A production management method and system for paint products

Abstract

The present invention relates to the technical field of data management, and particularly relates to a production management method and system for paint products. The method includes: obtaining a first evaluation index value of a 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 abscissa differences between all adjacent maximum value data points on the quality data point curve, and obtaining a third evaluation index value of the quality data sequence according to the difference between the maximum and minimum values on the quality data point curve and the average value of the extreme values on the quality data point curve, as well as the number of extreme points on the quality data point curve; 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. The present invention can improve or ensure the compression effect when compressing quality data related to paint products.

Description

Technical Field

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

[0002] Since product quality management is the core link for an enterprise to enhance its market competitiveness, currently in the field of paint products, it is also necessary to manage the quality parameters related to paint products. For example, it is necessary to manage quality parameters such as the viscosity, gloss, color difference, and density of paint products. However, as the production time increases, the quality parameters related to paint products will become more and more numerous and complex. Therefore, currently, 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 the quality parameters related to paint products generally compresses all types of quality parameters using the same compression algorithm. However, the quality parameters related to paint products are affected by factors such as data type, raw material batch changes, process condition fluctuations, and environmental factors, resulting in differences in the characteristics of different types of quality parameters obtained under different paint product batches. And the characteristics of the quality parameters include characteristics such as the regularity and repeatability of the quality parameters. Different compression algorithms have different compression effects on quality parameters with different characteristics. Therefore, when selecting a compression algorithm for compressing the quality parameters related to paint products currently, if the characteristics of different types of quality parameters are not considered and the compression algorithm is randomly selected directly, it will lead to a poor compression effect when compressing the 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 the 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 specific technical solutions adopted are as follows:

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

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

[0007] Obtain a first evaluation index value of the quality data sequence based on the fitting line slope of the quality data point curve, the abscissa difference between adjacent maximum value points on the quality data point curve, and the variance of the quality data sequence;

[0008] Obtain a second evaluation index value of the quality data sequence based on the difference between adjacent quality data points on the quality data point curve and the variance of the abscissa differences between all adjacent maximum value data points on the quality data point curve;

[0009] Obtain a third evaluation index value of the quality data sequence based on the difference between the maximum and minimum values on the quality data point curve and the average value of the extreme values on the quality data point curve, and the number of extreme points on the quality data point curve;

[0010] Select 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, and use the optimal compression strategy to compress the quality data sequence.

[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. The processor executes the 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 obtains the first evaluation index value of the quality data sequence based on the fitting line slope of the quality data point curve, the abscissa difference between adjacent maximum value points on the quality data point curve, and the variance of the quality data sequence, obtains the second evaluation index value of the quality data sequence based on the difference between adjacent quality data points on the quality data point curve and the variance of the abscissa differences between all adjacent maximum value data points on the quality data point curve, and obtains the third evaluation index value of the quality data sequence based on the difference between the maximum and minimum values on the quality data point curve and the average value of the extreme values on the quality data point curve, and the number of extreme points on the quality data point curve; finally, selects 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, and uses the optimal compression strategy to compress the quality data sequence. And based on the evaluation index value of the quality data sequence, the present invention can obtain the optimal compression strategy of the quality data sequence, and by using the optimal compression strategy of the quality data sequence to compress the corresponding quality data sequence, it can improve or ensure the compression effect when compressing the quality data related to the paint product. Description of the Drawings

[0013] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0014] Figure 1 It is a flowchart of a production management method for paint products of the present invention. Detailed implementation manners

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope protected by the embodiments of the present invention.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

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

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

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

[0020] The purpose of this embodiment is to select the most suitable or optimal compression strategy to compress the quality parameters to be compressed by analyzing the characteristics such as regularity and repeatability 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 during the target production period, and the paint produced by the production line during the target production period is placed in a paint storage container. Each paint storage container containing paint is denoted as a paint storage container to be detected. The paint contained in the paint storage container is the paint produced by the above production line during the target production period. The paint produced by the production line during the target production period is of the same variety. The types of the paint storage containers include, but are not limited to, stainless steel containers, special reaction kettles or mixing tanks, transitional plastics, iron containers, temporary storage tanks, etc.

