Production management method and system for paint products

Abstract

The invention relates to the technical field of data management, in particular to a production management method and system for paint products. The method comprises the steps of obtaining a first evaluation index value of a quality data sequence, and obtaining a second evaluation index value of the quality data sequence according to a difference between adjacent quality data points on a quality data point curve and a variance of abscissa differences between all adjacent maximum value data points on the quality data point curve, obtaining a third evaluation index value of the quality data sequence according to the difference between the extreme value on the quality data point curve and the extreme mean value on the quality data point curve and the number of extreme points on the quality data point curve; and selecting an optimal compression strategy corresponding to the quality data sequence according to the first evaluation index value, the second evaluation index value and the third evaluation index value, and compressing the quality data sequence by using the optimal compression strategy. According to the invention, the compression effect of compressing the quality data related to the paint product can be improved or ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a production management method and system for paint products. Background Art

[0002] Since product quality management is the core link for enterprises to enhance their market competitiveness, the quality parameters related to paint products also need to be managed in the field of paint products, such as viscosity, gloss, color difference, density and other quality parameters of paint products. However, as production time increases, the quality parameters related to paint products will become more and more numerous and more and more complex. Therefore, in order to improve management efficiency, the quality parameters related to paint products are generally compressed, and then the compressed data is transmitted and stored for subsequent product quality traceability.

[0003] However, the current method of compressing quality parameters related to paint products is generally to compress all types of quality parameters using the same compression algorithm. However, the quality parameters related to paint products will be affected by factors such as data type, changes in raw material batches, fluctuations in process conditions and environmental factors, resulting in differences in the characteristics of different types of quality parameters obtained from different paint product batches, and the characteristics of the quality parameters include regularity, repeatability and other characteristics of the quality parameters, and different compression algorithms have different compression effects on quality parameters with different characteristics. Therefore, when selecting a compression algorithm for compressing quality parameters related to paint products, if the characteristics of different types of quality parameters are not considered and the compression algorithm is directly selected at random, it will result in poor compression effect when compressing quality parameters related to paint products. Therefore, how to reasonably select a compression algorithm when compressing different types of quality parameters to improve or ensure the compression effect when compressing quality parameters related to paint products has become an urgent problem to be solved. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a production management method and system for paint products, and the technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a production management method for a paint product, comprising the following steps:

[0006] Acquire a quality data sequence of a paint product and a quality data point curve corresponding to the quality data sequence, wherein the quality data point curve is composed of quality data points;

[0007] Obtaining a first evaluation index value of the quality data sequence according to the slope of the fitting line of the quality data point curve, the horizontal coordinate difference between adjacent maximum value points on the quality data point curve, and the variance of the quality data sequence;

[0008] Obtaining a second evaluation index value of the quality data sequence according to the difference between adjacent quality data points on the quality data point curve and the variance of the horizontal coordinate difference between all adjacent maximum value data points on the quality data point curve;

[0009] Obtaining a third evaluation index value of the quality data sequence according to a difference between the maximum value on the quality data point curve and the mean value of the extreme values ​​on the quality data point curve and the number of extreme value points on the quality data point curve;

[0010] According to the first evaluation index value, the second evaluation index value and the third evaluation index value, an optimal compression strategy corresponding to the quality data sequence is selected, and the quality data sequence is compressed using the optimal compression strategy.

[0011] In a second aspect, an embodiment of the present invention provides a production management system for paint products, including a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the above-mentioned production management method for paint products.

[0012] Beneficial effects: The present invention first obtains the quality data sequence of the paint product and the quality data point curve corresponding to the quality data sequence; then, according to the slope of the fitting line of the quality data point curve, the horizontal coordinate difference between the adjacent maximum value points on the quality data point curve and the variance of the quality data sequence, obtains the first evaluation index value of the quality data sequence; according to the difference between the adjacent quality data points on the quality data point curve and the variance of the horizontal coordinate difference between all adjacent maximum value data points on the quality data point curve, obtains the second evaluation index value of the quality data sequence; according to the difference between the maximum value on the quality data point curve and the extreme value mean on the quality data point curve and the number of extreme value points on the quality data point curve, obtains the third evaluation index value of the quality data sequence; finally, according to the first evaluation index value, the second evaluation index value and the third evaluation index value, selects the optimal compression strategy corresponding to the quality data sequence, and compresses the quality data sequence using the optimal compression strategy. And the present invention can obtain the optimal compression strategy of the quality data sequence according to the evaluation index value of the quality data sequence, and compresses the corresponding quality data sequence by using the optimal compression strategy of the quality data sequence, which can improve or ensure the compression effect when compressing the quality data related to the paint product. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 The present invention is a flow chart of a production management method for paint products. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the embodiments of the present invention.

