveness of the analysis results, but also avoiding the waste of detection resources and time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0018] Figure 1 The present invention is a flowchart of the steps for implementing the method.

[0019] Figure 2 It is a schematic diagram of the use phases of a batch pesticide formulation sample of the present invention.

[0020] Figure 3 The present invention is a flow chart for implementing the grouping of test groups for the physical and chemical test. DETAILED DESCRIPTION

[0021] 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.

[0022] See also Figure 1 As shown, the present invention proposes a visualization-based pesticide preparation quality consistency management and evaluation method, comprising the following steps: S1, after preparing samples of the pesticide preparations currently produced in batches, dynamically collect the appearance of each preparation sample to obtain the appearance image of each preparation sample.

[0023] It should be added that the pesticide preparation mentioned in the present invention is a liquid preparation.

[0024] It is important to know that if pesticide preparations are directly used for quality inspection after batch production, on the one hand, it will directly cause pollution to the produced pesticide preparations. On the other hand, directly using the produced pesticide preparations for quality inspection may lead to inaccurate results due to uneven sampling. By preparing uniform samples from batch pesticide preparations, the test results can more accurately reflect the actual quality of the preparations.

[0025] During the sample preparation process, it is necessary to use consistent tools and specifications under the same environmental conditions and prepare pesticide formulation samples in a uniform volume. This can effectively control possible variations in the production process through the standardized sample preparation process, ensuring that all samples can accurately reflect the quality of batch products.

[0026] Applied to the above implementation scheme, the appearance of each preparation sample is dynamically collected to obtain the appearance image of each preparation sample as follows: S11, extracting the appearance color from the qualified appearance image as the standard appearance color, thereby extracting the matching background color from the constructed matching relationship between the object color and the background color.

[0027] It is important to understand that choosing a background color that matches the color of the subject can make the subject stand out more clearly from the background. This contrast helps to quickly identify and notice the subject. If the background color is too close to the subject color, it may obscure the details of the subject, resulting in blurred images or loss of details. Therefore, in situations where image manipulation is required, it is necessary to reasonably select and adapt the background color, which can significantly improve the quality and effect of the image, so that the subject can be best displayed, while also improving the image's ability to communicate and express.

[0028] According to a preferred embodiment, the matching relationship between the color of the object and the background color can be initially constructed by selecting the complementary color of the color of the object as the background color based on color theory, and testing is performed on this basis, and then optimization is performed based on the test results, and finally the optimal background color configuration is recorded to establish the matching relationship between the color of the object and the background color.

[0029] In the implementation example applied to the above solution, when the color of the photographed object is red, the selected adaptive background color is green.

[0030] It should be explained that the background color extraction by shooting with the standard appearance color before acquiring the appearance image of the prepared preparation sample is based on the consideration that the appearance color of the sample should be close to the standard appearance color. Prioritizing the use of the background color corresponding to the standard appearance color for appearance image acquisition can ensure the quality of the appearance images of most samples, thereby reducing the frequency of background color changes to a certain extent, and helping to improve the efficiency of appearance image acquisition.

[0031] S12, after adjusting the state parameters of the shooting device according to the adapted background color, collecting the appearance image of each preparation sample to obtain a preliminary appearance image of each preparation sample.

[0032] S13. Perform image quality indicator detection on the preliminary appearance image of each preparation sample, specifically including edge intensity, contrast and color depth, thereby evaluating the appearance image quality score of each preparation sample.

[0033] It should be pointed out that edge strength indicates the clarity of boundaries and details in an image. High edge strength means that the outlines of objects in the image are clear and the details are sharp, which improves the readability and visual clarity of the image. Contrast refers to the degree of difference between the brightest and darkest parts of an image; a high-contrast image means that the difference between the bright and dark parts of the image is very obvious. Color depth refers to the number of colors that can be displayed in an image, usually expressed in bits (such as 24-bit color). The higher the color depth, the richer the color levels of the image. By using edge strength, contrast and color depth as image quality indicators, the quality of the image can be comprehensively evaluated. This multi-dimensional evaluation method helps to have a more comprehensive understanding of the visual effects and information communication capabilities of the image. In addition, edge strength, contrast and color depth can be used as standards for automated image analysis and processing. These indicators can be used to screen and process high-quality images more quickly and improve work efficiency.