[0022] In this embodiment, the selection process of the compression algorithm for compressing the quality data obtained by detecting the key performance items of the paint is analyzed. Also, since in actual detection, full inspection is generally used for key performance items, this embodiment then performs quality inspection on the paint in each paint-containing container to be detected, obtains different types of quality data corresponding to each paint-containing container to be detected, and then, in the order of production time, records the sequence composed of the same type of quality data as the quality data sequence of the corresponding type. That is, if the paint-containing containers to be detected are numbered in the order of production time, and the later the production time of the paint in the paint-containing container with a larger number, then the s-th quality data in any quality data sequence refers to the data obtained by performing quality inspection on the paint in the paint-containing container numbered s. For the convenience of understanding and analysis in this embodiment, the specific selection or determination process of the compression algorithm when any type of quality data is compressed will be described later. That is to say, the selection or determination process of the compression algorithm when any quality data sequence is compressed will be described later. Then, all the quality data sequences that appear later are the same quality data sequence. In this embodiment, the quality data sequence of this type is recorded as the quality data sequence of the paint product. For example, if the paint in the paint-containing container to be detected is mainly used in the construction field, the types of quality data collected include, but are not limited to, VOC content, formaldehyde content, heavy metal content, etc. Thus, the types of the quality data sequence 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 a time series sequence with the same type of quality data is that, compared with different types of data, the same type of data generally shows 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 detecting various quality data of paint products, it is necessary to adopt a combination of sampling inspection and full inspection according to the importance, risk level and industry norms of the inspection items for quality inspection. Generally, the rule is to conduct full inspection on key performance items and sampling inspection on routine items. The key performance items refer to those that directly affect safety or core functions, and the routine items refer to secondary or easily controllable items. For example, the key performance items and routine items of paint products applied in different fields are different. For aerospace paint, high temperature resistance, radiation resistance, salt spray corrosion resistance, etc. belong to key performance items, and full inspection of products in each batch is required to ensure flight safety. The uniformity, bubbles, cracks, etc. of the coating also belong to key performance items, and full inspection is generally also adopted. The color, viscosity, drying time, etc. of aerospace paint belong to routine items, and sampling inspection is generally carried out according to production batches. For architectural paint, VOC, formaldehyde, heavy metals, etc. belong to key performance items, and full inspection is required to meet national standards. Health-related indicators such as formaldehyde release amount also belong to key performance items, and full batch inspection is also required by force. For adhesion (cross-cut method), water resistance (immersion test), leveling property, etc. belong to routine performance items, and sampling inspection is usually carried out according to a certain proportion to control costs.

[0024] Therefore, through the above process, the quality data sequence of the paint product is obtained in this embodiment. Subsequently, this embodiment mainly analyzes the data characteristics in the quality data sequence to select a compression method suitable for the quality data sequence. Since the data characteristics in the quality data sequence need to be referred to the data point curve in the subsequent analysis, in this embodiment, the quality data point curve corresponding to the quality data sequence will be obtained through the data in the quality data sequence next. The specific obtaining process of the quality data point curve corresponding to the quality data sequence is as follows:

[0025] First, obtain the position number values of each quality data in the quality data sequence. The position number value of the g-th quality data in the quality data sequence is g. Then, construct a two-dimensional coordinate system. The abscissa value of the constructed two-dimensional coordinate system is the position number value, and the ordinate value is the quality data. Then, map each quality data and its position number value in the quality data sequence to the two-dimensional coordinate system, and obtain the quality data points corresponding to each quality data in the quality data sequence in the two-dimensional coordinate system. The abscissa value of the quality data point corresponding to the quality data is the position number value of the corresponding quality data, and the ordinate value is the corresponding quality data. Immediately afterwards, in the two-dimensional coordinate system, connect the quality data points in ascending order of the abscissa value, and record the connected curve 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, through the above process, the quality data sequence of the paint product and the quality data point curve corresponding to the quality data sequence are obtained in this embodiment.

