A self - contained data printing system, method and its application in an ultra - pure water machine

Abstract

The present invention relates to the technical field of data processing, specifically to a self - contained data printing system, method and its application in an ultra - pure water machine. By analyzing and processing the collected ultra - pure water quality requirement data and the established production standard data of different ultra - pure water quality grades, ultra - pure water quality grade data is generated, and the normal operation parameter range data of the corresponding ultra - pure water machine components is collected. The real - time operation parameter data of the ultra - pure water machine components is collected through sensors and processed to generate the real - time operation parameter data of the standard ultra - pure water machine components. Numerical matching processing is carried out on the normal operation parameter range data of the ultra - pure water machine components and the real - time operation parameter data of the standard ultra - pure water machine components. According to the numerical matching result, the real - time operation state analysis data of the ultra - pure water machine is generated. Finally, the ultra - pure water production data is constructed and stored. When printing is required, data printing is carried out through a data printing device, realizing the intelligent analysis of ultra - pure water machine data and the accurate printing of the analysis data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically to a data printing system and method with built-in functions and its application in an ultrapure water machine. Background Art

[0002] An ultrapure water machine is a water treatment device that uses methods such as pretreatment, reverse osmosis technology, ultra-purification treatment, and post-treatment to almost completely remove conductive media in water and reduce colloidal substances, gases, and organic matter that do not dissociate in water to a very low level.

[0003] The Chinese invention patent with the publication number CN111367917B introduces a data storage method for the state evaluation of a water pump unit. It collects fast variable and process variable state data through a water pump unit state monitoring system, calibrates the collected state data under working conditions, and then classifies the state data after working condition calibration into five types of data: real-time wave recording data, real-time data, current data, unit operation characteristic data, and historical data for hierarchical storage and stores them in a database. It can improve the comprehensive database system of the monitoring system by hierarchical data storage, ensure that enough useful data is stored for the state analysis of the unit, reduce the data storage volume at the same time, and improve the database retrieval performance. However, it does not give an effective determination standard for the operating state of the water pump unit, making it difficult to realize the intelligent analysis of the unit's operating state. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] To solve the deficiencies in the background art, the present invention provides a data printing system and method with built-in functions and its application in an ultrapure water machine, realizing the intelligent analysis of ultrapure water machine data and the accurate printing of the analyzed data.

[0006] (II) Technical Solutions

[0007] The present invention discloses a data processing method, including the following steps:

[0008] S1. Collect ultrapure water quality requirement data;

[0009] S2. Establish production standard data for different ultrapure water quality grades, and perform ultrapure water quality grade analysis and processing based on the ultrapure water quality requirement data and the production standard data for different ultrapure water quality grades to generate ultrapure water quality grade data;

[0010] S3. Collect the normal operating parameter ranges of the ultrapure water machine components corresponding to the ultrapure water quality grade data;

[0011] S4. Collect the real-time operating parameter data of the standard ultrapure water machine components;

[0012] S5. Perform numerical matching processing on the normal operation parameter range data of the ultra-pure water machine components and the real-time operation parameter data of the standard ultra-pure water machine components to generate real-time operation status analysis data of the ultra-pure water machine;

[0013] S6. Construct ultra-pure water production data based on the ultra-pure water quality requirement data, the ultra-pure water quality grade data, the real-time operation parameter data of the standard ultra-pure water machine components, and the real-time operation status analysis data of the ultra-pure water machine, and store it in the ultra-pure water production data management platform.

[0014] In the present invention, by performing ultra-pure water quality grade analysis processing on the collected ultra-pure water quality requirement data and the established production standard data of different ultra-pure water quality grades, ultra-pure water quality grade data is generated, and the corresponding normal operation parameter range data of the ultra-pure water machine components is collected. The real-time operation parameter data of the ultra-pure water machine components is collected through sensors, and abnormal data cleaning and average value measurement processing are performed to generate real-time operation parameter data of the standard ultra-pure water machine components. Numerical matching processing is performed on the normal operation parameter range data of the ultra-pure water machine components and the real-time operation parameter data of the standard ultra-pure water machine components, and real-time operation status analysis data of the ultra-pure water machine is generated according to the numerical matching result. Finally, ultra-pure water production data is constructed and stored. When printing is required, data printing is performed through a data printing device, realizing intelligent analysis of ultra-pure water machine data and accurate printing of analysis data.

[0015] Preferably, the specific steps for collecting ultra-pure water quality requirement data are as follows:

[0016] S11. Enter the quality requirement data of the ultra-pure water to be produced currently through the ultra-pure water production control interface to generate ultra-pure water quality requirement data A. The ultra-pure water quality requirement data includes ultra-pure water resistivity, ultra-pure water microbial content, ultra-pure water organic carbon content, ultra-pure water endotoxin content, and ultra-pure water specific ion concentration.