[0034] The above-mentioned appearance image quality score is as follows: the deviation of each image quality indicator is obtained by subtracting the qualified image quality indicator from the image quality indicator in the corresponding preliminary appearance image of each preparation sample and dividing it by the qualified image quality indicator. If an image quality indicator reaches the qualified image quality indicator, the deviation of the image quality indicator is recorded as 0. If the qualified image quality indicator is not reached, the above-mentioned deviation calculation method is used to calculate the value 1 and the weighted average calculation result of the deviation of each image quality indicator to obtain the appearance image quality score corresponding to each preparation sample.

[0035] S14. Compare the appearance image quality score of each preparation sample with the qualified score. For example, the qualified score may be 0.8, and select the preparation samples with scores lower than the qualified score as the samples to be adjusted.

[0036] S15. Perform image enhancement processing on the sample to be adjusted, and perform appearance image quality scoring after processing, and then compare it with the standard score. If there are still preparation samples with scores lower than the standard score, reselect the background color of the preparation sample to collect the appearance image and continue to perform appearance image quality scoring until the standard score is reached.

[0037] In the example of the above solution, the image enhancement processing may be contrast adjustment, sharpening processing, noise removal processing, etc.

[0038] In the above, the background color can be reselected by extracting the appearance color of the preparation sample from the constructed matching relationship between the color of the photographed object and the background color.

[0039] In the present invention, when there are samples that need to be adjusted in the appearance image acquisition based on the standard appearance color selection background color, the sample that needs to be adjusted is first image enhanced, and the appearance image quality score is scored after the processing, and then compared with the standard score. If there are still preparation samples with scores lower than the standard score, the background color of the preparation sample is re-selected to perform appearance image acquisition. Compared with directly re-acquiring the appearance image of the sample that needs to be adjusted, this method greatly reduces the workload and time of repeated acquisition, and can significantly improve the efficiency of image acquisition, save resources and improve data consistency.

[0040] S2. Extract qualified appearance images based on the specifications and models of the current batch of pesticide preparations, thereby comparing the appearance features of the appearance images of each preparation sample with the qualified appearance images to analyze the appearance conformity.

[0041] Preferably, the appearance conformity analysis refers to the following steps: S21, extracting appearance features, specifically color and transparency, from the appearance images of each preparation sample, and quantifying the features to obtain quantitative appearance feature data of each preparation sample. Similarly, the qualified appearance images are subjected to appearance feature extraction and feature conversion to obtain qualified quantitative data of each appearance feature.

[0042] S22. Subtract the quantitative data of the appearance characteristics of each preparation sample from the qualified quantitative data and take the absolute value. The reciprocal of a set constant is used as the base, wherein the set constant is greater than 1. For example, the set constant is 3. An exponential function is constructed by multiplying the absolute value of the difference of each appearance characteristic by the qualified quantitative data to obtain the appearance conformity of each preparation sample.

[0043] In the example implementation of the above scheme, the quantization operation of the appearance features is as follows: color quantization can calculate the average color value of the image, such as calculating the average RGB value; transparency quantization evaluates transparency by analyzing the grayscale value of the image, and areas with higher transparency usually appear as higher brightness values ​​in the grayscale image.

[0044] S3. Calculate the appearance qualification rate from the appearance conformity analysis results of the batch pesticide formulations, and select the formulation samples with qualified appearance as preliminary formulation samples.

[0045] In a further innovation of the above scheme, the appearance qualification rate is calculated as follows: the appearance conformity of each formulation sample is compared with the set standard appearance conformity, the number of formulation samples that meet the standard appearance conformity is counted, and the percentage is compared with the number of pesticide formulations currently produced in batches to obtain the appearance qualification rate.