[0027] Step S002: Obtain the first evaluation index value of the quality data sequence according to the fitting straight line slope of the quality data point curve, the abscissa difference between adjacent maximum value points on the quality data point curve, and the variance of the quality data sequence; obtain the 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 abscissa differences between all adjacent maximum value data points on the quality data point curve; obtain the third evaluation index value of the quality data sequence according to the difference between the maximum and minimum values on the quality data point curve and the average value 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 the data regularity, repeatability, etc. among different types of quality data sequences, and different compression strategies or compression algorithms have different compression effects on quality data sequences with different characteristics. For example, 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 stronger regular changes. Therefore, in this embodiment, in order to improve the compression effect and optimize the storage space, and also to provide more reliable data for the analysis of quality trends, potential problems, and abnormal fluctuations in the production process, this embodiment will subsequently determine the optimal compression algorithm or compression strategy for the quality data sequence by analyzing the index 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 in this embodiment, the first evaluation index value, the second evaluation index value, and the third evaluation index value of the quality data sequence are obtained 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 optimal compression algorithm suitable for the quality data sequence can be obtained through the obtained first evaluation index value, second evaluation index value, and third evaluation index value in the subsequent process, or the optimal compression strategy for the quality data sequence can be obtained through the obtained first evaluation index value, second evaluation index value, and third evaluation index value.

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

[0030] First, use the least squares method to linearly fit the quality data point curve corresponding to the quality data sequence, and denote the fitted straight line obtained from the linear fitting as the target fitting straight line. Denote the absolute value of the slope of the target fitting straight line as the slope to be analyzed. Then, perform a negative correlation mapping on the slope to be analyzed, and denote the mapping result as the first eigenvalue. Then, obtain the variance of the quality data sequence, perform a negative correlation mapping on the variance of the quality data sequence, and denote the mapping result as the second eigenvalue. Immediately afterwards, obtain all the maximum value points on the quality data point curve, and denote the sequence composed of all the maximum value points on the quality data point curve as the maximum value point sequence. And the f-th maximum value point in the maximum value point sequence is the f-th maximum value point on the quality data point curve. Then, according to the abscissa difference between adjacent maximum value points in the maximum value point sequence, obtain the span value sequence, and obtain the mean value of the span value sequence. Denote the normalized value of the mean value of the span value sequence as the third eigenvalue. And the h-th span value in the span value sequence is the result of subtracting the abscissa value of the h-th maximum value point in the maximum value point sequence from the abscissa value of the (h + 1)-th maximum value point in the maximum value point sequence. Finally, obtain the mean value of the first eigenvalue, the second eigenvalue, and the third eigenvalue, and use the mean value of the first eigenvalue, the second eigenvalue, and the third eigenvalue 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 the exponential function with the constant e as the base, k0 is the slope of the target fitting straight line, is the variance of the quality data sequence, d0 is the mean value of the span value sequence, tanh() is the hyperbolic tangent function, is the first eigenvalue, is the second eigenvalue, is the third eigenvalue. Here, the hyperbolic tangent function tanh() is used to normalize d0.

[0034] And when is closer to 0, is smaller, and The larger it is, the larger the value of W1 is. And the larger the value of W1 is, the more horizontal and less variable the overall trend of the quality data sequence is. At the same time, the data fluctuations in the quality data sequence are relatively stable or have a high degree of stationarity. This also indicates that the data in the quality data sequence has a high degree of repeatability. When the data in the sequence has a high degree of repeatability, run-length encoding or differential encoding can achieve better compression effects. Run-length encoding or differential encoding is very suitable for dealing with situations with a large number of repeated data. It compresses the data by recording the number of occurrences of the same data value or the difference between adjacent data points internationally, 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 abscissa differences between all adjacent maximum value data points on the quality data point curve. Then, in this embodiment, the specific process of obtaining the second evaluation index value of the quality data sequence 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, a difference comprehensive representation value is obtained; then, the variance of the span value sequence is obtained, and a negative correlation mapping is performed on the variance of the span value sequence, and the mapping result is denoted as the mapped span variance value; then, the mean value of the difference comprehensive representation value and the mapped span variance value is obtained, and the mean value of the difference comprehensive representation value and the mapped span variance value is used as the second evaluation index value of the quality data sequence.