[0016] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0017] This embodiment provides a production management method for paint products, which is described in detail as follows:

[0018] like Figure 1 As shown, the production management method for paint products comprises the following steps:

[0019] Step S001, obtaining a quality data sequence of a paint product and a quality data point curve corresponding to the quality data sequence.

[0020] The purpose of this embodiment is to select the most appropriate or optimal compression strategy to compress the quality parameters to be compressed by analyzing the regularity, repeatability and other characteristics of the quality parameters to be compressed, so as to achieve the purpose of ensuring or improving the compression effect.

[0021] This embodiment first obtains the paint produced by any production line in the target production time period, and the paint produced by the production line in the target production time period is contained in a paint containing container, and each paint containing container is recorded as a paint containing container to be detected, the paint contained in the paint containing container is the paint produced by the above-mentioned production line in the target production time period, the types of paint produced by the production line in the target production time period are the same, and the types of the paint containing containers include but are not limited to stainless steel containers, special reactors or mixing tanks, transitional plastics, iron containers, temporary storage tanks, etc.

[0022] In this embodiment, the main focus is on analyzing the selection process of the compression algorithm when compressing the quality data obtained by testing the key performance items of the paint. Because in actual testing, a full inspection is generally used for the key performance items, the present embodiment then performs a quality inspection on the paint in each paint container to be tested to obtain different types of quality data corresponding to each paint container to be tested, and then records the sequence consisting of the same type of quality data as the quality data sequence of the corresponding type in chronological order of production time. That is, if the paint containers to be tested are numbered in chronological order of production time, and the paint in the paint container to be tested with a larger number has a later production time, then the sth quality data in any type of quality data sequence refers to the data obtained by quality testing the paint in the paint container to be tested numbered s. In order to facilitate understanding and analysis, the present embodiment will subsequently use any type of quality data in the sequence. The specific selection or determination process of the compression algorithm when compressing is described, that is, the selection or determination process of the compression algorithm when compressing any type of quality data sequence will be described later, then all quality data sequences that appear later are the same quality data sequence, and in this embodiment, this type of quality data sequence is recorded as the quality data sequence of the paint product. For example, if the paint in the paint container to be tested is mainly used in the construction field, then the types of quality data collected include but are not limited to VOC content, formaldehyde content, heavy metal content, etc., so the types of quality data sequences of the paint product include but are not limited to VOC content sequence, formaldehyde content sequence, heavy metal content sequence, etc.; and in this embodiment, the reason for forming the same type of quality data into a time series sequence is that compared with different types of data, the same type of data generally presents certain regular characteristics, stable characteristics or correlation characteristics, which is more conducive to improving the compression effect.

[0023] In addition, it should be noted that when testing the various quality data of paint products, it is necessary to conduct quality inspections by combining random inspections with full inspections based on the importance of the inspection items, risk levels and industry specifications, and generally follow the rules of full inspections of key performance items and random inspections of routine items. Key performance items refer to items that directly affect safety or core functions, and routine items refer to secondary or easily controllable items. For example, there are differences in key performance items and routine items of paint products used in different fields. For example, for aerospace paints, high temperature resistance, radiation resistance and salt spray corrosion resistance are key. For performance items, a full inspection is required for each batch of products to ensure flight safety. Coating uniformity, bubbles, cracks, etc. are also key performance items, and full inspection is generally adopted. The color, viscosity, drying time, etc. of aerospace paints are routine items, and they are generally sampled according to production batches. For architectural paints, VOC, formaldehyde, heavy metals, etc. are key performance items and need to be fully inspected to meet national standards. Health-related indicators such as formaldehyde emission are also key performance items, and full batch testing is also mandatory. Adhesion (grid method), water resistance (immersion test), leveling, etc. are routine performance items, and they are usually sampled in proportion to control costs.