[0017] Preferably, the specific steps for establishing production standard data of different ultra-pure water quality grades and performing ultra-pure water quality grade analysis processing based on the ultra-pure water quality requirement data and the production standard data of different ultra-pure water quality grades to generate ultra-pure water quality grade data are as follows:

[0018] S21. Set the ultra-pure water quality grade set B = {b 1 , b 2 , …, b i , …, b k}, where b i represents the i-th ultra-pure water quality grade, and k represents the total number of ultra-pure water quality grades;

[0019] S22. Establish the production standard data corresponding to each ultra-pure water quality grade in the ultra-pure water quality grade concentration, and generate a production standard data set for different ultra-pure water quality grades. Among them, represents the production standard data of the i-th ultra-pure water quality grade;

[0020] S23. Perform ultra-pure water quality grade analysis and processing on the ultra-pure water quality requirement data A and the production standard data of different ultra-pure water quality grades in the production standard data set of different ultra-pure water quality grades, and generate ultra-pure water quality grade data D;

[0021] S231. Construct a salp swarm, set the salp swarm size to N, the current iteration number to t, the maximum iteration number to t max and the search space dimension of the production standard data of different ultra-pure water quality grades to P;

[0022] Randomly generate N groups of production standard data in the search space of the production standard data of different ultra-pure water quality grades. Each group of production standard data corresponds to a salp individual in the salp swarm. Initialize the initial position set of the salp swarm as X = {X 1 , X 2 , …, X i , …, X N}, where X i represents the initial position of the i-th salp individual in the salp swarm;

[0023] S232. Calculate the fitness function value of each salp individual in the salp swarm, arrange each salp individual in the salp swarm in descending order according to the fitness function value, and select the salp individual with the highest fitness function value as the current optimal individual; the fitness function formula is as follows:

[0024]

[0025] Among them, Fit i represents the fitness function value of the i-th salp individual in the salp swarm, x i represents the ability of the production standard data corresponding to the i-th salp individual in the salp swarm to meet the ultra-pure water quality requirement data, and φ represents the correction value;

[0026] S233. Each salp individual in the salp swarm hunts and forages by forming a salp chain by gathering together in the search space of the production standard data of different ultra-pure water quality grades. The salp individual ranked first in the salp chain is used as the leader individual, and the remaining salp individuals are used as follower individuals;

[0027] S234. The individual leader is affected by the food position and updates its position in the search space of production standard data for different ultra-pure water quality levels. The position update formula is as follows:

[0028]

[0029] where, represents the position of the individual leader after position update in the search space of production standard data for different ultra-pure water quality levels in the j-th dimension, X best represents the position of the current optimal individual, ub j and lb j represent the upper and lower limits of the search space of the production standard data for different ultra-pure water quality levels respectively, rand 1 and rand 2 both represent random numbers uniformly distributed between [0, 1], α 1 represents the convergence factor, and

[0030]

[0031] S235. The individual follower follows the previous salp individual and updates its position in the search space of production standard data for different ultra-pure water quality levels. The position update formula is as follows:

[0032]

[0033] where, represents the position of the i-th follower individual after position update in the search space of production standard data for different ultra-pure water quality levels in the j-th dimension, and represent the positions of the i-th and (i - 1)-th follower individuals in the search space of production standard data for different ultra-pure water quality levels in the j-th dimension respectively;

[0034] S236. Calculate the fitness function values of each salp individual in the salp population after position update, arrange each salp individual in the salp population in descending order according to the fitness function values, and select the salp individual with the highest fitness function value as the current optimal individual;

[0035] S237. Determine whether the current iteration number t is less than the maximum iteration number t max , if the current iteration number t is less than the maximum iteration number t max , then increment the current iteration number t by 1 and return to S233; otherwise, take the current optimal individual as the global optimal solution, and perform data identification on the ultra-pure water quality level corresponding to the production standard data corresponding to the global optimal solution to generate the ultra-pure water quality level data D.

[0036] The analysis and processing of the quality grade of ultrapure water is carried out by the salp swarm optimization algorithm, which ensures the accuracy of the analysis results of the quality grade of ultrapure water, speeds up the convergence rate of the analysis process, effectively reduces the time cost of data processing. At the same time, the salp swarm optimization algorithm has good robustness, can resist the interference caused by data noise to a certain extent, and maintains the stability and reliability of the analysis process.

[0037] Preferably, the specific steps for collecting the normal operation parameter interval data of the components of the ultrapure water machine corresponding to the ultrapure water quality grade data are as follows:

[0038] S31. Call the normal operation parameter interval data of the ultrapure water machine equipment corresponding to the ultrapure water quality grade data D from the historical operation data storage unit of the ultrapure water machine to generate a dataset of the normal operation parameter intervals of the components of the ultrapure water machine Among them, and respectively represent the minimum and maximum values of the operation parameter data of the i-th component of the ultrapure water machine under normal operation, and l represents the total number of components of the ultrapure water machine.