[0037] In this embodiment, the specific process of obtaining the difference comprehensive representation value 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 a-th characteristic difference value in the characteristic difference value sequence is the result of subtracting the ordinate value of the (a + 1)-th quality data point on the quality data point curve from the ordinate value of the a-th quality data point on the quality data point curve. 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 b-th deviation degree in the deviation degree sequence is , is the b-th characteristic difference value in the characteristic difference value sequence, is the reference difference value; finally, the mean value of the deviation degree sequence is obtained, and a negative correlation mapping is performed on the mean value of the deviation degree sequence, and the result of the negative correlation mapping is denoted as the difference comprehensive representation value.

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

[0039]

[0040] Among them, W2 is the second evaluation index value of the quality data sequence, is the variance of the span value sequence; and when is smaller and is smaller, the value of W2 is larger. And when the value of W2 is larger, it indicates that the change of the data in the quality data sequence is more regular. On the contrary, when W2 is smaller, it indicates that the change of the data in the quality data sequence is less regular; and because the LZW compression algorithm has a better compression effect on the quality data sequence showing regular changes, so when W2 is larger, it indicates that the LZW compression algorithm has a better compression effect on the quality data sequence; reflects the deviation degree of data fluctuation. The smaller the deviation degree is, the more consistent the fluctuation amplitude is, the more stable the data fluctuation range is, and 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 property can effectively identify and compress this regular change, find these regular substrings in the data, especially in the face of this strong data repetition, greatly reducing the data redundancy and improving the compression ratio.

[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 and minimum values on the quality data point curve corresponding to the quality data sequence and the average value of the extreme values on the quality data point curve, and the number of extreme points on the quality data point curve. Then, in this embodiment, the specific obtaining process of the third evaluation index value of the quality data sequence is as follows:

[0043] First, obtain the total number of extreme points on the quality data point curve. The total number of extreme points is the sum of the number of minimum points and the number of maximum points on the quality data point curve. Then, obtain the total number of quality data points on the quality data point curve, and record 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 as the characteristic ratio.

[0044] After that, obtain the average of the ordinates of all the maximum points on the quality data point curve, and denote it as the maximum average value. Obtain the average of the ordinates of all the minimum points on the quality data point curve, and denote it as the minimum average value. Obtain the maximum point and the minimum point on the quality data point curve. Then, obtain the result of subtracting the maximum average value from the ordinate value of the maximum point on the quality data point curve, and denote it as the first difference. Obtain the result of subtracting the minimum average value from the ordinate value of the minimum point on the quality data point curve, and denote it as the second difference. Immediately afterwards, obtain the sum of the first difference and the second difference, and perform a negative correlation mapping on the sum of the first difference and the second difference, and denote the mapping result as the characteristic difference. Then, obtain the average of the characteristic ratio and the characteristic difference, and use it 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 used to normalize; and when the value is larger and is smaller, it indicates that the data disorder and volatility in the quality data sequence are stronger, that is, there is no obvious change rule in the data in the quality data sequence, and the data fluctuation range in the quality data sequence is in a stable interval; and since the SDT rotation gate compression algorithm has a good compression effect on the quality data sequence with strong disorder and volatility but a stable fluctuation range, when W3 is larger, it indicates that the SDT rotation gate compression algorithm has a better compression effect on the quality data sequence.

[0048] The SDT rotation gate compression algorithm can eliminate redundant parts by identifying the stable changes of data within a certain interval, and only record new data points when the data crosses the set threshold. This means that for the part of the data without significant fluctuations, SDT will merge these data points, reduce redundancy, and only store the key points representing changes. Therefore, the SDT rotation gate compression algorithm can effectively compress the redundant data in the stable interval, minimize the storage redundancy and save computing resources to the greatest extent, while retaining the important information of the data; and although SDT is a lossy compression, for the production management of products, there is usually a certain tolerance in data accuracy. SDT can ensure that the compression of data does not affect the effectiveness of actual decision-making by setting an appropriate range, sacrifice a certain amount of data accuracy, but retain the global trend of the data, and does not affect subsequent quality analysis, so as to efficiently compress the data.

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

[0050] Step S003: According to the first evaluation index value, the second evaluation index value, and the third evaluation index value, select the optimal compression strategy corresponding to the quality data sequence, and use the optimal compression strategy to compress the quality data sequence.