[0024] Therefore, the present embodiment obtains the quality data sequence of the paint product through the above process, and the present embodiment subsequently mainly selects a compression method suitable for the quality data sequence by analyzing the data characteristics in the quality data sequence; since the data point curve needs to be referred to when analyzing the data characteristics in the quality data sequence later, the present embodiment will then obtain the quality data point curve corresponding to the quality data sequence through the data in the quality data sequence, and the specific acquisition process of the quality data point curve corresponding to the quality data sequence is:

[0025] First, the position number value of each quality data in the quality data sequence is obtained, and the position number value of the g-th quality data in the quality data sequence is g, and then a two-dimensional coordinate system is constructed, the horizontal coordinate value of the constructed two-dimensional coordinate system is the position number value, and the vertical coordinate value is the quality data, and then each quality data in the quality data sequence and the position number value of each quality data are mapped to the two-dimensional coordinate system, and the quality data point corresponding to each quality data in the quality data sequence is obtained in the two-dimensional coordinate system, and the horizontal coordinate value of the quality data point corresponding to the quality data is the position number value of the corresponding quality data, and the vertical coordinate value is the corresponding quality data; then, in the two-dimensional coordinate system, the quality data points are connected in sequence according to the horizontal coordinate value from small to large, and the connected curve is recorded as the quality data point curve corresponding to the quality data sequence, that is, the quality data point curve is composed of quality data points.

[0026] Therefore, this embodiment obtains the quality data sequence of the paint product and the quality data point curve corresponding to the quality data sequence through the above process.

[0027] Step S002, obtaining a first evaluation index value of the quality data sequence according to the slope of the fitting straight line of the quality data point curve, the difference in horizontal coordinates between adjacent maximum points on the quality data point curve, and the variance of the quality data sequence; obtaining a second evaluation index value of the quality data sequence according to the difference between adjacent quality data points on the quality data point curve and the variance of the horizontal coordinate differences between all adjacent maximum data points on the quality data point curve; obtaining a third evaluation index value of the quality data sequence according to the difference between the maximum value on the quality data point curve and the mean of the extreme values ​​on the quality data point curve and the number of extreme value points on the quality data point curve.

[0028] Since there are differences in data regularity, repeatability and other characteristics in different types of quality data sequences, and different compression strategies or compression algorithms have different compression effects on quality data sequences with different characteristics, such as run-length encoding or differential encoding has a better compression effect on quality data sequences with higher repeatability, and the LZW compression algorithm has a better compression effect on quality data sequences with lower repeatability but showing stronger regular changes. Therefore, in order to improve the compression effect, optimize the storage space, and provide more reliable data for the analysis of quality trends, potential problems and abnormal fluctuations in the production process, this embodiment will further determine the optimal compression algorithm or compression strategy for the quality data sequence by analyzing the indicator values ​​that can evaluate the data characteristics in the quality data sequence. That is, after obtaining the quality data sequence and the quality data point curve corresponding to the quality data sequence, this embodiment obtains the first evaluation index value, the second evaluation index value and the third evaluation index value of the quality data sequence by analyzing the quality data sequence and the quality data point curve corresponding to the quality data sequence. Since the first evaluation index value, the second evaluation index value and the third evaluation index value can reflect the data characteristics in the corresponding quality data sequence, the compression algorithm most suitable for the quality data sequence can be obtained through the obtained first evaluation index value, the second evaluation index value and the third evaluation index value, or the optimal compression strategy for the quality data sequence can be obtained through the obtained first evaluation index value, the second evaluation index value and the third evaluation index value.

[0029] Therefore, based on the above analysis, it can be seen that in this embodiment, the first evaluation index value of the quality data sequence is obtained according to the slope of the fitting line of the quality data point curve, the horizontal coordinate difference between adjacent maximum value points on the quality data point curve, and the variance of the quality data sequence. Then, in this embodiment, the specific acquisition process of the first evaluation index value of the quality data sequence is as follows:

[0030] First, the quality data point curve corresponding to the quality data sequence is linearly fitted by the least square method, and the fitting straight line obtained by the linear fitting is recorded as the target fitting straight line, and the absolute value of the slope of the target fitting straight line is recorded as the slope to be analyzed, and then the slope to be analyzed is negatively correlated and mapped, and the mapping result is recorded as the first eigenvalue; then the variance of the quality data sequence is obtained, the variance of the quality data sequence is negatively correlated and mapped, and the mapping result is recorded as the second eigenvalue; then all the maximum value points on the quality data point curve are obtained, and the sequence composed of all the maximum value points on the quality data point curve is recorded as the maximum value point sequence, and the fth maximum value point sequence in the maximum value point sequence The maximum point is the fth maximum point on the quality data point curve; then, according to the difference in the horizontal coordinates between adjacent maximum points in the maximum point sequence, the span value sequence is obtained, and the mean of the span value sequence is obtained, and the normalized value of the mean of the span value sequence is recorded as the third eigenvalue, and the hth span value in the span value sequence is the result of subtracting the horizontal coordinate value of the h+1th maximum point in the maximum point sequence from the horizontal coordinate value of the hth maximum point in the maximum point sequence; finally, the mean of the first eigenvalue, the second eigenvalue and the third eigenvalue is obtained, and the mean of the first eigenvalue, the second eigenvalue and the third eigenvalue is used as the first evaluation index value of the quality data sequence.

[0031] In this embodiment, the specific calculation expression of the first evaluation index value of the quality data sequence is:

[0032]

[0033] Where W1 is the first evaluation index value of the quality data sequence, exp() is an exponential function with a constant e as the base, k0 is the slope of the target fitting line, is the variance of the quality data sequence, d0 is the mean of the span value sequence, tanh() is the hyperbolic tangent function, is the first eigenvalue, is the second eigenvalue, is the third eigenvalue, and the hyperbolic tangent function tanh() here is to normalize d0.

[0034] And when The closer to 0, The smaller and The larger it is, the larger the value of W1 is. The larger the value of W1 is, the more horizontal the overall trend of the quality data sequence is and the smaller the change is. At the same time, the data fluctuation in the quality data sequence is relatively stable or the steady-state is higher. This also indicates that the data in the quality data sequence has a high repeatability. When the data in the sequence has a high repeatability, the compression effect of run-length encoding or differential encoding is better. Run-length encoding or differential encoding is very suitable for processing situations with a large amount of repeated data. The data is compressed by recording the number of times the same data value occurs or the difference between adjacent data points, and the compression efficiency is extremely high.

[0035] After obtaining the first evaluation index value, the second evaluation index value of the quality data sequence is obtained according to the difference between adjacent quality data points on the quality data point curve corresponding to the quality data sequence and the variance of the horizontal coordinate difference between all adjacent maximum value data points on the quality data point curve. Then, the specific acquisition process of the second evaluation index value of the quality data sequence in this embodiment is as follows:

[0036] First, according to the difference between adjacent quality data points on the quality data point curve corresponding to the quality data sequence, the difference comprehensive characterization value is obtained; then the variance of the span value sequence is obtained, and the variance of the span value sequence is negatively correlated and mapped, and the mapping result is recorded as the mapping span variance value; then the mean of the difference comprehensive characterization value and the mapping span variance value is obtained, and the mean of the difference comprehensive characterization value and the mapping span variance value is used as the second evaluation indicator value of the quality data sequence.

[0037] In this embodiment, the specific process of obtaining the comprehensive characterization value of the difference according to the difference between adjacent quality data points on the quality data point curve corresponding to the quality data sequence is as follows: first, a characteristic difference value sequence corresponding to the quality data point curve is obtained, and the ath characteristic difference value in the characteristic difference value sequence is the result of subtracting the ordinate value of the a+1th quality data point on the quality data point curve from the ordinate value of the ath quality data point on the quality data point curve, and the total number of characteristic difference values ​​in the characteristic difference value sequence is M-1, where M is the total number of quality data points on the quality data point curve; then, the median in the characteristic difference value sequence is obtained, and the obtained median is used as the reference difference value; then, a deviation degree sequence corresponding to the characteristic difference value sequence is obtained, and the bth deviation degree in the deviation degree sequence is , is the bth feature difference value in the feature difference value sequence, is the reference difference value; finally, the mean of the deviation degree sequence is obtained, and a negative correlation mapping is performed on the mean of the deviation degree sequence, and the result of the negative correlation mapping is recorded as the comprehensive representation value of the difference.