[0046] S4. The preliminary preparation samples screened out are tested for ingredient content to obtain ingredient content data of each preliminary preparation sample, and compared with the qualified ingredient content standard to calculate the ingredient qualified rate. At the same time, preparation samples with qualified ingredient content are screened out from the preliminary preparation samples as test preparation samples.

[0047] In the specific operation of the above scheme, the component content of the preliminary preparation sample can be detected by extracting the component content from the chromatographic image using a chromatograph.

[0048] Applied to the above implementation scheme, the statistical component qualification rate is implemented as follows: S41. The component types existing in the qualified component content standard are numbered in descending order of content, and the qualified content of each component is divided by the sum of the contents of various components to obtain the weight factors of various components.

[0049] The weight factors mentioned above reflect the relative importance of each component in the total content.

[0050] It should be emphasized that when calculating the weight factor of each ingredient, it is necessary to ensure that the qualified ingredient content standards used are consistent to avoid errors caused by different standards.

[0051] S42. Match the ingredient types in the ingredient content data corresponding to each preliminary preparation sample with the qualified ingredient types in the qualified ingredient content standard to obtain the matching degree of various ingredients, where the matching degree has a value of 0 or 1. When the match is successful, the matching degree is 1, and when the match fails, the matching degree is 0.

[0052] S43. Compare the content of the component in the corresponding component content data of each preliminary preparation sample with the qualified component content in the qualified component content standard, and use the expression Calculate the composition compliance of the preliminary preparation samples , where Indicates The weight factor of the component, Indicates the ingredient type number. , Indicates the number of ingredient types present in the qualified ingredient content standard, Indicates The matching degree of the components, The value of is 0 or 1. Indicates the content of the corresponding component in the preliminary effective preparation The content of ingredients, Indicates the qualified ingredient content standard The qualified content of the ingredients.

[0053] S44. Compare the ingredient conformity of each preliminary preparation sample with the set qualified ingredient conformity, count the number of preparation samples that meet the qualified ingredient conformity, and compare it with the number of preparation samples that meet the qualified appearance conformity to obtain the percentage to obtain the ingredient qualification rate.

[0054] It should be added that the compliance with the standard appearance and the compliance with the standard ingredients are extracted from the production documents of the corresponding models based on the specifications of the batch pesticide preparations, among which the production documents contain the quality standard documents, which specifically stipulate the quality compliance standards for appearance and ingredients.

[0055] S5. Group the screened test preparation samples into several test groups according to the number of physical and chemical test items required, and determine the physical and chemical test items corresponding to each test group.

[0056] As an example of the above scheme, the physical and chemical test items that need to be carried out for pesticide formulations include emulsification test, dissolution test, precipitation test, etc.

[0057] It is important to understand that pesticide formulations are chemical substances, and their physical and chemical properties can best reflect their performance in actual applications.

[0058] In the optimization implementation of the above scheme, see Figure 3 As shown, the screened test preparation samples are grouped according to the number of physical and chemical test items that need to be performed to obtain several test groups. See the following process: S51. Divide the number of screened test preparation samples by the number of physical and chemical test items that need to be performed to identify whether they are divisible. If they are divisible, execute S52, otherwise execute S53.

[0059] In the above optimization implementation example, it is assumed that the number of test preparation samples screened is 58 and the number of physical and chemical test items to be conducted is 4. In this case, 58 cannot be divided by 4.

[0060] S52, the number of sample samples of the screened test preparations is evenly grouped according to the number of physical and chemical test items required. By evenly grouping, the systematic bias caused by uneven sample size can be reduced.

[0061] S53. Extract the remainder of the number of screened test preparation samples divided by the number of physical and chemical test items that need to be performed. In the above example, the quotient of 58 divided by 4 is 14, and the remainder is 2. The remainder is divided by the number of test preparation samples to obtain the remainder percentage value. In the above example, 2 divided by 58 is equal to 0.04, which is then compared with the set percentage threshold. Exemplarily, the remainder percentage value is 0.08. If the percentage threshold is not reached, the test preparation samples are randomly eliminated from the screened test preparation samples so that the elimination quantity meets the remainder. If the percentage threshold is reached, the number of screened test preparation samples is subjected to incompletely even distribution of physical and chemical test items.

