A reactor control optimization method and system based on quality inspection data analysis

Abstract

The invention discloses a method and system for optimizing reactor control based on quality inspection data analysis, and relates to the technical field of reactor control. Specifically, the invention discloses defining multiple performance parameters of a product; scientifically dividing the performance parameters into sections, constructing a performance parameter section mapping template, and providing a standard for subsequent analysis; comparing each performance parameter group with the mapping template, determining a triggering performance parameter section, and performing quality assessment and classification; taking the sensor data corresponding to an excellent triggering performance parameter section subset as a benchmark, analyzing the proximity of other data to the benchmark data, and forming a first comparative sensor data reference group; optimizing and adjusting the reactor control data based on the data difference characteristics in the reference group, so as to improve product quality, optimize production efficiency, and reduce energy consumption; the technical solution realizes the optimization of reactor control through precise data collection, scientific analysis, and optimization and adjustment, and provides strong support for the sustainable development of the chemical industry.

Description

Technical Field

[0001] The present invention relates to the technical field of reactor control, and in particular to a reactor control optimization method and system based on quality inspection data analysis. Background Art

[0002] In the chemical manufacturing industry, the reactor is a key equipment for chemical reactions, and its control effect is directly related to product quality, energy consumption and production efficiency. However, due to the complexity and variability of the chemical reaction process, traditional reactor control methods often fail to achieve ideal control effects; traditionally, reactor control mainly relies on the operator's experience and manual adjustment, which is highly subjective and uncertain, and easily leads to unstable product quality and waste of energy. At the same time, due to the mutual influence of various factors (such as temperature, pressure, reactant concentration, etc.) during the reaction process, the control of the reactor becomes more complicated. Summary of the invention

[0003] The object of the present invention is to provide a method and system capable of self-optimizing reactor control.

[0004] The present invention discloses a reactor control optimization method based on quality inspection data analysis, comprising:

[0005] Collect and record the production process data of the products, including the reactor sensor data and the reactor control data, and test the product quality of the products. Based on the test results, define multiple performance parameters of the product quality of the products to obtain a performance parameter group;

[0006] Analyze several performance parameter groups, divide each performance parameter into sections based on the fluctuation characteristics of each performance parameter of the production product, and obtain a performance parameter section mapping template;

[0007] Compare each performance parameter in each performance parameter group with its corresponding performance parameter segment mapping template, determine the performance parameter segment to which each performance parameter belongs in the performance parameter segment mapping template, and record the performance parameter segment as a triggering performance parameter segment to obtain a triggering performance parameter segment group;

[0008] Based on the quality requirements for different types of manufactured products, a quality assessment is performed on each trigger performance parameter segment group, and based on the quality assessment results, different trigger performance parameter segment groups are classified to obtain an excellent trigger performance parameter segment set, a good trigger performance parameter segment set, and an unqualified trigger performance parameter segment set, and the excellent trigger performance parameter segment set and the good trigger performance parameter segment set are each internally classified as equivalent to obtain a number of excellent trigger performance parameter segment subsets and a number of good trigger performance parameter segment subsets;

[0009] The reactor sensor data corresponding to the excellent trigger performance parameter segment subset is used as the benchmark sensor data, and the proximity between the reactor sensor data corresponding to the good trigger performance parameter segment subset and the unqualified trigger performance parameter segment set and the benchmark sensor data is analyzed. If the proximity is greater than or equal to a preset value, the corresponding reactor sensor data and the benchmark sensor data are combined and connected to obtain a first comparative sensor data reference group;

[0010] Based on the difference characteristics between the reactor sensor data and the benchmark sensor data in the first comparison sensor data reference group, the reactor control data corresponding to the reactor sensor data is optimized and adjusted.

[0011] In some embodiments disclosed in the present invention, the reactor control optimization method further includes:

[0012] If the degree of proximity between the reactor sensor data corresponding to the unqualified trigger performance parameter segment set and the benchmark sensor data is less than or equal to the preset value, the proximity between the reactor sensor data corresponding to the unqualified trigger performance parameter segment set and the reactor sensor data corresponding to the good trigger performance parameter segment subset is analyzed. If the degree of proximity is greater than or equal to the preset value, the reactor control data corresponding to the unqualified trigger performance parameter segment set is optimized and adjusted based on the difference characteristics between the reactor sensor data.

[0013] In some embodiments disclosed in the present invention, based on the fluctuation characteristics of each performance parameter of the produced product, the method of segmenting each performance parameter includes:

[0014] Establish a performance parameter variation axis, analyze several performance parameter groups, and map different types of performance parameters on the performance parameter variation axis respectively, and determine the number density of performance parameter mapping points at different nodes on the performance parameter variation axis;

[0015] Taking the performance parameter change axis as the horizontal axis and based on the performance parameter mapping point quantity density of different nodes, a performance parameter mapping point quantity density curve is constructed;

[0016] A comprehensive analysis is performed on the curvature change and peak value of the performance parameter mapping point quantity density curve, and based on the analysis results, the large section for segmentation is locked, and the division width parameter when dividing the large section into sections is determined.