[0039] Preferably, the specific steps for collecting the real-time operation parameter data of the components of the standard ultrapure water machine are as follows:

[0040] S41. Set the operation data collection period of the ultrapure water machine, evenly divide the operation data collection period of the ultrapure water machine into several operation data collection time points of the ultrapure water machine to generate a set F of operation data collection time points of the ultrapure water machine = {f 1 , f 2 , …, f i , …, f p}, where f i represents the i-th operation data collection time point of the ultrapure water machine, and p represents the total number of operation data collection time points of the ultrapure water machine;

[0041] S42. Through different high-precision sensors, collect the real-time operation parameter data of each component of the ultrapure water machine at each operation data collection time point in the set of operation data collection time points of the ultrapure water machine to generate a real-time operation parameter data matrix E' of the components of the ultrapure water machine as follows:

[0042]

[0043] Among them, e' ij represents the real-time operation parameter data of the i-th component of the ultrapure water machine at the j-th operation data collection time point of the ultrapure water machine, and the high-precision sensors include but are not limited to pressure sensors, temperature sensors, flow sensors, and liquid level sensors;

[0044] S43, using the Z-score method to clean abnormal data of the real-time operating parameter data of the ultrapure water machine components in the real-time operating parameter data matrix E' of the ultrapure water machine components, and generate a real data matrix of the real-time operating parameters of the ultrapure water machine components as follows:

[0045]

[0046] in, represents the real data of the real-time operating parameters of the jth ultrapure water machine component of the i-th component of the ultrapure water machine, o i It represents the total number of real data of real-time operating parameters of ultrapure water machine components obtained by real-time collection of operating parameter data of the i-th component of the ultrapure water machine; the abnormal data cleaning process is as follows:

[0047] S431, selecting the real-time operating parameter data of the i-th component of the ultrapure water machine at the j-th ultrapure water machine operating data collection time point in the real-time operating parameter data matrix of the ultrapure water machine components for abnormal data cleaning, When e' ij The abnormal point of the real-time operating parameter data of the ultrapure water machine components is e' ij Remove; otherwise, determine e' ij The real data of the real-time operating parameters of the ultrapure water machine components will be e' ij reserve;

[0048] Among them, μ i and σ i They respectively represent the mean and standard deviation of the real-time operating parameter data of all ultrapure water machine components obtained by real-time collection of the operating parameter data of the i-th component of the ultrapure water machine;

[0049] S432, repeating the operation in S431 until all the real-time operating parameter data of the ultrapure water machine components in the real-time operating parameter data matrix E' of the ultrapure water machine components are traversed, and then the abnormal data cleaning operation is terminated;

[0050] S44, using the mean formula to calculate the real data matrix of the real-time operating parameters of the ultrapure water machine components The real data of the real-time operating parameters of the ultrapure water machine components in the ultrapure water machine are averaged and measured to generate a standard ultrapure water machine component real-time operating parameter data set in, represents the real-time operating parameter data of the standard ultrapure water machine component of the i-th component of the ultrapure water machine, and

[0051] By using the Z-score method to clean abnormal data from the real-time operation parameter data of the ultra-pure water machine components, abnormal data in the real-time operation parameter data of the ultra-pure water machine components can be accurately identified and scientifically eliminated. Then, the mean formula is used to perform average numerical measurement on the cleaned data, so that the obtained data can better reflect the overall operation level of the ultra-pure water machine over a period of time, ensuring the comprehensiveness of the obtained data.

[0052] Preferably, the specific steps for generating the real-time operation status analysis data of the ultra-pure water machine by performing numerical matching on the normal operation parameter interval data of the ultra-pure water machine components and the real-time operation parameter data of the standard ultra-pure water machine components are as follows:

[0053] S51. Use the breadth-first search algorithm to search for the normal operation parameter interval data of the ultra-pure water machine components corresponding to each real-time operation parameter data of the standard ultra-pure water machine components in the normal operation parameter interval data set of the ultra-pure water machine components. Numerically match each real-time operation parameter data of the standard ultra-pure water machine components in the real-time operation parameter data set of the standard ultra-pure water machine components with the corresponding normal operation parameter interval data of the ultra-pure water machine components in the normal operation parameter interval data set of the ultra-pure water machine components, and generate the real-time operation status analysis data F of the ultra-pure water machine according to the numerical matching result. fenxi ;

[0054] If all the real-time operation parameter data of the standard ultra-pure water machine components in the real-time operation parameter data set of the standard ultra-pure water machine components are successfully matched with the corresponding normal operation parameter interval data of the ultra-pure water machine components in the normal operation parameter interval data set of the ultra-pure water machine components, then output the real-time operation status analysis data F of the ultra-pure water machine. fenxi is normal; otherwise, output the real-time operation status analysis data F of the ultra-pure water machine. fenxi is abnormal, and issue a warning through the abnormal operation status alarm light of the ultra-pure water machine.

[0055] Generate the real-time operation status analysis data of the ultra-pure water machine according to the numerical matching result between the normal operation parameter interval data of the ultra-pure water machine components and the real-time operation parameter data of the standard ultra-pure water machine components, and scientifically analyze the real-time operation status of the ultra-pure water machine to ensure the accuracy and reliability of the analysis results.

[0056] Preferably, the specific steps for constructing the ultra-pure water production data based on the ultra-pure water quality requirement data, the ultra-pure water quality grade data, the real-time operation parameter data of the standard ultra-pure water machine components, and the real-time operation status analysis data of the ultra-pure water machine and storing it in the ultra-pure water production data management platform are as follows:

[0057] S61. Combine the ultra-pure water quality requirement data A, the ultra-pure water quality grade data D, the real-time operation parameter data set of the standard ultra-pure water machine components and the ultra-pure water machine real-time operation status analysis data F fenxi to perform data combination and construct ultra-pure water production data

[0058] S62. Push the ultra-pure water production data to the ultra-pure water production data management platform through the Internet of Things communication network for data storage.