[0062] As an example of the above scheme, the remainder ratio is 0.04, which is less than 0.08, indicating that the remainder is small relative to the number of test preparation samples. At this time, eliminating a small number of samples will not have a significant impact on the test. At the same time, the test management can be simplified so that the number of samples in each group is consistent, which is convenient for subsequent data analysis and management. If the ratio threshold is reached, it means that the remainder is large relative to the number of test preparation samples. If it is directly eliminated, it may have a significant impact on the test. At this time, the number of screened test preparation samples is incompletely evenly distributed among the physical and chemical test items. In the specific example, assuming that the number of screened test preparation samples is 59, the number of physical and chemical test items to be performed is 6. 59 cannot be divided by 6, and the remainder of 59 is 5. The remainder ratio of 5 divided by 59 is 0.084, which is greater than the threshold value. At this time, the 59 samples are divided into 6 groups according to incomplete equal distribution. The number of samples in each group is: Group 1: 10 samples, Group 2: 10 samples, Group 3: 10 samples, Group 4: 10 samples, Group 5: 10 samples, Group 6: 9 samples. This ensures that the number of test reagent samples allocated to each test group is as close as possible. On the one hand, all samples can be fully utilized to avoid wasting some samples because they cannot be completely evenly distributed. On the other hand, the differences between the groups can be minimized.

[0063] It should be further added that when incomplete equal distribution is used for sample allocation, since the number of samples allocated to each group varies, the physical and chemical test items corresponding to each test group can be determined based on the difficulty of the test. For example, if some test items are more complicated or time-consuming, a test preparation with a larger number of samples can be used for that test item.

[0064] S6. Obtain appropriate environmental conditions based on the physical and chemical test items corresponding to each test group, where the environmental conditions can be temperature, humidity, light intensity, etc., thereby controlling the test environmental conditions of each test group to ensure that the test preparation samples in each test group are subjected to physical and chemical tests under uniform and appropriate environmental conditions to avoid the impact of the environment on the test results.

[0065] The suitable environmental conditions for each test group mentioned above can be extracted from the production documents of the corresponding model based on the specifications of the batch pesticide formulation.

[0066] S7. Perform performance tests on the test preparation samples in each test group under the control of test environment conditions to obtain test result images and test stabilization time, and perform performance consistency evaluation and processing based on the intra-group comparison results of the test result images and test stabilization time to obtain the final test result images of each test group.

[0067] In the specific implementation of the above scheme, the test stability time is obtained as follows: when the test result appears during the test of the test preparation samples in each test group, the test result appearance time is counted, and when the test result changes, the timing is ended. The difference between the two timings is the test stability time. When the test result has not changed, the timing is ended with the end time of the test.

[0068] The above-mentioned performance consistency evaluation and processing are as follows: S71, compare the test result data extracted from the test result images of each test preparation sample in each test group, select the maximum value data, make a difference and divide it by the maximum test result data to obtain the test result difference corresponding to each test group.

[0069] S72. Similarly, the experimental stability difference is calculated based on the experimental stability time of each experimental preparation sample in each experimental group.

[0070] S73. Perform weighted average statistics on the test result differences and test stability differences corresponding to each test group to obtain the performance consistency of each test group. The specific calculation formula is: , where Indicates performance consistency, , They represent the test result difference and test stability difference respectively. , They represent the weight values ​​corresponding to the test result difference and test stability difference respectively. The specific data can be set by yourself, and , it can be seen from the formula that both the test result difference and the test stability difference have a negative impact on the performance consistency, and compared with the consistency threshold, for example, the consistency threshold is 0.6. If the performance consistency corresponding to a test group is less than the consistency threshold, the test equipment is calibrated and then retested to obtain the final test result image of each test group.

[0071] It should be understood that each test group conducts the test under uniform and appropriate environmental conditions. In theory, the test results should not differ too much. When the test results differ greatly, it is likely caused by improper use of the test equipment, incorrect settings of the test equipment, test errors in the test equipment, etc. At this time, the validity of the test results can be ensured by recalibrating the test groups with large differences in test results and then conducting the test again.