[0017] In some embodiments disclosed in the present invention, a method for comprehensively analyzing the curvature change and peak value of the performance parameter mapping number density curve includes:

[0018] Continuously scan and analyze the density curve of the number of performance parameter mapping points, and record the absolute value accumulation of the curvature and the peak value accumulation of several consecutive acquisition points in real time. If the absolute value accumulation of the curvature is greater than or equal to the preset value, or the peak value accumulation is greater than or equal to the preset value, the acquisition point scanned at this time is identified as the truncation point, and the section between the truncation points is identified as the large section for segmentation;

[0019] Based on the curvature absolute value accumulation value and the peak value accumulation value, a division width parameter for dividing the large section is determined;

[0020] Among them, the expression for calculating the partition width parameter is:

[0021] ;

[0022] Among them, d is the partition width parameter, D is the segment width of the large segment for segmentation, The conversion factor for the number of segment divisions, is the cumulative value of the absolute value of curvature in the large segment, q is the cumulative value of the peak value, is the curve characteristic adjustment coefficient of the performance parameter mapping number density curve, and b is the curve characteristic adjustment constant of the performance parameter mapping number density curve.

[0023] In some embodiments disclosed in the present invention, the method of internally classifying the excellent trigger performance parameter segment set and the good trigger performance parameter segment set as equivalent includes:

[0024] Mapping different types of triggered performance parameter segments in a plurality of triggered performance parameter segment sets on a performance parameter segment template to obtain a plurality of performance parameter segment mapping points, analyzing clustered segments of performance parameter segment mapping points of the same type, and calculating an average performance parameter segment mapping point in the clustered segments;

[0025] Step S402, based on the equality of different types of performance parameter segment mapping points, the trigger performance parameter segment groups in the trigger performance parameter segment set are first classified into the same category;

[0026] The combination of performance parameter segment mapping points of each first equivalent classification is determined and recorded as an equivalent category performance parameter segment mapping point group, and the distance characteristics of the triggering performance parameter segment group from the equivalent category performance parameter segment mapping point group on the performance parameter segment template are analyzed. Based on the distance characteristics, the triggering performance parameter segment group after the first equivalent classification is subjected to a second equivalent classification.

[0027] In some embodiments disclosed in the present invention, a method for analyzing the distance characteristics of a triggering performance parameter segment group from a performance parameter segment mapping point group of the same category on a performance parameter segment template includes:

[0028] Respectively determine the performance parameter segment mapping points of each type of trigger performance parameter segment in the trigger performance parameter segment group on the performance parameter segment template, and determine the distance between the mapping points of each type of performance parameter segment mapping point relative to the average performance parameter segment mapping point;

[0029] Based on the emphasis on product quality requirements, type weights are set for different types of distances between mapping points, and the classification distance parameters are calculated based on the distances between mapping points.

[0030] Among them, the expression for calculating the classification distance parameter is:

[0031] ;

[0032] Among them, H is the classification distance parameter, is the type weight coefficient of the distance between mapping points of type i, is the distance between the i-th mapping points, and n is the total number of types in the trigger performance parameter segment group.

[0033] In some embodiments disclosed in the present invention, the method for analyzing the proximity between the reactor sensor data and the benchmark sensor data includes:

[0034] The reactor sensor data and the benchmark sensor data are analyzed respectively to generate a reactor sensor parameter curve group and a benchmark sensor parameter curve group;

[0035] The sensor parameter curves of the same category in the reactor sensor parameter curve group and the benchmark sensor parameter curve group are aligned to determine the trend similarity characteristics of their changing trends and the difference similarity characteristics between the two. Based on the trend similarity characteristics and difference similarity characteristics between each pair of sensor parameter curves, the degree of proximity between the reactor sensor data and the benchmark sensor data is determined.

[0036] In some embodiments disclosed in the present invention, a method for determining trend similarity characteristics and difference similarity characteristics between sensing parameter curves includes:

[0037] Set a number of detection points for the sensor parameter curve, and determine the parameter difference values ​​between the curves of the detection points, as well as the curve trends at the detection points;

[0038] If the parameter difference value between the curves of the detection point is less than or equal to the preset value, and the curve trends are the same, the detection point is marked as a similar detection point, and the number of similar detection points between the sensor parameter curves is identified as the sub-degree of closeness between the sensor parameter curves. The sum of the sub-degrees of closeness between all the sensor parameter curves is determined as the closeness between the reactor sensor data and the benchmark sensor data.