[0038] In addition, the specific calculation expression of the second evaluation index value of the quality data sequence is:

[0039]

[0040] Where W2 is the second evaluation index value of the quality data sequence, is the variance of the span value sequence; and when The smaller and The smaller the value, the larger the value of W2. When the value of W2 is larger, it indicates that the data changes in the quality data sequence have more regular characteristics. On the contrary, when W2 is smaller, it indicates that the data changes in the quality data sequence have less regular characteristics. And because the LZW compression algorithm has a better compression effect on the quality data sequence showing regular changes, the larger the value of W2 is, the better the compression effect of the LZW compression algorithm on the quality data sequence is. It reflects the degree of deviation of data fluctuation. The smaller the deviation, the more consistent the fluctuation range. The more stable the data fluctuation range, the more likely it is to have a certain regularity. The smaller it is, the more regular the distribution of extreme points is.

[0041] LZW can build a dictionary, and the dictionary feature can effectively identify and compress such regular changes, and find these regular substrings in the data, especially when faced with such regular data repetitions, which greatly reduces data redundancy and improves the compression rate.

[0042] After obtaining the second evaluation index value, the third evaluation index value of the quality data sequence is obtained according to the difference between the maximum value on the quality data point curve corresponding to the quality data sequence and the mean value of the extreme values ​​on the quality data point curve and the number of extreme value points on the quality data point curve. Then, the specific acquisition process of the third evaluation index value of the quality data sequence in this embodiment is as follows:

[0043] First, the total number of extreme points on the quality data point curve is obtained, which is the combination of the number of minimum points and the number of maximum points on the quality data point curve. Then, the total number of quality data points on the quality data point curve is obtained, and the normalized value of the ratio of the total number of extreme points on the quality data point curve to the total number of quality data points on the quality data point curve is recorded as the characteristic ratio.

[0044] Then, the mean of the ordinates of all the maximum points on the quality data point curve is obtained and recorded as the maximum mean, the mean of the ordinates of all the minimum points on the quality data point curve is obtained and recorded as the minimum mean, and the maximum and minimum points on the quality data point curve are obtained; then, the result of subtracting the maximum mean from the ordinate value of the maximum point on the quality data point curve is obtained and recorded as the first difference, and the result of subtracting the minimum mean from the ordinate value of the minimum point on the quality data point curve is obtained and recorded as the second difference; then, the sum of the first difference and the second difference is obtained, and a negative correlation mapping is performed on the sum of the first difference and the second difference, and the mapping result is recorded as the characteristic difference; then, the mean of the characteristic ratio and the characteristic difference is obtained and used as the third evaluation index value of the quality data sequence.

[0045] In addition, the specific calculation expression of the second evaluation index value of the quality data sequence is:

[0046]

[0047] Where W3 is the third evaluation index value of the quality data sequence, N is the total number of extreme points on the quality data point curve, M is the total number of quality data points on the quality data point curve, D1 is the first difference, D2 is the second difference, and the hyperbolic tangent function tanh() here is to Normalize; and when The larger the value, The smaller the value, the more disordered and volatile the data in the quality data sequence is, that is, there is no obvious change pattern in the data in the quality data sequence, and the data fluctuation range in the quality data sequence is in a stable range; and since the SDT revolving door compression algorithm has a better compression effect on quality data sequences that are disordered and volatile but have a fluctuation range in a stable range, the larger the W3 is, the better the compression effect of the SDT revolving door compression algorithm on the quality data sequence is.

[0048] The SDT revolving door compression algorithm can eliminate redundant parts by identifying stable changes in data within a certain range, and only records new data points when the data crosses a set threshold. This means that for parts of the data that do not fluctuate significantly, SDT will merge these data points to reduce redundancy, and only store key points that represent changes. Therefore, the SDT revolving door compression algorithm can effectively compress redundant data within a stable range, minimize storage redundancy, save computing resources, and retain important data information. Although SDT is a lossy compression, for product production management, there is usually a certain tolerance for data accuracy. SDT can ensure that data compression will not affect the effectiveness of actual decisions by setting an appropriate range. It can efficiently compress data by sacrificing a certain amount of data accuracy but retaining the global trend of the data without affecting subsequent quality analysis.