[0072] S8. Compare the final test result image of each test group with the qualified test image to calculate the qualified rate of physical and chemical performance. The specific statistics are as follows: S81. Compare the test result data extracted from the final test result image corresponding to each test preparation sample in each test group with the qualified test data in the qualified test image of the corresponding physical and chemical test item of the test group, and count the number of test preparation samples that meet the qualified test data.

[0073] S82. Divide the number of test preparation samples that meet the qualified test data in each test group by the number of test preparation samples in the corresponding test group and take the percentage to obtain the physical and chemical performance qualification rate of each test group, which is used as the physical and chemical performance qualification rate of the physical and chemical test items of each test group.

[0074] See also Figure 2 As shown, the use stages of batch pesticide formulation samples are: first, the batch pesticide formulation samples are subjected to appearance quality testing to obtain preliminary formulation samples with qualified appearance quality, then the preliminary formulation samples are subjected to ingredient content testing to obtain test formulation samples with qualified ingredient content, and finally the test formulation samples are subjected to physical and chemical performance tests.

[0075] S9. Conduct production quality compliance evaluation based on the appearance qualification rate, ingredient qualification rate and physical and chemical performance qualification rate of the current batch of pesticide preparations. The specific evaluation process is as follows: Multiply the appearance qualification rate, ingredient qualification rate and physical and chemical performance qualification rate of each physical and chemical test item of the current batch of pesticide preparations to obtain the comprehensive quality qualification rate of the current batch of pesticide preparations.

[0076] The comprehensive quality pass rate of the current batch of pesticide preparations is compared with the set standard quality pass rate, wherein the standard quality pass rate can be extracted from the production documents of the pesticide preparations of corresponding specifications. If the comprehensive quality pass rate is greater than the standard quality pass rate, it is judged that the production quality of the current batch of pesticide preparations meets the standards; otherwise, it is judged that the production quality of the current batch of pesticide preparations does not meet the standards.

[0077] Furthermore, when it is determined that the current batch of pesticide formulations does not meet the production quality standards, the appearance qualification rate, the ingredient qualification rate, and the physical and chemical performance qualification rate of each physical and chemical test item are arranged in order from the smallest to the largest qualification rate, and a ranking result of the qualification rate is obtained. At this time, the parameters with the lowest qualification rate are eliminated from the ranking results in turn, and the cumulative result is recalculated and compared with the qualified quality rate. If the new cumulative result reaches the qualified quality rate, the eliminated parameter can be regarded as a quality parameter that tends to be substandard. For example, when the new cumulative result reaches the qualified quality rate after the appearance qualification rate is eliminated, the appearance can be regarded as a quality parameter that tends to be substandard.

[0078] It should be noted that the comprehensive quality pass rate is calculated by multiplying the appearance pass rate, ingredient pass rate and the physical and chemical performance pass rate of various physical and chemical test items. This is because the comprehensive quality pass rate will be high only when the pass rate of each item is high in the cumulative calculation. This method ensures the overall quality of the product in all aspects. When the pass rate of a certain indicator is low, the overall pass rate will drop significantly. At this time, the substandard quality parameters can be quickly located through the cumulative calculation of the pass rate, helping the team to quickly identify and solve problems and shorten response time.