[0039] In some embodiments disclosed in the present invention, there is also disclosed a reactor control optimization system based on quality inspection data analysis, comprising:

[0040] The first module is used to collect and record the production process data of the production products, including the reactor sensor data and the reactor control data, and to detect the product quality of the production products. Based on the detection results, multiple performance parameters are defined for the product quality of the production products to obtain a performance parameter group;

[0041] The second module is used to analyze several performance parameter groups, divide each performance parameter into sections based on the fluctuation characteristics of each performance parameter of the production product, and obtain a performance parameter section mapping template;

[0042] The third module is used to compare each performance parameter in each performance parameter group with its corresponding performance parameter segment mapping template, determine the performance parameter segment to which each performance parameter belongs in the performance parameter segment mapping template, and record the performance parameter segment as a triggering performance parameter segment to obtain a triggering performance parameter segment group;

[0043] The fourth module is used to perform quality assessment on each trigger performance parameter segment group based on the quality requirements of different types of products, and classify different trigger performance parameter segment groups based on the quality assessment results to obtain an excellent trigger performance parameter segment set, a good trigger performance parameter segment set, and an unqualified trigger performance parameter segment set, and perform internal equivalent classification on the excellent trigger performance parameter segment set and the good trigger performance parameter segment set to obtain a number of excellent trigger performance parameter segment subsets and a number of good trigger performance parameter segment subsets;

[0044] The fifth module is used to use the reactor sensor data corresponding to the excellent trigger performance parameter segment subset as the benchmark sensor data, and analyze the proximity between the reactor sensor data corresponding to the good trigger performance parameter segment subset and the unqualified trigger performance parameter segment set and the benchmark sensor data. If the proximity is greater than or equal to a preset value, the corresponding reactor sensor data and the benchmark sensor data are combined and connected to obtain a first comparative sensor data reference group;

[0045] The sixth module is used to optimize and adjust the reactor control data corresponding to the reactor sensor data based on the difference characteristics between the reactor sensor data and the benchmark sensor data in the first comparison sensor data reference group.

[0046] The invention discloses a method and system for optimizing reactor control based on quality inspection data analysis, and relates to the technical field of reactor control. Specifically, the invention discloses defining multiple performance parameters of a product; scientifically dividing the performance parameters into sections, constructing a performance parameter section mapping template, and providing a standard for subsequent analysis; comparing each performance parameter group with the mapping template, determining a triggering performance parameter section, and performing quality assessment and classification; taking the sensor data corresponding to an excellent triggering performance parameter section subset as a benchmark, analyzing the proximity of other data to the benchmark data, and forming a first comparative sensor data reference group; optimizing and adjusting the reactor control data based on the data difference characteristics in the reference group, so as to improve product quality, optimize production efficiency, and reduce energy consumption; the technical solution realizes the optimization of reactor control through precise data collection, scientific analysis, and optimization and adjustment, and provides strong support for the sustainable development of the chemical industry.

[0047] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a method step diagram of a reactor control optimization method based on quality inspection data analysis disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0050] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solution of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should be the common meanings understood by the technical personnel described in the present invention. Example

[0051] The present invention discloses a reactor control optimization method based on quality inspection data analysis, see Figure 1 ,include:

[0052] Step S100, collecting and recording the production process data of the produced products, including reactor sensor data and reactor control data, and testing the product quality of the produced products. Based on the test results, multiple performance parameters of the product quality of the produced products are defined to obtain a performance parameter group.

[0053] The core of this step is to comprehensively and accurately collect data from the production process and define the performance parameters of the product. By installing high-precision sensors and advanced data acquisition systems, the sensor data (such as temperature, pressure, flow, etc.) and control data (such as stirring speed, heating power, etc.) of the reactor can be recorded in real time. At the same time, strict quality inspections are carried out on the products produced. Based on the inspection results, multiple performance parameters that can fully reflect the product quality are defined, such as purity, yield, color, etc., to form a performance parameter group. This step provides a solid foundation for subsequent performance parameter analysis and control data optimization.

[0054] Step S200, analyzing several performance parameter groups, dividing each performance parameter into sections based on the fluctuation characteristics of each performance parameter of the production product, and obtaining a performance parameter section mapping template.

[0055] The main purpose of this step is to scientifically segment the performance parameters. By statistically analyzing the data of a large number of performance parameter groups, the fluctuation characteristics and distribution laws of each performance parameter can be revealed. Based on these characteristics, each performance parameter is divided into different segments to form a performance parameter segment mapping template. This template provides a unified standard and basis for subsequent performance parameter comparison and quality assessment.