[0049] Therefore, this embodiment obtains the first evaluation index value, the second evaluation index value and the third evaluation index value of the quality data sequence through the above process.

[0050] Step S003: selecting an optimal compression strategy corresponding to the quality data sequence according to the first evaluation index value, the second evaluation index value and the third evaluation index value, and compressing the quality data sequence using the optimal compression strategy.

[0051] After obtaining the first evaluation index value, the second evaluation index value and the third evaluation index value of the quality data sequence, the optimal compression strategy corresponding to the quality data sequence is selected according to the first evaluation index value, the second evaluation index value and the third evaluation index value of the quality data sequence, and the quality data sequence is compressed using the optimal compression strategy, and the specific process is:

[0052] Determine whether the first evaluation index value of the quality data sequence is greater than the preset first threshold value. If so, use run-length coding or differential coding as the optimal compression strategy corresponding to the quality data sequence. Otherwise, determine whether the second evaluation index value of the quality data sequence is greater than the preset second threshold value. If so, use the LZW algorithm as the optimal compression strategy corresponding to the quality data sequence. Otherwise, continue to determine whether the third evaluation index value of the quality data sequence is greater than the preset third threshold value. If so, use the revolving door compression algorithm as the optimal compression strategy corresponding to the quality data sequence.

[0053] In addition, it may also happen that the first evaluation index value of the quality data sequence is not greater than the preset first threshold, the second evaluation index value of the quality data sequence is not greater than the preset second threshold, and the third evaluation index value of the quality data sequence is not greater than the preset third threshold. When the first evaluation index value of the quality data sequence is not greater than the preset first threshold, the second evaluation index value of the quality data sequence is not greater than the preset second threshold, and the third evaluation index value of the quality data sequence is not greater than the preset third threshold, the LZ77 compression algorithm is used as the optimal compression strategy corresponding to the quality data sequence. That is to say, at this time, the most common LZ77 compression algorithm can be selected to compress the quality data sequence. The LZ77 compression algorithm has strong adaptability and can handle most data types well.

[0054] In this embodiment, the implementer needs to set the preset first threshold, the preset second threshold and the preset third threshold according to actual conditions. For example, in this embodiment, the preset first threshold can be set to 0.7, the preset second threshold can be set to 0.8, and the preset third threshold can be set to 0.7.

[0055] Therefore, this embodiment can obtain the optimal compression strategy corresponding to each type of quality data sequence through the above process, and then use the optimal compression strategy corresponding to each type of quality data sequence to complete the compression of the corresponding quality data sequence. The compressed data will be subsequently transmitted and stored to facilitate the production management of paint products.

[0056] In addition, for ease of understanding, this embodiment will be described below by taking the determination process of the optimal compression strategy corresponding to the viscosity data sequence of a paint product as an example. The specific process is as follows:

[0057] If the viscosity data sequence obtained through the paint product quality data detection process in step S001 is {120.5, 122.3, 122.9, 125.8, 123.6, 123.7, 121.9, 123.3, 124.5, 123.1}, then according to the position number value of each viscosity data in the viscosity data sequence, a quality data point curve corresponding to the obtained viscosity data sequence is drawn, the abscissa of the data point on the curve is the position number value of the viscosity data in the viscosity data sequence, and the ordinate is the viscosity data, and then the least squares method is used to perform linear fitting on the quality data point curve corresponding to the viscosity data sequence to obtain a target fitting straight line.

[0058] After obtaining the viscosity data sequence, the mass data point curve corresponding to the viscosity data sequence, and the target fitting straight line, the first evaluation index value of the viscosity data sequence is calculated according to the specific calculation process of the first evaluation index value in step S002 as follows: 0, and 0.177 in the formula is the slope of the target fitting line obtained by linear fitting the mass data point curve corresponding to the viscosity data sequence, 1.874 is the variance of the viscosity data sequence, and 1.2 is the mean of the span value sequence calculated according to the maximum point on the mass data point curve corresponding to the viscosity data sequence, that is, the first evaluation index value of the viscosity data sequence is 0.60 at this time.