[0059] A data processing method further includes S7;

[0060] S7. When data printing is required, use a data printing device to print the ultra-pure water production data in the ultra-pure water production data management platform to generate ultra-pure water production paper data.

[0061] Preferably, S7 includes the following steps:

[0062] S71. When data printing is required, use a data printing device to print the ultra-pure water production data in the ultra-pure water production data management platform to generate ultra-pure water production paper data. The data printing device includes any one of an inkjet printer, a laser printer, a dot matrix printer, and a 3D printer.

[0063] The present invention also discloses a self-contained data printing system, including an ultra-pure water quality requirement data acquisition module, an ultra-pure water quality grade data analysis module, a normal operation parameter range data collection module, an ultra-pure water machine real-time operation parameter data acquisition module, an ultra-pure water machine real-time operation status analysis module, an ultra-pure water production data construction module, and a paper data printing module;

[0064] The ultra-pure water quality requirement data acquisition module enters the quality requirement data of the currently required ultra-pure water through the ultra-pure water production control interface to generate ultra-pure water quality requirement data;

[0065] The ultra-pure water quality grade data analysis module performs ultra-pure water quality grade analysis and processing according to the ultra-pure water quality requirement data and the established production standard data of different ultra-pure water quality grades to generate ultra-pure water quality grade data;

[0066] The normal operation parameter range data collection module calls the ultra-pure water machine equipment normal operation parameter range data corresponding to the ultra-pure water quality grade data from the ultra-pure water machine historical operation data storage unit;

[0067] The real-time operation parameter data acquisition module of the ultrapure water machine collects the real-time operation parameter data of each component of the ultrapure water machine at each set data acquisition time point of the ultrapure water machine operation data through different high-precision sensors, and performs abnormal data cleaning and average value measurement processing on the collected data to generate the real-time operation parameter data of the standard ultrapure water machine components;

[0068] The real-time operation status analysis module of the ultrapure water machine performs numerical matching processing based on the normal operation parameter interval data of the ultrapure water machine components and the real-time operation parameter data of the standard ultrapure water machine components to generate the real-time operation status analysis data of the ultrapure water machine;

[0069] The ultrapure water production data construction module combines the ultrapure water quality requirement data, the ultrapure water quality grade data, the real-time operation parameter data of the standard ultrapure water machine components and the real-time operation status analysis data of the ultrapure water machine to construct the ultrapure water production data, and pushes it to the ultrapure water production data management platform through the Internet of Things communication network for data storage;

[0070] When data printing is required, the paper data printing module prints the ultrapure water production data in the ultrapure water production data management platform through a data printing device to generate the paper data of the ultrapure water production.

[0071] (III) Beneficial effects

[0072] 1. By performing ultrapure water quality grade analysis and processing on the collected ultrapure water quality requirement data and the established production standard data of different ultrapure water quality grades, the invention generates the ultrapure water quality grade data, collects the corresponding normal operation parameter interval data of the ultrapure water machine components, collects the real-time operation parameter data of the ultrapure water machine components through sensors, and performs abnormal data cleaning and average value measurement processing to generate the real-time operation parameter data of the standard ultrapure water machine components. Numerical matching processing is performed on the normal operation parameter interval data of the ultrapure water machine components and the real-time operation parameter data of the standard ultrapure water machine components, and the real-time operation status analysis data of the ultrapure water machine is generated according to the numerical matching result. Finally, the ultrapure water production data is constructed and stored. When printing is required, data printing is performed through a data printing device, realizing the intelligent analysis of the ultrapure water machine data and the accurate printing of the analysis data;

[0073] 2. By using the salp swarm optimization algorithm for ultrapure water quality grade analysis and processing, the accuracy of the ultrapure water quality grade analysis result is ensured, the convergence speed of the analysis process is accelerated, the time cost of data processing is effectively reduced. At the same time, the salp swarm optimization algorithm has good robustness, can resist the interference brought by data noise to a certain extent, and maintain the stability and reliability of the analysis process;

[0074] 3. Anomaly data cleaning is performed on the real-time operation parameter data of the ultra-pure water machine components through the Z-score method, which can accurately identify the anomaly data in the real-time operation parameter data of the ultra-pure water machine components and scientifically eliminate them. Then, the mean formula is used to perform average numerical measurement processing on the cleaned data, so that the obtained data can better reflect the overall operation level of the ultra-pure water machine over a period of time, ensuring the comprehensiveness of the obtained data.

[0075] 4. Generate real-time operation status analysis data of the ultra-pure water machine according to the numerical matching result between the normal operation parameter interval data of the ultra-pure water machine components and the real-time operation parameter data of the standard ultra-pure water machine components, and scientifically analyze the real-time operation status of the ultra-pure water machine to ensure the accuracy and reliability of the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the invention. For those of ordinary skill in the art, without creative efforts, additional drawings can be obtained based on these drawings.

[0077] Figure 1 It is a flowchart of a data processing method provided by the present invention;

[0078] Figure 2 It is a schematic diagram of a module of a data printing system provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0079] 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0080] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "top", "middle", "inner", etc. indicating the orientation or position relationship are only for the convenience of describing the invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the invention.