[0056] In some embodiments disclosed in the present invention, based on the fluctuation characteristics of each performance parameter of the produced product, the method of segmenting each performance parameter includes:

[0057] Step S201, establish a performance parameter variation axis, analyze several performance parameter groups, and map different types of performance parameters on the performance parameter variation axis respectively, and determine the number density of performance parameter mapping points at different nodes on the performance parameter variation axis.

[0058] Step S202, taking the performance parameter variation axis as the horizontal axis and based on the performance parameter mapping point quantity density curve of different nodes, constructing a performance parameter mapping point quantity density curve.

[0059] Step S203, comprehensively analyzing the curvature change and peak value of the performance parameter mapping point quantity density curve, and based on the analysis result, locking the large segment for segment division this time, and determining the division width parameter when dividing the large segment into segments.

[0060] In the above steps, a performance parameter change axis is established, several performance parameter groups collected are analyzed in detail, and various types of performance parameters are accurately mapped to this axis, thereby determining the number density of performance parameter mapping points at different nodes on the axis. Subsequently, the performance parameter change axis is used as the horizontal axis, and a performance parameter mapping point number density curve is constructed based on these number densities. Finally, by conducting an in-depth comprehensive analysis of the curvature change and peak value of the curve, the division of large sections is locked, and the division width parameters for further subdividing the large sections are accurately determined, thereby providing a scientific basis for the segment division of performance parameters.

[0061] In some embodiments disclosed in the present invention, a method for comprehensively analyzing the curvature change and peak value of the performance parameter mapping number density curve includes:

[0062] Step S2031, continuously scan and analyze the density curve of the number of performance parameter mapping points, and record the cumulative absolute value of the curvature and the cumulative peak value of several consecutive acquisition points in real time. If the cumulative absolute value of the curvature is greater than or equal to the preset value, or the cumulative peak value is greater than or equal to the preset value, the acquisition point scanned at this time is identified as a truncation point, and the segment between the truncation points is identified as a large segment for segment division.

[0063] Step S2032, determining a partition width parameter for partitioning the large segment based on the curvature absolute value cumulative value and the peak value cumulative value;

[0064] Among them, the expression for calculating the partition width parameter is:

[0065] .

[0066] Among them, d is the partition width parameter, D is the segment width of the large segment for segmentation, The conversion factor for the number of segment divisions, is the cumulative value of the absolute value of curvature in the large segment, q is the cumulative value of the peak value, is the curve characteristic adjustment coefficient of the performance parameter mapping number density curve, and b is the curve characteristic adjustment constant of the performance parameter mapping number density curve.

[0067] Step S300, compare each performance parameter in each performance parameter group with its corresponding performance parameter segment mapping template, determine the performance parameter segment to which each performance parameter belongs in the performance parameter segment mapping template, and record the corresponding performance parameter segment as a triggering performance parameter segment to obtain a triggering performance parameter segment group.

[0068] In this step, the system will compare the performance parameters in each performance parameter group with the performance parameter segment mapping template one by one. Through the comparison, the segment to which each performance parameter belongs in the mapping template can be determined and recorded as the triggering performance parameter segment. In this way, the triggering performance parameter segment group corresponding to each performance parameter group can be obtained, providing specific data support for subsequent quality evaluation and optimization adjustment.

[0069] Step S400, based on the quality requirements for different types of products, perform quality assessment on each trigger performance parameter segment group, and based on the quality assessment results, classify different trigger performance parameter segment groups to obtain an excellent trigger performance parameter segment set, a good trigger performance parameter segment set, and an unqualified trigger performance parameter segment set, and perform internal equivalent classification on the excellent trigger performance parameter segment set and the good trigger performance parameter segment set, respectively, to obtain several excellent trigger performance parameter segment subsets and several good trigger performance parameter segment subsets.

[0070] The core of this step is to conduct quality assessment on the trigger performance parameter segment groups and classify them scientifically. According to the quality requirements of different types of products, corresponding assessment standards are formulated. Then, each trigger performance parameter segment group is quality assessed and classified into three categories according to its quality level: excellent, good or unqualified. At the same time, the excellent and good trigger performance parameter segment sets are internally classified as equivalent to form several subsets for subsequent more in-depth analysis and optimization.

[0071] In some embodiments disclosed in the present invention, the method of internally classifying the excellent trigger performance parameter segment set and the good trigger performance parameter segment set as equivalent includes:

[0072] Step S401, maps different types of triggered performance parameter segments in several triggered performance parameter segment sets on a performance parameter segment template to obtain several performance parameter segment mapping points, analyzes the clustered segments of the same type of performance parameter segment mapping points, and calculates the average performance parameter segment mapping point in the clustered segments.

[0073] The core of this step is to map the various segments in the trigger performance parameter segment set to the performance parameter segment template to form mapping points. This operation helps to determine the distribution of various types of performance parameter segments on the template. Then, by analyzing the clustered segments of the same type of mapping points and calculating their average performance parameter segment mapping points, we can understand the main distribution areas and central trends of various types of performance parameter segments, providing a basis for subsequent classification.