[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. The specific process is as follows:

[0052] Judge whether the first evaluation index value of the quality data sequence is greater than a preset first threshold. If so, use run-length encoding or differential encoding as the optimal compression strategy corresponding to the quality data sequence. Otherwise, judge whether the second evaluation index value of the quality data sequence is greater than a preset second threshold. If so, use the LZW algorithm as the optimal compression strategy corresponding to the quality data sequence. Otherwise, continue to judge whether the third evaluation index value of the quality data sequence is greater than a preset third threshold. If so, use the rotational gate compression algorithm as the optimal compression strategy corresponding to the quality data sequence.

[0053] In addition, there may also be a situation where 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 also 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 also 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 used to compress the quality data sequence. The LZ77 compression algorithm has strong adaptability and can better process most data types.

[0054] In this embodiment, the implementer needs to set the preset first threshold, the preset second threshold, and the preset third threshold according to the actual situation. 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, through the above process, this embodiment can obtain the optimal compression strategy corresponding to each type of quality data sequence, 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. Subsequently, the data obtained by compression will be transmitted and stored for the production management of paint products.

[0056] In addition, for the convenience of understanding, this embodiment will take the determination process of the optimal compression strategy corresponding to the viscosity data sequence of paint products as an example for illustration. 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 values of each viscosity data in the viscosity data sequence, the quality data point curve corresponding to the viscosity data sequence is drawn. The abscissa of the data points on the curve is the position number value of the viscosity data in the viscosity data sequence, and the ordinate is the viscosity data. Then, the least squares method is used to linearly fit the quality data point curve corresponding to the viscosity data sequence to obtain the target fitting straight line.

[0058] After obtaining the viscosity data sequence, the quality 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 above: 0, and at this time, 0.177 in the formula is the slope of the target fitting straight line obtained by linearly fitting the quality 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 value of the span value sequence calculated according to the maximum value point on the quality 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] The sequence of characteristic difference values corresponding to the mass data point curve corresponding to the viscosity data sequence obtained immediately following the specific calculation process of the second evaluation index value in the above step S002 is {1.8, 0.6, 2.9, -2.2, 0.1, -1.8, 1.4, 1.2, -1.4}. The median of the sequence of characteristic difference values is 0.6, that is, the reference difference value is 0.6, and the degree of deviation calculated through the calculated sequence of characteristic difference values and the reference difference value is the sequence of deviation degrees {2.0, 0.0, 3.83, 4.67, 1.0, 4.0, 1.33, 1.0, 3.33}. Based on the mean value of 2.35 of the sequence of deviation degrees and the variance of 0.25 of the sequence of span values calculated according to the maximum value points on the mass data point curve corresponding to the viscosity data sequence, the second evaluation index value of the viscosity data sequence is: .

[0060] Afterwards, according to the specific calculation process of the third evaluation index value in the above step S002, first obtain 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, that is the value of, at this time , 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 mean value of the maximum values is 124.67, and the mean value of the minimum values is 122.75, then the calculation result of D1 is 1.13, the calculation result of D2 is -0.85, and thus the third evaluation index value of the viscosity data sequence is: .

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

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

[0063] In summary, in this embodiment, first, the quality data sequence of the paint product and the quality data point curve corresponding to the quality data sequence are obtained; then, according to the fitting line slope of the quality data point curve, the abscissa difference between adjacent maximum value points on the quality data point curve, and the variance of the quality data sequence, the first 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 and the variance of the abscissa difference between all adjacent maximum value data points on the quality data point curve, the second evaluation index value of the quality data sequence is obtained. According to the difference between the maximum and minimum values on the quality data point curve and the average value of the extreme values on the quality data point curve, and the number of extreme points on the quality data point curve, the third evaluation index value of the quality data sequence is obtained; finally, according to the first evaluation index value, the second evaluation index value, and the third evaluation index value, the optimal compression strategy corresponding to the quality data sequence is selected, and the quality data sequence is compressed using the optimal compression strategy. And in this embodiment, by analyzing the evaluation index values of the quality data sequence, the most suitable or optimal compression strategy can be used to compress the quality data sequence. That is, in this embodiment, based on the evaluation index values of the quality data sequence, the optimal compression strategy of the quality data sequence can be obtained. By using the optimal compression strategy of the quality data sequence to compress the corresponding quality data sequence, the compression effect when compressing the quality data related to the paint product can be improved or guaranteed.