[0081] Embodiment 1 is as follows:

[0082] Please refer to Figure 1 , a data processing method, including the following steps:

[0083] S1. Collect data on the quality requirements of ultra-pure water;

[0084] S11. Enter the quality requirement data of the ultrapure water to be produced currently through the ultrapure water production control interface to generate ultrapure water quality requirement data A. The ultrapure water quality requirement data includes ultrapure water resistivity, ultrapure water microbial content, ultrapure water organic carbon content, ultrapure water endotoxin content, and ultrapure water specific ion concentration.

[0085] S2. Establish production standard data for different ultrapure water quality grades, and perform ultrapure water quality grade analysis and processing based on the ultrapure water quality requirement data and the production standard data for different ultrapure water quality grades to generate ultrapure water quality grade data;

[0086] S21. Set the ultrapure water quality grade set B = {b 1 , b 2 , …, b i , …, b k}, where b i represents the i-th ultrapure water quality grade, and k represents the total number of ultrapure water quality grades;

[0087] S22. Establish the production standard data corresponding to each ultrapure water quality grade in the ultrapure water quality grade set to generate a production standard data set for different ultrapure water quality grades Among them, represents the production standard data of the i-th ultrapure water quality grade;

[0088] S23. Perform ultrapure water quality grade analysis and processing based on the ultrapure water quality requirement data A and the production standard data for different ultrapure water quality grades in the production standard data set for different ultrapure water quality grades to generate ultrapure water quality grade data D;

[0089] S231. Construct a salp swarm, set the salp swarm size to N, the current iteration number to t, the maximum iteration number to t max and the search space dimension of the production standard data for different ultrapure water quality grades to P;

[0090] Randomly generate N groups of production standard data in the search space of the production standard data for different ultrapure water quality grades. Each group of production standard data corresponds to a salp individual in the salp swarm. Initialize the initial position set of the salp swarm as X = {X 1 , X 2 , …, X i , …, X N}, where X i represents the initial position of the i-th salp individual in the salp swarm;

[0091] S232. Calculate the fitness function values of individual salps in the salp population, arrange the individual salps in the salp population in descending order according to the fitness function values, and select the salp individual with the highest fitness function value as the current optimal individual. The fitness function formula is as follows:

[0092]

[0093] Among them, Fit i represents the fitness function value of the i-th salp individual in the salp population, x i represents the ability of the production standard data corresponding to the i-th salp individual in the salp population to meet the ultrapure water quality requirement data, and φ represents the correction value;

[0094] S233. Each salp individual in the salp population hunts and forages by forming a salp chain by gathering together in the search space of the production standard data of different ultrapure water quality levels. The salp individual ranked first in the salp chain is used as the leader individual, and the remaining salp individuals are used as follower individuals;

[0095] S234. The leader individual is affected by the food position and updates its position in the search space of the production standard data of different ultrapure water quality levels. The position update formula is as follows:

[0096]

[0097] Among them, represents the position after the leader individual updates its position in the j-th dimension search space of the production standard data of different ultrapure water quality levels, X best represents the position of the current optimal individual, ub j and lb j represent the search upper limit and search lower limit of the search space of the production standard data of different ultrapure water quality levels respectively, rand 1 and rand 2 both represent random numbers uniformly distributed between [0, 1], α 1 represents the convergence factor, and

[0098]

[0099] S235. The follower individuals update their positions by following the previous salp individual in the search space of the production standard data of different ultrapure water quality levels. The position update formula is as follows:

[0100]

[0101] Among them, represents the position of the $i$-th follower individual after position update in the search space of production standard data for different ultra-pure water quality levels in the $j$-th dimension, and represent the positions of the $i$-th and $(i - 1)$-th follower individuals in the search space of production standard data for different ultra-pure water quality levels in the $j$-th dimension respectively;

[0102] S236. Calculate the fitness function values of each salp individual in the salp population after position update, arrange each salp individual in the salp population in descending order according to the fitness function values, and select the salp individual with the highest fitness function value as the current optimal individual;

[0103] S237. Determine whether the current iteration number $t$ is less than the maximum iteration number $t$ max , if the current iteration number $t$ is less than the maximum iteration number $t$ max , then the current iteration number $t$ is incremented by 1, and return to S233; otherwise, take the current optimal individual as the global optimal solution, and perform data identification on the ultra-pure water quality level corresponding to the production standard data of the global optimal solution to generate ultra-pure water quality level data D.

[0104] S3. Collect the normal operation parameter interval data of the ultra-pure water machine components corresponding to the ultra-pure water quality level data;

[0105] S31. Call the normal operation parameter interval data of the ultra-pure water machine equipment corresponding to the ultra-pure water quality level data D from the ultra-pure water machine historical operation data storage unit to generate a normal operation parameter interval data set of the ultra-pure water machine components where, and represent the minimum value and the maximum value of the operation parameter data of the $i$-th component under the normal operation state of the ultra-pure water machine respectively, and $l$ represents the total number of ultra-pure water machine components.