[0074] Step S402, based on the equality of different types of performance parameter segment mapping points, the trigger performance parameter segment groups in the trigger performance parameter segment set are first classified into the same category.

[0075] This step performs the first equivalence classification based on the equivalence of different types of performance parameter segment mapping points. Equality refers to the similarity of the distribution characteristics, value range, etc. of the same type of mapping points on the performance parameter segment template. Through the first equivalence classification, the triggered performance parameter segments with similar characteristics can be divided into the same category, thereby simplifying the subsequent analysis and optimization work.

[0076] Step S403, determine the combination of performance parameter segment mapping points of each first equivalent classification, and record them as equivalent category performance parameter segment mapping point groups, analyze the distance characteristics of the triggering performance parameter segment group from the equivalent category performance parameter segment mapping point groups on the performance parameter segment template, and based on the distance characteristics, perform a second equivalent classification on the triggering performance parameter segment group after the first equivalent classification.

[0077] The combination of performance parameter segment mapping points for each equivalent classification is determined to form an equivalent category performance parameter segment mapping point group. Then, the distance characteristics of the trigger performance parameter segment group from the equivalent category performance parameter segment mapping point group on the performance parameter segment template are analyzed. The distance characteristics reflect the similarity and difference between the trigger performance parameter segment and the equivalent category mapping point group. Finally, a second equivalent classification is performed based on the distance characteristics, and the trigger performance parameter segments with similar distances and characteristics are divided into the same category. Through the second equivalent classification, more accurate and detailed classification results can be obtained, providing more targeted guidance for subsequent optimization and adjustment.

[0078] In some embodiments disclosed in the present invention, a method for analyzing the distance characteristics of a triggering performance parameter segment group from a performance parameter segment mapping point group of the same category on a performance parameter segment template includes:

[0079] Step S4031, respectively determine the performance parameter segment mapping points of each type of trigger performance parameter segment in the trigger performance parameter segment group on the performance parameter segment template, and determine the distance between the mapping points of each type of performance parameter segment mapping point relative to the average performance parameter segment mapping point.

[0080] Step S4032: based on the emphasis on the quality requirements of the produced products, type weights are set for different types of distances between mapping points, and the classification distance parameters are calculated in combination with the distances between mapping points.

[0081] Among them, the expression for calculating the classification distance parameter is:

[0082] .

[0083] Among them, H is the classification distance parameter, is the type weight coefficient of the distance between mapping points of type i, is the distance between the i-th mapping points, and n is the total number of types in the trigger performance parameter segment group.

[0084] Step S500, taking the reactor sensor data corresponding to the excellent trigger performance parameter segment subset as the benchmark sensor data, and analyzing the proximity between the reactor sensor data corresponding to the good trigger performance parameter segment subset and the unqualified trigger performance parameter segment set and the benchmark sensor data; if the proximity is greater than or equal to a preset value, establishing a combined connection between the corresponding reactor sensor data and the benchmark sensor data to obtain a first comparative sensor data reference group.

[0085] In this step, the system will use the reactor sensor data corresponding to the excellent trigger performance parameter segment subset as a benchmark for data analysis. These data are combined with the benchmark data to form the first comparative sensor data reference group, providing a specific reference basis for subsequent optimization and adjustment.

[0086] In some embodiments disclosed in the present invention, the method for analyzing the proximity between the reactor sensor data and the benchmark sensor data includes:

[0087] Step S501 , analyzing the reactor sensor data and the benchmark sensor data respectively to generate a reactor sensor parameter curve group and a benchmark sensor parameter curve group.

[0088] Step S502, aligning the sensor parameter curves of the same category in the reactor sensor parameter curve group and the benchmark sensor parameter curve group, determining the trend similarity characteristics of their change trends and the difference similarity characteristics between the two, and determining the degree of proximity between the reactor sensor data and the benchmark sensor data based on the trend similarity characteristics and the difference similarity characteristics between each pair of sensor parameter curves.

[0089] In some embodiments disclosed in the present invention, a method for determining trend similarity characteristics and difference similarity characteristics between sensing parameter curves includes:

[0090] Step S5021, setting a number of detection points for the sensing parameter curve, and determining the parameter difference values ​​between the curves of the detection points, and the curve trends at the detection points.

[0091] Step S5022, if the parameter difference value between the curves of the detection point is less than or equal to the preset value, and the curve trends are the same, the detection point is marked as a similar detection point, and the number of similar detection points between the sensor parameter curves is determined as the sub-degree of closeness between the sensor parameter curves. The sum of the sub-degrees of closeness between all the sensor parameter curves is determined as the closeness between the reactor sensor data and the benchmark sensor data.