[0079] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A visualization-based pesticide formulation quality consistency management and evaluation method, characterized in that: The following steps are involved: S1. After preparing samples of the pesticide preparations currently produced in batches, dynamically collect the appearance of each preparation sample to obtain the appearance image of each preparation sample; The dynamic acquisition of the appearance of each preparation sample to obtain the appearance image of each preparation sample is implemented as follows: S11, extracting the appearance color from the qualified appearance image as the standard appearance color, thereby extracting the matching background color from the constructed matching relationship between the object color and the background color; S12, collecting appearance images of each preparation sample after adjusting the state parameters of the shooting device according to the adapted background color, to obtain a preliminary appearance image of each preparation sample; S13, performing image quality indicator detection on the preliminary appearance image of each preparation sample, specifically including edge intensity, contrast and color depth, thereby evaluating the appearance image quality score of each preparation sample; S14, comparing the appearance image quality score of each preparation sample with the standard score, and selecting the preparation samples with scores lower than the standard score as the samples to be adjusted; S15, performing image enhancement processing on the sample to be adjusted, and scoring the appearance image quality after the processing, and then comparing it with the standard score. If there are still preparation samples with scores lower than the standard score, reselect the background color of the preparation sample to collect the appearance image and continue to score the appearance image quality until the standard score is reached; S2, extracting qualified appearance images based on the specifications and models of the current batch of pesticide preparations, thereby comparing the appearance features of the appearance images of each preparation sample with the qualified appearance images, so as to analyze the appearance conformity; S3. Counting the appearance qualified rate from the appearance conformity analysis results of the batch pesticide formulations, and selecting the formulation samples with qualified appearance as preliminary formulation samples; S4. Detect the component content of the selected preliminary preparation samples to obtain the component content data of each preliminary preparation sample, and compare them with the qualified component content standard to calculate the component qualified rate, and at the same time, select the preparation samples with qualified component content from the preliminary preparation samples as the test preparation samples; The statistical component qualification rate is implemented as follows: S41, numbering the types of ingredients in the qualified ingredient content standard in descending order of content, and dividing the qualified content of each ingredient by the sum of the contents of various ingredients to obtain the weight factors of various ingredients; S42, matching the component types in the component content data corresponding to each preliminary preparation sample with the qualified component types in the qualified component content standard to obtain the matching degree of various components; S43. Compare the content of the component in the corresponding component content data of each preliminary preparation sample with the qualified component content in the qualified component content standard, and use the expression Calculate the composition compliance of the preliminary preparation samples , where Indicates The weight factor of the component, Indicates the ingredient type number. , Indicates the number of ingredient types present in the qualified ingredient content standard, Indicates The matching degree of the components, The value of is 0 or 1. Indicates the content of the corresponding component in the preliminary effective preparation The content of ingredients, Indicates the qualified ingredient content standard Qualified content of the ingredients; S44, comparing the ingredient conformity of each preliminary preparation sample with the set conformity of the qualified ingredients, counting the number of preparation samples that have met the conformity of the qualified ingredients, and comparing it with the number of preparation samples that have met the conformity of the qualified appearance to obtain the percentage to obtain the qualified ingredient rate; S5. Group the screened test preparation samples into test groups according to the number of physical and chemical test items required, and determine the physical and chemical test items corresponding to each test group; S6. Obtaining suitable environmental conditions based on the physical and chemical test items corresponding to each test group, thereby controlling the test environmental conditions of each test group; S7, performing performance tests on the test preparation samples in each test group under the control of the test environment conditions to obtain test result images and test stability time, and performing performance consistency evaluation and processing based on the intra-group comparison results of the test result images and the test stability time to obtain the final test result images of each test group; S8. Compare the final test result image of each test group with the qualified test image to calculate the qualified rate of physical and chemical properties; S9. Evaluate the compliance of production quality based on the appearance qualification rate, ingredient qualification rate and physical and chemical performance qualification rate of the current batch of pesticide preparations.

2. A visualization-based pesticide formulation quality consistency management and evaluation method as claimed in claim 1, characterized in that: The appearance conformity is shown in the following analysis steps: S21. Extracting appearance features, specifically color and transparency, from the appearance images of each preparation sample, and performing feature quantification to obtain quantitative data of the appearance features of each preparation sample. Similarly, performing appearance feature extraction and feature conversion on the qualified appearance images to obtain qualified quantitative data of each appearance feature; S22. Subtract the quantitative data of the appearance characteristics of each preparation sample from the qualified quantitative data and take the absolute value. Then, with the reciprocal of the set constant as the base, an exponential function is constructed by multiplying the absolute value of the difference of each appearance characteristic by the qualified quantitative data as the exponent to obtain the appearance conformity of each preparation sample.