[0106] S4. Collect the real-time operation parameter data of the standard ultra-pure water machine components;

[0107] S41. Set the ultra-pure water machine operation data collection period, evenly divide the ultra-pure water machine operation data collection period into several ultra-pure water machine operation data collection time points to generate an ultra-pure water machine operation data collection time point set $F=\{f 1 ,f 2 ,\cdots,f i ,\cdots,f p \}$, where $f i represents the $i$-th ultra-pure water machine operation data collection time point, and $p$ represents the total number of ultra-pure water machine operation data collection time points;

[0108] S42, using different high-precision sensors to collect the operating parameter data of each component of the ultrapure water machine in real time at each ultrapure water machine operating data collection time point in the ultrapure water machine operating data collection time point set, and generating a real-time operating parameter data matrix E' of the ultrapure water machine components as follows:

[0109]

[0110] Among them, e' ij Represents the real-time operating parameter data of the i-th component of the ultrapure water machine at the j-th ultrapure water machine operating data collection time point, wherein the high-precision sensors include but are not limited to pressure sensors, temperature sensors, flow sensors and liquid level sensors;

[0111] S43, using the Z-score method to clean abnormal data of the real-time operating parameter data of the ultrapure water machine components in the real-time operating parameter data matrix E' of the ultrapure water machine components, and generate a real data matrix of the real-time operating parameters of the ultrapure water machine components as follows:

[0112]

[0113] in, represents the real data of the real-time operating parameters of the jth ultrapure water machine component of the i-th component of the ultrapure water machine, o i It represents the total number of real data of real-time operating parameters of ultrapure water machine components obtained by real-time collection of operating parameter data of the i-th component of the ultrapure water machine; the abnormal data cleaning process is as follows:

[0114] S431, selecting the real-time operating parameter data of the i-th component of the ultrapure water machine at the j-th ultrapure water machine operating data collection time point in the real-time operating parameter data matrix of the ultrapure water machine components for abnormal data cleaning, When e' ij The abnormal point of the real-time operating parameter data of the ultrapure water machine components is e' ij Remove; otherwise, determine e' ij The real data of the real-time operating parameters of the ultrapure water machine components will be e' ij reserve;

[0115] Among them, μ i and σ i They respectively represent the mean and standard deviation of the real-time operating parameter data of all ultrapure water machine components obtained by real-time collection of the operating parameter data of the i-th component of the ultrapure water machine;

[0116] S432, repeating the operation in S431 until all the real-time operating parameter data of the ultrapure water machine components in the real-time operating parameter data matrix E' of the ultrapure water machine components are traversed, and then the abnormal data cleaning operation is terminated;

[0117] S44. Use the mean formula to perform an average numerical measurement on the real-time operation parameter real data matrix of the ultra-pure water machine components in the real-time operation parameter real data of the ultra-pure water machine components to generate a standard real-time operation parameter data set of the ultra-pure water machine components where represents the standard real-time operation parameter data of the i-th component of the ultra-pure water machine, and

[0118] S5. Based on the normal operation parameter interval data of the ultra-pure water machine components and the standard real-time operation parameter data of the ultra-pure water machine components, perform a numerical matching process to generate the real-time operation state analysis data of the ultra-pure water machine;

[0119] S51. Use the breadth-first search algorithm to search for the normal operation parameter interval data of the ultra-pure water machine components corresponding to each standard real-time operation parameter data in the standard real-time operation parameter data set of the ultra-pure water machine components in the normal operation parameter interval data set of the ultra-pure water machine components. Respectively, perform a numerical matching on each standard real-time operation parameter data in the standard real-time operation parameter data set of the ultra-pure water machine components and the corresponding normal operation parameter interval data of the ultra-pure water machine components in the normal operation parameter interval data set of the ultra-pure water machine components, and generate the real-time operation state analysis data F of the ultra-pure water machine according to the numerical matching result fenxi ;

[0120] If all the standard real-time operation parameter data in the standard real-time operation parameter data set of the ultra-pure water machine components are successfully matched with the corresponding normal operation parameter interval data of the ultra-pure water machine components in the normal operation parameter interval data set of the ultra-pure water machine components, then output the real-time operation state analysis data F of the ultra-pure water machine fenxi as normal; otherwise, output the real-time operation state analysis data F of the ultra-pure water machine fenxi as abnormal, and issue a warning through the abnormal operation state alarm light of the ultra-pure water machine.

[0121] S6. Based on the ultra-pure water quality requirement data, the ultra-pure water quality grade data, the standard real-time operation parameter data of the ultra-pure water machine components, and the real-time operation state analysis data of the ultra-pure water machine, construct ultra-pure water production data and store it in the ultra-pure water production data management platform;

[0122] S61. Combine the ultra-pure water quality requirement data A, the ultra-pure water quality grade data D, the standard real-time operation parameter data set of the ultra-pure water machine components and the real-time operation state analysis data F of the ultra-pure water machine fenxi to perform data combination to construct ultra-pure water production data

[0123] S62. Push the ultrapure water production data to the ultrapure water production data management platform through the Internet of Things communication network for data storage. Push the ultrapure water production data to the ultrapure water production data management platform through the Internet of Things communication network for data storage.

[0124] In some embodiments, after S6, there is also S7; S7. When data printing is required, use a data printing device to print the ultrapure water production data in the ultrapure water production data management platform to generate paper-based ultrapure water production data.

[0125] S71. When data printing is required, use a data printing device to print the ultrapure water production data in the ultrapure water production data management platform to generate paper-based ultrapure water production data. The data printing device includes any one of an inkjet printer, a laser printer, a dot matrix printer, and a 3D printer.