[0059] Then, according to the specific calculation process of the second evaluation index value in the above step S002, the characteristic difference value sequence corresponding to the quality data point curve corresponding to the viscosity data sequence obtained is {1.8, 0.6, 2.9, -2.2, 0.1, -1.8, 1.4, 1.2, -1.4}, the median in the characteristic difference value sequence is 0.6, that is, the reference difference value is 0.6, and the deviation degree calculated by the calculated characteristic difference value sequence and the reference difference value is the deviation degree sequence of {2.0, 0.0, 3.83, 4.67, 1.0, 4.0, 1.33, 1.0, 3.33}, based on the mean value of the deviation degree sequence of 2.35 and the variance of the span value sequence calculated according to the maximum point on the quality data point curve corresponding to the viscosity data sequence. The second evaluation index value of the viscosity data sequence is: .

[0060] Then, according to the specific calculation process of the third evaluation index value in step S002, the ratio of the total number of extreme value points on the mass data point curve corresponding to the viscosity data sequence to the total number of mass data points on the mass data point curve is obtained, that is, The value of , and since the ordinate value of the maximum value point on the mass data point curve corresponding to the viscosity data sequence is 125.8, the ordinate value of the minimum value point is 121.9, the maximum mean is 124.67, and the minimum mean is 122.75, then the calculation result of D1 is 1.13, and the calculation result of D2 is -0.85, and the third evaluation index value of the viscosity data sequence is obtained: .

[0061] Therefore, this embodiment calculates the first evaluation index value of 0.60, the second evaluation index value of 0.43, and the third evaluation index value of 0.61 for the viscosity data sequence through the above process, and the first evaluation index value, the second evaluation index value and the third evaluation index value of the viscosity data sequence are all results retained after two decimal points; and because the first evaluation index value of 0.60 of the viscosity data sequence is not greater than the preset first threshold value of 0.7, the second evaluation index value of 0.43 of the viscosity data sequence is not greater than the preset second threshold value of 0.8, and the third evaluation index value of 0.61 of the viscosity data sequence is not greater than the preset third threshold value of 0.7, then based on the selection rule of the compression algorithm when the evaluation index values ​​formulated in step S003 are not greater than the threshold value, it can be known that the LZ77 compression algorithm should be selected as the optimal compression strategy corresponding to the viscosity data sequence at this time, that is, the LZ77 compression algorithm is subsequently used to compress the viscosity data sequence.

[0062] A production management system for paint products in this embodiment includes a memory and a processor, and the processor executes a computer program stored in the memory to implement the above-mentioned production management method for paint products.

[0063] In summary, this embodiment first obtains the quality data sequence of the paint product and the quality data point curve corresponding to the quality data sequence; then, according to the slope of the fitting straight line of the quality data point curve, the horizontal coordinate difference between adjacent maximum value points on the quality data point curve, and the variance of the quality data sequence, obtains the first evaluation index value of the quality data sequence; according to the difference between adjacent quality data points on the quality data point curve and the variance of the horizontal coordinate difference between all adjacent maximum value data points on the quality data point curve, obtains the second evaluation index value of the quality data sequence; according to the difference between the maximum value on the quality data point curve and the mean of the extreme values ​​on the quality data point curve and the number of extreme value points on the quality data point curve, obtains the third evaluation index value of the quality data sequence; finally, according to the first evaluation index value, the second evaluation index value and the third evaluation index value, selects the optimal compression strategy corresponding to the quality data sequence, and compresses the quality data sequence using the optimal compression strategy. Moreover, this embodiment can compress the quality data sequence with the most appropriate or optimal compression strategy by analyzing the evaluation index value of the quality data sequence. That is, this embodiment can obtain the optimal compression strategy of the quality data sequence based on the evaluation index value of the quality data sequence, and compress the corresponding quality data sequence by using the optimal compression strategy of the quality data sequence, which can improve or ensure the compression effect when compressing the quality data related to the paint product.