[0064] The above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various 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 includes the following steps: Obtain a quality data sequence of a paint product and a quality data point curve corresponding to the quality data sequence, where the quality data point curve is composed of quality data points; Obtain a first evaluation index value of the quality data sequence according to the fitting line slope of the quality data point curve, the abscissa difference between adjacent maximum value points on the quality data point curve, and the variance of the quality data sequence; Obtain 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 abscissa differences between all adjacent maximum value data points on the quality data point curve; Obtain a third evaluation index value of the quality data sequence according to the difference between the maximum value and the mean value of the maximum values on the quality data point curve, the difference between the minimum value and the mean value of the minimum values on the quality data point curve, and the number of extreme value points on the quality data point curve; Select 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 compress the quality data sequence by using the optimal compression strategy; The method for 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 includes: determining whether the first evaluation index value is greater than a preset first threshold. If so, use run-length encoding or differential encoding 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. 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. If so, use the rotating door compression algorithm as the optimal compression strategy corresponding to the quality data sequence.

2. The production management method for paint products according to claim 1, characterized in that The method for obtaining the first evaluation index value of the quality data sequence includes: Denote the absolute value of the fitting line slope obtained by linearly fitting the quality data point curve as the slope to be analyzed, perform a negative correlation mapping on the slope to be analyzed, and denote the mapping result as the first eigenvalue; Perform a negative correlation mapping on the variance of the quality data sequence, and denote the mapping result as the second eigenvalue; Denote the sequence constructed by the abscissa differences between all adjacent maximum value points on the quality data point curve as the span value sequence, and denote the normalized value of the mean of the span value sequence as the third eigenvalue; Take the mean of the first eigenvalue, the second eigenvalue, and the third eigenvalue as the first evaluation index value of the quality data sequence.

3. A production management method for paint products as described in claim 2, characterized in that, The method for obtaining the second evaluation index value of the quality data sequence includes: Obtain a comprehensive difference representation value according to the difference between adjacent quality data points on the quality data point curve; Perform a negative correlation mapping on the variance of the span value sequence, and denote the mapping result as the mapped span variance value; Take the mean of the comprehensive difference representation value and the mapped span variance value as the second evaluation index value of the quality data sequence.

4. The production management method for paint products according to claim 3, characterized in that, The method for obtaining the comprehensive difference characterization value includes: Obtaining a sequence of characteristic difference values corresponding to the quality data point curve, where the a-th characteristic difference value in the sequence of characteristic difference values is the result of subtracting the ordinate value of the a-th quality data point from the ordinate value of the (a + 1)-th quality data point on the quality data point curve; taking the median in the sequence of characteristic difference values as the reference difference value; Obtain a deviation degree sequence corresponding to the feature difference value sequence, where the b-th deviation degree in the deviation degree sequence is , is the b-th feature difference value in the feature difference value sequence, is the reference difference value; Performing a negative correlation mapping on the mean value of the deviation degree sequence, and denoting the result of the negative correlation mapping as the comprehensive difference characterization value.

5. The production management method for paint products according to claim 1, characterized in that, The method for obtaining the third evaluation index value of the quality data sequence includes: Denoting 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 as the characteristic ratio; Taking the mean value of the ordinates of all maximum points on the quality data point curve as the maximum value mean, and taking the mean value of the ordinates of all minimum points on the quality data point curve as the minimum value mean; denoting the result of subtracting the maximum value mean from the ordinate value of the maximum value point on the quality data point curve as the first difference, and denoting the result of subtracting the minimum value mean from the ordinate value of the minimum value point on the quality data point curve as the second difference, obtaining the sum of the first difference and the second difference, and performing a negative correlation mapping on the sum of the first difference and the second difference, and denoting the mapping result as the characteristic difference; Taking the mean value of the characteristic ratio and the characteristic difference as the third evaluation index value of the quality data sequence.

6. 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 a production management method for paint products as described in any one of claims 1-5.

Images