[0126] Embodiment 2 is as follows:

[0127] Please refer to Figure 2 , a self - contained data printing system, including an ultrapure water quality requirement data acquisition module, an ultrapure water quality grade data analysis module, a normal operation parameter range data collection module, an ultrapure water machine real - time operation parameter data acquisition module, an ultrapure water machine real - time operation status analysis module, an ultrapure water production data construction module, and a paper data printing module.

[0128] The ultrapure water quality requirement data acquisition module inputs the quality requirement data of the currently required ultrapure water through the ultrapure water production control interface to generate ultrapure water quality requirement data.

[0129] The ultrapure water quality grade data analysis module performs ultrapure water quality grade analysis and processing according to the ultrapure water quality requirement data and the established production standard data of different ultrapure water quality grades to generate ultrapure water quality grade data.

[0130] The normal operation parameter range data collection module calls the normal operation parameter range data of the ultrapure water machine equipment corresponding to the ultrapure water quality grade data from the ultrapure water machine historical operation data storage unit.

[0131] The ultrapure water machine real - time operation parameter data acquisition module performs real - time acquisition of the operation parameter data of each component of the ultrapure water machine at each ultrapure water machine operation data acquisition time point set by different high - precision sensors at the set ultrapure water machine operation data acquisition time points, and performs abnormal data cleaning and average value measurement processing on the acquired data to generate standard real - time operation parameter data of the ultrapure water machine components.

[0132] The real-time operation status analysis module of the ultra-pure water machine performs numerical matching processing based on the normal operation parameter range data of the ultra-pure water machine components and the real-time operation parameter data of the standard ultra-pure water machine components to generate real-time operation status analysis data of the ultra-pure water machine;

[0133] The ultra-pure water production data construction module combines the ultra-pure water quality requirement data, the ultra-pure water quality grade data, the real-time operation parameter data of the standard ultra-pure water machine components, and the real-time operation status analysis data of the ultra-pure water machine to construct ultra-pure water production data, and pushes it to the ultra-pure water production data management platform through the Internet of Things communication network for data storage;

[0134] When data printing is required, the paper data printing module prints the ultra-pure water production data in the ultra-pure water production data management platform through a data printing device to generate ultra-pure water production paper data.

[0135] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0136] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. The present specification selects and specifically describes these embodiments in order to better explain the principle and practical application of the invention, so that those skilled in the art can well understand and utilize the invention.

Claims

1. A data processing method, characterized in that: The steps include: S1. Collect ultrapure water quality demand data; S2. Establish production standard data of different ultrapure water quality grades, perform ultrapure water quality grade analysis and processing according to the ultrapure water quality demand data and the production standard data of different ultrapure water quality grades, and generate ultrapure water quality grade data; The S2 comprises the following steps: S21, set the ultrapure water quality level set B = {b1, b2, ..., b i ,…,b k }, where b i represents the i-th ultrapure water quality grade, and k represents the total number of ultrapure water quality grades; S22, establishing production standard data corresponding to each ultrapure water quality grade in the ultrapure water quality grade set, generating production standard data sets for different ultrapure water quality grades in, represents the production standard data of the i-th ultrapure water quality level; S23, performing ultrapure water quality grade analysis and processing according to the ultrapure water quality demand data A and the different ultrapure water quality grade production standard data in the different ultrapure water quality grade production standard data sets to generate ultrapure water quality grade data D; The S23 comprises the following steps: S231, construct the salp population, set the salp population size to N, the current number of iterations to t, and the maximum number of iterations to t max And the dimension of the search space for production standard data of different ultrapure water quality grades is P; N groups of production standard data are randomly generated in the production standard data search space of different ultrapure water quality levels, each group of production standard data corresponds to a salp individual in the salp population, and the initial position set of the salp population is initialized as X = {X1, X2, ..., X i ,…,X N }, where X i represents the initial position of the i-th salp individual in the salp population; S232, calculating the fitness function value of each salp individual in the salp population, arranging the salp individuals in the salp population from large to small according to the fitness function value, and selecting the salp individual with the highest fitness function value as the current optimal individual; S233, the salps individuals in the salps population gather together to form a salps chain in the search space of the production standard data of different ultrapure water quality levels to hunt and forage, and the salps individual ranked first in the salps chain is used as the leader individual, and the remaining salps individuals are used as follower individuals; S234, the leader individual will be affected by the location of food and update its position in the search space of the production standard data of different ultrapure water quality levels; S235, the follower individual will follow the previous salp individual to update its position in the search space of production standard data of different ultrapure water quality levels; S236, calculating the fitness function value of each salp individual in the salp population after the position is updated, arranging the salp individuals in the salp population from large to small according to the fitness function value, and selecting the salp individual with the highest fitness function value as the current optimal individual; S237, determine whether the current number of iterations t is less than the maximum number of iterations t max , if the current number of iterations t is less than the maximum number of iterations t max , then the current iteration number t is increased by 1, and the process returns to S233; otherwise, the current optimal individual is taken as the global optimal solution, and the ultrapure water quality grade corresponding to the production standard data corresponding to the global optimal solution is data-labeled to generate ultrapure water quality grade data D; S3, collecting the normal operation parameter interval data of the ultrapure water machine components corresponding to the ultrapure water quality grade data; S4, collect real-time operating parameter data of standard ultrapure water machine components; S5, performing numerical matching processing based on the normal operation parameter interval data of the ultrapure water machine components and the real-time operation parameter data of the standard ultrapure water machine components to generate real-time operation status analysis data of the ultrapure water machine; S6. Construct ultrapure water production data based on the ultrapure water quality demand data, the ultrapure water quality grade data, the real-time operating parameter data of the standard ultrapure water machine components and the real-time operating status analysis data of the ultrapure water machine, and store them in the ultrapure water production data management platform.