[0064] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A production management method for paint products, characterized in that: The method comprises the following steps: Acquire a quality data sequence of a paint product and a quality data point curve corresponding to the quality data sequence, wherein the quality data point curve is composed of quality data points; Obtaining a first evaluation index value of the quality data sequence according to the slope of the fitting line of the quality data point curve, the horizontal coordinate difference between adjacent maximum value points on the quality data point curve, and the variance of the quality data sequence; Obtaining a second evaluation index value of the quality data sequence according to the difference between adjacent quality data points on the quality data point curve and the variance of the horizontal coordinate difference between all adjacent maximum value data points on the quality data point curve; Obtaining a third evaluation index value of the quality data sequence according to a difference between the maximum value on the quality data point curve and the mean value of the extreme values ​​on the quality data point curve and the number of extreme value points on the quality data point curve; According to the first evaluation index value, the second evaluation index value and the third evaluation index value, an optimal compression strategy corresponding to the quality data sequence is selected, and the quality data sequence is compressed using the optimal compression strategy.

2. A production management method for paint products as claimed in claim 1, characterized in that: The method for obtaining the first evaluation index value of the quality data sequence includes: The absolute value of the slope of the fitted straight line obtained by linearly fitting the quality data point curve is recorded as the slope to be analyzed, and the negative correlation mapping value of the slope to be analyzed is recorded as the first eigenvalue; Recording the negative correlation mapping value of the variance of the quality data sequence as the second eigenvalue; Recording a sequence constructed by the horizontal coordinate differences between all adjacent maximum value points on the quality data point curve as a span value sequence, and recording a normalized value of the mean of the span value sequence as a third eigenvalue; The average of the first eigenvalue, the second eigenvalue and the third eigenvalue is used as the first evaluation index value of the quality data sequence.

3. A production management method for paint products as claimed in claim 2, characterized in that: The method for obtaining the second evaluation index value of the quality data sequence includes: Obtaining a comprehensive characterization value of the difference according to the difference between adjacent quality data points on the quality data point curve; Recording the negatively correlated mapping value of the variance of the span value sequence as a mapping span variance value; The mean of the difference comprehensive characterization value and the mapping span variance value is used as the second evaluation index value of the quality data sequence.

4. A production management method for paint products as claimed in claim 3, characterized in that: The method for obtaining the comprehensive characterization value of the difference includes: Obtaining a characteristic difference value sequence corresponding to the quality data point curve, wherein the ath characteristic difference value in the characteristic difference value sequence is the result of subtracting the ordinate value of the a+1th quality data point on the quality data point curve from the ordinate value of the ath quality data point; and using the median in the characteristic difference value sequence as a reference difference value; Obtain the deviation degree sequence corresponding to the characteristic difference value sequence, the bth deviation degree in the deviation degree sequence is , is the bth feature difference value in the feature difference value sequence, is the reference difference value; The negative correlation mapping value of the mean of the deviation degree sequence is recorded as the difference comprehensive characterization value.

5. A production management method for paint products as claimed in claim 1, characterized in that: The method for obtaining the third evaluation index value of the quality data sequence includes: Recording a normalized value of the ratio of the total number of extreme value points on the quality data point curve to the total number of quality data points on the quality data point curve as a characteristic ratio; The mean of the ordinates of all the maximum value points on the quality data point curve is taken as the maximum value mean, and the mean of the ordinates of all the minimum value points on the quality data point curve is taken as the minimum value mean; the result of subtracting the maximum value mean from the ordinate value of the maximum value point on the quality data point curve is recorded as the first difference, and the result of subtracting the minimum value mean from the ordinate value of the minimum value point on the quality data point curve is recorded as the second difference, and the negative correlation mapping value of the result obtained by adding the first difference and the second difference is recorded as the characteristic difference; The average of the feature ratio and the feature difference is used as the third evaluation index value of the quality data sequence.

6. A production management method for paint products as claimed in claim 1, characterized in that: The method of selecting the optimal compression strategy corresponding to the quality data sequence according to the first evaluation index value, the second evaluation index value and the third evaluation index value includes: Determine whether the first evaluation index value is greater than a preset first threshold value. If so, use run-length coding or differential coding as the optimal compression strategy corresponding to the quality data sequence. Otherwise, determine whether the second evaluation index value is greater than a preset second threshold value. If so, use the LZW algorithm as the optimal compression strategy corresponding to the quality data sequence. Otherwise, continue to determine whether the third evaluation index value is greater than a preset third threshold value. If so, use the revolving door compression algorithm as the optimal compression strategy corresponding to the quality data sequence.

7. A production management system for paint products, comprising a memory and a processor, characterized in that: The processor executes the computer program stored in the memory to implement the production management method for paint products as described in any one of claims 1-6.

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