[0092] Step S600 , based on the difference characteristics between the reactor sensor data and the benchmark sensor data in the first comparison sensor data reference group, optimizing and adjusting the reactor control data corresponding to the reactor sensor data.

[0093] This step is the ultimate goal of the technical solution, that is, to optimize and adjust the control data corresponding to the reactor sensor data based on the data difference characteristics in the first comparison sensor data reference group. By analyzing the differences between the reactor sensor data in the reference group and the benchmark sensor data, the deficiencies and optimization directions in the control data can be found. Then, combined with advanced control algorithms and optimization techniques, the control data is adjusted and optimized to improve product quality, optimize production efficiency and reduce energy consumption.

[0094] In some embodiments disclosed in the present invention, the reactor control optimization method further includes:

[0095] Step S700, if the degree of proximity between the reactor sensor data corresponding to the unqualified trigger performance parameter segment set and the benchmark sensor data is less than or equal to the preset value, then analyze the degree of proximity between the reactor sensor data corresponding to the unqualified trigger performance parameter segment set and the reactor sensor data corresponding to the good trigger performance parameter segment subset; if the degree of proximity is greater than or equal to the preset value, then optimize and adjust the reactor control data corresponding to the unqualified trigger performance parameter segment set based on the difference characteristics between the reactor sensor data.

[0096] The technical solution further handles the unqualified trigger performance parameter segment sets. When the proximity of the reactor sensor data corresponding to these segment sets to the benchmark sensor data is lower than the preset standard, the system will turn to analyze their proximity to the sensor data corresponding to the good trigger performance parameter segment subset. If the proximity reaches or exceeds the preset value, the reactor control data corresponding to the unqualified segment set will be optimized and adjusted in a targeted manner based on the sensor data difference between the two, aiming to improve product quality and make it closer to the good or excellent standards.

[0097] In some embodiments disclosed in the present invention, there is also disclosed a reactor control optimization system based on quality inspection data analysis, comprising:

[0098] The first module is used to collect and record the production process data of the production products, including the reactor sensor data and the reactor control data, and to detect the product quality of the production products. Based on the detection results, multiple performance parameters are defined for the product quality of the production products to obtain a performance parameter group;

[0099] The second module is used to analyze several performance parameter groups, divide each performance parameter into sections based on the fluctuation characteristics of each performance parameter of the production product, and obtain a performance parameter section mapping template;

[0100] The third module is used to compare each performance parameter in each performance parameter group with its corresponding performance parameter segment mapping template, determine the performance parameter segment to which each performance parameter belongs in the performance parameter segment mapping template, and record the performance parameter segment as a triggering performance parameter segment to obtain a triggering performance parameter segment group;

[0101] The fourth module is used to perform quality assessment on each trigger performance parameter segment group based on the quality requirements of different types of products, and classify different trigger performance parameter segment groups based on the quality assessment results to obtain an excellent trigger performance parameter segment set, a good trigger performance parameter segment set, and an unqualified trigger performance parameter segment set, and perform internal equivalent classification on the excellent trigger performance parameter segment set and the good trigger performance parameter segment set to obtain a number of excellent trigger performance parameter segment subsets and a number of good trigger performance parameter segment subsets;

[0102] The fifth module is used to use the reactor sensor data corresponding to the excellent trigger performance parameter segment subset as the benchmark sensor data, and analyze the proximity between the reactor sensor data corresponding to the good trigger performance parameter segment subset and the unqualified trigger performance parameter segment set and the benchmark sensor data. If the proximity is greater than or equal to a preset value, the corresponding reactor sensor data and the benchmark sensor data are combined and connected to obtain a first comparative sensor data reference group;

[0103] The sixth module is used to optimize and adjust the reactor control data corresponding to the reactor sensor data based on the difference characteristics between the reactor sensor data and the benchmark sensor data in the first comparison sensor data reference group.

[0104] The invention discloses a method and system for optimizing reactor control based on quality inspection data analysis, and relates to the technical field of reactor control. Specifically, the invention discloses defining multiple performance parameters of a product; scientifically dividing the performance parameters into sections, constructing a performance parameter section mapping template, and providing a standard for subsequent analysis; comparing each performance parameter group with the mapping template, determining a triggering performance parameter section, and performing quality assessment and classification; taking the sensor data corresponding to an excellent triggering performance parameter section subset as a benchmark, analyzing the proximity of other data to the benchmark data, and forming a first comparative sensor data reference group; optimizing and adjusting the reactor control data based on the data difference characteristics in the reference group, so as to improve product quality, optimize production efficiency, and reduce energy consumption; the technical solution realizes the optimization of reactor control through precise data collection, scientific analysis, and optimization and adjustment, and provides strong support for the sustainable development of the chemical industry.