3. A visualization-based pesticide formulation quality consistency management and evaluation method as claimed in claim 2, characterized in that: The appearance qualification rate is as follows: The appearance conformity of each formulation sample is compared with the set standard appearance conformity, and the number of formulation samples that meet the standard appearance conformity is counted. The percentage is compared with the number of pesticide formulations currently produced in batches to obtain the appearance qualification rate.

4. A visualization-based pesticide formulation quality consistency management and evaluation method as claimed in claim 1, characterized in that: The test groups were grouped as follows: S51, dividing the number of screened test preparation samples by the number of physical and chemical test items to be performed to determine whether they are divisible. If they are divisible, execute S52; otherwise, execute S53; S52, grouping the number of the screened test preparation samples evenly according to the number of physical and chemical test items required; S53. Extract the remainder of the number of screened test preparation samples and the number of physical and chemical test items that need to be conducted, and divide the remainder by the number of test preparation samples to obtain the remainder ratio, and then compare it with the set ratio threshold. If the ratio threshold is not reached, the test preparation samples are randomly eliminated from the screened test preparation samples so that the elimination number meets the remainder. If the ratio threshold is reached, the number of screened test preparation samples is subjected to incompletely even distribution of physical and chemical test items.

5. A visualization-based pesticide formulation quality consistency management and evaluation method as claimed in claim 1, characterized in that: The performance consistency evaluation and processing process is as follows: S71, comparing the test result data extracted from the test result images of each test preparation sample in each test group, selecting the maximum value data therefrom, taking the difference and dividing it by the maximum test result data to obtain the test result difference degree corresponding to each test group; S72. Similarly, the test stability difference is calculated based on the test stability time of each test preparation sample in each test group; S73. Perform weighted average statistics on the test result differences and test stability differences corresponding to each test group to obtain the performance consistency of each test group, and compare it with the consistency threshold. If the performance consistency corresponding to a test group is less than the consistency threshold, calibrate the test equipment and re-test to obtain the final test result images of each test group.

6. A visualization-based pesticide formulation quality consistency management and evaluation method as claimed in claim 1, characterized in that: The statistics of the qualified rates of the physical and chemical properties are as follows: S81, comparing the test result data extracted from the final test result image corresponding to each test preparation sample in each test group with the qualified test data in the qualified test image of the corresponding physical and chemical test item of the test group, and counting the number of test preparation samples that meet the qualified test data; S82. Divide the number of test preparation samples that meet the qualified test data in each test group by the number of test preparation samples in the corresponding test group and take the percentage to obtain the physical and chemical performance qualification rate of each test group, which is used as the physical and chemical performance qualification rate of the physical and chemical test items of each test group.

7. A visualization-based pesticide formulation quality consistency management and evaluation method as claimed in claim 1, characterized in that: The production quality compliance evaluation process is as follows: The overall quality pass rate of the current batch of pesticide preparations is obtained by multiplying the appearance pass rate, ingredient pass rate and physical and chemical performance pass rate of each physical and chemical test item of the current batch of pesticide preparations; The comprehensive quality pass rate of the current batch of pesticide preparations is compared with the set standard quality pass rate. If the comprehensive quality pass rate is greater than the standard quality pass rate, it is judged that the production quality of the current batch of pesticide preparations meets the standard; otherwise, it is judged that the production quality of the current batch of pesticide preparations does not meet the standard.

8. A visualization-based pesticide formulation quality consistency management and evaluation method as claimed in claim 7, characterized in that: When judging that the current batch of pesticide preparation production quality does not meet the standard, the appearance qualified rate, the ingredient qualified rate and the physical and chemical performance qualified rate of each physical and chemical test item are arranged in order from the smallest to the largest qualified rate to obtain a qualified rate sorting result. At this time, the parameters with the lowest qualified rate are eliminated from the sorting result in turn, and the cumulative product result is recalculated and compared with the qualified quality rate. If the new cumulative product result reaches the qualified quality rate, the eliminated parameter is regarded as a quality parameter that tends to fail to meet the standard.

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