2. A data processing method according to claim 1, characterized in that: The S1 comprises the following steps: S11. Enter the quality requirement data of the ultrapure water currently required to be produced through the ultrapure water production control interface to generate ultrapure water quality requirement data A, wherein the ultrapure water quality requirement data includes ultrapure water resistivity, ultrapure water microbial content, ultrapure water organic carbon content, ultrapure water endotoxin content and ultrapure water specific ion concentration.

3. A data processing method according to claim 1, characterized in that: The S3 comprises the following steps: S31, calling the ultrapure water machine equipment normal operation parameter interval data corresponding to the ultrapure water quality grade data D from the ultrapure water machine historical operation data storage unit to generate an ultrapure water machine component normal operation parameter interval data set in, and They respectively represent the minimum and maximum values ​​of the operating parameter data of the ith component under normal operating conditions of the ultrapure water machine, and l represents the total number of ultrapure water machine components.

4. A data processing method according to claim 1, characterized in that: The S4 comprises the following steps: S41, setting an ultrapure water machine operation data collection cycle, evenly dividing the ultrapure water machine operation data collection cycle into a number of ultrapure water machine operation data collection time points, generating an ultrapure water machine operation data collection time point set F = {f1, f2, ..., f i ,…,f p }, where f i represents the i-th ultrapure water machine operation data collection time point, and p represents the total number of ultrapure water machine operation data collection time points; S42, using different high-precision sensors to collect the operating parameter data of each component of the ultrapure water machine in real time at each ultrapure water machine operating data collection time point in the ultrapure water machine operating data collection time point set, to generate a real-time operating parameter data matrix E' of the ultrapure water machine components; S43, using the Z-score method to clean abnormal data of the real-time operating parameter data of the ultrapure water machine components in the real-time operating parameter data matrix E' of the ultrapure water machine components, and generate a real data matrix of the real-time operating parameters of the ultrapure water machine components S44, using the mean formula to calculate the real data matrix of the real-time operating parameters of the ultrapure water machine components The real data of the real-time operating parameters of the ultrapure water machine components in the ultrapure water machine are averaged and measured to generate a standard ultrapure water machine component real-time operating parameter data set in, Represents the real-time operating parameter data of the standard ultrapure water machine component of the i-th component of the ultrapure water machine.

5. A data processing method according to claim 1, characterized in that: The S5 comprises the following steps: S51, using a width search algorithm to search for the ultrapure water machine component normal operation parameter interval data corresponding to each standard ultrapure water machine component real-time operation parameter data in the standard ultrapure water machine component real-time operation parameter data set in the ultrapure water machine component normal operation parameter interval data set, respectively numerically matching each standard ultrapure water machine component real-time operation parameter data in the standard ultrapure water machine component real-time operation parameter data set with the ultrapure water machine component normal operation parameter interval data corresponding to the ultrapure water machine component normal operation parameter interval data set, and generating ultrapure water machine real-time operation status analysis data F according to the numerical matching result. fenxi ; If the real-time operating parameter data of each standard ultrapure water machine component in the standard ultrapure water machine component real-time operating parameter data set matches the corresponding ultrapure water machine component normal operating parameter interval data in the ultrapure water machine component normal operating parameter interval data set, the ultrapure water machine real-time operating status analysis data F is output. fenxi Normal; otherwise, output the real-time operation status analysis data F of the ultrapure water machine fenxi It is abnormal and issues a warning through the abnormal operation status alarm light of the ultrapure water machine.

6. A data processing method according to claim 1, characterized in that: The S6 comprises the following steps: S61, the ultrapure water quality requirement data A, the ultrapure water quality grade data D, and the standard ultrapure water machine component real-time operating parameter data set and the real-time operation status analysis data F of the ultrapure water machine fenxi Combine data to construct ultrapure water production data S62, transmitting the ultrapure water production data through the Internet of Things communication network Push to the ultrapure water production data management platform for data storage.

7. A data processing method according to claim 1, characterized in that: Also includes S7; S7. When data printing is required, the ultrapure water production data in the ultrapure water production data management platform is printed by a data printing device to generate paper data of ultrapure water production; The S7 comprises the following steps: S71, when data printing is required, the ultrapure water production data in the ultrapure water production data management platform is printed by a data printing device. Data printing is performed to generate paper data of ultrapure water production, wherein the data printing device includes any one of an inkjet printer, a laser printer, a dot matrix printer and a 3D printer.

8. A self-contained data printing system for implementing the data processing method according to any one of claims 1 to 7, characterized in that: It includes ultrapure water quality demand data collection module, ultrapure water quality grade data analysis module, normal operation parameter interval data collection module, ultrapure water machine real-time operation parameter data collection module, ultrapure water machine real-time operation status analysis module, ultrapure water production data construction module and paper data printing module.

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