[0105] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A reactor control optimization method based on quality inspection data analysis, characterized in that: include: Collect and record the production process data of the products, including the reactor sensor data and the reactor control data, and test the product quality of the products. Based on the test results, define multiple performance parameters of the product quality of the products to obtain a performance parameter group; Analyze several performance parameter groups, divide each performance parameter into sections based on the fluctuation characteristics of each performance parameter of the production product, and obtain a performance parameter section mapping template; Compare each performance parameter in each performance parameter group with its corresponding performance parameter segment mapping template, determine the performance parameter segment to which each performance parameter belongs in the performance parameter segment mapping template, and record the performance parameter segment as a triggering performance parameter segment to obtain a triggering performance parameter segment group; Based on the quality requirements for different types of manufactured products, a quality assessment is performed on each trigger performance parameter segment group, and based on the quality assessment results, different trigger performance parameter segment groups are classified to obtain an excellent trigger performance parameter segment set, a good trigger performance parameter segment set, and an unqualified trigger performance parameter segment set, and the excellent trigger performance parameter segment set and the good trigger performance parameter segment set are each internally classified as equivalent to obtain a number of excellent trigger performance parameter segment subsets and a number of good trigger performance parameter segment subsets; The reactor sensor data corresponding to the excellent trigger performance parameter segment subset is used as the benchmark sensor data, and the proximity between the reactor sensor data corresponding to the good trigger performance parameter segment subset and the unqualified trigger performance parameter segment set and the benchmark sensor data is analyzed. If the proximity is greater than or equal to a preset value, the corresponding reactor sensor data and the benchmark sensor data are combined and connected to obtain a first comparative sensor data reference group; Based on the difference characteristics between the reactor sensor data and the benchmark sensor data in the first comparison sensor data reference group, optimizing and adjusting the reactor control data corresponding to the reactor sensor data; Based on the fluctuation characteristics of each performance parameter of the production product, the method of segmenting each performance parameter includes: Establish a performance parameter variation axis, analyze several performance parameter groups, and map different types of performance parameters on the performance parameter variation axis respectively, and determine the number density of performance parameter mapping points at different nodes on the performance parameter variation axis; Taking the performance parameter change axis as the horizontal axis and based on the performance parameter mapping point quantity density of different nodes, a performance parameter mapping point quantity density curve is constructed; Comprehensively analyze the curvature change and peak value of the performance parameter mapping point quantity density curve, and based on the analysis results, lock the large section for this section division, and determine the division width parameter when dividing the large section into sections; The method for comprehensively analyzing the curvature change and peak value of the performance parameter mapping number density curve includes: Continuously scan and analyze the density curve of the number of performance parameter mapping points, and record the absolute value accumulation of the curvature and the peak value accumulation of several consecutive acquisition points in real time. If the absolute value accumulation of the curvature is greater than or equal to the preset value, or the peak value accumulation is greater than or equal to the preset value, the acquisition point scanned at this time is identified as the truncation point, and the section between the truncation points is identified as the large section for segmentation; Based on the curvature absolute value accumulation value and the peak value accumulation value, a division width parameter for dividing the large section is determined; Among them, the expression for calculating the partition width parameter is: Among them, d is the division width parameter, D is the segment width of the large segment for segment division, K1 is the segment division number conversion coefficient, f is the cumulative value of the absolute value of the curvature within the large segment, q is the cumulative value of the peak value, K2 is the curve characteristic adjustment coefficient of the performance parameter mapping quantity density curve, and b is the curve characteristic adjustment constant of the performance parameter mapping quantity density curve.

2. The method for optimizing reactor control based on quality inspection data analysis according to claim 1, characterized in that: Also includes: If the degree of proximity between the reactor sensor data corresponding to the unqualified trigger performance parameter segment set and the benchmark sensor data is less than or equal to the preset value, the proximity between the reactor sensor data corresponding to the unqualified trigger performance parameter segment set and the reactor sensor data corresponding to the good trigger performance parameter segment subset is analyzed. If the degree of proximity is greater than or equal to the preset value, the reactor control data corresponding to the unqualified trigger performance parameter segment set is optimized and adjusted based on the difference characteristics between the reactor sensor data.

3. The method for optimizing reactor control based on quality inspection data analysis according to claim 1, characterized in that: The method of internally classifying the excellent trigger performance parameter segment set and the good trigger performance parameter segment set as equivalent includes: Mapping different types of triggered performance parameter segments in a plurality of triggered performance parameter segment sets on a performance parameter segment template to obtain a plurality of performance parameter segment mapping points, analyzing clustered segments of performance parameter segment mapping points of the same type, and calculating an average performance parameter segment mapping point in the clustered segments; Based on the equality of different types of performance parameter segment mapping points, the trigger performance parameter segment groups in the trigger performance parameter segment set are first classified into the same category; The combination of performance parameter segment mapping points of each first equivalent classification is determined and recorded as an equivalent category performance parameter segment mapping point group, and the distance characteristics of the triggering performance parameter segment group from the equivalent category performance parameter segment mapping point group on the performance parameter segment template are analyzed. Based on the distance characteristics, the triggering performance parameter segment group after the first equivalent classification is subjected to a second equivalent classification.

4. The method for optimizing reactor control based on quality inspection data analysis according to claim 3 is characterized in that: The method for analyzing the distance characteristics of the triggering performance parameter segment group from the same category performance parameter segment mapping point group on the performance parameter segment template includes: Respectively determine the performance parameter segment mapping points of each type of trigger performance parameter segment in the trigger performance parameter segment group on the performance parameter segment template, and determine the distance between the mapping points of each type of performance parameter segment mapping point relative to the average performance parameter segment mapping point; Based on the emphasis on product quality requirements, type weights are set for different types of distances between mapping points, and the classification distance parameters are calculated based on the distances between mapping points. Among them, the expression for calculating the classification distance parameter is: Among them, H is the classification distance parameter, R i is the type weight coefficient of the distance between mapping points of type i, Δh i is the distance between the i-th mapping points, and n is the total number of types in the trigger performance parameter segment group.

5. The method for optimizing reactor control based on quality inspection data analysis according to claim 1, characterized in that: Methods for analyzing the closeness of reactor sensor data to benchmark sensor data include: The reactor sensor data and the benchmark sensor data are analyzed respectively to generate a reactor sensor parameter curve group and a benchmark sensor parameter curve group; The sensor parameter curves of the same category in the reactor sensor parameter curve group and the benchmark sensor parameter curve group are aligned to determine the trend similarity characteristics of their changing trends and the difference similarity characteristics between the two. Based on the trend similarity characteristics and difference similarity characteristics between each pair of sensor parameter curves, the degree of proximity between the reactor sensor data and the benchmark sensor data is determined.

6. The method for optimizing reactor control based on quality inspection data analysis according to claim 5, characterized in that: The method for determining the trend similarity characteristics and the difference similarity characteristics between the sensing parameter curves includes: Set a number of detection points for the sensor parameter curve, and determine the parameter difference values ​​between the curves of the detection points, as well as the curve trends at the detection points; If the parameter difference value between the curves of the detection point is less than or equal to the preset value, and the curve trends are the same, the detection point is marked as a similar detection point, and the number of similar detection points between the sensor parameter curves is identified as the sub-degree of closeness between the sensor parameter curves. The sum of the sub-degrees of closeness between all the sensor parameter curves is determined as the closeness between the reactor sensor data and the benchmark sensor data.

7. A reactor control optimization system based on quality inspection data analysis, characterized in that: A method for optimizing reactor control for executing any one of claims 1 to 6, comprising: The first module is used to collect and record the production process data of the production products, including the reactor sensor data and the reactor control data, and to detect the product quality of the production products. Based on the detection results, multiple performance parameters are defined for the product quality of the production products to obtain a performance parameter group; The second module is used to analyze several performance parameter groups, divide each performance parameter into sections based on the fluctuation characteristics of each performance parameter of the production product, and obtain a performance parameter section mapping template; The third module is used to compare each performance parameter in each performance parameter group with its corresponding performance parameter segment mapping template, determine the performance parameter segment to which each performance parameter belongs in the performance parameter segment mapping template, and record the performance parameter segment as a triggering performance parameter segment to obtain a triggering performance parameter segment group; The fourth module is used to perform quality assessment on each trigger performance parameter segment group based on the quality requirements of different types of products, and classify different trigger performance parameter segment groups based on the quality assessment results to obtain an excellent trigger performance parameter segment set, a good trigger performance parameter segment set, and an unqualified trigger performance parameter segment set, and perform internal equivalent classification on the excellent trigger performance parameter segment set and the good trigger performance parameter segment set to obtain a number of excellent trigger performance parameter segment subsets and a number of good trigger performance parameter segment subsets; The fifth module is used to use the reactor sensor data corresponding to the excellent trigger performance parameter segment subset as the benchmark sensor data, and analyze the proximity between the reactor sensor data corresponding to the good trigger performance parameter segment subset and the unqualified trigger performance parameter segment set and the benchmark sensor data. If the proximity is greater than or equal to a preset value, the corresponding reactor sensor data and the benchmark sensor data are combined and connected to obtain a first comparative sensor data reference group; The sixth module is used to optimize and adjust the reactor control data corresponding to the reactor sensor data based on the difference characteristics between the reactor sensor data and the benchmark sensor data in the first comparison sensor data reference group.

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